Computational Intelligence in Sensor Networks

496 Pages·2019·15.42 MB·English

by  Bijan Bihari Mishra

Studies in Computational Intelligence 776
Bijan Bihari Mishra · Satchidanand Dehuri 
Bijaya Ketan Panigrahi · Ajit Kumar Nayak 
Bhabani Shankar Prasad Mishra 
Editors
Himansu Das
Computational
Intelligence
in Sensor
Networks
Studies in Computational Intelligence
Volume 776
Series editor
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
e-mail: [email protected]
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Bijan Bihari Mishra Satchidanand Dehuri
(cid:129)
Bijaya Ketan Panigrahi Ajit Kumar Nayak
(cid:129)
Bhabani Shankar Prasad Mishra
Himansu Das
Editors
Computational Intelligence
in Sensor Networks
123
Editors
Bijan Bihari Mishra Ajit KumarNayak
Department ofInformation Technology Department ofComputer Science
Silicon Institute of Technology andEngineering
Bhubaneswar Silicon Institute of Technology
India Bhubaneswar
India
Satchidanand Dehuri
Department ofInformation Bhabani ShankarPrasad Mishra
andCommunication Schoolof Computer Engineering
FakirMohanUniversity KIIT University
Balasore, Odisha Bhubaneswar, Odisha
India India
Bijaya Ketan Panigrahi HimansuDas
Department ofElectrical Engineering Schoolof Computer Engineering
Indian Institute of Technology KIIT University
NewDelhi Bhubaneswar, Odisha
India India
ISSN 1860-949X ISSN 1860-9503 (electronic)
Studies in Computational Intelligence
ISBN978-3-662-57275-7 ISBN978-3-662-57277-1 (eBook)
https://doi.org/10.1007/978-3-662-57277-1
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Bijan Bihari Mishra dedicates thiswork tohis
wife and kids.
Satchidanand Dehuri dedicates this work to
his wife: Dr. Lopamudra Pradhan, and kids:
Rishna Dehuri and Khushyansei Dehuri.
Bijaya Ketan Panigrahi dedicates this work to
his wife and kids.
Ajit Kumar Nayak dedicates this work to his
wife and kids.
Bhabani Shankar Prasad Mishra dedicates
this work to his parents: Gouri Prasad
Mishra and Swarnalata Kar,
wife: Dr. Subhashree Mishra and kids:
Punyesh Mishra and Anwesh Mishra.
Himansu Das dedicates this work to his wife
Swagatika Das for her love and
encouragement and also to his parents—
Jogendra Das and Suprava Das, for their
endless support and guidance.
Preface
During the last decade, the field of sensor network has attracted much of the
researchers’ attention worldwide. A sensor network is a network of distributed
autonomous tiny electronic devices that can sense/collect some natural environ-
mental behaviour, process and communicate the information. Consequently,
monitoring physical or environmental conditions became simple and effective.
Advances in sensor technology and computer networks have enabled sensor net-
workstoevolvefromsmallclustersoflargesensorstolargenetworksofminiature
sensors, from wired communications to wireless communications and from static
network topology to dynamic topology. In spite of these technological advances,
sensor networks still face the challenges of communication and processing of a
large amount of imprecise and partial data in resource-constrained environments.
Further, optimal deployment of sensors in an environment is also posed as an
NP-hard problem. Therefore, we realize that the computational intelligence
approaches can suitably address the challenges in both wired and wireless sensor
networks. In order to make the realization become true, this volume entitled
Computational Intelligence in Sensor Networks has been taken into shape with an
inclusion of 20 chapters contributed by potential authors.
InChap.1,theauthorfocusesondistributedqueryprocessinginwirelesssensor
networktogenerateanoptimizeddistributedqueryplanefficiently.Optimizationof
distributed query plan is based on various resources such as processing cost,
communication cost and response time. The author studies the Artificial Immune
System to solve Distributed Query Processing Problem in wireless sensor network
with a focus on the affinity between antibody and antigen to generate query plans
with minimum query processing cost and deploy on the sensor network system.
Inrecentyears, sensornodelocalizationisanemerging research area inWSNs.
The sensor data become useless if we do not know the location of the reporting
node.Coordinatesdeterminationofthesensornodeisachallengingproblem,andit
isreferredaslocalizationproblem.Singhetal.havepresentedafewcomputational
intelligence paradigms in Chap. 2 for addressing the problem of localization in
WSNs.
vii
viii Preface
InChap.3,theauthorseffortistoprovideabettersolutiontoreducetheenergy
consumption of sensors. Here, the beauty of DBSCAN clustering technique has
beenfullyexploitedinordertodevelopaspatio-temporalrelationalmodelofsensor
nodes, followed by the selection of representative subset using measure trend
strategy and finally meeting the criteria for identifying the best optimal path for
transmissionofdatausingfewnature-inspiredalgorithmslikeACO,BCOandSA.
In Chap. 4, the authors describe the seven different types of routing protocols
such as Location-based Protocols, Data-centric Protocols, Hierarchical Protocols,
Multipath-based Protocols, Heterogeneity-based Protocols and Quality of Service
based protocols. This chapter focuses on various types of routing protocols, their
advantages and disadvantages along with the field of application.
In Chap. 5, the author gives emphasis on Distance-based Enhance Threshold
Sensitive Stable Election Protocol (DETSSEP) in which CH selection is based on
networksaverageenergy,nodesremainingenergyanddistancebetweennodesand
BaseStation(BS).DualhopcommunicationisusedbetweendistantCHsandBSto
achieve uniform energy consumption in the network. The authors also have
observedthatDETSSEPoutperformsEnhanceThresholdSensitiveStableElection
Protocol (ETSSEP) in various performance matrices, viz. stability period,
throughput, lifetime and remaining energy of the network.
Chapter 6 describes the deployment strategy in a wireless sensor network
towards construction of network topology. However, with the advancement in
wireless sensor network technologies, it is now proved that efficient sensor node
placementisessentialforqualityofserviceenhancementsofsuchnetworksbeitin
termsofbatteryconservation,lifetimeimprovement,interferenceorsimplyefficient
communications.
In Chap. 7, Babber and Randhawa present the communication lacks among
adjacentlayers.Optimizationoftheselayersthroughcross-layerapproachhasbeen
proposed. This chapter outlines the requirements and prevalent practices, and
presents challenges in standardized architecture. Afterwards, a cross-layer solution
through inter- and intra-layer communication and optimization of layers and a
framework for next-generation wireless networks has been addressed.
Chapter 8 provides information to the users on how to build and investigate a
hybrid Feedforward Neural Network (FNN) using nature-inspired meta-heuristic
algorithms such as the Gravitational Search Algorithm (GSA), Binary Bat
Algorithm (BBAT) and hybrid BBATGSA algorithm for the prediction of sensor
network data. Here, Feedforward Neural Network is trained using a hybrid
BBATGSA algorithm for predicting temperature data in sensor network. The
developed predictive model is evaluated by comparing it with existing two
meta-heuristic models such as FNNGSA and FNNBBAT.
Chapter 9 deals with the nature of loosely connected human nodes Pocket
Switched Network (PSN) which is a unique kind of Delay Tolerant Network
(DTN) has been instigated. This book chapter holds a brief discussion about all
these routing protocols which have helped us to get to this level of successful
communication through PSN where we are successful in sharing essential infor-
mation in the event of any kind of natural disasters, war situations, environmental
Preface ix
monitoring and urban sensing even in the space with the help of wireless tech-
nologies. The authors also discussed the challenges faced in the PSN environment
that are yet to overcome and its future application domain.
Chapter 10 discusses the several challenging factors and issues that affect the
routing protocol design. In this chapter, the authors categorize various routing
protocols into three major categories, namely, the networks routing protocols, the
hierarchical networks routing protocols and the QoS aware routing protocols. The
chapterexploresthenetworksroutingprotocolsasRe-active,Pro-activeandHybrid
Protocols and hierarchical networks routing protocols as chain-based, grid-based,
tree-basedandarea-basedprotocols.Thechapteralsodiscussesthevarioustypesof
QoSroutingprotocols.Finally,theauthorspresentcertainopenissuesregardingthe
design of routing protocols.
In Chap. 11, the authors have discussed the energy efficiency issues associated
with the sensor nodes.
Chapter 12 gives a prelude on the integration of cloud computing with WSNs
and discusses the functional architectures, design issues, benefits and the applica-
tions of the sensor cloud infrastructure. In addition, the author also proposed a
general architectural model for precision agriculture application and farmers
awareness using sensor cloud.
In Chap. 13, the authors analyses the trends of big data and deep learning
techniques to handle large data volumes and explore the ways and means for their
application while handling the stochastic wireless channel. The authors formulate
certain learning-based approach which is expected to contribute towards spectrum
conservationandachievebetterlinkreliability.Itfocusesonsomeoftheemerging
issues involving big data and the roles played by the capabilities of 5G and the
advantages that could be achieved due to the use of deep learning.
In Chap. 14, attempt has been made to find out the gap associated with sensor
networks and integrated neural network algorithms by maximizing lifespan uses,
and their function to envelop monitoring circumstances for groundwater sustain-
ability.Anoutlineoftheefficienttechnologyandrelevanttechniquesrelatedtothe
issues is presented. Back Propagation Neural Network (BPNN) and Radial Basis
Neural Network (RBNN) are proposed in terms of optimization of sensor data to
model the sensitivity of groundwater availability in arid region. It is found that
BPNN is suitable for optimizing and searching groundwater in arid region.
In Chap. 15, the authors present the growing needs to deploy Computational
Intelligence(CI)techniquesaswellasMachineLearning(ML)algorithmstocreate
smooth actuation, so that exoskeletons are able to predict the user intentions and
consequently operate in parallel with human intention.
Chapter 16 presents the design and implementation of power saving technique
for wireless sensor node with power management unit (DVFS + Clock gating)
controlledbycooperativecustomunit withparallelexecutioncapabilityonFPGA.
The customizable cooperative unit is based on customization of OS acceleration
using dedicated hardware and applies its soft-core processor. This unit will reduce
OSCPUoverheadinvolvedinprocessor-basedsensornodeimplementation.Inthis
chapter, the performance and power consumption of FPGA-based power saving
x Preface
technique for sensor node can be compared with the power consumption in the
processor-based implementation of sensor nodes.
Chapter17focusesonseveralefficientmethodsfortexturefeatureextractionand
similaritymeasuremethodsexist.Theobjectiveofthepresentchapteristopropose
efficient texture feature extraction algorithms which should have high retrieval
accuracy.
Chapter 18 discusses a one-round identity-based key agreement protocol
(AORID-KAP) based on the lightweight pairing-based cryptosystem. Authors
proposedschemeAORID-KAPisauthenticatedandscalabletolargenetworksize,
and secure against man-in-middle attack, and node capture. In terms of computa-
tionalcost,bandwidthcostandmessageexchange,ourproposedsystemperformed
better as compared to the other related schemes.
In Chap. 19, the author presents a detailed survey of different spectrum sharing
techniques in CRN. This chapter also presents different performance evaluation
parameters to ensure the quality of the spectrum sharing technique. At last, it
presents various challenges and issues associated with spectrum sharing and the
futureresearchopportunities inthisarea. Theauthorsalsopresentaclearvisionto
the young researchers to carry out their research in this domain by knowing the
future scope of it.
Chapter 20 focuses on sediment concentration which is measured using sensors
in a river reach. Sediment transport is basically in two forms, bed load and sus-
pended load. The amount of load carried in suspension by a river mainly depends
on the volume and velocity of the stream. The development of flow and sedi-
mentation prediction models for each month of monsoon period using artificial
neural networks. The framework is tested on the river Mahanadi.
Bhubaneswar, Odisha, India Bijan Bihari Mishra
Balasore, Odisha, India Satchidanand Dehuri
Bhubaneswar, Odisha, India Bijaya Ketan Panigrahi
Bhubaneswar, Odisha, India Ajit Kumar Nayak
Bhubaneswar, Odisha, India Bhabani Shankar Prasad Mishra
Bhubaneswar, Odisha, India Himansu Das