# Containing Future Epidemics with
Trustworthy Federated Systems for Ubiquitous Warning and Response

Dick Carrillo, Lam Duc Nguyen, Pedro H. J. Nardelli, Evangelos Pournaras,
Plinio Morita, Demóstenes Z. Rodríguez, Merim Dzaferagic, Harun Siljak,
Alexander Jung, Laurent Hébert-Dufresne, Irene Macaluso, Mehar Ullah, Gustavo
Fraidenraich, Petar Popovski DC, PHJN and MU are with LUT University, Finland.
LDN and PP is with Aalborg university, Denmark. EP is with University of
Leeds, Leeds, UK, PM is with university of Waterloo, Canada, DZR is with
federal university of Lavras, Brazil, MD, HS and IM are with Trinity College
Dublin, Ireland, AJ is with Aalto University, Finland, LHD is with University
of Vermont, USA, GF is with State University of Campinas, Brazil, This paper
is partly supported by Academy of Finland via EE-IoT project n.319009. Part of
this manuscript has been released as a pre-print at LUTPub [1]. Contact:
<EMAIL_ADDRESS>
###### Abstract

In this paper, we propose a global digital platform to avoid and combat
epidemics by providing relevant real-time information to support selective
lockdowns. It leverages the pervasiveness of wireless connectivity while being
trustworthy and secure. The proposed system is conceptualized to be
decentralized yet federated, based on ubiquitous public systems and active
citizen participation. Its foundations lie on the principle of informational
self-determination. We argue that only in this way it can become a trustworthy
and legitimate public good infrastructure for citizens by balancing the
asymmetry of the different hierarchical levels within the federated
organization while providing highly effective detection and guiding mitigation
measures towards graceful lockdown of the society.

To exemplify the proposed system, we choose a remote patient monitoring as use
case. This use case is evaluated considering different number of endorsed
peers on a solution that is based on the integration of distributed ledger
technologies and NB-IoT (narrowband IoT). An experimental setup is used to
evaluate the performance of this integration, in which the end-to-end latency
is slightly increased when a new endorsed element is added. However, the
system reliability, privacy, and interoperability are guaranteed. In this
sense, we expect active participation of empowered citizens to supplement the
more usual top-down management of epidemics.

###### Index Terms:

COVID-19, epidemiological warning system, wireless, privacy-preserving data
collection, communication networks

1G

    first generation of mobile network
1PPS

    1 pulse per second
2G

    second generation of mobile network
3G

    third generation of mobile network
4G

    fourth generation of mobile network
5G

    fifth generation
ARQ

    automatic repeat request
ASIP

    application specific integrated processors
AWGN

    additive white Gaussian noise
BER

    bit error rate
BCH

    Bose-Chaudhuri-Hocquenghem
BRICS

    Brazil-Russia-India-China-South Africa
BS

    base station
CDF

    cumulative density function
CoMP

    cooperative multi-point
CP

    cyclic prefix
CR

    cognitive radio
CS

    cyclic suffix
CSI

    channel state information
CSMA

    carrier sense multiple access
DFT

    discrete Fourier transform
DFT-s-OFDM

    DFT spread OFDM
DSA

    dynamic spectrum access
DVB

    digital video broadcast
DZT

    discrete Zak transform
eMBB

    enhanced mobile broadband
EPC

    evolved packet core
FBMC

    filterbank multicarrier
FDE

    frequency-domain equalization
FDMA

    frequency division multiple access
FD-OQAM-GFDM

    frequency-domain OQAM-GFDM
FEC

    forward error control
F-OFDM

    Filtered Orthogonal Frequency Division Multiplexing
FPGA

    field programmable gate array
FTN

    faster than Nyquist
FT

    Fourier transform
FSC

    frequency-selective channel
GFDM

    generalized frequency division multiplexing
GPS

    global positioning system
GS-GFDM

    guard-symbol GFDM
IARA

    Internet Access for Remote Areas
ICI

    intercarrier interference
IDFT

    Inverse Discrete Fourier Transform
IFI

    inter-frame interference
i.i.d.

    independent and identically distributed
IMS

    IP multimedia subsystem
IoT

    Internet of Things
IP

    Internet Protocol
ISI

    intersymbol interference
IUI

    inter-user interference
LDPC

    low-density parity check
LLR

    log-likelihood ratio
LMMSE

    linear minimum mean square error
LTE

    Long-Term Evolution
LTE-A

    Long-Term Evolution - Advanced
M2M

    Machine-to-Machine
MA

    multiple access
MAR

    mobile autonomous reporting
MF

    Matched filter
MIMO

    multiple-input multiple-output
MMSE

    minimum mean squared error
MRC

    maximum ratio combiner
MSE

    mean-squared error
MTC

    Machine-Type Communication
NEF

    noise enhancement factor
NFV

    network functions virtualization
OFDM

    orthogonal frequency division multiplexing
OOB

    out-of-band
OOBE

    out-of-band emission
OQAM

    offset quadrature amplitude modulation
PAPR

    peak-to-average power ratio
PDF

    probability density function
PHY

    physical layer
QAM

    quadrature amplitude modulation
PSD

    power spectrum density
QoE

    quality of experience
QoS

    quality of service
RC

    raised cosine
RRC

    root raised cosine
RTT

    round trip time
SC

    single carrier
SC-FDE

    Single Carrier Frequency Domain Equalization
SC-FDMA

    Single Carrier Frequency Domain Multiple Access
SDN

    software-defined network
SDR

    software-defined radio
SDW

    software-defined waveform
SEP

    symbol error probability
SER

    symbol error rate
SIC

    successive interference cancellation
SINR

    signal-to-interference-and-noise ratio
SMS

    Short Message Service
SNR

    signal-to-noise ratio
STC

    space time code
STFT

    short-time Fourier transform
TD-OQAM-GFDM

    time-domain OQAM-GFDM
TTI

    time transmission interval
TR-STC

    Time-Reverse Space Time Coding
TR-STC-GFDMA

    TR-STC Generalized Frequency Division Multiple Access
TVC

    ime-variant channel
UFMC

    universal filtered multi-carrier
UF-OFDM

    Universal Filtered Orthogonal Frequency Multiplexing
UHF

    ultra high frequency
URLL

    Ultra Reliable Low Latency
V2V

    vehicle-to-vehicle
V-OFDM

    Vector OFDM
ZF

    zero-forcing
ZMCSC

    zero-mean circular symmetric complex Gaussian
W-GFDM

    windowed GFDM
WHT

    Walsh-Hadamard Transform
WLAN

    wireless Local Area Network
WLE

    widely linear equalizer
WLP

    wide linear processing
WRAN

    Wireless Regional Area Network
WSN

    wireless sensor networks
ROI

    return on investment
NR

    new radio
SAE

    system architecture evolution
E-UTRAN

    evolved UTRAN
3GPP

    3rd Generation Partnership Project
MME

    mobility management entity
S-GW

    serving gateway
P-GW

    packet-data network gateway
eNodeB

    evolved NodeB
UE

    user equipment
DL

    downlink
UL

    uplink
LSM

    link-to-system mapping
PDSCH

    physical downlink shared channel
TB

    transport block
MCS

    modulation code scheme
ECR

    effective code rate
BLER

    block error rate
CCI

    co-channel interference
OFDMA

    orthogonal frequency-division multiple access
LOS

    line-of-sight
VHF

    very high frequency
pdf

    probability density function
ns-3

    Network simulator 3
Mbps

    mega bits per second
EH

    energy harvesting
SWIPT

    simultaneous wireless information and power transfer
AF

    amplify-and-forward
DF

    decode-and-forward
WIT

    wireless information transfer
WPT

    wireless power transfer
FSFC

    frequency selective fading channel
DC

    direct current
FFT

    fast Fourier transform
RF

    radio frequency
SISO

    single-input single-output
RRC

    root raised cosine
TSR

    time-switching relaying
IFFT

    inverse fast Fourier transform
LIS

    large intelligent surfaces
URLLC

    ultra-reliable low-latency communication
ZMCSCG

    zero mean circularly symmetric complex Gaussian
PPSINR

    post-processing SINR
mMTC

    massive machine-type communication
NR

    New radio
RIS

    reconfigurable intelligent surface
RAN

    radio access network
i.i.d.

    independent and identically distributed
NOMA

    non-orthogonal multiple access
SDN

    software defined networks
EMC

    edge-mobile computing
D2D

    device-to-device
LPWAN

    low-power wide-area network
WAN

    wide-area network
PAN

    personal-area network
BLE

    Bluetooth low energy
NB-IoT

    narrowband Internet of things
COVID-19

    coronavirus disease
WHO

    world health organization
CAPEX

    capital expenses
OPEX

    operational expenses
GEEWS

    global epidemiological early warning and response systems
GDP

    gross domestic product
DLT

    distributed ledger technology
NB-IoT

    narrowband IoT
KPI

    key performance indicator

##  I Introduction

The COVID-19 pandemic has clearly shown that, in many senses, the world as a
whole was not prepared for dealing with a disease of such magnitude. Although
a final assessment is infeasible at this point (October, 2020), in which a
second wave is creeping back in Europe and is poised to rage across the
continent by fall 2020 [2]. The current statistics available about COVID-19
indicate that the most successful policies for monitoring and controlling the
virus propagation are employing various digital technologies and connectivity
[3, 4]. These technologies can be used in two principal ways: (1) to provide
status and predictions of the epidemiological spread and facilitate actions,
such as administration of diagnostic tests or preparation of medical
equipment; (2) to implement active policies that facilitate societal processes
and safe citizen movement, such as shop schedules, organized delivery of
goods, and similar. The success of such informed policies is also strongly
related to the timing of their implementation for the respective place, as
virus contagion is a spatiotemporal phenomenon that usually leads to
exponential growth in the number of infected individuals [5].

Unfortunately, even the most successful timely interventions are in some way
overreaching, shutting down abruptly most of the economic activities and
creating social distancing between citizens. While acknowledging that this is
our current societal need to contain the propagation of this pandemic, our
main hypothesis is that, in the near future, digital technologies and wireless
connectivity can enable a _graceful lockdown_ by having the following main
roles.

  * •

Facilitate selective lockdowns by allowing flexible transfer of the work,
learn and play activities online to the desired extent.

  * •

Assist in maintaining physical distancing and tracking the spreading of the
disease, while offering digital tools to control the level of
lockdown/reopening.

  * •

Provide real-time information for targeted and efficient testing.

Note that these are also important today (October, 2020), when a second wave
is forcing several countries to rethink a step-by-step reopening based on the
aforementioned points.

This will be achieved based on an integrated solution for the ubiquitous and
privacy-preserving data acquisition, explainable predictive methods for
contagion risk assessment, and digitized policies for selective lockdown and
scheduling of societal activities. Data will be locally acquired via online
social networks, personal smart phone devices and Internet-of-Things (IoT)
sensors in general. It is foreseen that an effective global solution shall be
decentralized, yet hierarchical (system-of-systems), to provide fine-tuned
coordination among the federated entities to orchestrate actions and then
avoid both the human rights cost of overly stringent solutions [6] and the
human life cost of “hands-off” approaches. Beyond more traditional centralized
policy-making, such a decentralized platform, which will be built using
trustworthy distributed ledger technology (DLT), should also provide citizens
with tools for more direct participation in mitigation measures based on
tailored individual incentives.

In this sense, we expect to bring managerial decisions closer to the citizens.
With such a federated governance, we attempt to convert the usually passive
data acquisition process to an active participatory one. Hence, our aim is to
move away from a purely top-down management approach. In addition, the
proposed platform aims at achieving the minimal use of personal data,
processed with the maximum possible privacy-preservation to eliminate global
health risks, while protecting the economy [7]. The COVID-19 pandemic has
demonstrated that nations and governments need to be better prepared and
coordinated to detect and react to global threats focusing on early detection,
early response, moving beyond traditionally passive disease monitoring based
on voluntary reporting systems. Hence, a rapid, participatory responsive
detection system must be in place to support public health officials. A
synergistic relationship with public health officials, policymakers, and
citizens’ active participation will be critical to align the mandate for
public health with the protection of privacy, freedom and democracy [8, 9]. In
this context, one main factor is to design a special set of incentives that
would allow the citizens to provide secured anonymized access to their data
while actively participating in the crowd platform to support early disease
detection, a public information system, and possible mitigation measures.
Building up such an incentive structure that maps those trade-offs is a key
aspect (beyond purely technical ones) for the success of the proposed
federated architecture. Furthermore, this system should be constructed and
evaluated rigorously following ethical guidelines as in [4, 10].

Our contribution in this paper is threefold:

  1. 1.

a comprehensive analysis of epidemiological models, data collection, and
wireless connectivity is done based on key relevant scientific references on
pandemics.

  2. 2.

a federated global epidemiological warning system is proposed based on DLTs.

  3. 3.

an experimental setup of the integration between DLT and NB-IoT is used to
evaluate the wireless network performance on the IoT infrastructure supporting
a remote patient monitoring use case.

The rest of the paper is organized as follows: Section II presents a
discussion of epidemiological models and their limitations. Section III
describes the relevance of wireless connectivity on pandemic scenarios. In
Section IV, the federated global epidemiological warning system is proposed,
here is also detailed the experimental setup of the integration of DLT and
narrowband IoT (NB-IoT). Section V is reserved for some discussion and future
perspectives.

##  II Epidemiological Models and Data Collection

Biomedical data alone do not contain the information required for preventing
and mitigating pandemics. In a highly interconnected globalized society,
social interactions, environmental data, spatiotemporal events, and collective
nonlinear phenomena observed on complex infrastructures such as transport
systems, require the mining of heterogeneous pandemic big data that are a
result of a complex system of systems processes. In fact, modern disease
monitoring systems, as the Global Public Health Intelligence Network (GPHIN)
[11] and the Global Outbreak Alert and Response Network (GOARN) [12], and
event-based approaches that use a combination of web crawlers, artificial
intelligence, and public health expertise in the detection of indicators [13,
14] provide good—although limited—examples of successful platforms to identify
latent indicators of an outbreak.

To go beyond those existing solutions, data scale, contextualization, and
granularity are key requirements for data quality, which is usually orthogonal
to privacy preservation, timely processing and analysis as well as storage and
processing cost. In the following section, more details will be provided about
existing epidemiological models and epidemic-related data collection.

###  II-A Models and their limitations

Models for emerging epidemics come in various forms, all relying on
differently coarse-grained individuals’ data. State-of-the-art population-
level models for COVID-19 rely on metapopulation models with an underlying
Susceptible-Exposed-Infectious-Recovered (SEIR) disease dynamics [15]. The
metapopulations represent regions surrounding travel hubs and are
interconnected through mobility data, for example, data from airlines, public
transportation systems, and traffic control systems. The disease dynamics
itself accounts for the fact that exposed individuals can be undergoing an
incubation period, or even presymptomatic and unaware that they could infect
others, as suggested by early case data[16]. Note that some models opt for a
simpler formulation [17], the Susceptible-Infectious-Recovered (SIR) dynamics
which ignore the exposed state and therefore eliminate a potentially important
delay in disease progression.

Unfortunately, while SEIR and SIR models can be  effective at providing large-
scale forecasts and predicting importations at aggregated levels,  they are
less informative at the individual level where we expect significant
heterogeneity across individuals; both in behavior and disease manifestation
[18]. One key assumption of these models is that all individuals of a given
state follow the same dynamics (where state can be, for example, a combination
of age and epidemiological status). However, the importance of superspreading
events have highlighted that pandemics are often not driven by the average
individual but by rare, stochastic, events where a synergy between individual
behavior and disease manifestation can significantly spread the disease [19].

Models informed by data at the individual level attempt to account for
stochastic events but are less used for forecasting purposes as the data
required are seldom available and hard to connect across multiple scales or
populations [20]. However, individual contact data can inform network models
which are powerful for other purposes: Such as informing or measuring the
impact of interventions and policies, such as school closures and lockdowns
[17]. Likewise, these network models can be instrumental in guiding real-time
contact tracing efforts during epidemics [21, 22]. One key limitation of this
model is that they are often not spatially or temporally explicit. While this
is not problematic when they are used to guide local efforts and forecasts
over a short period, this limitation precludes their integration with spatial
and temporal forecasts from metapopulation models.

Altogether, an integrated modeling framework requires accurate estimation of
mobility across regions to inform metapopulation model, as well as individual
level data on heterogeneity in contact patterns to guide local interventions.
Importantly, these two scales need not be connected in any identifiable way.
It is not critical to know the local behavior of individuals connecting
different populations. Multi-scale data on the same population, with anonymity
assured across scales, would be of itself invaluable improvement to the
current reliance on disparate (and often private) data sources on different
populations. Doing so could help guide more flexible control policies, beyond
blanket measures that ignore the often unknown heterogeneity and adaptive
nature of human behavior.

More information is therefore necessary to accurately estimate the probability
of epidemics given importation event, forecast their eventual spread, and
guide control policies. Any particular importation event has the potential to
become the initial point of a widespread contagion in a region not reached by
epidemics until that point. Since this event basically depends on one
individual, aggregated level statistics provide poor description of the
heterogeneity of individual level behavior and mobility. Given this
uncertainty and the lack of multi-scale real-time informative system, most
governments around the world introduced stay-at-home policies to reduce the
mobility of their citizens, with the now famous goal of “flattening of the
curve.” This expression refers to the process of slowing down the virus spread
to keep the need for hospital care below the health care capacity. Different
variations of the stay-at-home policy have been introduced, e.g. allowing
citizens to move in a predefined radius to walk with their pets and exercise,
restricting the number of times per day or the number of people per household
that can go out. As previously argued, this is, understandably, a necessary
abrupt solution, but one that could be improved by properly utilizing crowd-
sourced data and active citizens’ participation. Multi-scale data gathering
could inform more flexible restriction while accounting for local
heterogeneity in behavior and mobility patterns. We argue that this important
data collection efforts could be accomplished by trustworthy federated
systems.

###  II-B Data collection: current activities and potential existing sources

At higher levels, mining existing data sets collected by different types of
service providers is an obvious and powerful way to quantify to which extent
citizens follow stay-at-home policies, thereby providing an essential means of
assessing their effectiveness. For example, smart grid systems can provide
information about the changes in the energy consumption in different areas
suggesting that people, on average, spend more time at home. The network usage
information from broadband operators can also be used as an indicator of
change in human behavior related to their mobility. Another source of
information are mobility data sets from cellular network operators that are
already present on the network. Smart appliances, like smart TVs, smart
fridges, and smart light bulbs can be used to provide information about the
overall time spent at home versus the time spent outside. Finally, information
collected by several smart city applications, such as public transport usage
and traffic, could also be leveraged to estimate the change in mobility
patterns at the level of places, buildings and vehicles.

At a lower granularity, proximity-tracing platforms have been proposed to
collect data at the individual level, for instance the Pan-European Privacy-
Preserving Proximity Tracing (PEPP-PT) [23], often in partnership with mobile
service providers and operators. By aggregating proximity data over time,
these approaches can follow data protection regulations. Among others, two of
the largest Big Tech companies Apple and Google proposed a joint solution that
appears to focus on the protection of user privacy, for example by keeping
people anonymous in the central servers and making data submission voluntary.
Different technologies are being proposed to automate and extend contact
tracing (e.g. shared databases, GPS traces of confirmed cases, contact tracing
through Bluetooth). The use of Bluetooth-based solutions, either in the form
of the third-party dedicated apps or as a feature built into the mobile
devices’ operating systems, seems to be the most promising solution to date.
Although these solutions are a new source of granular data, they cannot cover
all relevant forms of infection, namely asymptomatic infections, and
infections occurring out of proximity through shared surfaces (e.g.
doorknobs). In this sense, our hope is to combine the metapopulation and
individual tracing models in a citizen science framework to consider more
possible routes of infection as well as both proximity (e.g. close contact)
and mobility data (e.g. location visited). In addition to this, machine
learning methods are expected to combine expert knowledge (e.g. from
virologists and sociologists) with global data collected from different
sources for fitting powerful predictive models to high-dimensional data. In
doing so, the target is to leverage the different straights of different data
sources at different granularity for privacy protection and allow for more
thorough probabilistic forecasts. Ultimately this will allow for graceful
lockdowns, tailored incentives for citizens’ participation, and fine-tuned
legitimate interventions.

###  II-C Data privacy and trustworthiness

Fine-grained data collection approaches raise two major concerns as
highlighted in [24]: (1) user privacy; and (2) trustworthiness of the shared
information. Clearly, sharing location and health information with third-party
entities can lead to the misuse of the data set for instance through unwanted
advertising and health insurance implications. The impact of fake health
information on the contact tracing system should be analyzed to assess its
implications in terms of access to testing. All these applications rely on the
premise that users will trust and adopt these applications. This brings us to
an additional problem that could hamper these approaches. The adoption of new
technologies is hard to predict, and it might introduce bias for example
related to age, wealth, level of education, and state of country development.
Citizens’ participation in warning and response of epidemic outbreaks requires
new incentives that reward the responsible use of citizens’ personal data for
protecting public health, while penalize and prevent citizens’ profiling
actions, manipulative nudging and power misuse.

###  II-D Challenges and the way forward

Designing new data fusion schemes tailored to fuel data analytics processes
for prevention and mitigation of pandemics is an open research question: Which
smart phone sensor data can model compliance of stay-at-home policies? How
such models can be enhanced with social media, smart grid, or transport data?
Pandemics require that we revisit and potentially reinvent how data should be
managed and how systems should be designed to manage data in a more
responsible way [10]. More specifically, discovering new ways to turn private
sensitive data of citizens into public good, while preventing massive
surveillance, profiling, and discriminatory analytics, becomes imperative. To
pioneer this capability, new transparent and accountable data commons [25],
for instance personal data stores [26, 27, 28], to which citizens have
incentives to contribute data, are required. The citizens retain the right for
their data to be "forgotten" [29]. Ultimately, they have control and give
their consent to how these data are used. These data commons are designed to
maximize the use of techniques for privacy preservation [30, 7], for example
homomorphic encryption, differential privacy, anonymity, and obfuscation,
while citizens are made aware of the privacy risks they experience.

##  III Wireless Connectivity and Epidemic-Relevant Information

The previous section covered different aspects related to the data required to
support a fine-grained epidemiological model. It is argued that a ubiquitous
system can be employed to manage such data in a privacy-preserving manner.
Here, we identify wireless connectivity and Internet of Things (IoT) devices
as a way to collect data following those principles.

Wireless connectivity and IoT devices come in many flavors, but can, in
general, be classified into (1) personal devices, such as mobile phones and
earbuds; and (2) unattended connected machines and things, such as
surveillance cameras and motion sensors. There are three principal sources of
epidemic-relevant data acquired through wireless connectivity: (1) online
social networks; (2) personal smart phone and mobile data; and (3) sensory and
Internet of Things (IoT) devices.

###  III-A Online social networks

In critical events, people tend to use online social networks to post comments
about the emergency and learn from other users’ comments. As a result, online
social networks become a rich source of diverse information that could help to
understand the main characteristics of the crises and their potential
magnitude in early stages. For instance, in [31], authors collected comments
posted in the Chinese social media channel Weibo during the first month of the
COVID-19 epidemic in Wuhan (China) to understand the evolution trend of the
public opinion. A similar approach was used on Twitter in [32, 33]. Government
entities can use this approach to give more attention to the needs of the
public during the beginning of the epidemic and adjust their responses
accordingly. Another form of data content, normally provided by explicit
consent from the citizens, is the GPS location data from personal devices. At
the application level, the Twitter platform offers various user information,
geographical location being one of the most important for a wide range of
applications. However, only a few users enable their geolocation as public. To
discover the user’s geolocation (geographical region or exact geocoordinates),
there are mainly two approaches called content-based and network-based
algorithms. The first one uses textual contents from tweets, and the latter
uses the connections and interactions between users. In [34], a neural network
model based on multiview learning by combining knowledge from both user-
generated content and network interaction is proposed to infer users’
locations.

###  III-B Personal smart phone devices

This source of information comes in many different forms, ranging from the use
of metadata in cellular mobile networks for user localization up to the
metadata associated with different applications, such as Twitter. The use of
data111They are referred to as metadata due to its role played in the
communication system to which it belongs; here we will also refer to it as a
“data” when there is no danger of ambiguity. collected in mobile networks
during the COVID-19 pandemic has received significant attention [35].
Agglomerated mobile network data can be used to verify if interventions, such
as school closures, are effective, and help to understand the geographical
spread of an epidemic. Different types of mobile network metadata are
collected at different levels of the communication system, and they offer
varying levels of information. At the lowest level, localization of mobile
network users is possible by evaluating the strength of the wireless signal
received at base stations. Depending on how many base stations are connected
to the device, the location can be determined at the level of entire cells or
down to a few meters using triangulation.

The resolution offered by cellular mobile localization maybe too coarse for
detecting citizens’ proximity and potential infections by face-to-face
interactions. Nevertheless, even low-resolution location data can give insight
into behavioral patterns (how much time is spent at home, office, and events)
of individuals. Proximity detection can be enhanced by using the metadata from
Bluetooth devices, such as beacons and discovery messages (e.g., [36]). Wi-Fi
is also a technology that is used ubiquitously, and its metadata can provide
information on user proximity. For example, proximity can be inferred by
comparing the lists of access points that each device can see within a given
time interval [37].

Fusing the metadata from these different sources, along with the context
information (how many family members are at home, which events) is very
relevant for monitoring the epidemic.

###  III-C Sensory and IoT devices

Many applications and devices become an important tool to provide epidemic-
relevant data. These can be related to surveillance/thermal cameras, drones
and even wearable devices. For example, surveillance cameras can be employed
to count the number of people entering and exiting a specific area. In other
scenarios, thermal cameras are used with specialized settings to focus on
human skin temperature range, and infrared spot sensors for individual
temperature scanning. A similar application to identify individual or group
activities in a given place is based on motion sensors as infrared lasers, or
ultrasonic sensors. In this approach, the motion sensors are installed and
deployed in a specific environment to recognize different human activities.

Other important source of IoT data are the smart healthcare devices, for
instance, remote monitoring systems to check body temperature, which is a key
sign in the support of homeostasis. Other popular applications are oxygen
saturation monitoring based on beat oximetry, electrocardiogram monitoring
with a specialized framework to estimate the heart rate, elderly monitoring
using doppler radar to identify risk movements of elderly people, sugar level
monitoring, and blood pressure monitoring. These IoT devices provide
information that is analyzed by the IoT data to support specific applications.

##  IV Federated Global Epidemiological Warning System

We envision a federated and decentralized coordination system for
epidemiological warning and response. This system is federated by citizen
communities that crowd-source data, personal smart phone devices, community-
level IoT devices (e.g. LoRaWAN networks) and other computational resources on
the edge. It can scale organically and bottom-up to city-level, national-level
and ultimately at global-level to coordinate in a socially responsible way the
international actions of public health organizations and governments. This
scaling requires tailored incentives that align public health policy-making
with citizens’ privacy and autonomy. Distributed ledgers with secure crypto-
economic incentive models running on edge serves and scaling-up on-demand at a
global level using wireless connectivity and public cloud infrastructure are
the means to support the federated nature of this proposed system. Figure 1
illustrates the our vision.

Figure 1: A federated ubiquitous systems for epidemiological warning and
response. An organic and bottom-up scaling at global level is envisioned based
on active citizens’ participation. Decentralized privacy-preserving
computations are performed from the the edge to the cloud based on crowd-
sourced obfuscated and anonymized data managed with distributed ledgers to
empower trust. Incentive mechanisms for responsible data sharing align the
public health mandate with citizens’ privacy and autonomy.

###  IV-A Key components

The proposed federated global epidemiological warning and response system is
built upon epidemic-relevant information obtained through wireless
connectivity introduced in Section III, and the internet infrastructure
including fiber-optic and satellite links. Thus, in addition to the devices
that are the data sources (e.g., smart phones, wearables, smart appliances,
and cameras) with their applications and existing communication networks,
there is a need for a computer infrastructure dedicated to store and process
the epidemic-relevant data in a secure and privacy-preserving manner. It is
argued that this infrastructure also needs to follow a similar federated
organization based on the geographic locations. Each municipality, city,
county, or larger neighborhood will rely on edge servers to process their
respective data contents. The premise is to keep the computations as local as
possible.

However, larger-scale computations related to the interrelations between
locations (e.g. mobility, commuting, and traveling) requires data from these
different places. Once again, the federated organization supports this
collaborative sharing (up and downstream between the federated entities), but
it will probably require computationally more demanding algorithms. It is
possible that those computational tasks require more powerful cloud servers,
or collaborative parallel computing at the edges. For security reasons, DLTs
shall be employed to store data from the edge servers at the global level. At
the technical level, the following aspects deserve special attention.

####  IV-A1 Privacy

In this paper, a privacy-by-design approach is proposed with which several
critical operations of the federated global epidemiological system can be
performed; for instance, decentralized data analytics [38, 39], social
interactions analysis [40, 41], decentralized planning and resource allocation
[42] and federated learning [43, 44]. Such operations integrate in a smart way
state-of-the-art techniques, for instance, informational self-determination,
homomorphic encryption, differential privacy, obfuscation, and anonymity.
Often, privacy may limit the quality of service known as the _system utility_
as the accuracy and quality of data are deteriorated to hide information
content. For instance, predicting the risk of infections for individuals based
on an epidemiological network model is a graph-based semi-supervised learning
problem. Privacy-preserving semisupervised learning over graphs has been
considered in [45]. However, the precise trade-off between (lowering of)
privacy protection and learning accuracy is largely open. Pareto optimal
trade-offs can be configured and regulated by tuning the parameters of the
privacy techniques as previously shown in [7]. Monetary and other incentives
can be used to coordinate data sharing choices in a crowd. However, new types
of social incentives are required for such an epidemiological system; for
instance, incentives related to well-being, receiving solidarity, and long-
term payoffs.

####  IV-A2 Interoperability

The big data required by the proposed solution have to be interoperable, i.e.
the several applications that are providing data to be used to accomplish a
specific task have to operate together and share a common "understanding"
[46]. This can be achieved by employing standards for health informatics such
as ISO TS22220:2011, TS21090:2011, or TS13606. For instance, the data
collected from different sources can be used to predict refinements to patient
care or new drug contraindication [47]. This key issue, though, has deserved
little attention in large-scale epidemiological studies [48, 49]; it is
usually assumed that heterogeneous data sources are compatible with each
other. In practice, though, the highly heterogeneous data sources lead to poor
interoperability, which creates barriers to effectively combat pandemics like
COVID-19, as indicated by [50].

####  IV-A3 User interfaces

A successful platform also involves a suitable end-user interface [51]. In
this sense, data consumption by public health officials and global health
agencies will require user-friendly web-based interfaces, using common
dashboarding techniques. However, beyond this, the proposed federated platform
has to consider citizen participation and the heterogeneity of end-users.
Therefore, the following characteristics should be taken into consideration in
the design of the platform: it should be (a) explainable/accountable to
improve for instance awareness and engagement [52]; (b) gamified to engage and
incentivize participation [53, 54]; and (c) customizable for different user
groups at an international level.

###  IV-B Proposed architecture

The proposed architecture has to articulate the key elements from data
acquisition to analytics, following the best practices related to privacy and
cyber-security. At the acquisition level, in addition to the existing data
retrieval from the web, wireless-connected devices will send data to edge
nodes that will be associated with specific regions. Data will be anonymously
preprocessed at the edge, including some intelligent detection of anomalies or
event detection, and then sent to regional (cloud) servers to run a more
complete model that will fuse the geolocated timestamped data and run
detection models based on explainable AI approaches combined with mathematical
computational methods. In this case, crowd-sourcing models are applied based
on hardware as a public good in an approach similar to the DIASPORA social
network [55], also extending this approach to software and data. Regional
models will be associated in federations resembling the governance structure
of the actual regions under interest, which is customizable to local
policies/governance models.

Following this federated and collaborative organization, it will be possible
to build organically a fine-tuned early detection system at the global level
in a decentralized yet hierarchical manner to support graceful lockdowns. This
would turn the proposed federated architecture into a holarchy, where each
level is independent and self-sustained, but it can also be encapsulated at a
level above to capture new goals [56].

This system will rely on DLTs to guarantee a trustful system without a
responsible third party and minimize the risk of data manipulation[57].
Distributed ledgers could also be used to facilitate different (crypto-
economic) incentive mechanisms, such as token curated registries. Its main
objective is to build a crowd-sensing trustworthy platform based on privacy-
preserving methods to detect potential harmful symptoms in a specific region
in almost real-time and flag them to relevant authorities. This alarm needs to
be accurate, reliable, and explainable. It will also require a user-tailored
interface that could empower citizens by providing detailed explanations and
easy-to-access information, while being a tool for policymakers (from the city
level to global organizations) designing the correct interventions (a variety
of incentives, sanctions, and other persuasive measures) given the specific
context of the epidemic in some specific region in a given period of time.

###  IV-C Representative Use Case

To obtain insights from a practical implementation, we define a specific use
case that matches the premises of the proposed architecture. This key industry
use case focuses on remote patient monitoring. Here, many issues in treating
chronic patients could be reduced or resolved through more efficient patient
care. In the case of pandemics, such as COVID-19, the remote monitoring of
diagnosed patient can represent a key difference to avoid the virus
acceleration. Besides that, the application should guarantee privacy and
security on the gathered data. To guarantee people participation, the system
can provide incentives, such as bonus or tax compensations. In the context of
the proposed federated global epidemiological warning system, the remote
patient monitoring is a representative use case, in which the integration
between DLTs and IoT devices plays a key role. We analyze this integration
through an experimental setup based on NB-IoT, which is one of the most
representative low-power wide-area network (LPWAN) technologies in cellular
networks. Considering that the proposed architecture requires massive end
devices deployment, low power consumption, superior coverage, and worldwide
deployment, NB-IoT becomes a key solution to support the federated global
epidemiological warning system. Some details of the integration setup are
described in the next sub-section, emphasizing data workflow, and end-to-end
(E2E) latency. We consider latency because the experimental results indicate
that it is the most sensible network parameter when DLTs are built in the top
of the connectivity system.

###  IV-D Experimental Setup and Results

####  IV-D1 Setup Description

In this section, we provide brief explanation of the integration between the
NB-IoT network and the DLT-based system, which is illustrated in Figure 2.

Figure 2: Data workflow in both scenarios: standard NB-IoT in yellow
background, which is complemented by DLT-based system in purple background.

In a conventional NB-IoT system, the uplink data generated by the user
equipments is transmitted though specialized packet messages and routed toward
an edge data center to be stored and processed (yellow background in Fig. 2 \-
representing a standard-NB-IoT). At this point, the monitoring system has no
control on the collected data, so modification, corruption, and losses may
occur. Conversely, in our DLT-enabled NB-IoT setup, the uplink data generated
by the UEs is transmitted to a randomly chosen group of endorsing peers of
Hyperledger Fabric, a type of DLT used in our study, as transaction proposals
(check the purple background in Fig. 2). Then, each of the peers signs the
transaction using the elliptic curve digital signature algorithm (ECDSA) and
adds the signature before returning the signed message back to the IoT devices
(UEs).

The peers that provide an endorsement of the ledger send an update to the
application, but do not immediately apply the proposed update to their copy of
the ledger. Instead, a response is sent back to the IoT device to confirm that
the transaction proposal is correct, has not been previously submitted to the
ledger, and has a valid signature. Therefore, the security increases with the
number of endorsing peers. In addition, smart contracts can be executed to
update or query the ledger.

Then the IoT device broadcast the confirmed transaction proposals along with
the confirmation to the ordering service. The received transactions are
ordered chronologically to create blocks. These transaction blocks are
delivered to all peers for validation. Then, the peers append the block to the
ledger, and the valid transactions are committed to the current state
database. Finally, a confirmation message is transmitted back to the IoT
devices to report that the submitted transaction has been immutably published
to the ledger. The importance of confirmation is analyzed in [58].

####  IV-D2 Results

We evaluate the performance of an integrated DLT and IoT system with
permissioned Hyperledger Fabric and NB-IoT. Our experimental setup is based on
Hyperledger Fabric v1.4, NB-IoT development kits SARA EVK N211 and one NB-IoT
Amarisoft eNB station. We compare the E2E latency of conventional NB-IoT with
DLT-based NB-IoT in various scenarios. The number of endorsing peers is varied
from 1 to 4 peers. The IoT device transmits packet every minute, and the total
number of messages transmitted is around 1000.

Figure 3 shows that the end-to-end (E2E) latency is computed based on the
total uplink and downlink transmission latency and validation latency in the
ledger. We observe that when adding an new endorsing node to the same channel,
this will increase the endorsing latency. In comparison with conventional NB-
IoT system, the DLT-based NB-IoT perform a higher latency due to the
validation process from ledger to verify transaction as well as building
block. This is a trade-off between DLT-based system and conventional IoT
system. DLT-based IoT system guarantees the trust; however, the latency is
slightly degraded.

Figure 3: End-to-end latency performance of the proposed integration between
DLT and NB-IoT with different number of endorsed peers.

##  V Discussion and Future Perspective

###  V-A Latency analysis on practical DLT and NB-IoT Integration Setup

A federated global epidemiological warning system was proposed. Based on it, a
specialized use case focused on remote patient monitoring was considered. As
one critical piece of this use case is the integration of DLTs and IoT
devices, we implemented an experimental integration setup between DLTs and NB-
IoT.

In this study, we conclude that the latency increased when the number of
endorsing peers is increased. Results indicate that in average the latency is
increased 0.25 seconds per endorsed peer that is added. However, this
increases the guarantees in terms of secrecy, privacy, and interoperability.

The experimental setup also reveals additional challenges, such as the
performance of DLT-based system with massive number of connections, as well as
a trade-off between privacy, interoperability and system performance.

###  V-B Federated Global Epidemiological Warning System

Although the proposed architecture is designed to be fair among all citizens,
few important practical points still deserve attention. Good quality data
usually follow the wealth distribution over all scales of the proposed
federation. In other words, good quality data is very likely to be less
available in poorer regions of a city. The same is valid for poor regions
within a country and poor countries. This existing gap needs to be considered
when building incentives for participation, otherwise the proposed solution
has the potential of reinforcing inequality [59].

Another current issue we cannot neglect is the public perception of wireless
technologies. In this sense, part of the general public perceives IoT as
insecure [60] and fifth generation (5G) as a health hazard [61] to the point
of claiming an astonishing causal relation between 5G and COVID-19, which has
caused destruction of base stations across UK. Beyond these conspiracy
theories that are hard to combat, there exist legitimate concerns of
anonymized data not being anonymous [62], and of novel surveillance techniques
introduced in times of crisis that are maintained for monitoring (legally or
illegally) populations, e.g. post-9/11 surveillance in the USA [63].

All these have to be carefully addressed from the early stages of the system
development. Furthermore, it has to be clear that neither _technology_ nor
_data_ can prevent another outbreak on their own, but can only provide the
extremely valuable tools to enable the holy grail of controlling epidemics:
early detection, early response along all relevant actors within the federated
organization. In other words, technology and data detect and identify
potential harms and suggest actions and reactions, but the final diagnostic
and further interventions are due to the responsible institutions within the
federated structure and citizens’ active participation. Our proposal answers
those challenges based on informational self-determination as the way to build
trustworthy and secure public infrastructure that shall enable graceful
lockdowns as advocated here. In this sense, the proposed solution introduces a
more balanced management strategy, moving away from purely top-down approaches
toward a participatory system where the citizens are active. We further expect
that, even without being implemented, the high-level architecture introduced
here could offer important technological suggestions to decision-makers of how
to start smoothly resuming activities after lockdowns.

##  VI Conclusions

The federated system warning response envisioned for epidemiological warning
and response is a potential enabler to scale a global-level coordination
between public health organizations and governments. As it is based on DLTs,
secure crypto-economic incentive models can be used to motivate citizens and
institutions. Simultaneously, privacy and interoperability is assured.

An specific use case of the proposed system is based on the integration of
DLTs and a popular IoT transport technology, such as NB-IoT. A laboratory
setup implementation of this integration was proposed and detailed in this
paper. It indicated that there is a trade-off between privacy and some key
network key performance indicators, such as latency. This trade-off should be
studied and analyzed in terms of interoperability and system performance in
order to maximize the benefit of the proposed trustworthy federated system.

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  *[DLT]: distributed ledger technology
  *[DLTs]: distributed ledger technology
  *[NB-IoT]: narrowband IoT
  *[IoT]: Internet of Things
  *[LPWAN]: low-power wide-area network
  *[UEs]: user equipment
  *[5G]: fifth generation