# Abnormality Detection and Localization Schemes using Molecular Communication
Systems: A Survey

Ali Etemadi11, Maryam Farahnak-Ghazani11, Hamidreza Arjmandi2, Mahtab
Mirmohseni3,
and Masoumeh Nasiri-Kenari1
, 3University of Surrey  1Sharif University of Technology, 2University of
Warwick

###### Abstract

††1Co-first authors

Abnormality detection and localization (ADL) have been studied widely in
wireless sensor networks (WSNs) literature, where the sensors use
electromagnetic waves for communication. Molecular communication (MC) has been
introduced as an alternative approach for ADL in particular areas such as
healthcare, being able to tackle the shortcomings of conventional WSNs, such
as invasiveness, bio-incompatibility, and high energy consumption. In this
paper, we introduce a general framework for MC-based ADL, which consists of
multiple tiers for sensing the abnormality and communication between different
agents, including the sensors, the fusion center (FC), the gateway (GW), and
the external node (e.g., a local cloud), and describe each tier and the agents
in this framework. We classify and explain different abnormality recognition
methods, the functional units of the sensors, and different sensor features.
Further, we describe different types of interfaces required for converting the
internal and external signals at the FC and GW. Moreover, we present a unified
channel model for the sensing and communication links. We categorize the MC-
based abnormality detection schemes based on the sensor mobility, cooperative
detection, and cooperative sensing/activation. We also classify the
localization approaches based on the sensor mobility and propulsion mechanisms
and present a general framework for the externally-controllable localization
systems. Finally, we present some challenges and future research directions to
realize and develop MC-based systems for ADL. The important challenges in the
MC-based systems lie in four main directions as implementation, system design,
modeling, and methods, which need considerable attention from
multidisciplinary perspectives.

##  I Introduction

Abnormality detection and localization (ADL) have been studied widely and have
established literatures in different application areas such as fraud detection
in credit cards, healthcare, insurance [1], intrusion detection in cyber
security [2], fault detection in safety-critical systems [3], injection and
denial-of-service attacks in networks [4, 5, 6], detection of physical entity
malfunctions in smart grids [7], underwater target detection and localization
[8, 9], cancer detection and localization in medicine [10, 11], and odor
source detection and localization [12, 13].

###  I-A Motivation

Abnormality, defined as any abnormal feature in the system, is also referred
to as _anomaly_ , _target_ , _outlier_ , _exception_ , _surprise_ ,
_contaminant_ and _peculiarity_ in different domains [14]. In one hand, due to
the emergence of the Internet of things (IoT) and artificial intelligence (AI)
into many applications in industry, education, and medicine, resulting in a
large amount of data be gathered from the devices, the systems are more
exposed to the abnormalities, and the ADL have become even more important
problems [15]. On the other hand, the new advances in technology have paved
the way for new techniques to address the problem.

Conventionally, wireless sensor networks (WSNs) are used to detect and
localize the abnormality in a variety of healthcare, environmental, and
industrial applications [16, 17, 18, 19, 20]. In particular, wireless body
area networks (WBANs), which are one of the important classes of WSNs, are
used for health monitoring [21, 22, 23, 24, 25, 26, 27, 28, 29]. In [21, 22,
23, 24, 25], WBANs are introduced to detect various abnormal conditions of
physical health. In [29, 28, 26, 27], physical activity detection is
investigated using WBANs.

WSNs employ electromagnetic (EM) waves to convey information with some
disadvantages in some environments such as inside structural metal pipes [30].
Also, invasiveness, high energy consumption, and incompatibility with the
environment limit the applications of WSNs in some areas, such as inside
living bodies. Moreover, the conventional approaches are not reliable in
applications that demand fast detection and accurate localization of the
abnormality. For instance, the light symptoms at the early stage of cancer may
not be measured by these methods [31, 32]. In these applications, the
combination of molecular communication (MC) nano-networks inside the body and
wireless communication networks outside the body are promising systems [33].
Considering these types of applications, we focus on the MC-based systems for
abnormality detection and localization in this paper.

MC is a new paradigm, which is inspired by the communication between living
cells in nature, and it is biocompatible and energy-efficient, and hence more
suitable for in-body applications [34, 35, 36]. MC systems use molecules as
information carriers with many healthcare, industrial, and environmental
applications. In bio-medicine, MC can be used for targeted drug delivery,
health monitoring, and immune system support [35, 37]. The industrial
applications of MC include pattern and structure formation, water and food
quality control, and functionalized materials. The environmental applications
include environment monitoring, air pollution control, and biodiversity
control [35, 37]. In most of these applications, it is required to monitor the
environment to detect and localize the occurrence of abnormal changes, e.g.,
the presence of tumor cells inside the body, pollution in the air or water,
leakage or pressure drop in oil pipelines. MC systems are envisioned to detect
abnormalities in both micro-scale (range of a nano-meter to a micro-meter)
applications, such as early detection of cancer cells inside the body [38],
and macro-scale (range of a micro-meter to a meter) applications, such as
detection of virus in the air [39], and detection of leakage or pressure drop
in oil-pipelines [40].

Figure 1: Schematic view of MC-based ADL system.

###  I-B Multi-tier MC-based ADL Layout

An MC-based ADL system may consist of different tiers as shown in Fig. 1.
Here, we introduce a general multi-tier framework to comprehensively consider
the existing works. However, some tiers may not appear in some applications
and use-cases. In the first tier, there is an abnormal entity that changes a
parameter in the medium. The abnormality may affect the environment in
different ways, including molecule release (such as releasing biomarkers),
medium effect (such as changing temperature or pressure), and molecule
absorption that we describe in this paper. The sensors sense some variables in
the medium to capture one or multiple effects, to obtain information about the
abnormality. The abnormality sensing may be molecular or non-molecular. For
example, pressure measurement is non-molecular and biomarker concentration
measurement is molecular. More specifically, we consider a molecule
release/absorption method for the abnormality recognition as molecular
sensing, because the sensors measure the concentration of molecules, released
by the abnormality source, and then diffused in the environment. Thus, it
requires to analyze the movement of molecules from the molecule source to a
molecular receiver at the sensors. However, detecting medium effects such as
the pressure or temperature measurements are non-molecular since they are not
based on direct concentration measurements related to the abnormality source.
We explain the functional units of a sensor including the recognition layer,
transducer, processor, memory, and power units in this paper. Moreover, we
describe different sensor features for abnormality detection and localization,
including sensing, transduction, processing, memory, communication, molecule
release, mobility, propulsion, manufacturing technology, and energy.

In abnormality detection, after obtaining the information about different
variables that may be affected by an abnormality, the sensors can make the
decision about the existence of the abnormality themselves (called non-
cooperative detection). Alternatively, they can send their information (either
the raw information or their local decision about the abnormality) to an FC to
make the final decision (called cooperative detection). In the existing works,
for non-cooperative detection, only one sensor is used, which makes the final
decision about a single abnormal event [41, 39].

In the second tier, for cooperative abnormality detection, stationary or
mobile sensors may send their decisions to FC, through the sensor-to-FC (S2FC)
communication link, to overcome the uncertainty in sensor decision [42, 43].
As another approach, the sensors may move towards the FC, where their
activation levels could be read [31]. Mobile sensors can be useful when there
is no physical access to the sensing area and the sensing variables are very
weak outside this area. Moreover, if the sensing area is wide, it is more
feasible to use mobile sensors instead of multiple stationary sensors.

The sensors may also communicate with other sensors, through the sensor-to-
sensor (S2S) communication link, and cooperatively activate each other upon
detecting the abnormality [40] (which we call cooperative sensing/activation).
In non-cooperative sensing/activation, the sensors rely on their own
measurements to be activated and do not cooperate or interact with each other
in being activated. The FC processes the information and makes the final
decision itself, or transfers its raw samples or (soft/hard) decisions to a GW
through the FC-to-GW (FC2GW) communication link (third tier). The sensors may
also send their information directly to the GW through a sensor-to-GW (S2GW)
communication link [44]; see Fig. 1. The GW may send these information to an
external agent for complex processes [45]. The GW or external agent may also
control the sensors based on the received information [46]. The priority of
the information stream identifies the level of communication tier in which the
decision should be made. In addition to priority, introduction of cross-tier
complexities (e.g., data processing and signal conversion capabilities of the
FC and GW) is pivotal to make such a decision. For low-priority events, the
decision is made at the FC or possibly GW. However, for high-priority events,
which may need extensive global processing, the information is transmitted to
the cloud computing system.

The information gathered from the sensors can also be used to localize the
abnormality by the FC, GW, or external agents [40]. In contrast to the
stationary localization approaches, the sensors may move towards the
abnormality using different mobility mechanisms, e.g., chemotaxis, or using
external forces. The sensors may perform an action on the abnormality
themselves such as releasing drugs. Also, the leader sensors may release some
attractant molecules to passively attract follower sensors to the location of
the abnormality for a particular action [47, 48]. The leader sensors may also
communicate with the external environment through a GW to actively localize
the abnormality [49].

The communication in each tier may be based on wireless links (radio-frequency
(RF), optical, and acoustic) or molecular links. An ADL approach is called MC-
based if it uses molecules as carriers of information in at least one
communication link (mostly in the second communication link) or it uses
molecule release/absorption method in abnormality sensing link. Hence, the MC-
based layout consists of at least one molecular sensing or communication link.

For transferring the information to an external node in MC-based layout, there
should be an interface to convert the internal signals to the external
signals. We explain different options for constructing _wireless to wireless_
and _molecular to wireless_ interfaces. For example, carbon nano-tubes (CNT)
and Graphene nano-ribbons (GNR), embedded with molecular nano-sensors, can be
used as promising molecular to wireless interfaces in the FC or GW. Other
examples of interfaces to convert molecular signals to EM waves or vice versa
are constructed using genetically engineered cells and artificially
synthesized materials [46].

Moreover, we summarize the modeling of sensing and communication channels and
signal reception for molecular and non-molecular channels (including magnetic,
ultrasonic, or RF-based channels). Molecular channels can be generally modeled
by advection-reaction-diffusion partial differential equations (PDEs) with
some boundary conditions according to the medium structure and receiver type
[50]. Magnetic, ultrasonic, or RF-based channels can also be modeled by wave
equations following the second-order linear PDEs [51, 52].

###  I-C State-of-the-Art MC-based ADL Schemes

ADL have been studied widely in the literature of MC. We first formulate the
abnormality detection problem, in which the detection of the abnormality is
investigated and the location of the abnormality is not of interest. Different
abnormality detection schemes will be discussed later in detail. These schemes
are categorized based on using stationary or mobile sensors. For schemes with
stationary sensors, the abnormality may be detected using either a single
sensor (non-cooperative detection) or multiple sensors that send their
decisions to an FC for final decision (cooperative detection). As mentioned
above, the sensors may cooperatively activate each other (cooperative
sensing/activation). However, this possibility for stationary sensors has not
been studied yet. The schemes using stationary sensors are categorized based
on cooperative or non-cooperative detection. For example, in [53], a single
stationary sensor placed in a room is used for detecting virus-laden aerosols
in the room, and in [54], multiple stationary sensors and an FC are placed
near the suspicious tissue and the target detection is investigated at the
sensors and the FC. On the other hand, all detection schemes using mobile
sensors in the MC literature use cooperative detection (multiple sensors and
an FC). Hence, the schemes using mobile sensors are categorized into schemes
with non-cooperative or cooperative sensing/activation. For example, in [31],
multiple mobile sensors with non-cooperative sensing/activation are inserted
into the circulatory system for early cancer detection. In [40], the
abnormality detection inside pipelines is accomplished using multiple mobile
sensors with cooperative sensing/activation, where the pipeline is divided
into multiple regions with an FC placed at the end of each region.

The next step after the abnormality detection is to localize the abnormality.
The MC literature has recently proposed schemes to estimate the location of
the transmitter as a source of releasing molecules that leads to a diffusion
field [55, 56, 57, 58, 59]. In many abnormality localization applications, the
navigation of a swarm of sensors towards the intended region is of high
interest, e.g., in the targeted drug delivery systems [60] and macro-scale
robot-assisted surveillance systems [61]. Similar to abnormality detection,
the abnormality can be localized using either mobile or stationary sensors.
The stationary sensors may employ classification, discrete-model, or
continuous-model-based approaches to infer the location of the abnormality
[12]. Moreover, the mobile sensors are divided into non-propelled or propelled
sensors. The propelled sensors are influenced by specific internal and
external forces which are referred to as self-propulsion and external
propulsion, respectively. The self-propelled sensors can be potentially
employed in a variety of applications, e.g., water decontamination, water
remediation, cargo transport, and biomedical applications [62]. The self-
propulsion mobilities are originated in response to the natural mechanisms
(i.e., using taxis-based approaches) or relying on synthetic motors (i.e.,
using control-driven approaches). Taxis-based approaches, e.g., chemotaxis
[63], optotaxis [64], anemotaxis [65], and infotaxis [66], are well-studied
self-propulsion mechanisms in the literature on physical chemistry. In
biological environments, biocompatibility is vital for self-propelled sensors
which means that the sensors should emulate biological locomotion mechanisms
to efficiently localize an abnormality. On the other hand, control-driven
approaches [67] may perform more efficiently than simple taxis-based
approaches at the cost of more computational complexity. The important
challenge in self-propulsion mechanisms is that the sensors have to
autonomously locate the abnormality taking into account the limited resources
(e.g., energy, memory, etc) of the sensors. As another approach, external
triggers may be employed to control the locomotion of the swarm of sensors. To
this end, the communication between sensors, FC, GW, and possibly the cloud
computing servers is required to localize the abnormality externally. Thereby,
data gathered by the sensors from the abnormality micro-environment may be
employed locally by the sensors or globally by either FC, GW, or cloud
computing servers to make a local or global decision. The proposed multi-tier
framework for the abnormality detection schemes is still valid for abnormality
localization schemes. It should be notified that the third tier (including
S2GW and FC2GW links), which enables controlling of the externally-propelled
sensors, are more important for abnormality localization schemes compared to
the abnormality detection schemes. To motivate the role of the externally-
controllable systems, a variety of application-centric frameworks are
developed, e.g., a novel touch communication (TouchCom) framework for drug-
delivery systems [68] and a novel tumor detection and localization framework
using external devices [69], which are clarified throughout the paper. Recent
advancements in the field of medical imaging and actuating systems in line
with network cloudification promises future externally-controllable health
monitoring systems.

###  I-D Contribution Summary and Paper Organization

In this paper, we provide a comprehensive survey on the MC-based ADL systems.
We describe the most important challenges in modeling, designing, and
implementing the MC-based ADL systems and provide future research and
development directions. The MC-based systems are envisioned to be mostly used
in nano-scale in-body applications. Therefore, the most important challenges
to implement these systems are to design bio-nano-sensors with multiple
functionalities (such as mobility mechanisms, sensing methods, communication
strategies, storage capabilities, and energy harvesting mechanisms), and
interfaces with different types of signal conversion, for connecting the
internal and external networks [36]. The modeling of these systems is another
important challenge that requires a well understanding of biological systems
and environments. The existing models introduced for MC-based systems are
simplified models, which are far from reality in some cases. Therefore more
realistic, yet tractable models, are needed for these systems. Further, the
detection and localization approaches, which can be categorized in general
into classic, bio-inspired, and machine learning methods, can be improved by
appropriately combining different approaches.

The main contributions of the paper are summarized as:

  * •

An end-to-end MC-based ADL framework considering a multi-tier layout is
proposed to encompass most of the models in the ADL literature. The proposed
multi-tier layout consists of at least one molecular link, typically
considered the first or second tier in our model.

  * •

A detailed description of the sensors, interfaces, and external networks
including the functionality and inter-connectivity options, customized for the
ADL framework, is explained.

  * •

A general channel model describing the physical layer of each single tier
(molecular and non-molecular) based on the existing literature is presented.

  * •

A comprehensive classification of the MC-based ADL schemes based on the
mobility and propulsion of the sensor type is presented.

  * •

Finally, practical challenges and important future research directions in the
area of MC-based ADL systems are clarified.

This paper is organized as follows: in Section II, the ADL problem and the
important elements of the systems including abnormality, sensors, FC and GW
(interfaces), and sensing/communication links are described. In Section III,
the abnormality detection schemes and in Section IV, abnormality localization
schemes are reviewed. Finally, in Section V, the important challenges of the
MC-based ADL systems are explained and some future research directions are
introduced.

##  II Problem and System Description

In this section, we describe the MC-based ADL systems, represented
schematically in Fig. 1. In the following, we describe the important elements
of this system including abnormality, sensors, FC/GW (interfaces), and
sensing/communication channels. We first describe different abnormality
recognition approaches and explain the ADL problems in Subsection II-A. In
Subsection II-B, we illustrate different features for the sensors in these
systems and in Subsection II-C, we describe the possible technical options for
FC/GW (interfaces). Finally, in Subsection II-D, we provide the modeling of
sensing and communication channels and signal reception for molecular and some
non-molecular channels.

###  II-A Abnormality

Abnormality is defined as any abnormal feature, characteristic, or occurrence
that interferes with the normal status of a system [40].

####  II-A1 Abnormality Recognition Methods

The abnormal entities can be recognized in different ways. We consider the
following abnormality recognition methods, which are mostly considered in MC-
based systems (see Fig. 2):

  * •

Molecule release: an abnormal entity may release certain molecules into the
medium. For example, cancer cells as abnormal entities in the body may release
biomarkers, which can be used to detect and localize the cancerous site [70,
71]. Further, pathogens (viruses, bacteria, etc) in the air or inside the body
may be detected by the sensors [41, 44]. Viruses may be released to the air by
an infected person (as an abnormal entity). On the other hand, bacteria (as
abnormal entities) may release quorum-sensing molecules inside the body.
Further, antibodies inside the body may be released by the immune system in
the result of viral or bacterial infections. Moreover, pollutants may be
present in the air, water, or oil pipelines (released by cars, factories, etc,
as abnormal entities), which can be detected by certain sensors [40]. The
authors in [71] introduce the biomarkers released by different cancers and the
corresponding sensors to detect them. The biomarker release rate over time,
noted by $x_{\textrm{B}}(t)$, can be modeled by Weibull function as follows
[72]:

$\displaystyle x_{\textrm{B}}(t)=x_{\textrm{B},0}(1-\exp{(-kt^{b})}),$ (1)

where $b$ and $k$ are biomarker power-law and biomarker release coefficients,
respectively; and $x_{\textrm{B},0}$ is the steady-state release rate when
$t\rightarrow\infty$. The authors in [73] provide a more realistic model for
biomarker release rate from healthy and cancerous cells. According to this
model, biomarker release rates from healthy and cancerous cells that reach the
blood vessels, noted by $x_{\textrm{H}}(t)$ and $x_{\textrm{C}}(t)$,
respectively, are obtained as follows [31]:

$\displaystyle\begin{cases}x_{\textrm{H}}(t)=f_{\textrm{H}}r_{\textrm{H}}(t)n_{\textrm{H}}(t),\hskip
12.80365pt\text{for healthy cells,}\\\
x_{\textrm{C}}(t)=f_{\textrm{C}}r_{\textrm{C}}(t)n_{\textrm{C}}(t),\hskip
14.22636pt\text{for cancerous cells,}\end{cases}$ (2)

where $n_{\textrm{H}}(t)$ and $n_{\textrm{C}}(t)$ are the number of healthy
and cancerous cells over time, respectively; $r_{\textrm{H}}(t)$ and
$r_{\textrm{C}}(t)$ are the biomarker release rates from each healthy and
cancerous cell (assumed to be the same for all cells), respectively; and
$f_{\textrm{H}}$ and $f_{\textrm{C}}$ are the fractions of released biomarkers
from healthy and cancerous cells that reach the blood vessels (while the rest
of them remain in tissues), respectively. It is assumed that biomarker release
rates from each cancerous and healthy cell are constant, i.e.,
$r_{\textrm{C}}(t)=r_{\textrm{C}}$ and $r_{\textrm{H}}(t)=r_{\textrm{H}}$.
Further, the number of healthy cells is assumed to be constant over time,
i.e., $n_{\textrm{H}}(t)=n_{\textrm{H},0}$, and the number of cancerous cells
is modeled by Gompertzian function as follows [74]:

$\displaystyle
n_{\textrm{C}}(t)=n_{\textrm{C},0}\exp{(\frac{k_{\textrm{Gr}}}{k_{\textrm{Dec}}}(1-\exp{(-k_{\textrm{Dec}}t)}))},$
(3)

where $k_{\textrm{Dec}}$ and $k_{\textrm{Gr}}$ are the fractional decay and
growth rates of the cancerous cells, respectively, and $n_{\textrm{C},0}$ is
the number of cancerous cells at time $t=0$. For small values of
$k_{\textrm{Dec}}$, i.e., $k_{\textrm{Dec}}\rightarrow 0$ (which is the case
in the early stage of cancer), the number of cancerous cells can be simplified
to the following mono-exponential growth function:

$\displaystyle n_{\textrm{C}}(t)=n_{\textrm{C},0}\exp{(k_{\textrm{Gr}}t)}.$
(4)

Hence, we have

$\displaystyle x_{\textrm{C}}(t)$
$\displaystyle=f_{\textrm{H}}r_{\textrm{H}}n_{\textrm{C},0}\exp{(k_{\textrm{Gr}}t)}.$
(5)

  * •

Medium effects: abnormality may appear as the change in the medium parameters
such as flow velocity [75], pressure [76], temperature [77], and geometry
[78]. For example, a leakage leads to a pressure drop in oil pipelines [76]; a
tumor may change the temperature of the nearby environment [77], and
Atherosclerotic Lesion decreases the cross-section of the blood vessel [78].
The sensors may directly measure the environment parameters and detect the
changes. Also, a communication system could be influenced by the medium change
and indirectly detect the abnormality [42].

  * •

Molecule absorption: the abnormal entity may absorb certain molecule types in
the environment. For example, an eavesdropper (considered as an abnormality)
may absorb information or signaling molecules to interfere with a natural or
synthetic molecular communication link [79]. Moreover, cancerous cells may act
as competitor cells for other cells inside the body and absorb their released
molecules (such as Oxygen) [42]. The concentration change of the absorbed
molecules can be used to detect this abnormal entity.

The sensors may use one (or more than one) of these abnormality recognition
methods to obtain information about the abnormality in the environment.

Figure 2: Different abnormality recognition methods. Figure 3: Functional
units of the sensors. Figure 4: Sensor features.

###  II-B Sensors

The functional units of sensors are shown in Fig. 3 [80]. A sensor generally
consists of a recognition layer, where the abnormal events are recognized, a
transducer unit, where the perceived abnormal events are converted into
another type of signal to be processed further, a processing unit, where the
signal is processed, a power unit, which provides the energy for the
processor, a memory unit to store information, and a transceiver to
communicate with other nodes or to perform supplementary interventions at the
sensor (e.g., releasing drug molecules) [80]. The communication signal inputs
may contain controlling signals for the sensor, e.g., controlling the motion
of the sensor. The sensing input signals may also cause the moving of sensors
towards the abnormality. In the following, we categorize the features of the
sensors used for ADL (see Fig. 4):

  * •

Sensing: As the first mission, sensors may sense environmental parameters like
temperature [81] and pressure [82] (non-molecular sensing), or sense and
measure the concentration of certain molecules (biomarkers) in the environment
(molecular sensing), such as different gases and chemicals [83, 84]. For
molecular sensing, the receiving process of the sensors can be modeled by
transparent, absorbing (fully or partially), and reactive receivers [50] (see
Table II for different abnormality detection schemes assuming these models).

  * •

Transduction: The converted signal in the transducer may be electrical,
optical, and mechanical [85]. The optical and mechanical sensors need macro-
scale elements to detect the transducer’s output to be ready for processing,
while the electrical sensors can process the inputs in a self-operated manner
at the nano-scale. Electrochemical biosensors are examples of electrical
sensors [86] and biosensors based on nano-mechanical systems are examples of
mechanical sensors [87]. Surface plasmon resonance biosensors [88] and
Terahertz refractive index sensors [89] are examples of optical sensors.

  * •

Processing: The sensors may be able to process their measurements and make
decisions on the existence of the abnormality. The decisions made by the
individual sensors can be _hard_ , representing the absence or presence of the
abnormality, or _soft_ , interpreting a probability of abnormality existence
[90].

  * •

Memory: The sensors may use memory for information processing. For instance,
aggregate sensors in [40] keep their measurements in different time instances
and aggregate them for the final decision. A higher memory enables more
complicated information processing algorithms leading to more efficient ADL.

  * •

Communication: Besides sensing, the sensors may communicate with other agents
in the system (e.g., sensors and FC) to report their measurements and/or
decisions for further actions regarding the abnormality [40]. Depending on the
application, the sensors may be designed to communicate, which can be
accomplished using wireless (e.g., RF [91, 92], optical [93], ultrasonic
[94],) and molecular signals [38]. In [92], nano-sensors with RF-based
communication, operate in Terahertz (THz) band. MC is a promising approach for
communication in nano-scale in-body applications since it is bio-compatible
and energy-efficient [95].

  * •

Molecule release: The sensors may be able to release molecules (single type or
multiple types) for various purposes, for instance, to communicate with other
sensors and FC [40], and to deliver drug molecules at the target site [42].
The molecules may be stored in the sensor storage or produced through chemical
processes by the sensor itself. The release of molecules should be controlled
by the sensor, which can be realized using ion channels [96] and ion pumps
[97].

  * •

Mobility: Sensors may be _mobile_ or _stationary_. The locations of the
stationary sensors are fixed, e.g., skin-integrated and implantable sensors
[98, 99], tissue-integrated sensors [100], and virus or air pollutant sensors
in a room [39]. On the other hand, the sensors may be able to move passively
[43] or actively (autonomously) [49, 101] in the environment.

  * •

Propulsion: Mobile sensors move using different propulsion methods. The
sensors may move passively in the fluid medium due to the diffusion and/or
flow [43], which is called non-propulsion. Sensors may move using their own
means for active (autonomous) displacement, which is referred to as self-
propulsion. For example, flagellated bacteria autonomously move towards the
higher gradient of attractant molecules to find food [49]. Moreover, the
sensors may be externally-propelled for active (autonomous) displacement,
i.e., external electric or magnetic fields can control the sensors’ motion
[101, 102].

  * •

Manufacturing technology: The manufacturing technology of the sensors at nano-
and micro-scales includes synthetic (based on micro-electromechanical systems
(MEMS)), bio-inspired (engineered biological elements), and hybrid [45].

  * •

Energy: Based on the manufacturing technology, the sensors need a source of
energy for their processing that can be harvested or stored in advance in
their energy storage (battery). The sensors may harvest energy using different
techniques including vibration energy harvesting, thermal energy harvesting,
biochemical energy harvesting, and wireless energy harvesting [103].
Biological sensors may use the foods in their environment to harvest energy
(biochemical energy harvesting) [103]. The sensors may use thermal energy
(temperature difference) to harvest energy [103, 104]. They may also harvest
energy from the kinetic energy (such as flow) in their environment (vibration
energy harvesting) [103, 105, 106]. Further, they may harvest energy from
wireless energy sources [103, 107].

###  II-C FC and GW (Interface)

To provide more processing capabilities in the MC-based ADL layout, the
sensors may send their collected information to external computing units
through FC and GW, which is referred to as out-messaging. External units can
conversely impose controlling commands to the sensors in the reverse direction
referred to as in-messaging. GW connects the internal communication network
(e.g., the nano-network inside the body, called the in-body network) to the
external communication network (e.g., the macro-network outside the body,
called the off-body network).

In healthcare applications, the GW can be placed inside or outside the body
[108]. Hence, either the FC or the GW needs a nano-macro interface to convert
the data from the in-body network to the appropriate form for the off-body
network. The nano-network inside the body can use THz or molecular signals,
while for the off-body networks, EM signals are mostly used for communication.
For example, [109, 110] use THz signals and [46] uses molecular signals for
communication in the nano-network. In THz-based nano-networks, the nano-
devices inside the body are non-biological devices and use EM waves for
communication, which may be invasive to the human body, while MC-based nano-
networks are more bio-compatible and less invasive, due to using molecules and
biological elements for communication, and hence they are of most interest
[34, 35, 36].

Based on the communication technologies used in the S2S, S2FC, S2GW, and FC2GW
links, the interfaces can be categorized into two main types:

  * •

Wireless to wireless interface: For sensors that use THz signals for
communication, an interface is needed to convert the input THz signal to the
output EM signal and vice versa. Since both signals are wireless, the
interface is an electrical device with a dual transceiver enabling cross-tier
communications. For example, in [109], the nano-devices are located inside
coronary arteries, which communicate in THz band with a GW. The GW is a
wireless to wireless interface located in the intercostal space of the rib
cage of the patient which communicates with the off-body networks using EM
signal. In [110], multiple nano-devices are inserted into the circulatory
system and communicate in THz band with a nano-router playing the role of an
FC. The FC is a wireless to wireless interface implanted in the hand skin
between the dermis and epidermis. The FC communicates in THz band with an
external portable device as a GW, which is placed on the back of the hand.

  * •

Molecular to wireless interface: For MC networks, an interface is needed to
convert the input molecular signal (originating from the internal network) to
the output EM signal (appropriate for the external network) and vice versa.
Different interfaces for in-messaging and out-messaging can be implemented
using genetically engineered cells [46, 111, 112], artificially synthesized
materials (ARTs) (such as photosensitive, temperature-sensitive, magnetic, and
luminescent materials) [46], CNTs [108, 113], and GNRs [108, 114].
Implementing different types of signal conversion is complex using genetically
engineered cells, while ARTs provide a wide range of options for signal
conversion in interfaces, e.g., converting electrical, optical, or magnetic
signals to molecular signals and vice versa. ARTs can be generated using
Polystyrene bead and pHrodo molecules [46]. pHrodo molecules are pH-sensitive
fluorogenic dyes that are only fluorescent under acidic environments (low pH)
and almost non-fluorescent under neutral environments. Hence, the intensity of
the fluorescence can be measured using fluorescence microscopy to get
information about the acidity and neutrality of the cells (out-messaging).
Further, polystyrene bead can be used to implement in-messaging, e.g., it can
be designed to release or absorb molecules upon receiving signals from
external devices. However, the need for a fluorescence microscope makes it
complicated for ordinary use. CNTs and GNRs are more recent approaches to
construct the interfaces [108, 113]. In these mechanisms, chemical nano-
sensors are embedded in CNT or GNR. These nano-sensors absorb certain
molecules that generate an electrical signal in the CNT or GNR by changing the
number of electrons that move through the carbon lattice.

###  II-D Unified Channel Model and Signal Reception

In this section, we provide the general channel model for the system
considered in Fig. 1. The link from the abnormality to the sensors is referred
to as the sensing channel. Other links, e.g., S2FC, FC2GW, and S2GW links are
all referred to as communication channels. We model the sensing and
communication channels using a generalized advection-diffusion-wave equation
in viscoelastic111Viscoelasticity of a body identifies its capability to
simultaneously dissipate and store mechanical energy in a generic continuous
media exhibiting a combination of viscosity (i.e., fluid-like materials
supporting types of anomalous diffusion processes) and elasticity (i.e.,
solid-like materials exhibiting wave propagation) [115, 116]. environments,
which characterizes the Brownian motion of particles as well as the wave
propagation. The generalized space-time fractional advection-diffusion-wave
equation of order $(\alpha,\beta)$ for spatio-temporal field
$\mathcal{C}(\bm{r},t)$ is described as follows [115, 116]:

$\displaystyle\mathcal{D}^{\beta}_{t}\big{(}\mathcal{C}(\bm{r},t)\big{)}$
$\displaystyle=\nabla^{\alpha}\big{(}a_{{\bm{r},t}}\mathcal{C}({\bm{r},t})\big{)}-u_{\beta}\nabla.\big{(}b_{{\bm{r},t}}\mathcal{C}(\bm{r},t)\big{)}$
$\displaystyle\quad-
u_{\beta}\mathcal{F}(k_{d},\mathcal{C}(\bm{r},t))+\mathcal{P}\big{(}S(\bm{r},t)\big{)},$
(6)

where $u_{\beta}=\mathbf{1}_{\beta\neq 2}$ and $u_{2}=0$. Also,
$\nabla^{\alpha}(\cdot)$ and $\mathcal{D}^{\beta}_{t}(\cdot)$ denote the Riesz
space-fractional derivative of order $\alpha$ and the Caputo time-fractional
derivative of order $\beta$ [115, 116], respectively. For $\beta\neq 2$,
(II-D) describes the space-time fractional diffusion equation with
$a_{\mathrm{\bm{r},t}}$ and $b_{\mathrm{\bm{r},t}}$ denoting the spatio-
temporal diffusion and drift parts, respectively. Moreover,
$\mathcal{F}(\cdot)$ and $\mathcal{P}(\cdot)$ describe the degradation
reaction rate function with degradation rate $k_{d}$ and the source equation,
respectively. In the case of diffusion processes, (II-D) models a wide range
of diffusion processes from normal to anomalous diffusion. For normal
diffusion, i.e., $(\alpha,\beta)=(2,1)$, the Fokker-Planck equation in (II-D)
is given by
$\mathcal{P}\big{(}S(\mathrm{\bm{r},t})\big{)}=S(\mathrm{\bm{r},t})$. Also,
$\beta=2$ characterizes the time-fractional wave equation with
$a_{\mathrm{\bm{r},t}}=c^{2}$ where $c$ denotes the corresponding wave speed.
In the sequel, the provided model in (II-D) is clarified for special cases of
reaction-coupled diffusion and wave-propagation channels.

####  II-D1 Reaction-Coupled Diffusion Channels

In a real biological microenvironment, there may be multiple transceivers,
obstacles, and boundaries in the presence of flow-induced diffusion, coupled
with arbitrary homogeneous boundary conditions. The molecules diffused in the
environment may react with the receptors on the boundaries or obstacles in the
environment. The reactions at the boundaries may lead to complicated and non-
linear boundary conditions, which can be simplified to linear reaction chains.
A general homogeneous boundary condition that simplifies modeling of a wide
range of reactions at the boundaries is given as follows [117],

$\textbf{D}\nabla\mathcal{C}(\bm{r},t)\cdot\hat{n}=\textbf{L}\mathcal{C}(\bm{r},t),~{}~{}~{}\bm{r}\in\partial\mathcal{D},$
(7)

where $\nabla$ denotes the spatial gradient operator, $\hat{n}$ describes the
surface normal vector pointing towards the exterior of the diffusion
environment at $\bm{r}\in\partial\mathcal{D}$, where $\partial\mathcal{D}$
denotes the set of all points over the boundary of the environment, and D and
L are some time-domain differential operators which are characterized based on
the reactions involving information molecules across the boundary.

In the case of a normal diffusive MC system in a viscous medium with constant
diffusion coefficient $a_{\mathrm{\bm{r},t}}={D}$ and uniform bulk flow
$b_{\mathrm{\bm{r},t}}={v}$, (II-D) is simplified to:

$\displaystyle\mathcal{D}_{t}\big{(}\mathcal{C}(\mathrm{\bm{r},t})\big{)}$
$\displaystyle=D\nabla^{2}\big{(}\mathcal{C}(\mathrm{\bm{r},t})\big{)}$
$\displaystyle\quad-
k_{d}\mathcal{C}(\mathrm{\bm{r},t})-v\nabla.\big{(}\mathcal{C}(\mathrm{\bm{r},t})\big{)}+S(\mathrm{\bm{r},t}).$
(8)

Note that (7) models a wide range of boundary conditions including partially
absorbing boundaries and active transport mechanisms. (7) can potentially
describe an oversimplified model for Transcytosis across membrane announcing
physiochemical reactive properties using a second-order reaction-diffusion
system [117].

######  Example 1.

Simplified adevction-reaction-diffusion channel in an unbounded environment:
Given the impulsive instantaneous release source
$S(\mathrm{\bm{r},t})=\delta(\mathrm{\bm{r}}-\mathrm{\bm{r}_{0}})\delta(t-\tau_{0})$,
where $\delta(\cdot)$ denotes the Dirac’s delta function, and boundary
condition $\mathcal{C}(\mathrm{|\bm{r}|\rightarrow\infty,t})=0$, diffusion
coefficient $a_{\mathrm{\bm{r},t}}={D}$, uniform bulk flow
$b_{\mathrm{\bm{r},t}}={v}$, and the first-order degradation of rate $k_{d}$,
i.e.,
$\mathcal{F}(k_{d},\mathcal{C}(\mathrm{\bm{r},t}))=k_{d}\mathcal{C}(\mathrm{\bm{r},t})$,
the solution of (II-D) in an unbounded environment is obtained as [30, 50]:

$\displaystyle\mathcal{C}(\mathrm{\bm{r},t})$ $\displaystyle=\frac{1}{(4\pi
D(t-\tau_{0}))^{3/2}}$ (9)
$\displaystyle\quad\times\exp\big{(}-k_{d}(t-\tau_{0})-\frac{|\mathrm{\bm{r}}-(t-\tau_{0}){v}-\mathrm{\bm{r}_{0}}|^{2}}{4D(t-\tau_{0})}\big{)},$

for $t\geq\tau_{0}$.

####  II-D2 Wave-propagation Channels

In typical wireless communications, various technologies, e.g.,
electromagnetic, ultrasonic, and RF, are employed for information
transmission. The S2S, S2FC, FC2GW, and S2GW links may be modeled using (II-D)
with $\beta=2$, $a_{\mathrm{\bm{r},t}}=c^{2}$, and
$\mathcal{P}\big{(}S(\mathrm{\bm{r},t})\big{)}=\Gamma\frac{\partial}{\partial
t}\mathcal{S}(\mathrm{\bm{r},t})$, in which $\Gamma$ is the Grüneisen
coefficient which depends on the medium characteristic for the propagating
wave [94].

######  Example 2.

Opto-ultrasonic channel model: One of the interesting channels in WBAN is the
opto-ultrasonic channel described by (II-D) [94, 52]. This channel is based on
ultrasonic waves (i.e., high-frequency222For instance, wideband ultrasonic
signals (in the range of hundreds of MHz for both bandwidth and central
frequency) are generated in response to nano-seconds-long optical pulses.
acoustic waves) generated in response to irradiation of optical signals to the
medium. Joint deployment of light beams and ultrasonic waves allows for
external interventions at the nano-scale, which is critical for connecting in-
body nano-networks to off-body WBANs. Assuming Gaussian spatio-temporal pulse
profile, i.e., $S(\mathrm{\bm{r},t})=G(\mathrm{\bm{r}})\delta(t)$, the
received pressure at position $\bm{r}$ and time $t$ under the far-field
assumption is described as follows:

$\displaystyle\mathcal{C}(\mathrm{\bm{r},t})=-\frac{\Gamma}{2(2\pi)^{3/2}c^{2}\bm{r}}\frac{t-\tau}{\tau_{e}^{3}}\exp{\bigg{(}-\frac{1}{2}(\frac{t-\tau}{\tau_{e}})^{2}\bigg{)}},$
(10)

where $\tau_{e}$ depends on both the width of the optical beam and optical
pulse duration. Also, $\tau=\frac{\bm{r}}{c}$ and the source
${S}(\mathrm{\bm{r},t})$ is the normalized absorbed heat by tissue
representing the spatio-temporal source of pressure [52].

We note that the concentration signal and corresponding signal received by the
individual sensors, FC, or GW are random variables whose mean satisfies the
obtained average field given by (II-D). In most scenarios, the received signal
is well approximated by a Gaussian distribution, i.e.,
$y(t)\sim\mathcal{N}(\mu(\mathrm{\bm{r},t}),\sigma^{2}(\mathrm{\bm{r},t}))$
where $\mu(\mathrm{\bm{r},t})$ and $\sigma^{2}(\mathrm{\bm{r},t})$ denote the
mean and the variance of the received signal, respectively. The variance
$\sigma^{2}(\mathrm{\bm{r},t})$ can be either signal-dependent (diffusion-
based links) or signal-independent (wave-propagation-based links).

TABLE I: Summary of the ADL methods. Ref. |  Abnormality Detection | Abnormality Localization
---|---|---
Stationary sensors |  Mobile sensors (non-propelled) | Stationary sensors (passive) | Mobile sensors
Non-cooperative detection (single sensor) |  Cooperative detection (multiple sensors and an FC) |  Non-cooperative sensing/ activation |  Cooperative sensing/ activation |  Classification |  Discrete-model |  Continuous-model |  Non-propelled (passive) |  Self-propelled (active) |  Externally-propelled (active)
[53, 41, 39] | $\checkmark$ |  |  |  |  |  |  |  |  |
[42, 72, 118, 54, 119, 90, 70, 120, 121] |  | $\checkmark$ |  |  |  |  |  |  |  |
[31, 43, 122, 45, 78, 44] |  |  | $\checkmark$ |  |  |  |  |  |  |
[38] |  |  | $\checkmark$ |  |  |  |  |  | $\checkmark$ |
[40] |  |  |  | $\checkmark$ |  |  |  | $\checkmark$ |  |
[55, 56, 123, 124, 59, 12] |  |  |  |  |  |  | $\checkmark$ |  |  |
[125, 126, 127, 66, 128] |  |  |  |  | $\checkmark$ | $\checkmark$ |  |  |  |
[101, 129] |  |  |  |  |  |  |  |  | $\checkmark$ | $\checkmark$
[130] |  |  |  |  |  |  |  |  |  | $\checkmark$
[131] |  |  |  |  |  |  |  |  | $\checkmark$ |
[49, 48] |  |  |  |  |  |  |  |  | $\checkmark$ |
[132, 133, 134, 135] |  |  |  |  |  |  |  |  |  | $\checkmark$
[69, 136, 47] |  |  |  |  | $\checkmark$ |  |  |  |  | $\checkmark$
[137] |  |  |  |  |  |  |  |  | $\checkmark$ | $\checkmark$
[138, 139] |  |  |  |  |  |  |  |  | $\checkmark$ | $\checkmark$
[108, 68] |  |  |  |  |  |  |  | $\checkmark$ |  | $\checkmark$

##  III Abnormality Detection Schemes

MC-based abnormality detection schemes can be categorized based on using
stationary or mobile sensors. In both cases, the abnormality detection can be
cooperative or non-cooperative. In addition to the detection scheme, the
sensors’ sensing/activation can be achieved in either a cooperative or a non-
cooperative manner. See Table I for the list of abnormality detection schemes,
which will be discussed in this section. Further, we discuss the sensing and
communication features in abnormality detection schemes, including abnormality
recognition methods, sensor mobility, receiver type of the sensors and the FC,
and sensing and S2FC/GW communication links, summarized in Table II.

We first formulate the abnormality detection problem using the hypothesis
testing approach in Subsection III-A. Then, in Subsection III-B, we describe
the abnormality detection schemes using stationary sensors, and in Subsection
III-C, we describe the schemes using mobile sensors.

###  III-A Abnormality detection problem formulation

The abnormality detection problem is modeled as a binary hypothesis testing
problem; $H_{0}$, the absence of abnormality, and $H_{1}$, the existence of
abnormality, based on the observation vector $\bm{y}=[y_{1},y_{2},...y_{n}]$.
The decision is made using a test statistic $T(\bm{y})$ and a set $A$ as
follows:

$\displaystyle\begin{cases}H_{1},~{}T(\bm{y})\in A,\\\ H_{0},~{}T(\bm{y})\in
A^{\textrm{c}}.\end{cases}$ (11)

Assume $f_{H_{0}}(\cdot)$ and $f_{H_{1}}(\cdot)$ are the probability density
functions of $T(\bm{y})$ conditioned on hypotheses $H_{0}$ and $H_{1}$,
respectively. The probability of detection in binary hypothesis testing
problem, i.e., the probability of correctly deciding $H_{1}$, is

$\displaystyle P_{\textrm{d}}=\mathbb{P}(T(\bm{y})\in A|H_{1})=\int_{l\in
A}f_{H_{1}}(l)dl,$ (12)

and the probability of false alarm, i.e., the probability of falsely deciding
$H_{1}$, is

$\displaystyle P_{\textrm{fa}}=\mathbb{P}(T(\bm{y})\in A|H_{0})=\int_{l\in
A}f_{H_{0}}(l)dl.$ (13)

Further, the miss-detection probability, i.e., the probability that the
presence of $H_{1}$ is missed, is defined as

$\displaystyle P_{\textrm{m}}=\mathbb{P}(T(\bm{y})\in
A^{\textrm{c}}|H_{1})=1-P_{\textrm{d}}.$ (14)

There is a trade-off between the false alarm and miss-detection probabilities,
i.e., reducing one of them increases the other one. The goal in this problem
is to find a good test statistic $T$ and the set $A$, leading to a low miss-
detection probability $P_{\textrm{m}}$ as well as a low false alarm
probability $P_{\textrm{fa}}$.

We first describe a binary hypothesis test with a fixed sample size $n$ and
then we extend it to a sequential test with a variable sample size. For the
case of the binary hypothesis test, the Neyman-Pearson lemma gives the optimal
test, where the Neyman-Pearson criterion deals with the trade-off between
$P_{\textrm{fa}}$ and $P_{\textrm{m}}$ by solving the following optimization
problem [140]:

$\displaystyle\min_{T,A}P_{\textrm{m}}$ $\displaystyle\textrm{subject
to}~{}P_{\textrm{fa}}\leq\gamma.$ (15)

According to the Neyman-Pearson lemma, given $f_{H_{0}}$ and $f_{H_{1}}$, the
optimal decision rule that minimizes $P_{\textrm{m}}$ (or equivalently
maximizes $P_{\textrm{d}}$), for a fixed sample size $n$ and any
$P_{\textrm{fa}}$, is the likelihood ratio test (LRT) as follows [140]:

$\displaystyle\lambda_{\textrm{LR}}(\bm{y})\underset{H_{0}}{\overset{H_{1}}{\gtrless}}\mathcal{T},$
(16)

where $\mathcal{T}$ is the threshold that satisfies
$P_{\textrm{fa}}=\int_{\mathcal{T}}^{\infty}f_{H_{0}}(l)dl$ and
$\lambda_{\textrm{LR}}(\bm{y})$ is the likelihood ratio as follows:

$\displaystyle\lambda_{\textrm{LR}}(\bm{y})=\frac{\mathbb{P}(\bm{y}|H_{0})}{\mathbb{P}(\bm{y}|H_{1})}.$
(17)

The optimal decision rule can also be written using log-likelihood ratio
(LLR), i.e.,
$\lambda_{\textrm{LLR}}(\bm{y})=\log(\lambda_{\textrm{LR}}(\bm{y}))$, as

$\displaystyle\lambda_{\textrm{LLR}}(\bm{y})\underset{H_{0}}{\overset{H_{1}}{\gtrless}}\log(\mathcal{T}).$
(18)

An alternative approach to deal with the trade-off between $P_{\textrm{fa}}$
and $P_{\textrm{m}}$, introduced by Wald [141], is to assume a sequential test
with variable sample size $n$ and to minimize $n$ (i.e., to minimize the
decision delay) for predetermined values of $P_{\textrm{fa}}$ and
$P_{\textrm{m}}$. This criterion yields Wald’s sequential probability ratio
test (SPRT) [142], which uses the observation sequence $(y_{1},y_{2},...)$ and
obtains the likelihood ratio sequence $(\lambda_{1},\lambda_{2},...)$, where

$\displaystyle\lambda_{n}=\lambda_{\textrm{LR}}(y_{1},y_{2},...,y_{n})=\frac{\mathbb{P}(y_{1},...y_{n}|H_{0})}{\mathbb{P}(y_{1},...y_{n}|H_{1})},$
(19)

for $n\in\mathbb{Z}^{+}$. The decoder continues sampling as long as
$\mathcal{T}_{1}<\lambda_{n}<\mathcal{T}_{2}$ and stops collecting the samples
after the $\tilde{n}$-th observation and decides on a hypothesis if
$\lambda_{\tilde{n}}\leq\mathcal{T}_{1}$ (decides $H_{0}$) or
$\lambda_{\tilde{n}}\geq\mathcal{T}_{2}$ (decides $H_{1}$). Hence, Wald’s SPRT
can be characterized as

$\displaystyle\tilde{n}$
$\displaystyle=\inf\\{n>0:\lambda_{n}\notin(\mathcal{T}_{1},\mathcal{T}_{2})\\},$
(20)
$\displaystyle\qquad\begin{cases}H_{0}:~{}\lambda_{\tilde{n}}\leq\mathcal{T}_{1},\\\
H_{1}:~{}\lambda_{\tilde{n}}\geq\mathcal{T}_{2},\end{cases}$ (21)

where $\mathcal{T}_{1}$ and $\mathcal{T}_{2}$ are obtained according to the
predefined values of $P_{\textrm{fa}}$ and $P_{\textrm{m}}$ as
${P_{\textrm{fa}}=\int_{l\geq\mathcal{T}_{2}}f_{H_{0}}(l)dl}$ and
${P_{\textrm{m}}=\int_{l\leq\mathcal{T}_{1}}f_{H_{1}}(l)dl}$. This approach is
called sequential hypothesis testing which has been used for MC-based
abnormality detection in [120, 119].

TABLE II: Sensing and reporting features in abnormality detection schemes. Ref. |  Abnormality recognition |  Sensor and FC mobility |  Receiver type at the sensors and FC |  Sensing link |  S2FC/GW communication link |  Sensor decision
---|---|---|---|---|---|---
[41, 39] | Molecule release | Stationary sensor | Reactive sensor | Molecular | — | Hard
[42] | General |  Stationary sensors and FC | Reactive sensors | Molecular | General | Hard
[118] | General |  Stationary sensors and FC | Absorbing FC | General | Molecular | Soft
[72] | Molecule release |  Stationary sensors and FC | Absorbing | Molecular | Molecular | Hard
[54] | Molecule release |  Stationary sensors and FC | Reactive sensors and FC | Molecular | Molecular | Hard
[90, 120] | General |  Stationary sensors and FC | Absorbing FC | General | Molecular | Soft
[121, 119] | General | Stationary sensors and FC | Absorbing FC | General | Molecular | Hard
[31] | Molecule release |  Mobile (non-propelled) sensors and stationary FC | Transparent | Molecular | Molecular | Soft
[43] | General |  Mobile (non-propelled) sensors and FC | Absorbing FC | General | Molecular | Hard
[122] | Molecule release |  Mobile (non-propelled) sensors and stationary FC | Transparent FC | General | Molecular | Hard
[44] | Molecule release |  Mobile (non-propelled) sensors and stationary GW | General | Molecular | Wireless | Hard
[40] | General |  Mobile (non-propelled) sensors ans stationary FCs | Transparent FC | General | Molecular | Hard

Alternatively, the decision-maker may choose from a subset of actions to
control the information extracted from the observation data and enhance the
decision-making fidelity [143, 144]. This approach, which is called active
hypothesis testing, was first introduced by Chernoff [145] for the sequential
case. However, it can be used in both fixed sample size and sequential cases
[146]. Active hypothesis testing has been used for abnormality detection in
[147, 148]. However, there are no works in the MC literature that use this
approach.

###  III-B Abnormality Detection Schemes using Stationary Sensors

In this subsection, we review the abnormality detection schemes that use
stationary sensors. The schemes are divided into non-cooperative detection
schemes (single sensor) and cooperative detection schemes (multiple sensors
and an FC). In addition to the detection scheme, in principle, the sensors can
be cooperative or non-cooperative in their sensing/activation. However, there
are no schemes in the literature of MC that use stationary sensors with
cooperative sensing/activation.

####  III-B1 Non-cooperative detection (single sensor)

Motivated by to the COVID-19 pandemic, [41, 39] use a single electronic-based
bio-sensor (Silicon NanoWire (Si-NW) field-effect transistor (FET)) in the
environment to detect virus-laden aerosols from the exhaled breath of an
infected human to viruses (such as influenza or COVID-19). To detect viruses
using Si-NW FET, the antibody receptors of the virus are placed on Si-NW
between the transistor’s source and drain. The antigens on the surface of the
viruses bind to these receptors, and as a result, a current change occurs
across the source-drain channel.

In [41], the virus transmission using aerosols is modeled as an MC system. The
aerosol channels with continuous sources (associated with breathing) and
impulsive (jet) sources (associated with coughing and sneezing) are analyzed
for a large room in the presence of airflow. The steady-state and transient
channel responses are obtained for certain initial and boundary conditions.
The frequency response of the system is also derived for choosing the
appropriate sampling frequency. Further, a bio-sensor detector along with an
air-sampler is proposed to detect the viruses using a predefined threshold.
The air-sampler consists of an ionizer, which induces the aerosols with the
negative charge, and a charged electrode, which is induced with the positive
charge and hence attracts the negatively charged aerosols. The performance of
the system is evaluated by obtaining the miss-detection probability using
simulation, and the effects of different parameters such as distance, virus
flow rate, and air flow velocity are analyzed on the miss-detection
probability. It is shown that these parameters have a significant effect on
the system performance.

In [39], the viral aerosol detection problem is considered in a bounded
environment with quiescent air, and partial/full absorption and reflection
from the walls. The breathing, coughing, and sneezing sources are modeled as
clouds with a relatively symmetrical conical shape and an initial velocity
that vanishes eventually through space and time. Hence, there is a zero-
velocity zone where the aerosols move only due to diffusion. The authors in
[39] focus only on the zero-velocity zone and obtain the concentration of the
virus-laden aerosols in this zone using an impulsive point source and
continuous circular source. The maximum-likelihood threshold for the virus
detector and the probability of miss-detection are obtained and the effect of
the channel parameters (such as detector location and sampling time) is
investigated using simulation. The authors show that the reflecting and
absorbing boundaries have a significant effect on the concentration profile of
the aerosols and the virus detector performance.

####  III-B2 Cooperative detection (multiple sensors and an FC)

In [42, 118, 72, 54, 90, 120, 121, 119], cooperative abnormality detection is
considered with a two-tier network consisting of multiple stationary sensors
with non-cooperative sensing/activation and a stationary FC.

In [42], two scenarios are studied which consider different abnormality
recognition methods, categorized as molecule release, medium effects, and
molecule absorption. In the first scenario, specific molecules are released
from abnormal entities (molecule release). In the second scenario, it is
assumed that the abnormal entity changes the medium parameters or absorbs the
molecules released from normal entities. For example, it may change the
absorbing rate of the molecules at the sensors, devitalize the receptors or
change the temperature of the receptors (medium effects), or it may be a
competitor cell for the sensors that absorbs the molecules (molecule
absorption). The molecule sources in both scenarios are modeled by
transmitters. In the first scenario, the molecule source is the abnormal
entity that releases some molecules. In the second scenario, the molecule
sources are other normal entities that release some molecules both in the
presence and absence of the abnormality and the abnormality changes the
concentration of these molecules by absorbing the molecules or changing the
channel parameters. Multiple sensors that can react with these molecules,
acting as molecular receivers, are injected into the medium and the presence
of the abnormality is detected by noticing the change in the received
molecular concentration. The sensors communicate their hard decisions
(activation flags) to the FC through a micro-communication channel, which can
be molecular or non-molecular and is modeled by an additive white Gaussian
noise channel. The final decision is made by applying a threshold on the
received signal at the FC, where the optimum threshold is obtained using the
maximum a-posteriori (MAP) rule. This is equivalent to the sub-optimal OR
rule, i.e., the FC decides on the existence of the abnormality if at least one
of the sensors reports its existence. Further, the detection and false alarm
probabilities of the two-tier network are analyzed, and the number of sensors
is optimized for the desired performance. Moreover, [42] considers the impact
of temporal and spatial correlation of the Gaussian observations which may
lead to noticeably inaccurate results if they are not taken into account.

In [118], the sensors measure some input variables which are affected by the
abnormality (such as the concentration of biomarker molecules, temperature, pH
value). The sensors make soft decisions by normalizing and quantizing the
measured variables. These decisions are reported to an absorbing FC by
releasing the same types of molecules (STM) or different types of molecule
types (DTM) For the STM scenario, the optimal detector is obtained at the FC
using LLR, leading to a simple decision rule. For the DTM scenario, since the
optimal rule has a complex form, sub-optimal decisions are made based on the
max-log, maximum ratio combining, and Chair-Varshney approximations of LLR.
Further, a two-stage sub-optimal detector is proposed for the DTM scenario: In
the first stage, the FC makes local decisions for each sensor using their soft
observations; in the second stage, the FC makes the final decision using the
local decisions and applying a threshold on the sum of the decisions. The
overall detection and false alarm probabilities are investigated for both
scenarios. A numerical method is proposed for the performance evaluation of
LLR-based detectors in terms of their false alarm and miss-detection
probabilities. Closed-form expressions are derived for the performance of
optimal detector for STM and sub-optimal maximum ratio combining and two-stage
detectors for DTM. Further, the asymptotic performance for the large number of
sensors is evaluated for the STM and DTM, providing upper bounds for the
schemes. It has been shown that for small background noise, the optimal
detector for the DTM scenario has a better performance compared to the STM,
while for large noise, STM outperforms DTM.

In [72], the sensors are absorbing artificial cells and the FC is a fixed
smart probe, located along a blood vessel, which sends its information to a
bio-cyber interface (GW). The abnormal entities are diseased cells that
release biomarkers in the medium and the range of the biomarker concentration
value distinguishes the healthy and diseased cells. The biomarker release
waveform is modeled by the Weibull function, and the sensors detect the
abnormality by capturing the variations in the biomarker concentration based
on its steady-state value and the generalized LRT for independent Poisson
observations. The sensors send a molecular message to inform their hard
decision to the FC. If a sensor detects the abnormality, it releases a
concentration waveform over time, which is considered the Weibull function;
otherwise, it stays silent. The generalized LRT yields a complex decision rule
at the sensors. Hence, a simplified decision rule and a lower bound on its
detection probability for a limited false alarm probability are obtained by
bounding the likelihood ratio using the maximum likelihood (ML) estimate of
the average number of received molecules at the sensors. Further, assuming a
symmetric topology for the network, the decision rule at the FC, a lower bound
on the total detection probability, and an upper bound on the total false
alarm probability are derived. Moreover, the optimum thresholds at the sensors
and the FC, which minimize the total detection probability for a limited total
false alarm probability, are obtained numerically, and the effect of different
parameters on the system performance is numerically analyzed.

The authors in [54] investigate the early detection of the abnormality (such
as a tumor) inside the body. The sensors and FC, equipped with reactive
receivers, are located near a suspicious area to detect the abnormal entity
releasing biomarkers in the medium. The target location and the biomarker
release rate, assumed to be constant, are unknown at the sensors and the FC,
while the locations of the sensors are known at the FC. The average received
signal, i.e., the average number of activated receptors at each sensor, and
its steady-state probability mass function are derived, which is used to
obtain the optimal decision rule at the sensors. The presence of the target is
detected at the sensors using the optimal detector with unknown parameters
which turns out to be uniformly most powerful (UMP) test, having a performance
equal to the Neyman-Pearson detector with known parameters. The UMP test
compares the received signal with a threshold instead of using the LLR in the
Neyman-Pearson detector. Each sensor makes its hard decision using the optimal
decision rule and sends it to the FC using different molecule types (different
from the biomarker molecules). The final decision is made at the FC using sub-
optimal detectors including generalized LRT and generalized locally optimum
detector (the detector proposed in [149]). Further, a genie-aided detector
(aware of the location of the target and its biomarker release rate) is
developed at the FC leading to an upper bound on the performance of the
proposed detectors.

In [90, 120], the authors consider MC-based abnormality detection for a
variety of medical and environmental applications. The sensors sense some
variables in the medium affected by the abnormality (molecule
release/absorption or medium effects) and make general soft decisions based on
their measurements, i.e., the output of the sensors are quantized values
between $0$ and $1$ (this is equivalent to the hard decision scenario by
setting two quantization levels). The soft decision of the sensors is then
sent to an absorbing FC using the same molecule type for all sensors and the
final decision at the FC is made using neural network detectors in [90] and
sequential tests in [120]. In [90], feed-forward neural network (FF-NN) and
recurrent neural network (RNN) structures are used for abnormality detection
based on the training data obtained from the existing statistical models
(Poisson channel model). The training data is a set of received signal
sequences labeled with the corresponding hypothesis. The neural network
detector, trained by this set, decides on the hypothesis of a given sequence
of the received signal (test). It is shown that the neural network-based
detectors perform better than the LLR detector in [118] for unknown channel
parameters, since they provide a better approximation to the unknown
parameters. In [120], a decision fusion approach is proposed based on Wald’s
SPRT in (21), which uses a variable observation window size and minimizes the
average sample size required for the decision. The authors in [120] propose to
use the average LLR (taking the average from the likelihood function over the
sum of sensor outputs) for both fixed sample size and sequential detectors. It
is shown that the sequential detector needs a lower sample size on average and
hence a shorter decision delay compared to the fixed sample size approaches
for a given detection performance.

In [121], the event detection problem is considered in mediums with anomalous
diffusion such as turbulent flow, which is usually modeled by Lévy walk,
instead of the normal diffusion where molecules diffuse according to Brownian
motion. Multiple sensor nano-machines monitor the environment for certain
events which may occur in the medium. The sensors may detect the event in each
time slot and release one molecule of the same type upon detecting the event
at the beginning of the next time slot. The released molecule is assumed to
diffuse according to Lévy walk to reach an absorbing FC. The inter-symbol
interference caused by the released molecules from the previous time slots is
also considered. Thereby, the number of received molecules from the sensors
for each hypothesis, corresponding to the presence and absence of the event,
is modeled as a Poisson-Binomial random variable which is approximated to have
a Poisson distribution for a large number of time slots. Based on the Poisson
observations, the LLR detector is obtained at the FC to detect the
abnormality. Further, an online algorithm based on reinforcement learning is
proposed to optimize the time slot duration, and thereby the network
throughput. The performance of the proposed algorithm, in terms of the network
throughput, and the complexity of the algorithm, in terms of the training
phase duration, are investigated using numerical simulations. It is shown that
there is a tradeoff between performance and complexity. But, the proposed
algorithm has a good performance for a short training phase duration.

In [119], a moving abnormality is assumed in the medium starting from a random
location and time. A sensor network is considered to monitor the environment.
The sensors are placed at known locations and partition the environment into
different sections. Each sensor senses the environment for the abnormality
with certain probabilities of detection and false alarm and if it detects the
abnormality, it releases some molecules (which are of different types for
different sensors) to inform the FC with an absorbing receiver. Two scenarios
are considered at the FC, referred to as stopping time and monitoring
scenarios, in which the initiation time of the abnormality and how it spreads
in the environment are investigated, respectively. For the stopping time
scenario, binary (two hypotheses) and non-binary (more than two hypotheses)
cases are considered regarding the abnormality. In each case, to detect the
abnormality initiation time, the quickest change detection and quickest
detection and identification of change problems are investigated. For these
problems, the stopping time and its corresponding hypothesis that minimize the
decision delay are obtained, with constraints on the false alarm and/or miss-
detection probabilities. The optimal solutions for these problems are obtained
using a partially observable Markov decision process framework, which is
complex, and hence a sub-optimal solution is obtained using myopic policy,
which is shown to have an acceptable performance.

###  III-C Abnormality Detection Schemes using Mobile Sensors

Here, we describe the abnormality detection schemes using mobile sensors. In
most of these schemes, the sensors move due to the diffusion and/or the medium
flow, i.e., they are non-propelled mobile sensors. Similar to the schemes
using stationary sensors, the schemes using mobile sensors can have
cooperative detection (multiple sensors and an FC) or non-cooperative
detection (a single sensor). In addition, the sensors can be cooperative or
non-cooperative in their sensing/activation. However, there are no schemes
with a single mobile sensor in the literature and all existing schemes with
mobile sensors use cooperative detection. Hence, we divide the scheme into
cooperative and non-cooperative sensing/activation.

####  III-C1 Non-cooperative sensing/activation

In [31, 43, 38, 122, 45, 78], multiple mobile sensors with non-cooperative
sensing/activation and a stationary/mobile FC is used for cooperative
detection of the abnormality in the medium. In these schemes, the sensors may
be absorbed by the FC to report their measurements and data. More accurately
the sensors may move towards the FC and the FC absorbs them for reading their
activation levels [31, 40].

In [31], early cancer detection using multiple mobile nano-sensors in blood
vessels is studied. The cancer cells emit biomarkers in the blood vessels,
which propagate by the diffusion and flow, and may be detected by the sensors
with transparent receivers. A simplified 2-D model is considered for the
cardiovascular system, and the production of the biomarkers from the cancerous
and healthy cells are assumed as in (3). The sensors move in different paths
according to the topology of the blood vessel network and blood flow direction
and are eventually collected by a stationary FC (see Fig. 5), where the final
decision is made by reading the activation level of the sensors. The optimal
LRT is obtained at the FC, which depends on the knowledge of the cancerous
cell location and network topology, and hence it is not suitable for practical
applications. Therefore, a sub-optimal sum detector is proposed without
requiring the network topology. The system model assumptions are validated
using particle-based simulations, and the miss-detection probability is
obtained using Monte Carlo simulations for evaluating the performance of the
proposed scheme. It is shown that the proposed scheme substantially
outperforms the benchmark scheme, which uses stationary sensors fixed at the
location of the FC (equivalent to the conventional blood test), and applies
equal gain combining on the sensor observations (see Fig. 5).

Figure 5: Abnormality detection with mobile cells in the cardiovascular system
[31]. Figure 6: Probability of miss-detection versus biomarker release rate
($\mu$) [31]. During the 7 month time interval, that $\mu$ changes from
$0.5~{}\textrm{s}^{-1}$ to $2000~{}\textrm{s}^{-1}$, the miss-detection
probability of the benchmark scheme (conventional blood test) is more than
$10^{-1}$. Further, using the proposed sum detector, for the example
considered, the cancer is detected at least 6 months earlier than the
conventional blood test.

In [43], multiple mobile nano-sensors and a mobile FC with absorbing receivers
are used inside a single blood vessel to detect an abnormality. The sensors
and the FC move along the vessels due to the blood flow. The sensors sense the
abnormality with certain detection and false alarm probabilities and send
their local decisions to the FC using different molecule types and ON-OFF
keying modulation. The FC receives the sensors’ transmission signals and uses
a sub-optimal detector that is based on the approximation of LLR. The FC makes
the final decision about the presence of the abnormality using AND/OR rules.
For the AND rule, the FC decides on the existence of the abnormality if all of
the sensors report its existence.

In [122], the _in-vivo_ detection of the abnormality is considered using an MC
system consisting of multiple mobile nano-sensors and a stationary FC in a
fluid medium with drift (such as blood vessels). The abnormal entity releases
biomarkers at a specific rate to the blood vessels detectable by the sensors.
The nano-sensors are assumed transparent receivers randomly distributed in the
environment with limited movability, i.e., they can hold their position to
sense and sample the biomarkers to detect the presence of abnormality. Each
nano-sensor decides individually on the presence of the abnormal entity based
on its observation, with some probabilities of detection and false alarm, and
informs its decision to the FC using two different molecule types (type 1 for
the presence and type 2 for the absence of abnormality). The final decision is
made at the FC using the optimal Chair-Varshney fusion rule and sub-optimal
counting rule, also called $K$ out of $N$ fusion rule, i.e., the FC decides on
the presence of abnormality if $K$ out of $N$ sensors report its existence.
The false alarm and detection probabilities are obtained for the optimal and
sub-optimal fusion rules and it is shown that the sub-optimal counting rule
gives an acceptable performance.

In [38], an MC system is proposed for tumor detection using mobile sensors
inside the blood vessels and a stationary smart probe that communicates the
detection of the cancer cells to the outside of the body. The smart probe can
be used to detect the tumor cells originating from the tumor site and
circulating in the cardiovascular system. Due to the number of circulating
cells in the early stage of cancer, another approach is to use small mobile
sensors circulating in the blood vessels to detect circulating cells and
release a burst of molecules to inform their decisions to the smart probe. In
another scenario, small mobile sensors with slow speed (bacteriobots with
self-propulsion mechanism) move towards the tumor cells to detect the tumor
site. They inform their decisions to several large mobile sensors that move
faster in the blood vessels and eventually reach the smart probe, where the
final decision is made and sent to the outside of the body.

Figure 7: Abnormality detection with cooperative sensors in pipelines [40].
Figure 8: Probability of detection error versus the number of sensors
($N_{\textrm{s}}$) using cooperative sensors in pipelines [40]. $\alpha$ is
the detection probability of the sensors at the abnormality and $M$ is the
number of stored molecules at the sensors ($M=0$ corresponds to the non-
cooperative activation for the sensors).

In [44], early infection detection is considered using mobile nano-sensors
inside blood vessels, which is faster than conventional blood tests that take
48-72 hours. The bacterial infections release quorum-sensing molecules for
communication among each other. These molecules also reach the blood vessels
and may be detected by the nano-sensors. The sensors circulate inside blood
vessels and may reach the sensing region (where the concentration of the
quorum-sensing molecules is above a detection threshold), which is obtained
using COMSOL simulations. If a sensor enters the sensing region and detects
the infection, it reports its sensing data to a GW, which is placed out of the
body, using ultrasonic or THz signals (though the channel is considered to be
ideal). The GW applies the counting rule on the sensor observations to make
the final decision. To evaluate the performance of this system, the
probability of a sensor entering the sensing region is obtained using machine
learning. To this end, the traveling behavior of the sensors inside the human
circulatory system is modeled using BloodVoyagerS [150] (a simulation
framework for nano-networks). Further, the traveling path of the sensors in
the circulatory system (e.g., the vessels) is modeled as a Markov chain and
the transition probabilities are obtained using ML methods. Then, using the
transition probabilities, the stationary probability of a sensor located in a
vessel corresponding to the sensing region is obtained for each organ inside
the body.

Similarly, the authors in [45] propose an MC network to detect and treat
early-stage infections in the human body. Several nano-machines inside the
body sense their environment to detect infections and are able to send,
receive, and forward messages forming an in-body network, which may
communicate using molecular or wireless signals. The nano-machines communicate
with a control station out of the body (off-body network) using GWs which are
implants or on-body micro-organisms. Since the bio-nano-machines operate
inside the body, they are mobile and move in the vessels due to the blood
flow.

####  III-C2 Cooperative sensing/activation

In [40, 151], the sensors share their decisions with other sensors to
cooperate effectively in sensing/activation.

In [40], an MC setup is proposed in the cylindrical environment that uses
cooperative mobile sensors to detect and localize the abnormality. While most
of the ADL methods in MC literature focus on the micro-scale in-body
applications, [40] considers the macro-scale applications and investigates
detection and localization of the abnormality (such as leakage) in pipelines.
For cooperative activation, each sensor after detecting the abnormality and
getting activated releases molecules to activate other sensors. There are
multiple sensory regions with local FCs at the end of each region (see Fig. 7)
that collect the sensors and read their activation levels to decide about the
presence of the abnormality using binary hypothesis testing. Two types of
sensors are considered in [40] based on their activation strategies:
memoryless and aggregate. The memoryless sensor is activated based on its
observation in a single time instance. The aggregate sensor is activated based
on the summation of its observations in multiple time instances. The
probabilities of false alarm, miss-detection, and detection error are obtained
for each sensor type. As expected, the aggregate sensors outperform the
memoryless sensors to detect the abnormality. Further, the authors show that
cooperative activation of sensors improves the performance significantly (see
Fig. 8).

In [151], an MC system using multiple mobile nano-machines with reactive
receivers is proposed for information dissemination in the system. In this
system, the nano-machines have two _infected_ and _uninfected_ states that
correspond with awareness and unawareness of the information being
disseminated, respectively. The infected nano-machines release information
molecules, and the uninfected nano-machines may sense and detect them to
change their states and become infected. By repeating this process, the
information originating from a source node can be disseminated in the system
to reach a destination node. This information dissemination method can be used
for target detection by propagating the information about the target in the
system, in which the source node is the abnormal entity and the destination
node is an FC or a controlling node, and the sensors have cooperation in their
sensing/activation.

##  IV Abnormality Localization Schemes

In this section, we review the papers in the area of abnormality localization
mainly for medical applications. To localize the abnormality, mobile [40, 67,
66, 152] and stationary [66, 55, 12, 56, 79, 153, 57, 154, 58, 155, 124, 128,
156, 157, 123, 59, 126, 127] sensors may be used. The localization schemes are
also categorized into passive or active schemes. In passive localization, the
sensors solely determine the location of the abnormality while in the latter,
the sensors collectively migrate towards the abnormality using a propulsion
mechanism, e.g., in response to an abnormal gradient or actuating external
field; see Table I.

###  IV-A Abnormality Localization Problem Formulation

Generally, the abnormality propagation field is characterized by (II-D). For
the special case of a diffusion field, it is assumed that the abnormality
source releases type-$A$ molecules at an unknown rate $Q_{\mathrm{Ab}}$ at an
unknown position ${\mathrm{\bm{r}}_{\mathrm{Ab}}}$. The diffusion coefficient
for type-$A$ molecules is $D$. Assume that the $j$th sensor is located at
$\mathrm{\bm{r}}_{\mathrm{s}}(j)$. Considering an analytical concentration
profile
$\mathcal{C}(\mathrm{\bm{r}}_{\mathrm{s}}(j),{\mathrm{\bm{r}}_{\mathrm{Ab}}},Q_{\mathrm{Ab}})$,
the standard estimation problem centralized in the FC can be formulated using
the following least-square-based method:

$\displaystyle\underset{{\mathrm{\bm{r}}_{\mathrm{Ab}},Q_{\mathrm{Ab}}}}{\text{minimize}}$
$\displaystyle\sum_{j=1}^{M}\big{|}\mathcal{C}(\mathrm{\bm{r}}_{\mathrm{s}}(j),{\mathrm{\bm{r}}_{\mathrm{Ab}}},Q_{\mathrm{Ab}})-y_{j}\big{|}^{2},$
(22)

where $M$ denotes the number of the sensors in the environment and $y_{j}$ is
the noisy concentration measured by the $j$th sensor. Also, $Q_{\mathrm{Ab}}$
and ${\mathrm{\bm{r}}_{\mathrm{Ab}}}$ are optimization variables of the above
least-squares problem.

The system may include mobile sensors that should localize and potentially
follow the abnormality, as presented in Fig. 1. The sensors may be classified
into leaders or followers. A leader is responsible for both abnormality
detection and localization. Once the abnormality is detected, two options are
available to steer the follower agents towards the abnormality for further
tasks. The steering procedure depends on the sensor propulsion type. In the
case of self-propelled sensors in the MC framework, the leader may release
type-$B$ molecules to coordinate the follower sensors towards the abnormality.
For the externally-propelled sensors, a feedback system consisting of sensors,
FC, and GW is required to localize the abnormality and modulate the external
intervention, accordingly.

Figure 9:  Schematic representation of the hybrid localization system. The
solid lines refer to links having both MC and wireless (RF, magnetic, and
acoustic) technologies. The dashed lines refer to only the wireless
technology.

Fig. 9 depicts the hybrid feedback signaling flow among leader sensors (via
cooperative MC/THz links), follower sensors, FC, and GW. The solid lines refer
to the links having both MC and wireless (RF, magnetic, and acoustic)
technologies. The dashed lines refer to only the wireless technology.
Noteworthy, the links among the same sensor types or between leader sensors
and follower sensors are considered to be MC or THz (as a nano-scale RF
technology). The FC plays the role of a hub that can communicate with either
sensor (via S2FC links) or GW (via FC2GW link) to carry the inbound or
outbound messaging. The GW can directly communicate and control the leaders
and followers via GW2S inbound links to enforce a dedicated task in addition
to the localization. Once leader sensors detect the abnormality, the FC and/or
potentially the GW get engaged in the _hybrid localization process_ 333Our aim
of using _hybrid localization_ is to highlight the role of external
interventions (e.g., collaboration of FC and GW using various types of
transmission technologies) in addition to the MC-based localization.. The
hybrid localization enables the follower sensors to collectively migrate
towards the abnormality. In the sequel, we explain localization schemes using
different sensor types and related communication links in more detail.

###  IV-B Abnormality Localization using Stationary Sensors

Typically, multiple (a network of) sensors are used to localize the
abnormality [12, 55, 56]. The MC-based localization methods using stationary
sensors are mainly based on i) classification, ii) discrete models, and iii)
continuous models [12]. Classification methods [158] simulate the received
signals and measurements based on a set of pre-known abnormality positions and
compare them with the real measurements. Methods based on the discrete models
employ a lattice-based structure to discretize the spatial dispersion [127,
158]. The number of sensors should exceed the number of lattice nodes for
better performance which makes these methods costly. Methods based on
continuous models use analytical dispersion models to formulate the inverse
problems and estimate the parameters. These methods require an analytical
dispersion model of the molecule concentration profile in the medium and
convergent solutions for corresponding inverse problems. The methods using
continuous models are the most prevalent ones that we review in Subsection
IV-B2. We also review the methods using classification or discrete models in
Subsection IV-B1. See Table I for the list of abnormality localization
schemes.

####  IV-B1 Classification and Discrete Models

The classification models consider multiple _a-priori_ known positions for the
source of abnormality [125, 128]. Assuming _a-priori_ known position for every
source, simulation or experimental data is generated for all sensors. For
classification task, the simulated data is compared with the received signals
which is computationally inefficient. On the other hand, discrete models use
discretization of the dispersal model. To this end, a lattice-based model for
source localization assuming sensors and sources located at lattice points are
proposed in the literature [127, 158]. To attain an acceptable localization
accuracy, the number of sensors should be in the same order of the state-space
system.

Motivated by classification methods, the authors in [125] propose a recurrent
neural network for odor source localization (OSL) problem with stationary
sensors. The proposed deep learning approach is based on a combination of
classification and discrete-model methods. It is revealed that learning-based
approaches in line with probabilistic methods may provide more reliable
approaches. Also, feature extraction methods using the support vector machine
classification method to learn the environment with stationary sensors should
be manipulated to process the measured time series data, gathered from the
sensors. It is shown that the model learns the inverse problem and can
accurately estimate the leakage point as the source of abnormality.

The papers [12, 126, 127] have investigated the macro-scale abnormality OSL
problem using multiple sensors. In [12], the location of a continuously
releasing point source is estimated using multiple stationary sensors assuming
a turbulent advection-diffusion model. The localization problem in (22) is
formulated as a quadratically constrained least-squares problem. Motivated by
the discrete-model methods, the authors in [126] investigate the localization
of a diffusive point source provided by a distributed chemical sensor network
using the sequential ML estimation algorithm. In [127], a sensor network is
deployed to localize multiple sources of diffusion at the macro-scale. The
sources are classified into instantaneous and non-instantaneous sources. The
paper provides simple and exact closed-form inversion formulas based on the
dispersal model for both cases of concentration profiles. Using Green’s second
theorem [159], a sequence based on Prony’s method was developed to estimate
the intended source parameter. The use of Green’s second theorem links the
boundary and obstacle surface concentration measurements to the abnormality
sources. The discrete spatio-temporal samples may robustly construct diffusion
fields which is critical in multi-focal localization problems. Despite the
need for continuous full-field measurements of the environment, the proposed
method can be leveraged to recover the source by approximating the generalized
measurement model. The authors also address the problem in the case of noisy
measurements which needs the inversion formula to be adjusted to improve
robustness. It is revealed that the proposed method robustly combats noises
and model mismatches. The authors also implemented an experimental setup and
successfully recovered the multi-focal temperature fields.

In [128], a machine learning framework is developed to infer the diffusion
channel parameters in a complex turbulent diffusion environment. A rectangular
environment with non-reflecting transparent boundaries is considered with an
injector at the origin. The source of abnormality is considered to be the
velocity of individual puffs by the injector (as a transmitter) in the
environment. It is assumed that the abnormality injects multiple molecular
puffs into the environment having different velocities. The goal is to
discriminate the velocities using a time difference concentration method based
on the support vector machine. Also, a stepwise maximum variance algorithm is
proposed which selects dominant sensing points in a multi-sensor environment
(in a lattice as a discrete model approach), leading to the less dimension-
related complexities inherent in localization problems with stationary
sensors. Employing different regularization schemes [128], the methods based
on machine learning are approved to accurately estimate the velocities.

In [157], the receiver estimates the relative angle of an absorbing
interferer, which causes a perturbation in the diffusion environment. A point
transmitter is considered in the environment and both the receiver and
interferer are assumed to be fully absorbing. The results revealed non-trivial
behavior of the considered MC system where the variance of the localization
error increases with the distance between the transmitter and receiver and
their relative look angle. Notably, when the transmitter and receiver are very
closely located, the estimation is also unreliable. Moreover, a look angle of
$30^{\circ}$ is demonstrated to be optimal for accurate interferer
localization. The potential application of the proposed framework arises in
the self-propulsive motion planning for mobile MC systems.

####  IV-B2 Continuous Models

These methods use the analytical dispersal model based on (II-D), subject to
various boundary conditions. The standard localization problem, formulated in
(22), solves inverse problem to find $Q_{\mathrm{Ab}}$ and
${\mathrm{\bm{r}}_{\mathrm{Ab}}}$. However, finding closed-form analytical
solution to (II-D) is cumbersome and only semi-analytical or numerical
solutions [159] are attainable in case of realistic scenarios. Assuming the
analytical dispersion model in (22) and given the location of the sensors in a
$d$-dimensional space, at least $d+1$ sensors are theoretically needed to
localize the transmitter [12].

In [55], a multi-sensor framework was proposed to localize a diffusion source
of concentration having potential applications in biomedical engineering,
industrial applications, and environmental monitoring. Assuming the analytical
dispersal model in (9) with zero drift, the location of the transmitter as a
source of abnormality is estimated and the Hammersley-Chapman-Robinson lower
bound (as a generalization of Cramér-Rao lower bound) is investigated
considering time-dependent noisy measurements and ISI. Also, the condition for
a unique recovery of the abnormality’s location are obtained assuming known
and unknown positions of the sensors. The authors show that the proposed
algorithm has a low root-mean-squared (RMS) error.

In [56] a localization method using the peak of the channel impulse response
is proposed employing multiple sensing nodes and an FC with transparent
stationary receivers. The sensors report the measurements to the FC for
further processing. It is assumed that each sensor picks the largest value for
making further decisions. A method based on triangulation (equivalently known
as the least-square problem defined in (22) or multi-point positioning
method), as well as a gradient-descent method, are adopted to minimize a non-
convex cost function. The results demonstrate that the gradient-descent
approach outperforms the triangulation approach for a wide range of signal-to-
noise ratios.

In [57], the authors consider the source localization problem using a cluster
of a limited number of sensing points in a turbulent diffusion channel. A
novel back propagation-based neural network, based on Bayesian regularization,
is proposed in a bounded environment. A concentration total variation
comparison algorithm was employed to select the most important sensing points.
It is revealed that the proposed scheme characterizes the radius and location
of the abnormality with small error.

In [154], the authors investigate the localization of a silent abnormal entity
that absorbs molecules from the medium. The diffusive MC channel in the
presence of an absorbing target (abnormality entity) is modeled using the
probability equivalent modeling method. To localize the abnormality source,
the maximum-likelihood problem is formulated and solved based on the Newton-
Raphson algorithm. The proposed method is extended to the channel with
multiple absorbing abnormalities. The results reveal that the localization
accuracy depends on the perceived differentiated concentration of molecules at
the receiver.

In [79], the localization problem of an absorbing source (e.g., an
eavesdropper) is solved using the finite return probability of the random walk
process. Also, a 1-D probability density function of the molecules with
absorbing and reflecting boundaries, employing mirroring principles, is
derived. It is shown that abnormalities closer to the transmitter may need
more costly computations for reverse estimation, and for abnormalities closer
to the receiver, a simple first-term approximation of probability density
function is appropriate with high accuracy for low diffusion coefficient
scenarios. Furthermore, an eavesdropper very close to the transmitter is
highly probable to be detected due to the higher rate of molecule absorption.
The proposed method is complicated to localize the eavesdropper in a 3-D
environment.

In a framework similar to [79], authors in [153] characterize the 1-D closed-
form analytical channel impulse response in the presence of a point
transmitter located between two fully-absorbing receivers. The 1-D environment
is a good approximation for close inter-cellular regions, e.g., a synaptic
cleft. They analytically derive the fraction of absorbed molecules, the
corresponding hitting rate, and the asymptotic fraction of the absorbed
molecules as a function of time. The number of molecules absorbed at the
receiver is introduced as a metric to localize the eavesdropper abnormality.
It is shown that increasing degradation rate results in a reduction in the
mutual influence of two absorbing receivers.

In [58], the transmitter positioning is adopted using a single-input multiple-
output MC system. The cumulative number of molecules hitting a fully-absorbing
receiver until a predefined time is considered as the dispersal model. The
distance from the transmitter and the attenuation factor are estimated using a
curve-fitting method in the form of a non-linear least-square problem. This
optimization problem is solved using the Levenberg-Marquardt algorithm. After
estimating distances, the multi-point positioning method is employed to
localize the transmitter.

In [123], the abnormality localization in the macro-scale is studied using a
clustered sensor network in an experimental platform. The network consists of
a point transmitter and 24 MQ-3 alcohol sensing nodes placed on a rectangular
surface. Also, a Gaussian plume is assumed to model the release of the
molecules by evaporation at room temperature to identify the location of the
unknown transmitter. Assuming the turbulent dispersal model given by (9),
joint estimation of location and detection time is considered using numerical
algorithms. Moreover, a clustering method for estimating wind velocity is
proposed based on the estimated detection times. Then, the estimated velocity
is employed for estimating the evaporation rate of ethanol in the air.

In [124], the authors investigate the localization of a chemical abnormality
(e.g., submarine disaster or airplane crash discovery) using a mobile robot
equipped with a transparent receiver. The background chemical noise is
considered to be Gaussian distributed with its variance depending on the
receiver’s sensitivity defined as the limit of detection (LOD). The authors
assume that due to the motion of the molecules by oceanic waves, the LOD is
dominant compared to the counting noise which is typically assumed in the MC
literature. To solve the localization problem, a multi-stage gradient method
based on the Rosenbrock gradient ascent algorithm is proposed. The Rosenbrock
algorithm, as a numerical optimization algorithm or a particular form of
derivative-free search, is well adapted to searching environments with blind
methods in the presence of zones with zero gradients. The proposed methods
outperform the conventional acoustic methods for underwater source
localization.

In [59], a source localization approach for localizing multiple transmitters
(i.e., diffusing multi-focal abnormality sources) considering a fully
absorbing receiver is proposed. The scenario considers multiple point
transmitters located at different positions which release the same amount of
molecules in the environment. A 3-D unbounded environment with zero drift is
considered. The method employs clustering algorithms, i.e., $K$-means,
Gaussian mixture models, Dirichlet model, and Bayesian mixture models. The
Gaussian mixture and Dirichlet models are based on the iterative expectation-
maximization algorithm. After the clustering stage, the results are employed
to provide the direction of arrival by averaging the coordinates of the
hitting molecules at the surface of the absorbing receiver. Using estimated
distance and direction of arrival information, the source can be efficiently
localized.

THz-band communications can also be used to establish S2FC links or
cooperation among sensors [92, 160] to detect abnormal events at the nano-
scale. However, the wireless nano-sensors have low communication coverage
[132, 133]. To address this problem, multi-hop localization strategies may be
proposed at the cost of error propagation by increasing number of the hops.

###  IV-C Abnormality Localization using Mobile sensors

This section introduces the localization using mobile sensors, referred to as
mobile microrobots in the literature [129]. As Table I demonstrates, mobile
sensors can be classified into three categories including non-propelled
sensors [40], self-propelled sensors [66, 152], and externally-propelled
sensors [101, 129]. The non-propelled sensors do not have a propulsion source,
e.g., motor or other types of responses to the concentration changes in the
environment. They may be freely distributed in the environment and move due to
the inherent forces of the environment (e.g., the blood flow in the body,
other forms of flows like in the lymphatic system, and the laminar or bulk
flow in the industrial liquid ducts) to monitor and localize the abnormality.
For self-propelled sensors, the source of propulsion can be either a specific
response of the agent to the changes in the environment [62], e.g., taxis-
based approaches [161, 162, 163, 164, 165], or other cooperative control-
driven approaches [67, 130]. Understanding the motion control mechanisms
inspires designing multi-functional systems incorporating micro-agents [129].
In two latter types of motion, the agents are steered toward the target site
using self-propulsion mechanisms or an external field force. In the sequel, we
review the localization methods in these three categories in more detail.

####  IV-C1 Non-propelled sensors

While these sensors do not move intentionally, the inherent forces of the
environment may move them.

In [40], abnormality localization is conducted using non-propelled sensors,
non-cooperatively. An FC is required to integrate the local sensing
information to use a multi-hypothesis testing problem. Two types of FCs
(namely class-A and class-B FCs) are considered with/without requiring
sampling of the sensors’ marker signal. These markers can be further used by
the FC to find the location of the abnormality. The class-A FCs use markers
for localization (activated sensors are known but the corresponding sub-
regions where the sensors were activated are unknown) while the class-B FCs
know both the flag value (representing the activation statue of individual
sensors) and the storage level of each sensor. It is revealed that employing
multiple sensors, markers, and increasing resolution of the FC sensors storage
levels (an indicator of the location of the released molecules at the sensor)
improves the performance of the localization. Also, the performance of the
localization error in perfect and imperfect sensing regimes for both type-A
and type-B FCs is obtained. For class-A FCs in the perfect sensing regime, by
increasing the number of sensors, the probability of localization error
asymptotically vanishes. Also, class-B FCs in the perfect sensing regime can
accurately (with no error) localize the abnormality.

In [131], the security challenges of MC systems employing two types of
Blackhole and Sentry attacks are introduced. The sensors are classified into
two types: malicious and legitimate. In the Blackhole attack, the malicious
agent emits attractant molecules to disrupt the legitimate sensors from normal
searching for the target site. In the Sentry attacks, the malicious sensors
emit repellent chemicals to disperse the legitimate sensors from the target
site. A combination of the Bayes’ rule and a threshold-based decision-making
approach was conducted to induce robustness against such attacks. To this end,
a cooperative form of the engineered bacterial swarms is investigated. Once
any probable abnormality is detected by any individual legitimate sensors, it
switches from emitting repellents to emitting attractants to drag the other
legitimate sensors towards itself.

####  IV-C2 Self-propelled sensors

Establishing cooperation among sensors in MC is challenging due to the
inherent interference, time variability, and the need for synchronization.
However, swarm behaviors of biological organisms may be used to address this
challenge. Self-assembly, self-replication, self-feeding, locomotion, and
communication are operated mainly in swarm-like collections [166]. This
enables the swarm of sensors with limited intelligence (i.e., having limited
storage and computational capabilities) to realize the localization [161, 162,
163, 164, 165]. The self-propelled agents are considered for their potential
applications in water decontamination, DNA sensing and detection, water
remediation and cargo transport, wastewater treatment, biocompatible engine,
biomedical applications, and propagation of pollutants [62].

The self-propulsion mechanisms include bubble-based, self-electrophoresis, and
taxis-based propulsion mechanisms. The taxis-based propulsion mechanisms
include chemotaxis [161], magnetotaxis [162], phototaxis [163], rheotaxis
[164], and viscotaxis [165]. For instance, in chemotaxis, self-propelled
micro-agents are navigated due to chemoattractants and chemo-repellents
inducing direct migration of the agent towards and away from a diffusion
source, respectively [129]. Generally, the self-propulsion mechanism is
generated using various types of micro-motors, e.g., droplet swimmer as a
chemotactic-based method [167]. Also, some of the taxis-based approaches
(e.g., magnetotaxis) are classified as externally-propelled approaches which
will be discussed in the next subsection.

In [49], the authors design and model a mobile MC system based on the
Bacterium-inspired quorum-sensing method, in which repellent and attractant
molecules are used to search for and localize the abnormality. To widely
spread in the environment, the sensors release repellent molecules. Once an
abnormality is detected, the activated sensors start to release attractant
molecules to gather other sensors to the target site. To evaluate the
efficiency of the localization system, the density of the sensors gathered
around the target site is considered as a performance metric using a
simulation-based environment. Employing an FC (i.e., a millimeter-sized
implantable device communicating with GW via FC2GW link) in the monitoring
environment may help the sensors efficiently coordinate their movements
towards the abnormality. A mean-square displacement measure is used to account
for the efficiency of the network expansion in response to the repellent
molecules released by sensors. Similarly, without attractive sensors, the
abnormality localization becomes opportunistic and time-consuming. For
feasibility analysis, a wet-lab experiment is also conducted to show the
efficiency of the localization framework.

In the more recent work [48], a non-diffusion-based framework for a targeted
drug delivery system is proposed which uses adhesive molecules instead of
diffusive molecules for navigation. The sensors (or nano-machines) are divided
into two main groups of leaders and followers. The leaders are spatially
distributed and responsible for the exploration of the environment while the
followers carry the drug particles. The mentioned non-diffusion-based MC
system is based on the abundant adhesive molecules (e.g., fibronectin,
laminin, elastin, and collagen) in the extra-cellular matrix (ECM) [168],
which enable a probable binding mechanism for the cells. A wet-laboratory
experiment relying on mathematical techniques is adopted. This is a form of
chemotaxis where endothelial cells move up the fibronectin gradient. For
experimental setup, calf pulmonary artery endothelial cells were cultured at
$37^{\circ}$C. Maximum-likelihood estimation was adopted to estimate the
parameters of the Langevin equation for both leaders and followers. Fig. 10
depicts a sample realization of the leader-follower-based model in [48].
Abnormality is depicted on the top left side of Fig. 10 as a circular area in
red color. Once every leader sensor touches this area, it starts to release
attractant molecules and forms an attractant concentration profile. The
attractant molecules are assumed to be adhesive and not diffusive leading to
trails. This concentration gradient pattern makes the follower sensors be
gathered around the target site so that they can perform drug delivery to the
target site. The distribution of the leader and follower sensors around the
detected abnormality in Fig.10 indicates the correct leading and following
process of sensors that lead to the gathering of the sensors around the target
site. Fig. 10 (top) shows the time evolution of the leader sensors freely
distributed in the environment to sense the abnormality. Fig. 10 (middle)
demonstrates the concentration profile generated by the activated leaders.
Fig. 10 (bottom) depicts the time evolution of the followers towards the
activated leaders which lead to a reliable localization.

Figure 10:  Distribution of the leader and follower sensors around detected
abnormality [48]. (Top) The leader sensors freely distributed in the
environment, (Middle) the concentration generated due to the activated leader
sensors, (Bottom) the distribution of the followers attracted towards the
abnormality.

In [139], an MC system is developed which comprises leaders, followers, and
amplifying bio-nano-machines, as the sensors. The idea behind the amplifying
sensors is to strengthen the attractant field of the leaders in a leader-
follower-based framework. The followers migrate towards the abnormality using
a discretized space model. In contrast to [48], which lies on a non-diffusion-
based model, the authors in [139] consider a diffusion-based MC with the aid
of amplifying sensors. The amplifying sensors follow a simple random-walk in
the environment to distribute the attractant molecules switching between
reactive and non-reactive phases. The concentration of the attractant
molecules released by the leader sensors in the environment is compared to a
threshold and if it is higher than a threshold, the amplifying agents react by
releasing the same molecules to enhance the gradient field.

In a more recent work [138], as a complementary to [139], the collective
migration of the bio-nano-machines (as self-propelled sensors) towards the
abnormality in a 2-D unbounded environment is studied. Unlike [49, 48] where
the sensors do not relay the perceived concentration signal, the sensors in
[138] perform depending on the region where they are located. The sensors
outside the abnormality region use a stop-and-relay strategy, i.e., they relay
the perceived signaling molecules and stop for some time to coordinate their
motion towards the abnormality. Each sensor acts according to its internal
state, e.g., the number of activated cell-surface receptors or internal
signaling pathways. The rate at which the sensor releases the molecules in the
environment depends on its internal state and how it is located inside or
outside of the target region. The release function is designed to support
repetitive pulse dispersions if the sensors are located inside the target
region. The sensors outside the target region release molecules whenever their
perceived concentration increases. The locomotion of the relaying self-
propelled sensors (as followers) towards the activated sensors (as leaders) is
investigated by extensive simulations with 99 relay sensors, which shows that
only 10 nearest sensors react to the concentration fluctuations due to the
leader sensor. The results show that only the 4 nearest sensors can reliably
localize and reach the leader. Also, the disruptive effect of the signal
relaying by other sensors is confirmed in the simulations which is the main
idea behind developing the relay-and-stop strategy to compensate the
unintended motion fluctuations of the sensors. Finally, the relay-and-stop
strategy is examined in a practical setup (including abnormality), and its
efficiency to localize the abnormality is approved.

In [66], a non-gradient-based method based on the Fisher information matrix
referred to as infotaxis, is proposed to localize the concentration source.
This strategy locally maximizes the expected rate of information gained to
find the best route to the abnormality source. The idea behind this approach
is that entropy decreases faster in the proximity of the source.

From a more practical perspective, [169] introduces self-propelled
(autonomous) shape-morphing micro-machines consisting of stimuli-responsive
hydrogels for drug-delivery applications. Inspired by the locomotion of the
Leukocytes, synthetic thermo-responsive hydrogels (as self-folding nano-
sensors) are proposed to localize the abnormality for drug delivery
applications. The abnormality, e.g., tumor site, is primarily localized using
conventional medical imaging approaches in an _in-vitro_ environment. Then,
the abnormality is targeted with an external near-infrared light gradient
which can be sensed by the injected nano-sensors. Finally, a rotating magnetic
field is applied to steer the micro-machines to explore the monitoring
environment to find the abnormality. The results reveal that the sensors move
up along the gradient by increasing velocity and remain at the target site by
unfolding their shape to release the drug whenever they arrive. This is a
special kind of optotaxis where autonomous localization is conducted using
sensory information without external intervention (e.g., imaging feedback).

For monitoring wide environment or slowly-varying diffusion field, cooperative
multi-sensor ADL is of high interest. In [130], a stand-alone444Our aim by
using the term _stand-alone_ is to highlight that the sensor does not need its
own coordinate to localize the source. This is an inherent assumption
considered in many bio-inspired localization algorithms, e.g., chemotaxis.
control-driven approach is proposed to steer an equi-spaced circular formation
of the sensors towards the abnormality (e.g., a macro-scale heat source
located in a room) using distributed gradient-ascent algorithm. The sensors
can solely measure the relative angles concerning their neighbors. The method
of Poisson integral is used to find the concentration gradient which can be
approximated by a finite summation of measurements perceived by the sensors.
Notably, the center of the formed circle by the sensors moves along the
estimated gradient field. The main drawback of the proposed scheme is to use
the gradient-ascent algorithm which necessitates convexity assumption over the
spatial simulation domain. The provided steering control algorithms are
according to a ring-shaped communication topology where required information
can be handed over S2S links using the linear average-consensus algorithm. The
minimum number of sensors required for the proposed scheme is three without
restraining the maximum limit. The efficiency of the localization scheme using
an illustrative example was approved and theoretically investigated.

Recently, machine learning approaches have been investigated for OSL problems
considering self-propelled sensors in complex environments with time-varying
conditions. In [170], due to the spatio-temporal changes of the environment, a
continuous state-space and action space model based on a partially observable
Markov decision process (POMDP) is proposed. A deep reinforcement learning
method was used to enable sensors to optimize their interactions with their
surrounding environment to localize abnormality. The POMDP is designed as a
long short-term memory (LSTM) structure. Also, a belief-state model is used
instead of the conventional state-space model and selects its future action
(i.e., path planning) based on its history and current measurement. To
maximize the long-term cumulative reward function, an LSTM-based deterministic
policy gradient (DPG) algorithm is proposed. The training stage of the
proposed machine learning approach is accelerated by employing supervised
policies (i.e., features inherent in dynamic programming methods). A
simulation-based abnormality localization setup is considered for a turbulent
time-varying environment. To learn a searching strategy, the agent requires to
record and use the local concentration measurements, local flow velocity, and
the related motion information. Reynolds-averaged Navier-Stokes equations are
used to make the simulation environment. The results reveals that the LSTM-
based DPG algorithm outperforms the deep DPG algorithm both in efficiency and
effectiveness. Also, it is revealed that the LSTM-based DPG supplemented by
supervised policy accelerates the localization efficiency.

####  IV-C3 Externally-propelled sensors

Externally-propelled and controllable sensors promise non-invasive health
monitoring and interventions [46]. As the feedback system in Fig. 9 shows,
wireless links (e.g., RF, magnetic, or acoustic) are the only mechanisms for
external messaging to control the nano-network or stimulate the externally-
propelled sensors [92, 46]. Linking the nano-networks (based on MC/THz links)
to external communication systems (based on wireless links) is an important
ongoing research direction [95]. The bottleneck is to design appropriate
communication links (e.g., FC2GW or S2GW links) to interact with nano-scale
externally-propelled sensors. Particularly, the hybrid interconnectivity of
THz-based nano-networks and MC-based nano-networks was recently reviewed in
[108]. In the following, we review the proof-of-concept models which still
demand more theoretical research and investigations.

The authors in [68] propose a touch communication (referred to as TouchCom)
framework which employs externally-controllable sensors for the transportation
of pharmaceutical compounds to the target (e.g., tumor) site inside the body.
To this aim, a cross-scale (micro-to-macro) design of communication and
control is proposed. The sensors are transient in the sense that they remain
in the environment for a medically-useful time duration before any dissolving
and resorption by the body and comprise three compartments namely diffusion,
branching, and degeneration until they reach the tumor. Both silicon-based
(transient electronics) and bacteria-based (magnetotactic bacteria) structures
are appropriate as sensors for the TouchCom framework. The TouchCom can be
generalized to other applications including quality control of water and food,
environmental pollution, etc. The important part of the TouchCom is the
external control system (e.g., a GW cooperating with a local cloud computing
system) which can partially coordinate the movements of the externally-
propelled sensors using a feedback system; see Fig. 9. The GW intelligently
optimizes the localization strategy using previous trajectories of the sensors
combined with learning methods. The feedback loop can be implemented using
GW2S links. The GW2S link is a visual communication link that enables the GW
to control the localization by touching the tangible maneuvering space using
an external field. The propagation delay and attenuation loss minimizations
are important measures to evaluate the efficiency of the localization system.
The experimental setup is based on the blood vessels and capillaries in which
the sensors are conveyed and can be seen using an angiogram. The probability
distributions corresponding to the propagation time from the injection site to
the tumor, the angle of arrival, and the path loss are investigated. Moreover,
the delay spectrum and azimuth spectrum, as crucial parameters for drug-
delivery applications are studied.

Recent studies demonstrate the importance of the soft untethered grippers
[171] to pick-and-place biological materials in dynamic environments for
minimally-invasive surgery and lab-on-a-chip systems. Unlike traditional
approaches which use imaging as a mechanism to control the in-body systems,
ultrasound is more bio-compatible with medical applications. Ultrasound
provides a high frame rate, which is crucial to realize real-time
interventions which can also be the best choice for externally-controllable
self-propelled sensors [172]. An external device, e.g., a GW, can be used to
emit wireless signals (e.g., ultrasound signals) to directly accomplish the
localization or steering process. In [173], magnetic closed-loop motion
control of a soft miniaturized hydrogel-based gripper towards a target site
using feedback from ultrasound images is reported. The results demonstrates
that the proposed system employing ultrasound images can track the gripper
with an error in the order of $0.4~{}\textrm{mm}$ without payload and
$0.36~{}\textrm{mm}$ conveying a payload. The provided system enables the
control of miniaturized grippers in the absence of visual feedback cameras.

In [137], a more developed MC-based externally-controllable framework based on
[46] is proposed that combines RF communication and MC to realize externally
controllable MC systems to apply therapeutic interventions inside the body.
Furthermore, a proof-of-concept model for targeting cell and exosome-mediated
theranostic systems accompanying phenotypic switching and network formation is
proposed [137]. The authors introduce a novel brain-machine interface to
detect, localize, and control molecular activities bidirectionally. The
proposed framework consists of an external device (e.g., GW or the local cloud
as described in Fig. 1) incorporating a processing unit, storage, and
corresponding feedback mechanisms. However, the authors in [137] declare that
the migration mechanisms of the cells to reach the intended sites need further
investigation and research.

In [69], a novel sequential multifocal tumor localization approach using the
history of the biological gradient field (BGF) is proposed. The BGF concept is
related to the biological characteristics of high-risk tissues in the body.
For instance, the capillaries around the healthy tissues exhibit a uniformly-
distributed pattern while for the tumor environment, the capillaries are
tortuous and highly dense. This leads to varied BGFs which are captured by the
GW due to the changed trajectories and magnetic variations of sensors in the
multi-focal tumor environment. The localization is established by externally-
propelled sensors, e.g., Janus nanoparticles, which are maneuvered by a GW
(i.e., external actuating device) using magnetic-based S2GW links. The
efficiency of the non-intervening delivery methods depends on the body’s
natural circulatory system leading to extremely poor delivery performance
(about $0.7\%$ of injected particles). The sequential localization strategy
modifies the BGF and helps the sensors to be divided into multiple clusters to
find the tumors one after another. The sensors are maneuvered by an external
actuating device (e.g., a rotating magnetic field generated by a coil) and
steered in the fluid using a swarm intelligence algorithm. The typical imaging
systems can be deployed to follow the trajectory of the sensors and the BGF is
estimated using the sensors’ characteristics. A multi-modal optimization
problem to minimize the BGF is formulated whose solution is the global optimal
location of the tumors which is sequentially explored by a share of swarm
sensors. It is shown that the proposed imaging-and-control-assisted method
localizes the tumor sites and outperforms the conventional multi-modal
optimization algorithms [69].

In addition to magnetic resonance imaging (MRI) and ultrasound, other
technologies may be utilized for the S2GW and FC2GW links involving optical
tracking, magnetic particle imaging (MPI), X-ray fluoroscopy, and
fluorescence. In optical tracking, the 3-D localization of the sensor is hard
and it is easily disrupted by noise or other environmental optical sources.
Currently, this approach is only applicable in transparent micro-environments
like vitreous humor (also known as vitreous fluid) in eyes. The MRI is based
on the magnetic field, which is bio-compatible non-ionizing radiation causing
excellent tissue contrast. This approach is not only employed extensively for
imaging but also used to manipulate nano-machines which enables 3-D
localization. However, real-time data acquisition is challenging and costly.
The MPI has the same advantages as the MRI with high spectral resolution and
fast scanning speed. The drawback of the MPI is its limited application to
super-paramagnetic nano-particles. The X-ray family has the advantage of deep
penetration into the human body. As previously described, an interesting
approach is ultrasound which is real-time imaging with low cost. Among the
technologies mentioned, ultrasound has minimum adverse health effects making
it a proper solution for both imaging and nano-sensor control.

##  V Challenges and Directions for Future Works

In this section, we discuss the open challenges and directions linked to the
ADL problem. We classify them into four main categories: implementation,
system design, modeling, and methods in Fig. 11. Further, we extensively
discuss the mentioned challenges for the internet of things (IoT) systems as
an important part of beyond 5G networks, including the internet of nano-things
(IoNTs) and internet of bio-nano-things (IoBNTs).

###  V-A Implementation

Implementation of MC systems requires multidisciplinary collaborative research
at the intersection of biology, physics, communications, and computer
sciences. Developing multifunctional sensory systems in complex environments
is a challenging task that needs more investigation. Here, we provide the
challenges and recent trends in sensor design, developing interfaces, and
creation of testbeds and platforms devoted to the intersection of IoNTs and
the future beyond 5G networks.

Challenges and Directions Modeling Tractable Realistic Models Consistent and
Reproducibe Models Learning based Models System Design Security and Privacy
Design Strategies Comm. Links and Protocols Methods Machine Learning Methods
Bioinspired Methods Classic Methods Implementation Sensors Interfaces Testbeds
and platforms

Figure 11: Schematic of the research challenges and directions for MC-based
ADL.

Sensors: Developing multi-functional sensors with different physical and
technical capabilities such as mobility mechanisms, detection schemes, storage
capabilities, release techniques, sampling methods, reception processes, and
synchronization are very critical, particularly for nano-scale applications
which need great attention from multidisciplinary perspectives. While various
constructions have been provided for sensors in macro-scale applications, the
sensors cannot be necessarily scaled down to nano- and micro-scale dimensions
to be operated in a micro-environment. Designing nano-sensors is an ongoing
research trend in the physical chemistry field [85]. A promising method to
manufacturing nano-sensors is based on using biological elements. Sensors can
be electrical, optical, or mechanical based on the converted signal in the
transducer unit. However, electrical transducers gain more attention since
they do not need macro-scale elements for detecting optical and mechanical
signals. To realize electrical transducers, designing miniaturized signal
processing units at the nano-scale is urgent [85]. The design of biocompatible