# Reconfigurable Wireless Networks

Amr El-Mougy, Mohamed Ibnkahla, Ghaith Hattab, and Waleed Ejaz The authors are
with the department of Electrical and Computer Engineering at Queen’s
University, Kingston, ON, Canada (email: {mohamed.ibnkahla; 6ema; g.hattab;
we5}@queensu.ca).

###### Abstract

Driven by the advent of sophisticated and ubiquitous applications, and the
ever-growing need for information, wireless networks are without a doubt
steadily evolving into profoundly more complex and dynamic systems. The user
demands are progressively rampant, while application requirements continue to
expand in both range and diversity. Future wireless networks, therefore, must
be equipped with the ability to handle numerous, albeit challenging
requirements. Network reconfiguration, considered as a prominent network
paradigm, is envisioned to play a key role in leveraging future network
performance and considerably advancing current user experiences. This paper
presents a comprehensive overview of reconfigurable wireless networks and an
in-depth analysis of reconfiguration at all layers of the protocol stack. Such
networks characteristically possess the ability to reconfigure and adapt their
hardware and software components and architectures, thus enabling flexible
delivery of broad services, as well as sustaining robust operation under
highly dynamic conditions. The paper offers a unifying framework for research
in reconfigurable wireless networks. This should provide the reader with a
holistic view of concepts, methods, and strategies in reconfigurable wireless
networks. Focus is given to reconfigurable systems in relatively new and
emerging research areas such as cognitive radio networks, cross-layer
reconfiguration and software-defined networks. In addition, modern networks
have to be intelligent and capable of self-organization. Thus, this paper
discusses the concept of network intelligence as a means to enable
reconfiguration in highly complex and dynamic networks. Key processes in
network intelligence, such as reasoning, learning, and context-awareness are
presented to illustrate how these methods can take reconfiguration to a new
level. Finally, the paper is supported with several examples and case studies
showing the tremendous impact of reconfiguration on wireless networks.

###### Index Terms:

Cognitive radio networks, context-awareness, cross-layer reconfiguration,
learning, machine-to-machine communications, network intelligence,
reconfigurable networks, software-defined networks.

##  I Introduction

The last few years have witnessed an incredible revolution in wireless
networks. The number of wireless devices worldwide continues to increase at an
enormous rate and the applications of wireless networks are getting
increasingly versatile, sophisticated, and ubiquitous. Thus, future wireless
networks are expected to operate under several challenging conditions. First,
they are expected to provide efficient always-on reliable high data rate
access. Second, they have to be energy-efficient for the sake of user
convenience and for environmental concerns, since the widespread of wireless
devices leads to significant increase in their total energy consumption and
greenhouse gas (GHG) emission. Indeed, information and communication systems
consume over 10% of the world’s total energy [1]. Third, wireless networks
have to consider challenging and changing user demands (video traffic over
mobile devices now accounts for over 50% of global data traffic [2]). Fourth,
users expect their devices to be _smart_ , to learn about their interests, and
to provide them with information that is timely and convenient (over 2 billion
smartphone subscriptions have become available in 2014 [3]). Last but not
least, the spread of cloud resources and big data centres, together with the
advent of the Internet of Everything (IoE), promise to provide wireless users
with unprecedented services and opportunities.

These challenges have forced designers of wireless systems to completely
change their traditional design methodologies. Primarily, the the above
challenges mean that modern wireless networks have to be able to consider a
wide range of changing objects and conditions. Thus, modern wireless systems
have to be able to adapt to the evolving nature of wireless networks and
services. In other words, they have to be reconfigurable across multiple
layers of the protocol stack. For example, modern devices have to adapt their
transmission schemes at the physical (PHY) layer in order to operate in time-
varying conditions or high mobility, they have to reconfigure their routing
protocols to operate in heterogeneous or evolving networks, and they have to
adapt to their application requirements in order to satisfy a wide range of
user interests. Therefore, reconfiguration will be a key enabler for next-
generation wireless systems and services.

We can classify research in reconfigurable networks into three levels:
Reconfiguration at the PHY layer, reconfigurable networking, and network
intelligence. This hierarchy is shown in Fig. 1 with a few examples of
research topics at each level.

Figure 1: Classification of research in reconfigurable networks and examples
of research topics.

Reconfiguration at the PHY layer covers a broad range of topics such as
adaptive modulation, antenna beamforming, and software-defined radio (SDR).
Work in these functions has been covered comprehensively in the literature [4,
5, 6, 7, 8, 9, 10, 11, 12]. Section II focuses on reconfiguration at the PHY
layer, where network-wide considerations are taken into account during device
reconfiguration. With the recent emergence of cognitive radio networks (CRNs),
and the significant role CRNs are expected to play in next-generation wireless
networks, a special discussion on CRNs is given in Section II.

Reconfigurable networking is concerned with building adaptive networking
firmware that can address changing network topologies or application
requirements. Thus, Section III studies methodologies of implementing
reconfiguration at the networking level. Three particular aspects of
reconfigurable networking are considered: Cross-layer design, network
management, and software-defined networks (SDN). Several examples are
thoroughly analyzed in order to demonstrate how reconfiguration can solve
different network challenges. Performance analysis is also presented to show
the gain achieved from reconfiguration. For illustration purposes, this paper
considers the layered model presented in Table I. The model is composed of the
application, transport, network, medium access control (MAC), and PHY layers.
Cross-layer reconfiguration is the leading methodology in this area because it
has demonstrated significant improvements in the network performance when
inter-layer effects are taken into consideration [4, 13]. For example, PHY
layer parameters such as transmit power and bit error rate (BER) can have
profound effects on several processes in other layers of the protocol stack
such as scheduling at MAC layer and topology control at the network layer.
Therefore, the single-layer design approach can tangibly limit the adaptations
that can be implemented. In addition, network management is an important tool
for reconfiguring network parameters and protocols from a network-wide
perspective, which provides the potential to consider global network
objectives. On the other hand, SDN is a promising method that can leverage
capabilities in automation and virtualization of networks.

TABLE I: Reconfiguration can be performed at all layers Layers |  Examples of Possible Reconfigurations
---|---
Application |  Context information
User requirements
User interface
Transport |  Number of retransmissions
Congestion control
Network |  Routing
Admission control
QoS management
MAC |  Transmission and sleep scheduling
Contention/sensing window
Transmission rate
PHY |  Transceiver and antenna reconfiguration

The third level of reconfiguration in wireless networks adopts intelligence
and cognitive strategies throughout the wireless system and its surrounding
environment [14]. Intelligence and cognition include reasoning and learning,
which aim at improving networking decisions while fulfilling network end-to-
end goals. Of particular importance in this level is context-awareness (CA)
[15], which enables networking decisions to be made according to certain
relevant information. Here, the goal is to reconfigure the network to suit
system-wide architecture/deployment, user interests, or the environment, often
through predictions and proactive actions. This level is discussed in Section
IV.

It is clear from this introduction that research in reconfigurable wireless
networks has quite a wide scope. It also demands expertise from multiple
disciplines, mainly engineering and computer science. However, publications in
this field have been quite scattered, often focusing on a limited set of
issues. In this paper, we provide a unifying framework for research in
reconfigurable wireless networks. The objective is to link together ideas from
different fields and provide the reader with a holistic view of concepts,
methods, and strategies in reconfigurable wireless networks.

The rest of the paper is organized as follows. Section II covers the PHY layer
reconfiguration from a network perspective, focusing on CRNs. A case study is
provided to examine the performance gains achieved when network-wide
reconfiguration is implemented. Section III covers reconfigurable networking,
with emphasis on: Cross-layer reconfiguration, network management, and SDNs. A
case study is presented to show the performance gains archived when cross-
layer reconfiguration is used in machine-to-machine (M2M) networks. Section IV
is devoted to cognitive networking and context-awareness. A case study is
presented which investigates network management using reasoning and learning
in wireless sensor networks. Finally, the main conclusions and open research
areas are presented in Section V.

##  II PHY Layer Reconfiguration: A Network Perspective

PHY layer reconfiguration includes SDR, transceiver reconfiguration, adaptive
antennas and beamforming, adaptive modulation and coding (AMC), etc. [5, 6,
16]. Most publications in this field target device-level reconfiguration that
often does not take into consideration the network aspect (even when wireless
devices are part of a wider network). This section shows the importance of
network-based device reconfiguration. Without loss of generality, and in order
to illustrate the different concepts to the reader, this section explores the
case of CRNs.

CRNs are envisioned to tackle the problem of low utilization of the spectrum
by exploiting spectral opportunities in time, frequency, and space. These
opportunities can be used by secondary users (SUs), as illustrated in Fig. 2,
in situations where the incumbents (also known as primary users (PUs)) of
these resources are idle, or they can be shared by both PUs and SUs if the
latter conform to stringent rules to protect the former from harmful
interference.

Figure 2: There are unused spectral opportunities in time, frequency, and
space. SUs can orthogonally exist with PUs (interweave paradigm), or coexist
with them and share the resources (underlay paradigm).

Conventionally, a wireless network is primarily concerned with providing some
services at certain performance levels to its users (e.g., throughput,
fairness, etc.). However, the CRN must provide guarantees to both SUs and PUs.
In particular, the CRN must not degrade the quality-of-service (QoS) of PUs.
It must also provide QoS guarantees to SUs. The coexistence of CRNs with
heterogenous PU networks (PUNs) is challenging because:

  * •

Different PUNs may have different interference tolerance levels.

  * •

Some PUs are static (e.g., TV channels) while others are mobile (e.g.,
cellular users). Protecting the latter is more challenging.

  * •

Some PUs transmit at low powers (e.g., wireless microphones), and thus
detecting the presence of these incumbents is challenging.

In addition, providing a certain QoS for SUs remains a challenging task mainly
because:

  * •

The spectrum access is opportunistic. Hence, the resources are limited, and
they must be shared among multiple SUs efficiently and in a fair manner.

  * •

The PU activity may change. Thus, periodic monitoring of the spectral
opportunity is vital, meaning that data transmission interruptions or spectrum
handover to other available opportunities are inevitable.

To deal with these challenges, network level _reconfigurability_ and
_learning_ are key enablers for effective CRNs.

In the subsequent subsections, we discuss the key functionalities of a CRN,
and particularly, the cognitive management module, which acts as the brain of
the CRN. Then, we examine different levels of reconfiguration and evaluate the
throughput performance of a CRN in the absence and presence of network-based
reconfiguration and learning. We show that significant gains can be achieved
with proper reconfiguration at the SU device and the network level.

###  II-A Cognitive Management

The CRN must be equipped with a cognitive management module, which is composed
of a _cognitive spectrum_ manager and a _cognitive resource_ manager as shown
in Fig. 3 [17, 18]. The former helps _explore_ the spectral opportunities, and
the latter helps _exploit_ spectral resources and allocates them to different
SUs.

Figure 3: Spectrum exploration and exploitation must work in tandem to utilize
the spectral opportunities.

####  II-A1 The cognitive spectrum manager (CSM)

It is responsible for exploring spectral opportunities over the multi-
dimensional domain (time, frequency, and space). This can be realized through
accurate databases (e.g., databases of the location of TV channels), spectrum
sensing (e.g., energy detection), or a cooperation of both. In this section,
multiband sensing is assumed because it identifies a wider range of spectral
opportunities compared to single-band techniques [19]. However, instead of
exploring the entire spectrum by a single SU, multiple SUs can cooperate to
efficiently identify the available channels, where those with the highest
potential are explored first [19].

####  II-A2 The cognitive resource manager (CRM)

It has several key responsibilities. First, it collaborates with the CSM to:

  * •

Divide the multiband spectrum sensing task among different SUs, based on
different possible factors (e.g., the SU’s capability, battery level,
location, etc.).

  * •

Sense specific parts of the spectrum instead of sensing the entire spectrum.
For instance, to guarantee a certain level of QoS for SUs, the channels with
the highest signal-to-noise ratio (SNR) as well as the channels with the
lowest PU activity are sensed first.

Second, once the CSM identifies the spectral opportunities for current and
future use, the CRM must divide these resources among the different SUs. This
is realized through _resource usage_ (RU) and _resource control_ (RC) modules
[17]. The RU module is responsible for reconfiguring the PHY layer to exploit
the resources (e.g., spectrum shaping, which can be done via orthogonal
frequency division multiplexing (OFDM)). The RC module is responsible for
resource allocation (and reallocation in the event of returning PUs) and
mobility management (which includes spectrum handover).

###  II-B Cognitive Radio Layers Reconfiguration

Intuitively, the CSM and the CRM must work in tandem and provide cross-layer
optimization to guarantee QoS support for SUs and to protect PUs. Network
reconfiguration is not solely used at the PHY layer, but across all layers,
from the application layer down to the PHY layer as illustrated in Table I.
For instance, the application layer determines the QoS requirements for the
SUs (e.g., different applications have different bandwidth requirements, data
rates, delay-tolerance, etc.). Therefore, the wideband receiver front-end is
reconfigured to support the QoS requirement of a given application at the SU’s
device level. At the network level, the number of sensing SUs can be
reconfigured to meet these requirements (e.g., more SUs can be asked to sense
a channel that requires very high sensitivity for detecting a PU). At the
transport layer, delays and SU data packet losses may not only be induced due
to the channel, mobility, or congestion, but may also be due to spectrum
handover (e.g., when a PU reclaims a channel), or when an intermediate SU
(acting as a hop) cannot properly relay information [20, 21]. To overcome
these unique challenges, the CRM must identify a list of candidate and backup
spectral opportunities.

At the network layer, cognitive managers must learn and recognize the traffic
patterns of the PUNs (also known as _network tomography_ [22]). This is
necessary because it helps the network in routing reconfiguration and in
understanding the interference and spectrum usage patterns related to PUs.
Moreover, routing in CRNs must be spectrum-aware in order to consider the
dynamic nature of the available spectral opportunities. For example, routing
protocols must not only consider the shortest paths (to minimize energy) and
the strongest links (to maximize throughput), but also paths that generate the
minimal interference to PUs [22, 23].

At the MAC layer, access protocols must be intelligently aware of the
spectrum. Since the resources are dynamically changing, access control must be
flexible and must consider practical constraints such as the availability of a
common control channel for SUs to share the sensing results. In addition, the
MAC layer directly controls the reconfiguration of the SU’s transceiver. For
instance, reconfiguring the sensing schedule by distributing the task of
multiband sensing over multiple SUs is essential in order to reduce the high
complexity of multiband sensing. Moreover, the MAC frame includes a sensing
slot and a transmission slot. Thus, an inherent-tradeoff between sensing
accuracy and network throughput must be optimized by properly reconfiguring
the sensing time to maintain a balance between QoS delivery for SUs and
protection for PUs [24].

At the PHY layer, several reconfiguration strategies can be adopted for both
sensing and transmission:

  * •

The SU can reconfigure the spectrum sensor based on channel conditions, prior
channel state-information (CSI), prior knowledge about the PU signal, or any
combination of these. For example, energy sensors can be used in the absence
of any prior knowledge about the PU signal and when the channel condition is
good. Coherent and feature-based sensors can be used if the SU has prior
information about the PU signal [19, 23, 25].

  * •

In the case of multiband sensing, spectrum sensing can be reconfigured to
sense a subset of channels using, for instance, filter banks [26].

  * •

The parameters of the spectrum sensor can be dynamically reconfigured (e.g.,
the number of collected samples, the decision threshold, etc.).

  * •

In the case of multiple-input multiple-output (MIMO) SUs, antennas can be
reconfigured into transmitting antennas, sensing antennas, or both. In the
last case, concurrent sensing and transmission is feasible, under certain
conditions [27].

  * •

For transmission, multiple SUs may access non-contiguous channels, and thus
spectrum sculpting is essential to circumvent interfering with PUs (see [28]
for more details about spectrum sculpting techniques).

Besides all the aforementioned hardware reconfigurations, conventional
reconfiguration can also be used such as power control and AMC schemes.

###  II-C Case Study

In this section we study a CRN where network-based reconfiguration is
envisioned and compared with a basic CRN (in the absence of network
reconfiguration).

We consider a network that consists of $K$ SUs
$\in\mathcal{K}=\\{SU_{1},SU_{2},\ldots,SU_{K}\\}$, and $M$ channels at center
frequencies $\in\mathcal{F}=\\{f_{1},f_{2},\ldots,f_{M}\\}$. The PU activity
(i.e., the probability that the PU is active) for each channel, $0\leq
p_{m}\leq 1,~{}m=1,2,\ldots,M$, is assumed to be known (it can be obtained
using PU traffic estimation techniques [29]). For instance, $p_{m}=0.3$ means
that the $m$-th channel is occupied $30\%$ of the time.

Each SU senses multiple channels to determine whether they are occupied or
not. That is, $SU_{k}$ solves the following multiple binary hypothesis testing
problem [19]

$\displaystyle\mathcal{H}_{0}^{m,k}:$
$\displaystyle~{}\mathbf{y}_{m}^{k}=\mathbf{w}_{m}^{k}$ (1)
$\displaystyle\mathcal{H}_{1}^{m,k}:$
$\displaystyle~{}\mathbf{y}_{m}^{k}=\mathbf{x}_{m}+\mathbf{w}_{m}^{k},$

where $m=1,2,\ldots,I_{k}\leq M$ is the channel index, such that $SU_{k}$
senses $I_{k}$ channels,
$\mathbf{y}_{m}^{k}=[y^{k}_{m,1},y^{k}_{m,2},\ldots,y^{k}_{m,N_{m}^{k}}]^{T}$
is the sensed signal at the $m$-th channel by $SU_{k}$, $\mathbf{x}_{m}$ is
the transmitted PU signal, and $\mathbf{w}_{m}^{k}$ is a zero-mean additive
white Gaussian noise (AWGN) with unit variance. $SU_{k}$ decides
$\mathcal{H}_{0}^{m,k}$ if the $m$-th channel is unoccupied or decides
$\mathcal{H}_{1}^{m,k}$ otherwise.

In general, $SU_{k}$ forms a test statistic based on the likelihood ratio
test. Thus, the decision rule of $SU_{k}$ for the $m$-th channel is

$\Lambda(\mathbf{y}_{m}^{k})\overset{\mathcal{H}_{1}^{m,k}}{\underset{\mathcal{H}_{0}^{m,k}}{\gtrless}}\xi_{m}^{k},$
(2)

where $\Lambda(\mathbf{y}_{m}^{k})$ is the test statistic, and $\xi_{m}^{k}$
is the threshold that divides the decision region into $\mathcal{H}_{1}^{m,k}$
and $\mathcal{H}_{0}^{m,k}$. To form a test statistic for the $m$-th channel,
$SU_{k}$ collects $N_{m}^{k}$ samples. This parameter is commonly known as the
_sensing window_. It plays a key role in the sensing-throughput tradeoff [24].

We consider three spectrum sensors: An energy-based sensor and two feature-
based sensors (FBS). The first FBS exploits pilot patterns in the frequency
spectrum, and the second exploits the cyclic prefix in an OFDM-based PU. The
reader may refer to [19, 25, 23] for a comprehensive discussion of state-of-
the-art sensing techniques.

If the SU has no prior knowledge about the PU signal, or if the channel link
is good, then the SU may use the energy detector (ED), which is expressed as

$\frac{1}{N_{m}^{k}}\big{\|}\mathbf{y}_{m}^{k}\big{\|}^{2}\overset{\mathcal{H}_{1}^{m,k}}{\underset{\mathcal{H}_{0}^{m,k}}{\gtrless}}\xi_{m,ED}^{k},$
(3)

where $\|.\|^{2}$ is the Frobenius norm. This spectrum sensor has the
following receiver operating characteristic (ROC) performance under the AWGN
channel condition [24]

$P_{D_{k,m}}^{ED}=\mathcal{Q}\bigg{(}\frac{\mathcal{Q}^{-1}(P_{FA})-\sqrt{N_{m}^{k}}\textsl{SNR}_{k}}{\sqrt{2\textsl{SNR}_{k}+1}}\bigg{)},$
(4)

where $P_{D_{k,m}}^{ED}$ is the ED detection probability of $SU_{k}$ at the
$m$-th channel, $P_{FA}$ is the false alarm probability requirement,
$\textsl{SNR}_{k}$ is the SNR at $SU_{k}$, $\mathcal{Q}(.)$ is the
complementary distribution function of the standard Gaussian, and
$\mathcal{Q}^{-1}(.)$ is its inverse.

In many practical systems, the PU transmits a pilot pattern along with the
data-carrying signal. If the SU has a prior knowledge about the pilot pattern,
$\mathbf{x}_{m}^{P}$, then it may use the pilot detector (PD), which is
expressed as

$\frac{1}{N_{k}}(\mathbf{x}_{m}^{P})^{H}\mathbf{y}_{m}^{k}\overset{\mathcal{H}_{1}^{m,k}}{\underset{\mathcal{H}_{0}^{m,k}}{\gtrless}}\xi_{m,PD}^{k},$
(5)

where $(.)^{H}$ is the Hermitian operator. The ROC performance of this sensor
(in the AWGN channel case) is expressed as [30, 31]

$P_{D_{k,m}}^{PD}=\mathcal{Q}\bigg{(}\mathcal{Q}^{-1}(P_{FA})-\sqrt{2\theta
N_{m}^{k}\textsl{SNR}_{k}}\bigg{)},$ (6)

where $\theta$ is the power allocation factor for the pilot signal.

Finally, using the detector proposed in [32] for detecting OFDM-based PUs, the
ROC performance can be approximated by

$P_{D_{k,m}}^{OFDM}\approx\mathcal{Q}\bigg{(}\frac{\mathcal{Q}^{-1}(P_{FA})-\sqrt{2N_{m}^{k}}\textsl{SNR}_{k}}{\sqrt{4\textsl{SNR}_{k}+1}}\bigg{)}.$
(7)

Fig. 4 illustrates the detection performance of the three spectrum sensors in
the single SU single-band case. The sensing duration is $N=1000$ samples for
all sensors to achieve a fixed false alarm probability of 10%. It is observed
that the pilot-based detector achieves the best detection performance at very
low SNR, whereas the energy detector exhibits a poor performance because it
does not incorporate any prior knowledge about the PU signal.

We present now a number of reconfiguration strategies for the CRN:

Figure 4: Detection performance of the three spectrum sensors.

####  II-C1 Basic CRN

This is the simplest scenario where the network arbitrarily allocates the
sensing tasks to the SUs. Here, each individual SU merely configures its
transmission frequency according to the spectrum availability. Particularly,
in this scenario:

  * •

Each SU may have to sense all channels in the absence of any coordination.
Thus, $I_{k}=M~{}\forall k=1,2,\ldots,K$ regardless of the PU activity
profile. Note that this implies that the SU may sense a channel with
$p_{m}\approx 1$ (i.e., almost always busy), which is clearly a waste of
resources. Moreover, sensing the entire spectrum by an individual SU is
expensive and complex.

  * •

All SUs sense for a fixed duration (i.e., $N_{m}^{k}=N~{}\forall
k=1,2,\ldots,K,~{}\forall m=1,2,\ldots M$). That is, the SU does not
reconfigure its sensors based on the channel quality. This may lead to
throughput losses especially if a channel is in a good condition and the
sensing window is not reduced for that channel.

  * •

Each SU implements a _non-reconfigurable multiband detector_ (NMD), as shown
in Fig. 5. It consists, in this case, of $M$ EDs. Here, the same threshold is
used at $SU_{k}$, i.e., $\xi_{m,ED}^{k}=\xi^{k}_{ED}$, regardless of PU
activities and interference protection levels.

Once the sensing results are obtained for all channels, the SUs send their
local decisions to a fusion center (FC) to make a global decision on the
occupancy of the $M$ channels.

####  II-C2 CRN with device-based reconfiguration

Here, each SU optimizes the thresholds and the sensing duration of each branch
of its multiband detector to adapt to the interference levels, channel
quality, etc. For instance, the _multiband joint detector_ (MJD), which
consists of energy sensors, jointly optimizes $\xi_{m,ED}^{k}$ and $N_{m}^{k}$
(see [33, 34, 35] for more details) to maximize the CRN throughput. A higher
level of reconfiguration is obtained when the multiband detector consists of
sensors of various types, as illustrated in Fig. 5. We refer to it as the
_reconfigurable multiband detector_ (RMD).

Figure 5: (a) The NMD consists of multiple single-type detectors (in this
case, all detectors are EDs); (b) The RMD may consist of multiple detectors of
multiple types (in this case, there are EDs, PDs, and OFDM-based detectors).

####  II-C3 CRN with centralized network-based reconfiguration

Here, the FC reconfigures the network’s parameters and coordinates the
spectral sensing and exploitation tasks of SUs. In particular, the FC may
group the SUs into multiple clusters. Each cluster is responsible of sensing a
subset of the wideband spectrum. Furthermore, the FC can assign the sensing
tasks among the SUs based on various factors (e.g., the FC can ask an SU to
only sense the channels with the highest SNR at its location).

####  II-C4 CRN with distributed network-based reconfiguration

The centralized CRN has several limitations. First, the amount of information
exchange and network overhead between the SUs and the FC is tremendous due to
continuous spectrum sensing and decision updates performed by the SUs in real-
time. Second, in practice the communication links between the SUs and the FC
are not error-free. Indeed, the network’s performance can be limited if the
reporting channel is in a deep fade [36]. Third, many network architectures
lack a common central unit (e.g., ad hoc networks). For these reasons,
distributed network-based reconfiguration is appealing for CRNs. Due to the
absence of the FC, _learning_ becomes prominent in order to improve the
reliability of spectrum sensing [37, 23, 38, 39, 40, 41]. Here, a consensus-
based learning scheme is considered. It consists of two stages: In the first
stage, each SU senses its corresponding list of channels using either the NMD
or the RMD (depending which device reconfiguration is in effect). In the
second stage, each SU communicates with its neighboring SUs for information
exchange. Each $SU_{k}$ checks with its neighboring SUs, and if some of them
are sensing some channels that $SU_{k}$ is sensing, then it will use the
information obtained from these neighbours to update its own test statistics
as follows

$\Lambda(\mathbf{y}_{m}^{k}(i+1))=\Lambda(\mathbf{y}_{m}^{k}(i))+\mu\sum_{l\in\mathcal{N}_{k}}\Lambda(\mathbf{y}_{m}^{l}(i))-\Lambda(\mathbf{y}_{m}^{k}(i))$
(8)

where $i$ is the iteration index, $\mathcal{N}_{k}$ is a set of $SU_{k}$’s
neighbours, and $\mu$ is the learning step size.

For example, let $\Lambda(\mathbf{y}_{m}^{k})=E(k,m)$ be the energy of the
samples collected by $SU_{k}$ for the $m$-th channel. Then, $SU_{k}$ will
exchange information about this energy measurement with its neighbors until
all corresponding SUs reach a consensus about the energy level of that channel
(i.e., until $E(k,m)$ converges to some constant $\forall
k\in\mathcal{N}_{k}$). See [41] for an in-depth analysis of the convergence
properties of consensus-based algorithms.

####  II-C5 Performance analysis

The average aggregate throughput of the CRN is investigated for different
levels of reconfiguration and compared to the basic CRN (see [19] for more
details about the throughput in multiband CRNs). The case of $M=5$ channels
and $K=10$ users is considered. Each user senses $I_{k}=I=2~{}\forall k$
channels (the same assumption is made for the basic CRN for a fair comparison)
and transmits its own data over one available channel with transmit power of
10dB. The SNR of each channel at each SU is based on a Rayleigh fading model,
with an average SNR of $\bar{\gamma}$. Moreover, the PU activity over the
$m$-th channel is assumed to follow a uniform random process, i.e.,
$p_{m}\sim\mathcal{U}(0,1)$, and it is independent of the other channels.

In the centralized case (Fig. 6), SUs send their decisions to the FC, which
combines the results using OR-logic rule [42]. The resulting decisions are
then sent by the FC to the SUs for spectrum access.

For the basic CRN scenario, each SU senses two pre-assigned channels using the
ED-based NMD (i.e., it is assumed that the FC allocates two channels for each
SU such that each channel is sensed by $(I\times K)/M$ SUs as shown in Fig.
6).

Figure 6: Network-based reconfiguration: (a) In the centralized approach, the
FC allocates the channels to the SUs. For instance, in the basic CRN, the FC
allocates $f_{1}$ and $f_{2}$ to $SU_{1}$ and $SU_{6}$, as shown. In the
network-based reconfiguration, the FC will allocate the best channels at each
SU’s location; (b) In the distributed approach, SUs will sense the best
channels. For instance, it is shown here that $f_{1}$ and $f_{5}$ are the best
channels at $SU_{9}$. Then, $SU_{9}$ will exchange information with SUs that
sense these channels. That is, $SU_{9}$ will exchange information about
$f_{1}$ with $SU_{1}$ (in a single-hop) and $SU_{3}$ (in multiple hops), and
so on.

For the CRN with device-based reconfiguration, the channel allocation is
similar to that of the basic CRN case. However, the SUs jointly optimize the
sensing duration and the threshold of each channel (to maximize the throughput
over that channel subject to a false alarm constraint of 10%). Here, two cases
are studied: In the first one, the SUs use the MJD, and in the second case
they use the RMD. Without loss of generality, let us assume that an SU with
the RMD uses a PD for $f_{1}$ and $f_{2}$, an ED for $f_{3}$ and $f_{4}$, and
an OFDM-based detector for $f_{5}$.

For the CRN with network-based reconfiguration (centralized and distributed),
the sensing tasks are no longer randomly allocated. In this study, each SU
senses the channels that have the highest SNRs at their locations.

For the distributed network-based reconfiguration case (Fig. 6), it is assumed
that each SU is aware of its neighbours. In addition, each SU collects a fixed
number of samples, $N$, for the sensing process. The duration of the learning
process is set to 10 iterations, with a step size of $\mu=0.25$. It is
reasonable to use a fixed duration for the sensing and learning processes
because synchronization among SUs is essential in distributed networks.

The aggregate throughput of the centralized CRN versus the PU protection level
is illustrated in Fig. 7 for $\bar{\gamma}=-15$dB. Here, a 0.90 protection
level is equivalent to a global probability of detection of 90%. The sample
budget is set at $\tau=10000$, and the sensing window is set such that:

  * •

Each SU uses $N=2500$ samples in the basic CRN scenario.

  * •

Each SU uses $0\leq N_{m}^{k*}\leq 2500$ samples in the device-based and
centralized network-based reconfiguration scenarios, where $N_{m}^{k*}$ is
optimized together with the thresholds to maximize the throughput of $SU_{k}$
at the $m$-th channel.

It is clear from Fig. 7 that the higher the reconfiguration level in the
network, the higher the aggregate throughput. For instance, SUs that use the
MJD achieve a lower throughput than SUs that use the RMD. This is because the
former uses a single-type sensor (i.e., only the parameters of each sensor are
reconfigured), whereas the latter uses sensors of multiple types (i.e., the
sensor type and its specific parameters are reconfigured). More throughput
gains can be accomplished when the FC reconfigures the sensing tasks for the
SUs. Furthermore, for the same level of network reconfiguration, it is
observed that there is a significant gain when SUs sense two channels (i.e.,
$I=2$) compared to the single-channel case (i.e., $I=1$). For instance, when
the PU protection level is 90%, the throughput can be improved by 67% when
multiband sensing is used in the network-based reconfiguration compared to the
single-channel case. This illustrates the superiority of multiband spectrum
sensing over single-band techniques.

Figure 7: (a) The aggregate throughput of the centralized CRN with different
levels of reconfiguration versus the PU protection level; (b) Comparison of
the aggregate throughput between the centralized and distributed network-based
reconfiguration versus the average SNR.

Fig. 7 shows the aggregate throughput of the network-based reconfiguration as
a function of $\bar{\gamma}$. The false alarm requirement for each channel is
10%. In all scenarios, a fixed sensing window is implemented, where $N=5000$
samples. In the network-based reconfiguration, it is assumed that the SUs use
the RMD, where only the thresholds are optimized. It is clear that the
distributed reconfiguration approach outperforms the centralized
reconfiguration. This is expected since the latter implements the OR-logic
rule.

Fig. 8 illustrates how SUs reach consensus for different channels even though
the initial measurements vary between SUs due to the random condition of the
wireless environment (a snapshot of the network is shown in Fig. 6). Because
$f_{2}$ is being sensed by SUs using PDs, they will exchange information about
the correlation level (shown in the top plot), whereas SUs sensing $f_{3}$
will exchange information about the energy level because this channel is being
sensed using EDs (shown in the bottom plot). In both cases, it can be observed
that SUs quickly reach a consensus.

####  II-C6 Complexity

Distributed network-based reconfiguration is more complex than the other
approaches, yet no centralized units are required. Because leaning is done in
a distributed way, the burden is shared among the nodes, and hence the
additional complexity is worth it given the tremendous saving in terms of
throughput and energy consumption.

In what we have discussed so far, network-based reconfiguration has been
limited to the PHY layer. The following section covers reconfigurable networks
where all layers of the protocol stack are jointly reconfigurable.

Figure 8: Learning curves for different iterations ($\mu=0.1$).

##  III Reconfigurable Networking

Networking functionalities need to support a variety of issues such as
mobility, QoS, security, energy-efficiency, etc. Several research directions
have tried to achieve these goals. In this section, we focus on three areas of
reconfigurable networking. These are: Cross-layer-based reconfiguration,
network management, and SDNs.

###  III-A Cross-layer-based Reconfiguration

Cross-layer design reconfigurations break the boundaries between layers by
exchanging parameters or jointly designing layers in order to overcome the
limitations and the rigidity of the traditional protocol stack (or single
layer design) [4]. This opens significant possibilities for reconfiguration in
networks, primarily because it allows for the consideration of information and
objectives from multiple layers. For example, packet loss may be attributed to
many factors such as link breakages, which can be detected at the PHY and MAC
layers, or congestion, which can be detected at the network and transport
layers. Even though the resulting event in all these cases is the same (packet
loss), the reasons behind each case is quite different, and so the way to deal
with the event must also be different.

Thus, in order to design a reconfigurable system which is capable of producing
the appropriate decisions in response to some event, information from multiple
layers may be needed. Cross-layer protocols can be classified into two main
categories according to how information is exchanged between layers and how
the network is organized [43].

In the first category, cross-layer protocols exchange information in one of
two ways: Either by utilizing a database where parameters from all layers are
stored and can be accessed by any layer, or by allowing direct exchange of
information between any two layers. Having a database of parameters simplifies
information exchange and reduces the chance of conflict when two layers are
modifying the same parameter. However, the design of such a multi-layer
database may require modifications at multiple layers. On the other hand,
allowing direct information exchange allows for more localized cross-layer
implementations, but may produce conflicts when the number of layers and
parameters involved increases.

In the second category, cross-layered protocols are designed according to the
type of network architecture. Here, cross-layered approaches can be
implemented using centralized or distributed network architectures. In the
centralized approach, network-wide information is used, which may result in
more optimized reconfigurations. However, this may come at the cost of extra
overhead and latency associated with the required information gathering. On
the other hand, distributed approaches are faster and thus more adaptive.
However, the decisions produced can only be optimum in localized parts of the
network.

We can classify reconfigurations within each layer into processes that are
controlled or information that is passed between layers. At the PHY layer, two
important processes to be controlled are transmit power and data rate. In
addition, MIMO techniques have introduced other important processes such as
beamforming and precoding [7], whereas CRNs require spectrum control at the
PHY layer. On the other hand, information that can be passed from the PHY
layer to other layers include BER, CSI, SNR, interference temperature,
mobility information, remaining energy, etc.

At the MAC layer, processes to be controlled include scheduling, source rate
control, hybrid automatic repeat request (HARQ), maximum number of
retransmissions, and contention window size, etc. Information that can be
passed from the MAC layer to other layers include packet loss rate, queue
length, packet types, frame length, etc.

At the network layer, the main process to be controlled is routing, which
includes path discovery and maintenance. Other processes include admission
control, which is particularly challenging in distributed networks due to the
lack of complete information about interfering nodes. Congestion control and
QoS support also coincide at the network layer. Parameters that can be passed
from the network layer include path length and cost, session blocking and
dropping rates, and path quality (in terms of any parameters under
consideration). There is also some consideration of the transport layer, where
processes include transmission window, flow control, and information passed
includes error rate and congestion detection. This list of processes and
parameters is not exclusive, but intended to provide an idea about what can be
controlled at each layer.

To illustrate, several cross-layer implementations that enable reconfiguration
in networks can be found in long-term evolution (LTE) and LTE-Advanced (LTE-A)
technologies [44, 45]. One example is coordinated multipoint transmission and
reception (CoMP) [46], expected to appear in Rel. 11 of LTE. This technology
tries to improve performance at the cell edges by taking advantage of the fact
that users are receiving signals from more than one eNodeB. There are mainly
two types of CoMP: Coordinated scheduling/beamforming (CS/CB) and joint
processing (JP) [47]. In CS/CB, the user is still associated with a single
eNodeB, but the neighboring eNodeBs exchange information in order to
coordinate their activities and reduce inter-cell interference. In CS, the
exchanged information is used to coordinate scheduling to different users at
the cell edges, while CB uses the information exchange to adjust the antenna
beams to users. On the other hand, in JP, the neighboring eNodeBs exchange
information in order to coordinate simultaneous transmissions to the same
user.

Figure 9: CoMP techniques: (a) In CS, the scheduling is coordinated; (b) In
CB, antenna beams are coordinated; (c) In JP, the user simultaneously receives
information from the neighboring eNodeBs.

The reconfigurations done in different CoMP types are shown in Fig. 9. These
techniques represent a clear example of cross-layer reconfiguration of network
parameters and systems in order to reduce interference and improve performance
[8]. This is done based on feedback from the user equipment (UE) that
indicates CSI to the eNodeBs. This CSI can be used to assign resource blocks
(RBs - the smallest individual scheduling unit that can be assigned to users
and consists of a chunk of adjacent frequencies) in CS, reconfigure precoding
matrices for beamforming in CB, or perform JP. In any case, multiple layers
are involved in the process and the system behaves differently according to
the number of UEs and their CSI.

LTE has other examples where cross-layer reconfiguration improves user
experience such as downlink and uplink packet scheduling [48], which relies on
feedback for the UE about CSI to perform time-domain and frequency-domain
scheduling (deciding the set of users that will transmit in a particular time
slot and the RBs that will be assigned to each one of those users). This is
also an example of joint design between the PHY and MAC layers. Note that this
is possible only for UEs with low mobility. In high mobility, CSI is changing
quite rapidly and the eNodeBs will not be able to change the assignment of RBs
for all users that quickly. Thus, the protocol is reconfigured according to
the statuses of the UEs: If mobility is low, then frequency-domain scheduling
is applied in order to assign the optimum RBs to each UE according to their
CSI; otherwise, RBs are assigned over a broader part of the bandwidth to
utilize frequency diversity in case of high mobility.

The above examples represent centralized implementations of cross-layer
reconfiguration without the existence of a database of parameters. Centralized
implementations are straightforward in cellular networks due to the presence
of base stations (BSs) that perform most of the processing on behalf of all
UEs in the network. Thus, fine-grained optimizations, such as assigning RBs to
users based on their CSI, can be performed. For example, [49] proposes an
uplink scheduler for LTE-A networks that exploits multi-user-MIMO (MU-MIMO)
techniques. For cross-layer reconfiguration between the PHY and MAC layers,
MU-MIMO reconfigures matrix precoding techniques to allow multiple users to
use the same RBs without interfering with each other. Here, the eNodeB
receives feedback from every UE about CSI for each individual RB. Based on
this feedback, an optimization problem is formed where the objective function
is to maximize the rate assigned to every user for every RB. This is subject
to multiple constraints such as decodability (the rates have to be decodable
by the eNodeB), one precoder matrix per user, finite buffers per user, limits
on inter-cell interference, and a maximum of two chunks of contiguous RBs for
every user in any time slot. The output of the optimization function is the
set of RBs and the precoding matrix that will be assigned to each user.
Therefore, the assigned RBs and the antenna precoding matrices are
reconfigured according to the CSI of each user.

The performance of MU-MIMO and CoMP techniques is analyzed in [46]. Two
particular cases are considered: Homogeneous and heterogeneous networks. In
homogeneous networks, all BSs belong to the same power class. On the other
hand, heterogeneous networks use low-power nodes known as pico cells. The
percentage gain in throughput is evaluated in case of MU-MIMO, CS/CB, or joint
transmission (JT), which is the most common JP scheme. All nodes are assumed
to have a full buffer and thus can continuously transmit data. The case of the
homogenous network is illustrated in Fig. 10. As the figure shows, all
techniques achieve a minimum of 100% throughput gain. JT achieves the highest
gain, particularly at the cell edges. The gain is higher in heterogeneous
networks, since the introduction of pico cells means that there is a larger
number of cells and thus a larger number of cell edges [46].

Figure 10: Performance analysis of MU-MIMO and CoMP techniques [46].

Additional examples include the work in [50], which proposes a scheduler for
multicast services in LTE systems. This work exploits multi-user diversity and
uses AMC to maximize throughput. Another example is the work in [51], which is
a cross-layer channel selection scheme for WiMAX networks. It addresses the
performance degradation associated with high mobility and proposes a channel
selection scheme that utilizes AMC. A reward-based scheme is used for power
control in order to reduce interference. Prioritization for users is
considered as well.

Centralized cross-layer design has also been used in networks without
infrastructure such as ad hoc networks. However, one or more nodes have to
assume a central or manager role. Thus, in order for this central node to
perform the required processing, information typically has to be collected
from the nodes under its service. This is not as straightforward as in
cellular networks, where dedicated channels are used to relay the required
information. In addition, nodes in cellular networks are synchronized, and all
communications are single-hop (from UE to BS or BS to UE). Some of the
challenges in ad hoc networks include: First, the lack of synchronization
could mean collisions when relaying information, especially when the number of
nodes served by the central manager increases. Second, multi-hopping
complicates several issues such as routing, admission control, multiple
access, and third the lack of dedicated channels mean that control information
has to compete for bandwidth with data. This means that the protocols may not
be highly reactive and latency may be incurred in reconfiguring system
parameters.

Figure 11: Performance of the power control algorithms in metrics of (a)
average total power and (b) total throughput [52].

For these reasons, centralized cross-layer implementations in ad hoc networks
often target long-term planning or processes that do not require quick or
frequent reconfigurations. An example is the cross-layer protocol proposed for
ad hoc networks in [52], which targets the optimization of throughput in
sensor networks, where the existence of one or more BSs simplifies the
implementation of centralized protocols. The protocol in [52] is divided into
two parts: The first one performs topology control while the second one
performs joint routing and rate control to maximize throughput. Two algorithms
are proposed for topology control based on linear programming problems that
utilize power control. In the first algorithm, the linear programming problem
is solved to minimize total transmission powers at all nodes while
guaranteeing connectivity. The second algorithm configures transmit powers to
minimize the total interference in the network. After the topology has been
configured, the second part of the algorithm runs and another linear
programming problem is solved to maximize data rates over all links. The
algorithms are compared to the case where there is no power control. The
results in Fig. 11 show that these algorithms have better energy efficiency
and throughput.

As we can see, the protocol can only be implemented in a centralized node,
where network-wide information is available. In addition, each time the
algorithms are used, they are reconfigured according to the status of the
nodes in the network. However, the work in [52] does not consider the latency
involved in gathering this information and does not indicate how quickly the
system may react to network changes.

From the above discussion, it is clear that centralized cross-layer
implementations may not be suitable for highly dynamic networks, where
protocols are required to be highly adaptive. For example, in vehicular ad hoc
networks (VANETs), high vehicle mobility complicates the design of networking
protocols. For example, routing protocols have to recover quickly due to
frequent link breakages (links may not be alive for more than 30 seconds),
which makes heuristic approaches such as greedy routing [53] more favorable
than optimized solutions, even if greedy routing produces suboptimal results.
One protocol that employs greedy routing is the grid-based predictive
geographical routing (GPGR) protocol [54]. This protocol uses global
positioning system (GPS) devices on board vehicles to route packets between
road segments. In each segment, the protocol tries to forward data to the
farthest node within its communication range. GPGR uses information about
position, velocity, and direction of nodes to choose the next relay node along
the path in a dynamic way that reduces link breakages and packet loss. A
similar approach is adopted by reliability-improving position-based routing
(RIPR) [55]. Optimization is used to study cooperative routing, but no
analysis of the adaptability of the protocol is given. In all these routing
protocols, the devices have to reconfigure their routing paths faster than the
speed at which the network topology is changing.

Reconfiguration is particularly important in heterogeneous networks where
topologies, architectures, devices, and goals are different. In the following
section, we detail the case of cross-layer reconfiguration in M2M networks,
which are expected to be an important part of next-generation networks.

###  III-B Case Study: Cross-layer Reconfiguration in M2M Networks Based on
Game Theory

M2M networks have many applications which can lead to new business models and
opportunities. These applications include smart grid, intelligent
transportation systems, healthcare, smart houses, and environmental
monitoring. The deployment of indoor/outdoor M2M devices depends on the
specific application. Recent studies show that about 80% of M2M communication
take place indoor and 20% outdoor [56, 57]. M2M communications face many
challenges such as the massive deployment of devices, transmissions in dynamic
environments and architectures, high data traffic, and strict QoS requirements
(such as coverage, latency, bandwidth, lifetime, and scalability) [58].
Moreover, machines have to operate autonomously with minimal human
intervention. Therefore, system reconfiguration and self-organization are
required in order to meet user requirements in a cost effective way [59, 60,
61]. This section presents a cross-layer framework for M2M device
reconfiguration based on a game-theoretic approach.

####  III-B1 Network architecture

This section adopts the wireless M2M architecture presented in Fig. 12, where
all devices are assumed to use cognitive radio (CR) technology. For indoor
communication, many technologies can be used for short range communications
such as WiFi, ultra wide band, mm-Wave communications, radio frequency
identification (RFID), femtocell, Bluetooth, ZigBee, visible light
communication, etc. [56, 57, 62]. For outdoor communication, indoor BSs for
each access network can directly or indirectly communicate with antennas
installed outside, on the top of buildings, or in streets in the case of
residential areas. These antennas will then communicate with the outdoor macro
cell BSs (eNodeBs) [63].

Figure 12: Network architecture for support of M2M communication.

####  III-B2 System model

Let $N_{P}$ be the number of cognitive M2M devices and $N_{A}$ the number of
access networks available for uplink transmission in M2M communications. Each
access network $b$ has $\alpha_{b}$ channels in each cell, where the total
number of cells in each access network is $\gamma_{b}$. The system capacity
for the combination of all access networks can be written as

$\mathbb{C}^{S}=\sum_{b=1}^{N_{A}}\sum_{a=1}^{\gamma_{b}}\sum_{c=1}^{\alpha_{b}}B_{c}\log_{2}(1+\zeta^{b}_{a,c}),$
(9)

where $B_{c}$ is the bandwidth of channel $c$ and $\zeta^{b}_{a,c}$ is the
signal-to-interference-plus-noise ratio (SINR) at the M2M device on channel
$c$ belonging to cell $a$.

The total energy efficiency is defined as

$\mathbb{E}^{S}=\sum_{b=1}^{N_{A}}\sum_{a=1}^{\gamma_{b}}\sum_{c=1}^{\alpha_{b}}\frac{B_{c}\log_{2}(1+\zeta^{b}_{a,c})}{P_{A}(d_{a,c})^{\nu}+P_{RF}},$
(10)

where $P_{RF}$ is the energy consumed by the radio frequency circuit, $P_{A}$
is the amplifier energy required at the transmitter, $d_{a,c}$ is the distance
between the M2M device using channel $c$ and BS $a\in\gamma_{b}$, and $\nu$ is
the path loss exponent.

M2M applications have diverse QoS requirements including data rate, latency,
reliability, etc. Moreover, due to the massive deployment of M2M devices, the
environment and economic impact must be considered in the system design. This
is because an increase in service price will increase the overall cost of the
M2M communication system, and an increase in energy consumption is considered
as a major threat to the environment as it is directly linked to GHG
emissions.

Let $SC$ be the set of service classes in the M2M communication system. Each
class $sc\in SC$ has the following QoS parameters: Data rate ($R$), latency
($L$), reliability ($RE$), economic impact ($EC$), and environment impact
($EN$). Without loss of generality and for illustration purposes, we consider
a limited set of service classes with QoS parameter attributes are as
indicated in Table II. The parameters are defined over the levels 0 to 3
according to the significance, with 0 as unimportant, 1 as low, 2 as important
and 3 as most important.

TABLE II: QoS classes for M2M applications Service | $R$ | $L$ | $RE$ | $EC$ | $EN$
---|---|---|---|---|---
Real-time/Reliable (RtRe) | 2 | 3 | 3 | 0 | 1
Non-real-time/reliable (NRtRe) | 1 | 0 | 3 | 3 | 3
Real-time/non-reliable (RtNRe) | 2 | 3 | 1 | 2 | 3
Non-real-time/non-reliable (NRtNRe) | 0 | 0 | 1 | 3 | 3

####  III-B3 Reconfiguration based on game theory

Here a cross-layer reconfigurable (CLR) scheme is adopted for M2M device
reconfiguration and resource allocation. The scheme is based on potential
games where the M2M devices act as players.

A potential game is a type of strategic non-cooperative game in which all
players autonomously learn their strategies [64]. There are three fundamental
components of non-cooperative games which include players, strategies, and
utilities. A game can be represented as $G=\\{N_{P},\\{S_{n}\\}_{n\in
N_{P}},\\{U_{n}(.)\\}_{n\in N_{P}}\\}$, where $S_{n}$ is the set of strategies
of the $n$-th player, $S=\prod_{n\in N_{p}}S_{n}$, and $U_{n}$ is the utility
of the $n$-th player, $U_{n}:S\rightarrow\mathbb{R}$ which maps strategy
profiles $S$ into a real value $\mathbb{R}$. The utility function $U_{n}$
shows the satisfaction of the $n$-th player while considering its own strategy
$S_{n}$ and the strategies of other players denoted by
$S_{-n}=\\{S_{1},S_{2},\ldots,S_{n-1},S_{n+1},\ldots,S_{N_{P}}\\}$.

In the CLR scheme, each player (or M2M device) is capable of reconfiguring its
operating parameters according to the best available access network (Table
III). The PHY layer is responsible for the CR module, operating frequency, and
transmit power. The MAC layer is responsible for access network selection and
resource allocation according to the requirement of service class mentioned in
Table II. It is assumed that the network layer uses spectrum-aware routing
protocols that can be classified into four classes: Power-based, delay-based,
throughput-based, and reliability-based where the routing protocol targets the
energy efficiency, end-to-end delay, achievable throughput, and link
stability, respectively [65][66].

TABLE III: Reconfigurable M2M device Layers |  Reconfigurable Parameters/Protocols
---|---
Application |  QoS requirements
Network |  Spectrum aware routing protocol
Data Link & Mac |  Network selection
Channel allocation
PHY |  CR module
Operating frequency
|  Transmit power

Reconfiguration is performed according to the diverse QoS requirements for
classes RtRe, NRtRe, RtNRe, and NRtNRe (Table II) based on weighted
instantaneous date rate, latency, reliability, economic impact, environment
impact, and cross-co-tier interference. The utility function of the resource
allocation and reconfiguration problem for each M2M device can be formulated
as:

$\displaystyle U_{n}(s_{n},s_{-n})$
$\displaystyle=\omega_{n}^{R}R_{n}^{b,a}+\omega_{n}^{L}L_{n}^{b,a}+\omega_{n}^{RE}RE_{n}^{b,a}$
(11)
$\displaystyle+\omega_{n}^{RP}h_{n,c}^{b,a}P_{n}^{b,a}-\omega_{n}^{EC}EC_{n}^{b,a}B_{n}^{b,a}$
$\displaystyle-\omega_{n}^{EN}EN_{n}^{b,a}P_{n}^{b,a}-\sum_{m=1,m\neq
n}^{N_{P}}h_{m,c}^{b,a}\sigma_{c_{n}c_{m}}$ $\displaystyle-\sum_{m=1,m\neq
n}^{N_{P}}h_{n,c}^{b,a}\sigma_{c_{m}c_{n}},$

and the potential function used for the potential game is

$\Phi(S)=\frac{1}{2}\sum_{n=1}^{N_{P}}U_{n},$ (12)

where $R_{n}^{b,a}$ is the data rate, $L_{n}^{b,a}$ is the latency,
$RE_{n}^{b,a}$ is the reliability, $EC_{n}^{b,a}$ is economic price for
bandwidth $B_{n}^{b,a}$ corresponding to particular application class, and
$EN_{n}^{b,a}$ is the environment cost of power $P_{n}^{b,a}$ corresponding to
a particular application class of the $n$-th player in cell $a$ of access
network $b$, respectively. $\omega_{n}^{R}$, $\omega_{n}^{L}$,
$\omega_{n}^{RE}$, $\omega_{n}^{EC}$, and $\omega_{n}^{EN}$ are the
coefficients which intensify the QoS requirements according to the service
classes mentioned in Table II. $\omega_{n}^{RP}$, the reward/penalty
coefficient is set to 1 when the access network is cost effective and have
less impact on environment (e.g., small cells and WLAN) and is set to $-1$
otherwise. $h_{m,c}^{b,a}$ is the channel gain between the $m$-th M2M device
and BS $a$ which serves the $n$-th M2M device. $\sigma_{c_{n},c_{m}}$ is the
interference function which is one if channel $c$ is same for the $n$-th and
the $m$-th M2M device and zero otherwise.

In this game theoretical framework, each player executes the three-step cycle
shown in Fig. 13:

Figure 13: Main steps in the CLR scheme.

  * •

In the _sensing step_ , each player interacts with the radio environment for
spectrum sensing to determine available channels and acquire their link gains.

  * •

During the _analysis and reasoning_ step, each player maximizes its utility by
choosing appropriate strategies while considering the current strategies of
other players. Nash Equilibrium is considered as a steady state point after
which the players cannot change their strategies to maximize their utilities.
It has been demonstrated that for the potential function in (12), the players
reach Nash Equilibrium after a certain number of iterations. For an in-depth
study of the Nash Equilibrium and the convergence of this game, the reader is
referred to [64, 67].

  * •

In the _decision_ step, each device decides to update its strategy according
to the best response update strategy. If the device updates its strategy then
it will share this strategy with its neighboring devices.

####  III-B4 Performance analysis

The performance of the system is evaluated through extensive computer
simulations. Four service classes are considered with 25% of total M2M devices
in each service class. For illustration purposes, it is assumed that cellular,
WLAN, and mmWave channels are available for data transmission where the
parameters for cellular bands are according to 3GPP specifications [68], WLAN
are according to WiFi [69], and mmWaves are according to specifications in
[69, 70]. It is assumed also that four routing protocols are available for M2M
devices and these are power-based [71], delay-based [72], throughput-based
[73], and reliability-based [74]. The reconfigurable approach is compared to a
user-optimized scheme called joint resource allocation and network selection
(JRANS), where the resources are allocated based on the interference factor
and the channel gain only [75]. The performance metrics considered in the
simulations are throughput, energy efficiency, economic cost, and environment
impact.

Figure 14: Convergence of the CLR and the JRANS schemes in terms of system
throughput (the number of M2M devices is 300).

Fig. 14 illustrates the convergence in terms of throughput of the CLR scheme
and comparison is carried out with JRANS scheme. In the CLR scheme, each M2M
device selects suitable strategy to improve its utility while considering
strategies of other players. After some iterations, a steady state (Nash
Equilibrium) is reached from where no one can increase its utility. The JRANS
scheme converges faster because it only considers interference and channel
gains for channel allocation and network selection. However, the CLR
outperforms the JRANS in terms of total throughput.

Fig. 15(a) compares the system throughput versus the number of M2M devices for
the CLR scheme, JRANS scheme, and two other schemes that consider different
variants of (11) (without economic and environment impact, and without
reconfigurable routing). It should be noted that the higher throughput in the
case of non-reconfigurable routing is due to the fact that in that scheme,
throughput-based routing protocols are used for all M2M devices. However, the
CLR based scheme reconfigures the routing protocols according to the
corresponding service classes. The CLR scheme outperforms the JRANS scheme and
the other scheme that excludes economic and environment impact from the
utility function. The economic and environment impact pushes M2M devices to
unlicensed and low power resources such as mmWaves, which offer high bandwidth
in indoor areas while reducing energy consumption and satisfying other QoS
requirements. This explains the degradation of system throughput when economic
and environment impacts are excluded from utility function.

Fig. 15(b) shows energy efficiency comparison for the aforementioned schemes.
The CLR scheme outperforms all other competitors. The performance degrades in
terms of energy efficiency when the economic and environment impacts are
removed from the utility function because in that case the system will choose
channels without considering their energy footprint. Moreover, energy
efficiency without considering reconfigurable routing further degrades the
performance because in that case the system uses the routing protocol with the
highest throughput regardless of its energy cost.

Figure 15: (a) Throughput and (b) energy efficiency with variations of the
number of M2M devices.

Fig. 16 compares economic cost and environment impact of the CLR and JRANS
schemes versus the number of M2M devices, respectively. It can be seen that
the CLR scheme has about 25% less economic cost and 27% less environment cost
in comparison with the JRANS scheme when number of M2M devices is 400.

Figure 16: Economic cost and environment impact versus the number of M2M
devices.

####  III-B5 Complexity

It should be noted that the computational complexity of CLR is higher than
JRANS. However, CLR uses a decentralized scheme where the complexity cost is
distributed among all M2M devices. For the communication overhead (due to
information exchange required by the game-theoretical approach), it can be
significantly reduced if M2M devices use existing common control channels.
This complexity cost is fully justified given the significant performance
improvement that CLR offers in terms of throughput, energy efficiency as well
as economic and environment costs compared to JRANS.

###  III-C Network Management

Network management covers a broad range of functions that generally have to do
with network reconfiguration. These functions can generally be classified into
three categories: Parameter configuration, connectivity management, and
network monitoring. Parameter configuration consists of choosing the
appropriate network parameters to achieve a given set of objectives. For
example, topology control algorithms typically focus on choosing the
appropriate transmit power that achieves network-wide connectivity [76]. This
can be done in a centralized way [76] or in a distributed way [77]. It can
also be homogeneous (all nodes transmit using the same transmit power) or
heterogeneous. On the other hand, connectivity management encompasses a wide
variety of tasks such as mobility management [78] [79], radio resource
management (RRM) [48] [80], and QoS support [48]. Finally, network monitoring
refers to monitoring the links between users and the serving stations or
between users. It is a function used to collect information that serves other
networking functions such as CSI or mobility information.

Clearly, network management is cross-layer in nature. However, the main
difference between network management protocols and other cross-layer
protocols is that network management focuses on functions that have network-
wide effect, while other cross-layer protocols may optimize individual links
or a small set of functions. In this sense, network management acts as the
brain behind the network. In this section, we will consider two examples that
illustrate how network management is used across different layers.
Particularly, we consider the cases of RRM in LTE, and network management in
WLANs.

In LTE, RRM is performed using several protocols [44, 48]. At the topmost
layer of the LTE protocol stack, the network layer, the radio resource control
(RRC) protocol performs three main functions: QoS management, admission
control, and semi-persistent scheduling. QoS management builds a profile for
the requirements of the upcoming communication session, admission control
decides if these requirements can be satisfied given the current load, and
semi-persistent scheduling is used for periodic allocation of resources (used
for deterministic communication sessions such as video streaming). Then, at
the MAC layer, the radio link control (RLC) and MAC protocols perform three
other functions: HARQ, dynamic scheduling, and link adaptation. HARQ is an
efficient error control mechanism that adjusts the number of error control
bits according to link quality. On the other hand, dynamic scheduling and link
adaptation are performed jointly according to the algorithm presented in
Section III.A. Finally, at the PHY layer we find mainly the channel quality
indicator (CQI) manager, which is basically a tool to monitor link quality.
Thus, these protocols perform network management functions in the categories
of parameter configuration (HARQ, link adaptation, dynamic scheduling, and
semi-persistent scheduling), connectivity management (QoS management and
admission control), and monitoring (CQI management). The reconfigurations
performed by these protocol stack are numerous. For example, whether the
system will apply semi-persistent scheduling or not depends on the type of
application. In addition, admission control considers existing connections as
well as new requests to determine the ones that will be dropped. Furthermore,
dynamic scheduling may switch between scheduling techniques according to the
type of applications requested by the users. Generally speaking, the main
objective of the reconfigurations in the LTE protocol stack is QoS support.
The protocols are designed to consider a large number of users with varying
application requirements.

RRM functions in LTE are performed at the eNodeBs, which control the
operations of the UEs in their service areas. In addition, in LTE-A, eNodeBs
communicate with each other and coordinate their transmissions, as explained
before. In any case, RRM functions are coordinated and can be optimized even
across cells. However, this is not the case in WLANs with multiple interfering
access points (APs). Here, management functions are performed independently at
each AP, with minimum collaboration with the other APs. Nevertheless, there
are provisions for network management within the IEEE 802.11 family of
standards. For example, 802.11r [81] allows for fast negotiations between APs
by allowing multiple negotiation sets to occur in parallel, 802.11k [82]
defines how APs exchange management information, while 802.11s [83] allows APs
to operate in a mesh topology. However, each AP still manages the users within
their cells independently of any other APs. These management functions include
channel monitoring, priority management, and power configuration. However,
there are two particular management functions that require collaboration
between APs: Channel selection and AP association. The first is the process by
which the AP chooses the channel over which it will operate, while the second
is the process by which the user chooses a serving AP. Traditionally, channel
selection is done by considering the channels utilized by the neighboring APs,
in order to reduce inter-AP interference. Once the AP chooses the channel, it
starts transmitting its beacon messages to announce its presence. Then, users
typically associate with the AP with the highest signal strength. It is worth
noting that other protocols for AP association have been proposed that
consider metrics such as packet error rate [84] or throughput [85], which are
more efficient than signal strength alone.

According to [86], channel selection and AP association must be done in a
user-centric way in order to improve throughput. This means that the channel
conditions seen by the user must be considered, and load balancing must be
performed. Thus, the authors in [86] propose a user-centric protocol for
network management in WLANs. This protocol starts in the traditional way by
having the AP select a channel and send beacons. Then the user sends its CSI
seen with every AP from which it is receiving beacons. The APs then calculate
the potential throughput according to this CSI and send the information back
to the user. Using this information, the user can associate with the AP that
provides the maximum throughput. In addition, each AP periodically collects
user performance statistics from its users and may trigger a channel
adjustment operation if the aggregate throughput drops below a certain
threshold. Since AP association is based on throughput, load balancing is
implicitly included since loaded cells will provide users a potentially low
throughput estimation, discouraging it from associating with it. These
reconfigurations are illustrated in Fig. 17.

Figure 17: Topology reconfiguration in three WLANs: (a) After initial
associations; (b) After reporting their CSIs. Some device associations have
been reconfigured to achieve load balancing.

The performance of the user-centric algorithm is evaluated in [86] from the
perspective of the throughput observed by the user and the throughput observed
by the AP. Several experiments are conducted. In one experiment, one user is
tagged as the observing user and its throughput is measured for different
number of users per unit area. This experiment evaluates the performance of
the AP association scheme proposed in [86]. Fig. 18 shows that the proposed
scheme achieves performance gains from 25% to about 90% over the self-
organization management framework, and the gain is higher in lower user
densities.

Figure 18: Performance results for the AP association scheme.

Network management protocols have also been proposed for ad hoc networks.
However, due to the distributed nature of these networks, it is difficult to
propose protocols that can have a network-wide effect. In addition, the multi-
hop nature of these networks make collecting information costly. Thus, network
management protocols for ad hoc networks typically focus on a narrow set of
objectives such as topology control [77], admission control and scheduling
[87] [88], or congestion control [89].

###  III-D Software-Defined Networking (SDN)

Conventional networks rely on protocols that implement a particular set of
functions that target some specified objectives, as we have seen in previous
sections. However, modern networks are becoming increasingly complex in terms
of challenging requirements such as changing user demands, reliability,
scalability, and security. These demands may even be time-varying. Thus,
protocols (especially those that are implemented in hardware) may not adapt so
easily. This has led to the revolutionary idea of SDNs. The main concept in
SDN is to decouple the control plane from the data plane [90] [91]. Thus, all
network policy rules and configurations, forwarding rules, packet handling
rules and priorities, etc., are implemented using reprogrammable software
called the _controller_ [92]. These rules are forwarded to special SDN-
compliant switches and routers, where they are stored in tables called _flow
tables_.

SDN architecture is illustrated in Fig. 19, where there are several controller
implementations such as POX [93], NOX [93], Beacon [94], and Floodlight [95].
Each one provides a base on which any application can be implemented. On the
other hand, communication between the controller and the SDN-complaint devices
is based on an SDN protocol such as forwarding and control element
specification (ForCES) [96] or interface to the routing system (IRS) [97].
Nevertheless, the most prominent SDN protocol is OpenFlow [90, 98]. Using,
OpenFlow, entries in the flow table contain match fields (forwarding rules),
counters and instructions (prioritization, dropping rules, etc.). Upon
receiving a packet, the SDN-compliant device checks its flow table for a
match. If found, the match will be used to forward the packet. However, if a
match is not found, the packet is forwarded to the controller for analysis.
The controller then uses software to decide what to do with the packet, and
may then forward a new matching rule to the device. Since the controller is
reprogrammable, all networking policies can be re-adjusted as needed.

Figure 19: SDN architecture.

SDN can be implemented in many ways [99]. For example, a centralized
implementation is to have one controller managing multiple devices, while a
distributed implementation is to have one controller per device and to have
the controllers communicate with each other to coordinate their operations.
Hybrid and hierarchical implementations (one controller managing multiple
controllers, each managing one or more switches) are also possible.

Due to this flexible architecture, SDN can have many possible applications.
For one thing, it allows for experimenting with protocols without having to
implement on hardware. It also has huge implications on interoperability in
multi-vendor networks [100] [101]. For this reason, major cloud providers
(such as Google and Amazon) are highly in favor of SDNs. Using SDNs, these
cloud providers can purchase equipment from any vendor, and they can operate
together using instructions from the controller if they are SDN-compliant
[102]. SDNs also simplify network management significantly and allow for large
scale automated management. They also allow for easy management of VLANs
[102], since managing network IDs and the size of each network becomes simple
using the software-based controller.

SDNs also make load balancing and congestion control quite simple since the
controller can get statistics from the devices and optimize network traffic
distribution [103] [104]. Another important application is network security
[105] [106]. This is because the presence of the controller provides powerful
tools for traffic analysis. The software can also be modified and adapted to
consider new forms of attacks. Thus, security attacks such as distributed
denial of service (DDoS) can be handled more easily [90]. The controller also
provides means for traffic isolation, simply by adjusting the forwarding and
dropping rules. Finally, SDNs provide significant tools for flexible QoS
support [107] [108]. Controllers can inspect packet types and payload, and
adjust priorities and queues accordingly. If demand changes, the controller
can re-adjust its configuration. Note that all these reconfigurations can be
performed automatically and in software, a capability that cannot be easily
achieved outside the scope of SDN. An illustration of some SDN applications is
shown in Fig. 20.

Figure 20: Examples of SDN applications: (a) Load balancing using SDN; (b)
Security using SDN; (c) QoS support using SDN.

It is worth mentioning that there are multiple versions of the OpenFlow
protocol [90]. The earliest version is OpenFlow 1.0.0 and is the most widely
deployed version. Here, the controller has a single flow table and there is no
support for IPv6, multi-protocol label switching (MPLS), or simultaneous
communication with multiple controllers. MPLS matching and multiple flow
tables are included in version 1.1.0. Then, version 1.2.0 adds support for
IPv6 and inter-controller communication. Finally, the latest version is 1.3.0
and has more flexible capabilities for communications between controllers.

SDN can be used in wired and wireless networks and can provide a revolutionary
way for network reconfiguration and adaptability. It is certainly a very
promising technology that is expected to continue rising in the near future.
However, there are challenges that need to be considered, and several of them
stem from the dependency of the network on the controller. This presents
reliability and scalability threats. If the controller fails, the network will
not function. Moreover, if the controller is managing a large number of nodes,
it may only be able to handle a limited amount of packets being forwarded to
it for analysis. This also makes the controller an attractive target for
attackers not only because it represents a single point of failure, but also
because it holds critical information about the network. Thus, security
measures have to be in place such as intrusion detection, policy adaptation,
and network forensics. Finally, recovery time of SDNs needs investigation. In
order for a node to recover from an error, it needs to send the packet to the
controller and receive a decision back. This may cause some latency. This has
to be considered in the controller design.

##  IV Network Intelligence

Cross-layer reconfiguration has some limitations [109, 110]. For example,
there is a risk known as adaptation loops, where improving one aspect may lead
to downgrading another. There are concerns regarding the coexistence of
several cross-layer designs. Since cross-layer designs are generally
application-specific, it may not be straightforward to integrate multiple
solutions. Thus, wide-scale implementations are challenging.

In this section, we focus on cognitive networking and context-awareness as
means of improving cross-layer adaptation and adaptability.

###  IV-A Cognitive Networking

Cognitive networking is based on the cognitive loop [111], shown in Fig. 21.
It contains an observation entity responsible for monitoring the external
world through sensory measurements. It also collects information about the
internal state of the system. All this information is processed and passed to
a reasoning machine. Here, the information is analyzed and decisions are made.
These decisions can be short-term or long-term (requires planning).
Furthermore, the decisions may change the internal state of the system and may
require implementation using physical actions. In addition, feedback
information from different parts of the system is passed to a learning entity
that builds a knowledge database. This database is used to enhance the
decisions produced by the reasoning machine over time.

The cognitive loop implements degrees of intelligence in the network. Rather
than targeting specific applications or algorithms, cognitive networking
focuses on methods of analyzing information, whatever it may be, and producing
decisions, while focusing on some specific goals or objectives. Since learning
is a highly important cognitive tool in humans, it is also implemented in the
cognitive networking loop. Just as humans improve their decision-making by
learning from their past experiences, networks too can optimize their
decisions by observing the results of their previous actions and of the
changes in the external world.

The importance of cognitive networking has led standardizing bodies to develop
frameworks for management and control based on the cognitive loop. Three
standards in particular are worth mentioning [112]: Reconfigurable radio
systems (RRS) [113], IEEE 1900.4 [114], self-organizing networks (SON) [115].

Figure 21: Cognitive loop [111].

Cognitive networking has also found implementations in ad hoc networks, where
the lack of centralized nodes poses challenges. Several tools of computational
intelligence (CI) have been utilized for this task. For example, evolutionary
algorithms such as particle swarm optimization and ant colonies have been used
in [116, 117] to perform topology maintenance and congestion control in
wireless sensor networks (WSNs). Particle swarm optimization considered node
positions and data transfer rate, while ant colony optimization considered the
time taken to traverse a path and the number of successful iterations in the
search process. Simulation results show that the proposed algorithms are able
to reduce congestion and delay and increase throughput. Other CI tools that
have been used include game theory [118, 119, 120] and Bayesian Networks [121,
122, 123].

Learning transforms the operation of the system from policy-based to
intelligence-based [124]. Policy-based systems follow static rules that are
predefined and hardcoded, while intelligence-based systems have the ability to
learn new states and evolve the operation of the system to achieve optimal or
near optimal performance. These systems also have the ability to detect
statistical patterns in the environment, such as regions with good or bad
channel conditions or the nature of the traffic transmitted and adapt to them.
Since most wireless systems exhibit dynamic properties, it is clear that
learning can be a powerful tool for improving system performance.

There are mainly three categories of learning algorithms [125, 126]:
Supervised learning, unsupervised learning, and reinforcement learning (RL).
In supervised and unsupervised learning, examples or training sequences are
used to accomplish the learning process. Learning agents use these sequences
to identify patterns or recurring behavior and learn about the system. In
supervised learning, each entry of the training sequence consists of an input
signal and a desired output value, and the job of the learning protocol is to
find an inferred function in all the sequences that can be used for
calculating new values. On the other hand, the desired output is unknown in
unsupervised learning. The learning algorithm simply observes the input and
tries to detect patterns. In RL, the training sequence is not needed. The
learning agent relies on feedback from the normal interactions of the system
and identifies the actions that yield the most reward or least cost.

RL is the most prominent learning tool in wireless networks. This is because
RL is a simple and model-free algorithm [124]. This means that RL does not
need to model the entire operating environment in order to perform learning,
only the desired parameters are monitored. This also means that RL can focus
on metrics that enclose a wide range of networking issues, rather than having
to learn about all the parameters in the network. For example, the RL
algorithm can monitor end-to-end delay in order to discover the states that
minimize this metric rather than monitoring a wide range of issues such as
queue length, routing metrics, channel quality, etc. Delay, not only
encompasses all these issues, but also has direct impact on the QoS observed
by the user.

RL has been used in various ways in wireless networks and at different layers.
For example, RL is used in [127] to design an opportunistic routing protocol.
The learning protocol rewards paths with good transmission success
probabilities in order to minimize the cost of relaying a packet. In addition,
this protocol balances the exploitation of paths that have been given previous
rewards versus the exploration of new paths in order to improve the efficiency
of routing decisions. This is done using probe packets that are sent
periodically to explore new paths. In another example [128], RL is used to
design a MAC protocol where nodes cooperate to maximize the throughput per
unit of consumed energy. Nodes exchange the RL values in order to explore the
efficiency of the decisions from a network-wide perspective. Thus, nodes
adjust their transmission power and transmission probabilities according to
the accumulated rewards. RL has also been used in [129] to improve the
efficiency of spectrum sharing in CRNs. A warm-up stage is defined where nodes
test the quality of different channels. Afterwards, the nodes will only learn
about the set of channels that are discovered to be promising during the warm-
up stage. This reduces the sensing frequency and improves the quality of
transmission by focusing on good channels. In [130], RL is used in defining
energy management policies for sensor nodes with finite buffers. The goal of
this scheme is to maximize network lifetime in the presence of energy
harvesting sources.

It is worth mentioning that RL has challenges. First, exploration versus
exploitation has to be addressed. Exploration may lead to better solutions,
but it also means slower algorithm convergence. In addition, traditional RL
algorithms have to be implemented either in a centralized way or at every node
in the network. Some modifications have been proposed in order to enable
distributed implementation. Examples are the distributed value function (DVF)
[131] and the distributed reward and value function (DRV) [124], which combine
the values learned at different agents. Nevertheless, these functions require
the exchange of such values, thus incurring higher overhead. Finally, there is
a trade-off between learning time and granularity of the learning process.
Having more states may mean that accuracy of the learning process is
increased. However, it also means that it may take longer for the protocol to
visit all these states and learn about their performance.

There have also been limited efforts on using supervised and unsupervised
learning in WSNs. For example, supervised learning is used in [132] to design
a neural network that exploits the spatial correlation of sensed events in
nodes with close proximity in order to reduce packet loss. However, the
algorithm has to be implemented in a centralized way. On the other hand,
unsupervised learning is used in [133] to classify SUs and PUs in CRNs. The
work also considered malicious attacks from SUs that misclassify themselves as
PUs. In another paper [134], Bayesian learning is used as a method for
unsupervised learning in support to a game-theoretical spectrum sensing
technique. An auctioning game is proposed where nodes entered _bids_ (to a
centralized coordinator) on using the available spectrum holes. Bayesian
learning is used to determine whether a node should enter a bid in each round
or not, based on previous experience. However, as mentioned before, supervised
and unsupervised learning algorithms often do not adapt quickly enough in
dynamic environments. In addition, if goals or conditions change, the training
sequences may have to be redesigned.

###  IV-B Case Study: Network reconfiguration based on reasoning and learning
for WSN

In WSNs, there are numerous issues that have to be managed while performing
planning and negotiations [3, 110]. Connectivity, coverage, sensing frequency,
QoS requirements, routing, packet loss rate, and congestion are among those
issues. All tasks have to be performed while ensuring energy efficiency.
Furthermore, WSNs may be highly dynamic: Sensor nodes may be mobile, duty
cycling may continuously change network topology, the wireless environment may
be changing, etc. Thus, managing network resources and achieving the goals
become highly challenging. Since most of these issues may involve multiple
layers, cross-layer approaches have been typically involved. However, despite
the different approaches that have been tried, there is still a need for a
tool that can address these challenges as well as the dynamic nature of the
network. The characteristics of such a new tool may include the ability to
adapt to changing goals, addressing conflicting constraints, supporting QoS
requirements, and being able to be implemented in a distributed way.

####  IV-B1 Basic concepts

A mathematical tool known as weighted cognitive map (WCM) has been recently
introduced [135, 136]. It presents significant promises in achieving the
aforementioned requirements. A WCM is a graphical model that can represent any
dynamic network using the underlying causal relationships between the
different parts of the system. Vertices of the map represent concepts (such as
transmit power control or BER from the real system). A characterizing scalar,
$A_{i}$, is associated with each vertex, $C_{i}$, which identifies its
activation level. $A_{i}$ can take any real value from [-1, 1]. Negative
values imply a decreasingly active concept (for example packet loss ratio
(PLR) becoming lower), positive values imply an increasingly active concept,
while a value of zero imply that a concept is inactive. On the other hand,
edges of the WCM connect concepts that are causally related. Weights are
associated with each edge to identify the strength of the causality. These
weights can also take any real value from [-1, 1]. Changes in the activation
levels of the concepts, which can occur due to changes in the environment
(such as an increase in PLR), trigger the WCM to operate. The inference
properties then enable the WCM to calculate new activation levels using simple
mathematical operations. The new activation levels translate into actions that
are executed by the network (for example, an increase in the activation level
of PLR may lead to an increase in the activation level of transmit power,
which causes the system to use the next higher transmit power).

As an example, the WCM in Fig. 22 models the relationships between six
processes (concepts) affecting a node, namely, transmit power, data rate,
maximum number of retransmissions allowed before a packet is dropped, BER,
throughput, and the expected transmission time (ETT). The edge weights shown
represent the strength of causality between concepts. We can classify concepts
in any WCM into end-to-end goals, environment variables, and processes. All
processes interact to achieve the end-to-end goals. On the other hand,
environment variables cannot be manipulated directly, but only as a result of
other actions. It can be seen from Fig. 22 that the end-to-end goal is
throughput (C5), since all other concepts can cause changes to it. It can also
be seen that there are two environment variables considered, namely BER and
ETT, since they cause changes in other processes but cannot be changed
directly by them. They can only be affected by changes in the environment or
as a result of actions taken by the WCM. The processes available to the WCM
are transmit power, data rate adaptation, and changing the maximum number of
retransmissions.

Figure 22: WCM representing six processes within a wireless node.

The WCM can be represented in a matrix form as

$W=\bordermatrix{\text{}&C_{1}&C_{2}&C_{3}&C_{4}&C_{5}&C_{6}\cr
C_{1}&0&0&0&0&1&0\cr C_{2}&0&0&0&0&1&0\cr C_{3}&0&0&0&0&0.9&0\cr
C_{4}&0.3&-0.5&0.8&0&-1&0\cr C_{5}&0&0&0&0&0&0\cr C_{6}&0.4&0&0&0&-0.7&0}.$
(13)

For a WCM with $N_{C}$ concepts, its status at time $t$ can be given by

$A(t)=[A_{1},A_{2},A_{3},\ldots,A_{N_{C}}].$ (14)

The inference process of WCMs is the one by which the values of concepts
change according to their underlying causal relationships. Thus, according to
the inference properties of WCMs, the status at time $t+1$ can be given by

$A(t+1)=f\big{(}A(t)W\big{)},$ (15)

where $f(t)$ is a threshold function that determines the type of the WCM. The
inference process is initialized when a particular concept is triggered,
causing its activation value to change (e.g., a sudden increase in BER).
Therefore, the input to the WCM at time $t$ is $A(t)$, including the new value
of the concept that is just triggered. The triggered concept influences other
concepts according to $W$, thus producing an output to the WCM, $A(t+1)$, as
given by (15). Thus, WCMs can be implemented in a distributed way and has
powerful reasoning capabilities. In addition, since WCMs are based mainly on
the causal relationships between concepts, conflicting constraints can be
easily considered. Furthermore, if goals or constraints change, modifying the
edge weights is sufficient to allow the WCM to adapt to the new requirements.

In this case study, we will illustrate how WCMs can be used to design a
reasoning machine for WSNs [137, 138]. For the WSN architecture, a cluster
hierarchy is employed for its popularity and simplicity. The WCM is
implemented at the cluster heads (CHs) and a sink node, which are assumed to
have higher energy and processing capabilities than regular sensor nodes (Fig.
23). Sensor nodes are deployed in uniform random positions across the area.
Thus, the WCM at each CH is responsible for determining the parameters to be
used by all nodes in its cluster. It is assumed that nodes are synchronized
and that time is slotted. Every $T$ consecutive time slots, the WCM determines
the appropriate number of nodes that must be activated in order to guarantee
connectivity and coverage, using the theorem that is proposed in [139]. These
nodes remain active for the next $T$ time slots. On the other hand, the WCM
monitors the cluster every $T/\eta$ time slots to check if actions need to be
taken. The value $\eta$ depends on the dynamics of the network. In highly
dynamic networks, $\eta$ can be equal to $T$ so that the WCM is activated in
every time slot.

Figure 23: Network topology with random deployment of nodes. The WCM is
implemented in a distributed way on the CHs which act as intelligent nodes.

The first step in designing the WCM is to specify the end-to-end goal(s).
Since network lifetime and throughput are the two main goals of the WSN, they
must be directly incorporated as goals of the WCM. This way the WCM can
constantly monitor these goals and make sure they are constantly achieved.
Therefore, in order to consider network lifetime, one goal of the WCM will be
energy consumption. Given that the initial battery power of nodes is $E$ mAhr,
and that a target lifetime, $X$ time slots, is defined by the user, the task
of the WCM is to make sure that the rate of energy consumption does not exceed
$E/X$ mAhr per time slot per node. However, due to the expected variation in
the energy consumption of nodes, forcing the WCM to react whenever there is an
increase in energy consumption in a time slot will result in frequent changes,
which is undesirable. Thus, if $N_{C}$ is the number of nodes within the
cluster, the WCM at the CH will monitor the energy consumption, $EnC$, within
its cluster and makes sure that the total energy consumption satisfies

$\sum_{i=1}^{N_{C}}EnC_{i}<\frac{T\cdot N_{C}\cdot E}{4X},$ (16)

mAhr per $T/4$ time slots, where monitoring takes place every $T/4$ time slots
so that the WCM has enough time to take action before the next scheduling
round takes place. Similarly, if $N_{WSN}$ is the total number of nodes in the
network, the sink node is responsible for making sure that the total energy
consumption of the network satisfies

$\sum_{i=1}^{N_{C}}EnC_{i}<\frac{T\cdot N_{WSN}\cdot E}{X},$ (17)

mAhr per $T$ time slots. Note that monitoring at the sink node takes place
every $T$ time slots. On the other hand, the end-to-end throughput can be
monitored at the sink node by counting the number of packets successfully
received per time slot. Thus, if $R_{WSN}$ is the throughput required by the
user, the task of the WCM at the sink is to make sure that the number of
packets successfully received at the sink, $\rho_{S}$, in $T$ time slots
satisfies

$\rho_{S}\geq T\cdot R_{WSN}.$ (18)

Similarly, since $N_{C}/N_{WSN}$ is the ratio of the number of nodes within a
cluster to the total number of nodes, the WCM at the CH can calculate its
throughput by counting the number of successfully received packets per time
slot and making sure that the number of packets successfully received at the
CH, $\rho_{CH}$, in $T/4$ time slots satisfies

$\rho_{CH}\geq\frac{T\cdot N_{C}\cdot R_{WSN}}{4N_{WSN}}.$ (19)

Thus, the designed WCM considers $EnC$ and $R_{WSN}$ as the end-to-end goals.
It considers transmit power, data rate, duty cycle control, routing, source
loading rate control, and scheduling active nodes as tools for achieving these
goals. The environment variables that trigger the WCM to operate are PLR,
channel utilization, buffer capacity, and node failure. Fig. 24 shows the
overall WCM that is installed at the CHs and sink node.

The dashed edges in Fig. 24 are the ones going in/out of the goals of the WCM.
Thus, every $T/\eta$ time slots the WCM checks if any environment variable or
goal violate their predefined threshold in order to take appropriate actions.
For example, if cluster energy consumption is too high, the WCM has the option
to decrease transmit power, decrease the number of active nodes, decrease the
source loading rates, or invoke routing to find more energy efficient paths.
This action is determined based on the current status of the network. For
instance, if energy consumption is too high and the throughput is too low,
then the WCM will not decrease the number of active nodes or loading rate
because this will decrease throughput further. Instead routing may be invoked
and transmit power reduced. This way, the WCM is able to consider multiple
conflicting issues and deal with several challenges simultaneously.

Figure 24: Internal structure of the WCM engine as implemented in the cluster
heads.

####  IV-B2 Performance analysis

In order to study the performance of the WCM system, extensive computer
simulations are performed. The WCM system is compared to TABU-RCC [140], which
is a prominent network planning protocol, and Symphony [141], which is an
energy-efficient transmit power and data rate control protocol. Different
mobility scenarios are considered in order to examine the performance of the
WCM system in dynamic environments. In the simulation set-up, sensor nodes
have an average speed of 1.5 m/s and the network performance is tested under
different network sizes. The WSN is assumed to be running an application with
target network lifetime of at least 1500 time slots and target throughput of
at least 50 kbps.

Figure 25: Simulation results of WCM: (a) Network lifetime; (b) Network
throughput.

It can be seen from the simulation results in Fig. 25 that the WCM system is
the only one capable of achieving both (conflicting) goals at most network
sizes. This is because of the ability of the WCM system to find compromises
between conflicting goals and consider multiple issues simultaneously. TABU-
RCC achieves good network lifetime results at the expense of throughput, while
Symphony achieves only good throughput results at larger network sizes.

The performance of the WCM can be further improved through RL. As mentioned
earlier, RL can be implemented in a distributed way and requires limited
computations. This section presents $Q$-learning [137] to illustrate how to
design a distributed learning process. Here the learning process will observe
the actions of the WCM system (i.e., the reconfigurations made) and assigns
rewards to those reconfigurations that achieve the predefined goals.

####  IV-B3 Learning

Since the WCM engine is implemented at the CHs, the learning process is also
implemented there. The $Q$-learning algorithm consists of: Policy, reward
system, and value function. Thus, the state of any node will be represented
using the combination {transmit power ($T_{x}$), data rate ($R_{WSN}$), next
hop index ($NI$)}. These parameters are chosen because they can be controlled
by the WCM and can have significant impact on the performance of several nodes
in the network. This means that the set of actions (or reconfigurations)
observed by the learning protocol are {increase transmit power, decrease
transmit power, increase rate, decrease rate, change next hop}. The action
_change the next hop_ occurs when routing is invoked.

To determine how the rewards are assigned, the application requirements are
used to specify thresholds that can be monitored by the learning process.
Therefore, if the reconfigurations made by the WCM lead to values of the
aforementioned goals that are within the thresholds, a reward will be given.
Otherwise the reward is zero. We specify an additional constraint that the
thresholds of all goals have to be satisfied in order for the rewards to be
assigned. If any goal is violated, the total reward is zero. This is to avoid
accumulating rewards at actions that only achieve a subset of goals.

To implement distributed learning, DVF is used to combine knowledge from
different CHs. Using DVF, the Q-values for each {state, action} pair is
calculated as

$\mathcal{Q}(S_{t},a_{t})=r_{t+1}+\sigma\sum_{j\in
common\\_zone}\omega_{Q}\times V_{t}^{j}(S_{t+1})$ (20)

where

$V_{t}^{j}(S_{t+1})=\max_{a_{t+1}}\mathcal{Q}(S_{t+1},a_{t+1})$ (21)

and $\omega_{Q}$ is a weight factor that can be used to give more importance
to information from certain nodes. In this example, equal importance is given
to all nodes. Thus, $\omega_{Q}=1/|common\\_zone|$, where $|common\\_zone|$ is
the number of nodes in the common zone. However, in order to limit the
overhead incurred from exchanging values from the $Q$-table, only the values
concerning the nodes on the boundaries of each cluster are exchanged. This is
because these boundary nodes are the ones expected to interfere with the
performance of other nodes in neighboring clusters as illustrated in Fig. 23.

In the simulation results shown in Fig. 26, the mobility of the nodes is fixed
at 1.5 m/s. In addition, the learning protocol is compared to the energy-
efficient coverage and connectivity routing (EECCR) protocol [142], as well as
TABU-RCC, and Symphony. Moreover, an independent learning protocol is
simulated, where CHs do not exchange values and do not use the DVF. This will
give an idea about how the cooperation of CHs can enhance performance. This
experiment considers an application with target network lifetime of at least
1500 cycles and target throughput of at least 50kbps, while in the second
experiment assumes that the objective is to maximize network lifetime with no
regard to throughput.

Figure 26: Simulation results of the learning protocol: (a) Network lifetime;
(b) Network throughput.

It can be seen that the learning protocol improves the performance of the WCM
system. This is because of the ability of the learning protocol to accumulate
knowledge that allows the WCM system to make choices for the reconfiguration
actions to take place. Without the learning protocol, these choices are made
blindly. In addition, distributed learning achieves better performance than
independent learning, illustrating the importance of CH cooperation.
Furthermore, the performance improvement is higher in larger networks. This is
because larger networks have more options for the WCM to choose from.

####  IV-B4 Complexity

The WCM system imposes additional computational complexity and communication
overhead on the regular WSN. However, it is important to notice that there are
no loops in the execution of WCM algorithms. In addition, the WCM is an event-
based system and the algorithms are executed upon detecting a significant
change in one of the environment variables such as PLR or buffer capacity.
Algorithms in other protocols that we have been using for comparison such as
TABU-RCC or EECCR are executed periodically. This means that there is a chance
they execute without there being a real need (e.g., when there are no changes
in the network). With regards to communication overhead, the WCM system
piggybacks control information on data packets. Special packets may be
transmitted only if required, for example in the event of node failure, where
immediate action is needed. This minimizes overhead and ensures robustness
without incurring the risk of missing or delaying actions. Furthermore, the
WCM system does not require centralized implementation. As we have seen, it
can be implemented in CHs, with each CH controlling its local area. Note that
TABU-RCC and EECCR protocols have to be implemented at the centralized sink
node. Centralized solutions have to collect network-wide data and thus may
incur high delays and communication overhead. Moreover, WCM is an entirely
software-based network reconfiguration solution. Thus, implementing it does
not require dedicated nodes or hardware modifications. To conclude, even
though the WCM system imposes additional complexity, like any network
management system, this extra complexity is minimum and justified by the
significant improvement in performance.

###  IV-C Context-Awareness (CA)

CA is a classical research area in pervasive computing that has recently been
adopted in mobile and wireless networking [15, 143]. Technically speaking, CA
can be a part of any cognitive networking or cross-layer reconfiguration
framework, which is why we think it deserves a section on its own. In
addition, the growth of heterogeneous and ubiquitous networking makes CA quite
important. CA targets the minimization of uncertainty in decision making by
providing necessary information [15]. This improves the efficiency,
confidence, and user experience in the application. CA is basically a
collection of measured and inferred knowledge that arises from the general
activity of a system and can exist independently of any system interactions
[15].

There are four main types of uncertain context information that can appear in
wireless networks [15]: Imperfect, ambiguous, wrong, or unknown context
information. Imperfect context information can be used directly or can be used
to infer other information. It can arise from inaccurate, incomplete, or
outdated context information. Unreliable communication links can be a cause of
that. For example, in high mobility situations such as VANETs, links may break
frequently causing context information to be missed. Eventually, information
within one node may become obsolete. The second type of uncertain context
information is ambiguous information. This can be the outcome of operating in
heterogeneous environments where conflicting policies exist. For example, one
node may decide to act selfishly to maximize its own throughput, thus causing
a negative impact on the overall network performance. The third type of
uncertain context information is wrong information, resulting from erroneous,
inadequate, or inapplicable information. This can result from delivering
information to the wrong entity or delivering incomplete information due to
channel unreliability. Finally, unknown context information is generally
expected information that has not been delivered. This may result from
failures or node isolations.

CA systems try to bridge some of these information uncertainties. For example,
all three cognitive standards discussed in the previous section include
different forms of CA [112]. RRS includes specifications for acquisition of
long-term and short-term context information. On the long-term the network
collects information such as network status and spectrum availability, while
on the short-term the information includes channel conditions, neighbor nodes,
QoS parameters, and other information that may be necessary for JRRM.
Similarly, IEEE 1900.4 collects context information about available spectrum
bands, policies and regulations, and status of RANs. In addition, SON has an
extensive CA framework that addresses different cases. For example, to
minimize inter-cell interference, eNodeBs exchange information about
interference indicators and transmit power used in the uplink and downlink
directions. In addition, eNodeBs may exchange information about their current
traffic load in order to perform load balancing.

The importance and potential of CA has been recognized by many researchers.
Thus, in [144] the mobility management protocol known as Tramcar is proposed,
which uses context information to make vertical handover decisions. This
protocol considered context information about the cost of wireless services,
power consumption, resource availability, network conditions, and security
issues. At the application layer, user preferences are first translated into
networking parameters. Then, a handoff necessity estimator uses context
information about location, time, and places of events to avoid frequent
handovers. In addition, the transport layer detect the presence of other
networks and implement the decisions of the protocols at the application
layer.

With regards to mobility management, the work in [78] has defined twelve
necessary requirements for a context-aware system. These are: The availability
of reliable information from all layers, standardized network discovery
(particularly based on the IEEE 802.21 Media Independent Handover system
[136]), post-handover self-management, consideration of the application
profile, use of a single radio interface, pre-authentication (before handovers
are executed), the consideration of multiple available networks, use of vendor
independent handoff trigger indicators such as the number of packet
retransmissions, it must take into account quality-of-experience (QoE) factors
(such as costs and user preferences) as well as QoS factors, it must work with
several protocols, admission control, and finally making the actual handover
decision after considering all the parameters. Thus, the work in [78] provides
a framework for any researcher wishing to propose context-aware mobility
management systems. Other works include the framework for context management
for next generation networks [138], the framework for context mediation
proposed in [139], and the framework for collaborative CA proposed in [140].

CA has been utilized in ad hoc networks as well. However, CA systems in ad hoc
networks usually take a narrower perspective (such as focusing on one
particular issue or a small subset of objectives) due to the lack of a
centralized node. Location awareness in particular has found extensive use in
different types of ad hoc networks such as in VANETs, where it has been used
for issues such as routing [141], QoS support [142], and security [145].
Mobility awareness has also been utilized for routing in mobile ad hoc
networks [146]. In addition, CA has been used for data fusion in WSNs [147] to
improve the efficiency of the process.

##  V Conclusions and Open Research Topics

The demand for sophisticated and ubiquitous applications has shifted the
paradigm of wireless networks to reconfigurable wireless networks.
Nevertheless, the scattered research in this area hinders the gains that can
be achieved by reconfiguration. This paper presented an overview of
reconfigurable wireless networks with an in-depth discussion of
reconfiguration at all layers of the protocol stack. Reconfigurable networks
have been classified in the paper into three levels: Reconfiguration at the
physical layer, reconfigurable networking, and network intelligence. The paper
focused on reconfigurable systems in emerging areas such as cognitive radio
networks, cross-layer reconfiguration and software-defined networks. Moreover,
the paper covered context awareness and cognitive networking as key enablers
of reconfiguration in highly complex and dynamic networks. Furthermore, three
case studies have been detailed, one for each level of reconfigurable
networks. The first targeted management of CRNs where centralized and
distributed network-based reconfigurations were used. The second was dedicated
to cross-layer reconfiguration in M2M networks based on game theory. The third
case study covered network intelligence in WSNs where a reasoning and learning
framework was established to enable effective reconfiguration in WSNs. Several
examples and experimental results have been presented in the paper
illustrating the superiority of reconfigurable networks over non-
reconfigurable ones, especially in the case of highly complex and dynamic
networks. These results show that the reconfigurable wireless network paradigm
will be a key enabler for next-generation wireless communications networks and
services.

Although research in reconfigurable wireless networks has been quite
extensive, there remain areas that need more attention. In this section, some
of these areas are briefly discussed.

####  V-1 Learning

This is quite a powerful tool with great potential, which is why it has been
recommended by standards such as RRS. Nevertheless, work in this area still
faces challenges. One issue is the learning speed. The learning system has to
observe the environment in order to accumulate knowledge. The time this takes
depends on the parameters being considered. In dynamic environments, the
parameters may be changing too frequently, rendering the learning system
useless. To address this challenge, researchers typically confine the focus of
the learning system to only a few parameters that can guarantee convergence.
Another solution is to implement learning in a distributed way, across
multiple agents. In this case, each agent collects information only from a
subset of its neighbors. This may lead to suboptimal solutions and requires
collecting agents that can aggregate the learned information. It also may
require asynchronous operation, where each agent executes its learning
algorithm according to the parameters in its neighborhood. In addition, the
performance of learning systems in noisy environments, interference, changing
topologies, or node failures needs investigation. In these situations, the
learning system may become unstable and may have a negative effect on network
performance. Finally, combining learning tools with CA approaches can have
great potentials. Learning algorithms can be adapted to choose the most
important parameters, given the right context, while CA systems can get more
information about the history of the data being considered.

####  V-2 Coexistence and Interoperability

The techniques discussed in this paper provide solutions for a wide variety of
challenges. However, there is limited work on the performance of
reconfigurable systems under the coexistence of multiple solutions. For
example, a learning system that targets the minimization of energy consumption
may guide nodes to reduce their transmit powers. This may have an impact on
packet loss, interference, and routing. Thus, an independent routing solution
that is not considered in the learning process may suffer negatively.
Interoperability is also another challenge. The world of wireless networks now
includes a variety of standards, protocols, vendors, operators, etc. These are
not always compatible. SDN is a promising solution to address this challenge,
as explained above. However, SDN requires hardware modifications that may be
expensive. The alternative direction that is currently pursued is to build
increasingly complex reconfigurable devices capable of dealing with the
incompatibilities of the different network components.

####  V-3 Intelligence in Reconfigurable CRNs

Most of the research in CRNs still focuses on the PHY layer. As indicated in
this paper, tangible performance gains can be achieved if network-based
reconfiguration is applied. Such reconfiguration can be achieved globally via
an FC (in centralized CRNs), or locally via learning and consensus-based
schemes (in decentralized CRNs). For multiband sensing, the existing
literature has been limited to homogenous multiband detectors (which consist
of single-type sensors), and thus reconfiguration has been restricted to
adapting the sensors’ parameters. Nevertheless, as shown in this paper,
further gains can be accomplished if more than one sensor type are available
at the SU device. In particular, it has been shown that the RMD outperforms
the well-known MJD in terms of the aggregate throughput of the CRN. This must
motivate the research community to further investigate low-cost and
reconfigurable heterogenous multiband detectors.

####  V-4 Context-Awareness

There are still open issues in wireless network reconfiguration based on CA.
One important challenge is the tradeoff in considering higher level versus
lower level context information. On one hand, lower level context information
enables fine-grained adaptation of network parameters. However, the parameters
available in this level are numerous and the effect of considering only a
subset of parameters must be considered. On the other hand, higher level
information has a better chance to directly address the QoE of the user, but
may not lead to fine adaptations. A promising direction that needs further
investigation is context sharing. In a cooperative network, exchanging context
information may lead to the improvement of all cooperating nodes. However,
this consumes bandwidth. Thus, deciding which information is worth sharing
with which node is important. There is also a need for researching various
reasoning and learning techniques, and how they may coexist especially in
complex dynamic networks.

####  V-5 Security and Privacy

Research in this area is usually performed independently of research in other
branches in networking. In today’s ubiquitous networking world, security has
to be considered at the design stage, not as an afterthought. For example, CA
requires gathering information about particular parameters. This may
potentially violate certain privacy issues such as location, which is quite
important in many networks such as VANETs. In addition, reconfiguration in
heterogeneous environments has potential security risks. Not all networks have
the same security guarantees. Thus, choosing a particular wireless service may
introduce vulnerabilities. If the violated node includes critical information
about another network (such as an SDN controller managing multiple networks),
then the vulnerability will stretch across multiple networks. Thus, trust
management and security have to be an integral part of the reconfiguration
process.

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