Review Article A Review on Sensor Network Issues...

Review ArticleA Review on Sensor Network Issues and Robotics

Ji Hyoung Ryu1 Muhammad Irfan2 and Aamir Reyaz1

1Chonbuk National University Jeonju Republic of Korea2The Infrastructure University Kuala Lumpur (IUKL) Kuala Lumpur Malaysia

Correspondence should be addressed to Aamir Reyaz aamir8095gmailcom

Received 21 November 2014 Accepted 11 February 2015

Academic Editor Iftikhar Ahmad

Copyright copy 2015 Ji Hyoung Ryu et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

The interaction of distributed robotics and wireless sensor networks has led to the creation of mobile sensor networks There hasbeen an increasing interest in building mobile sensor networks and they are the favored class of WSNs in which mobility plays akey role in the execution of an application More and more researches focus on development of mobile wireless sensor networks(MWSNs) due to its favorable advantages and applications In WSNs robotics can play a crucial role and integrating static nodeswith mobile robots enhances the capabilities of both types of devices and enables new applications In this paper we present anoverview on mobile sensor networks in robotics and vice versa and robotic sensor network applications

1 Introduction

Technological advances as well as the advent of 4G commu-nications and of pervasive and ubiquitous computing havepromoted a new interest in multihop networks (ad hoc com-munications) In particular the interest is in self-organizingwireless multihop networks composed of a possibly largenumber of motes which can bemobile and static and can alsobe used for computational and power capabilities Wirelesssensor networks (WSNs) are typical examples of these kindsof networksMost of the research inWSNs concerns networkswhose nodes cannot be replaced and do not move Mobilityof the sensor nodes has been exploited for improving orenabling altogether communication coverage and sensing [1]The credit for the creation of mobile sensor networks goes towireless sensor networks and to the interaction of distributedroboticsThe class of networks where small sensing devices ina collaborative way move in a space to observe and monitorenvironmental and physical conditions are known as mobilesensor networks [2] Mobile sensor network is composed ofnodes and all nodes have sensing computation communica-tion and locomotion modules (Figure 1) Each sensor nodeis capable of navigating autonomously or under the controlof humans [3] MSNs have emerged as an important area forresearch and development

Though MSNs are still in the developing stages they canbe used for monitoring of environmental habitat healthcareagriculture defense applications disaster prone areas haz-ardous zones and so forth MWSNs can also be used formonitored control and more and more practical applica-tions of MSNs also continue to emerge [4] And roboticsis the science of technology with applications in variousfields such as design fabrication and theory [5] It canalso be considered as the area of technology dealing withconstruction operation control of robotic applications andcomputer systems sensory feedback and information pro-cessing The main advantage of this technology is that it canreplace humans in manufacturing processes and dangerousenvironments or can also resemble humans in behavior cog-nition or experience [5] A breakthrough in the autonomousrobot technology occurred in the mid-1980s with work inbehavior based robotics We can say that this work was thefoundation for many current robotic applications [6] Byincorporating intelligent mobile robots directly into sensornetworksmost of the problems in traditional sensor networksmay be addressed Mobile robots offer the ways to interactand survey the environment in a decentralized and dynamicway The new system of robots and networked sensorsled to the development of new solutions to the existingproblems such as navigation and localization [7] Mobile

Hindawi Publishing CorporationJournal of SensorsVolume 2015 Article ID 140217 14 pageshttpdxdoiorg1011552015140217

2 Journal of Sensors

nodes can be implemented as autonomous perceptive mobilerobots or as perceptive robots whose sensor systems addressenvironmental and navigational tasks So we can say roboticsensor networks are the distributed systems in which mobilerobots carry sensors around an area to sense phenomenaand to produce detailed environmental assessments [8]The use of multirobot systems for carrying sensors aroundthe environment represents a solution that has received asignificant attention and can also provide some extraordinaryadvantages A number of applications have been addressedso far by robotic sensor networks such as rescue searchand environmental monitoring In wireless sensor networksrobotics can also be used to solve many problems to advanceperformances such as responding to a particular sensorfailure node distribution and data aggregation Similarlyfor solving the problems that exist in the field of roboticswireless sensor networks can play a crucial role Problems likelocalization path planning coordination for multiple robotand sensing can be solved by using wireless sensor networks[9] Today we have many applications of sensor networkson ground air underwater and underground In mobileUWSNsensormobility can bring twomajor benefits Floatingsensors can increase system reusability and can also help toenable dynamicmonitoring and coverageMobile sensors canhelp to track changes in water masses thus providing 4D(space and time) environmental monitoring As comparedto ground based sensor networks mobile UWSNs have toemploy acoustic communications because in hard waterenvironments radio does not work Similarly undergroundsensor network can be used tomonitor a variety of conditionssuch as properties of soil and environmental monitoringfor toxic substances They are buried completely under-ground and do not require any wired connections On theground they can be used for target tracking environmentalmonitoring detecting forest fire industrial monitoring andmachine health monitoring Wireless sensor nodes are intothe service from a long time and were being used for differentapplications such as earthquake measurements and warfareThe recent growth of small sensor nodes dates back to theyear 1998 NASA SensorWebs project and smart dust projectTo make autonomous sensing and communication possiblewithin a cubic millimeter of space was the main purposeof the smart dust project This project led to many moreresearch projects including major research centers in CENSand Berkeley NESTThe termmotewas coined by researchersworking in these projects to refer to a sensor node pod isthe same term used in the NASA Sensor Webs project for aphysical sensor node although in a Sensor Web the sensornode can be another Sensor Web itself [10]

The main components of a sensor node are as followstransceiver a microcontroller external memory one or moresensors and power source [10] The controller processes thedata and controls functionality of other components in thesensor nodes The feasible option of wireless transmissionmedia is infrared radio frequency (RF) and optical commu-nication As far as external memory is concerned the mostrelevant kinds of memory are the flash memory and theon-chip memory of a microcontroller The most importantfeature in the development of a wireless sensor node is to

Internet

User

Sink node

Sensor node

Target

Wireless sensor network

Figure 1 Wireless sensor network architecture

Figure 2 Temperature humidity sensor module

make sure that there is always sufficient energy available tothe power system Sensor nodes consume power for dataprocessing sensing and communication power is stored incapacitors or batteries Batteries can be both rechargeableand nonrechargeable and for sensor nodes they are the mainresource of power supply And sensor is a device that detectsor senses heat light sound motion and so forth and thenresponds to it in a particular way [11] (Figure 2)

The crossbow radioprocessor boards usually known asmotes permit many sensors scattered over a large area towirelessly transmit their data back to the base station whichis attached to the computer (Figure 3) These motes runTinyOS operating system which is an open source operatingsystemdesigned for low-powerwireless devices such as thoseused in PANs smart meters ubiquitous computing sensornetworks and smart buildings [12] It controls power radiotransmission and networking transparent to the user and thenetwork which is formed as an ad hoc network [13]

TheMICA2Mote is a third generationmote module with512 Kbytes of measurement (serial) flash memory 128 Kbytesof program flash memory and 4Kbytes of programmableread-only memory (Figure 4)

Stargate is a 400MHZ Intel PXA255 Xscale processorwith 32Mbytes of flash memory and 64Mbytes of syn-chronous dynamic random access memory A number ofclasses of sensors are available these include barometricpressure acceleration seismic acoustic radar magneticcamera light temperature relative humidity magnetic cam-era and global positioning system (GPS) Usually sensorsare classified into 3 types passive omnidirectional passivenarrow-beam and active sensors Passive sensors are self-powered they sense the data without actually manipulatingthe environment by active probing while active sensorsactively probe the environment Narrow beam sensors havea well-defined notion of direction of measurement Omnidi-rectional sensors have no notion of direction involved in theirmeasurements [10] (Figure 5)

Journal of Sensors 3

SensorsMemory Processor Radio

Battery

Io interface

Figure 3 A sensor node architecture

Figure 4 MICA2 processor

Figure 5 Stargate processor

2 Mobile Sensor Networks

Mobile sensor networks are a class of networks where smallsensing devices move in a space over time to collabora-tively monitor physical and environmental conditions [2]The research on mobile sensor networks has been plentyworldwide For MSN there could be a lot of valuableapplication with attached sensors as well as capabilities suchas locomotion environmental information sensing and deadreckoning The architecture of MSNs can be divided intonode server and client layer [3] The job of the node layeris to acquire all sorts of data as it is directly embedded intophysical world This layer also consists of all the static as wellas mobile sensor nodes Server layer comprises single boardcomputer running server software or a personal computerThe client layer devices can be any smart terminals thesedevices also include remote and local clients Mobility is anunrealistic or undesirable characteristic of sensor nodes asit can address the objective challenges [14] Research issueson Mobile sensor networks can be analyzed into two aspects[2] communication issues and data management issues Ourwork is focused on communication issues which includecoverage and localization issues (Figure 6)

21 Coverage In the sensor networks coverage can be seen asthe measure of quality of service The quality of surveillancethat the network can provide also depends upon the coverage

of a sensor network [15 16] It can be seen that for allthe applications of mobile sensor networks coverage is oneof the most fundamental issues [17] It will decrease dueto sensor failure and undesirable sensor deployment Gage(Gage 92) defines coverage as the maintenance of spatial rela-tionship which adjusts to exact local conditions to optimizethe performances of some functions Gage describes threecoverage behavior types Blanket coverage its objective is toachieve a static arrangement of nodes thatminimizes the totaldetection area Barrier coverage the main goal of the barriercoverage is to reduce the probability of unnoticed penetrationthrough the barrier Sweep coverage the concept of sweepcoverage is from robotics which is more or less equivalentto moving barrier The lifetime of sensors is strongly affectedby hardware defects battery depletions some harsh externalenvironments (eg fire wind) and so forth [2] In MSNspreviously uncovered areas became covered when sensorsmove through them and when sensors move away thealready covered areas become uncovered As a result theareas covered by sensors change over time and more areaswill be covered at least once as time continues For roboticapplications Khatib [18] was the first one to describe potentialfield techniques for tasks like local navigation and obstacleavoidance Similar concept of ldquomotor schemasrdquo was alsointroduced which uses the superposition of spatial vectors togenerate behavior [19] Howard et al [20] also used potentialfields but for the deployment problem they consider theproblem of arranging mobile sensors in an unknown envi-ronment where fields are constructed such that each node isrepelled by other nodes and also throughout the environmentobstacles forces the network to spread Reference [21] alsoproposed potential field technique which is distributed andscalable and does not require prior map of the environmentIn [22] for the uncovered areas by the sensor network newnodes are always placed on the boundary of those areas Itis also able to find a suboptimal deployment solution andalso makes it sure that each node must be in line of sightwith another node In order to increase the coverage [23]proposed algorithms to calculate the desired target positionswhere sensors should move and identify the coverage holesexisting in the network To find out the coverage holesVoronoi diagram was used by Wang et al [24] and he alsodesigned three movement-assisted sensor deployment pro-tocols namely VEC (vector based) VOR (Voronoi-based)and Minimax based on the principle of sensors movingfrom densely deployed areas to sparsely deployed areasA virtual force algorithm (VFA) was proposed by [25] toincrease sensor field coverage by combining repulsive forcesand attractive forces to determine randomly deployed sensorsmovement and virtual motion paths Reference [26] dealswith both static and mobile sensors and within the sensorfield the job is to be served bymobile sensors which appear atrandom locations The static sensors then guide the mobilesensors to the position where the task occurs when theyget aware about the arrival of tasks Researchers deal withthe dynamic aspects of coverage in mobile sensor networksand also characterized area coverage at specific time instantsand during time interval and detection time of the randomlylocated target [27] The problem of coverage and exploration


Client

Internet

Client

LAN

Server

Client

RS-232USB

Sensor node

Base stationMobile node

Figure 6 The system architecture of a mobile sensor network

through the utilization of deployed network was consideredand the algorithmwhich assumes that the global informationis not available was also presented [28] In [29] the problemof sensor relocation is being focused and two-phase sensorrelocation solution has been proposed in which redundantsensors are identified first using Grid-Quorum and then arerelocated in a cascaded movement in a timely efficient andbalanced way [2]

22 Localization Recently there has been much focus onbuilding mobile sensors and we have seen the developmentof small-profile sensing devices that are quite capable ofcontrolling their own movement Mobility has become animportant area of research for mobile sensor networksMobility enables sensor nodes to target and track movingphenomena such as vehicles chemical clouds and packages[30] One of themost significant challenges for mobile sensornodes is the need for localization Localization is the abilityof sensor nodes to find out its physical coordinates andlocalization on mobile sensors is performed for navigationaland tracking purposes Localization is required in manyapplications in wireless sensor networks such as healthmilitary and industry Extensive research has been done sofar on localization Many location discovery schemes haveproposed to eliminate the need of GPS on every sensor node[31] GPS is commonly considered to be a good solution foroutdoor localization However GPS is still expensive andhence insufficient to be used for large number of devices inWSN Some of the problems with GPS are as follows

These are some situations in which GPS will not workreliably because GPS receiver needs line of sight to themultiple satellites and it does not work well in the indoorenvironment And GPS receivers are available for mote scaledevices only They are still expensive and undesirable formany applications Even if GPS receivers became cheaper andare used in every node the nodes cannot actively use GPSin mobile sensor networks Typically GPS node consumesmore energy than sensors and low-power transceivers Theproblemof usingGPS in a real environment also exists inGPSitself GPS shows 10sim20m of error when used in normal out-door environments unless it uses a costly mechanism such as

differential GPS Deploying a large number of GPS in mobilesensor network has both limits and possibilities [32] Thereare two types of localization algorithms namely centralizedand distributed algorithms [31] These centralized locationtechniques depend on sensor nodes transmitting data to acentral location where computation is performed to find outthe location of each node [33] Distributed algorithms do notneed a central base station and for determining its location itrelays on each node with only limited communication withnearby nodes [31] Localization algorithms in MWSNs canbe categorized into (1) range-based method (2) range-freemethod (3)mobility based method [2] All of these methodsvary in the information used for localization purposesRange-based methods use range measurements while range-free techniques only use the content of messages [34] Range-based methods also require expensive hardware to measuresignal arrival time and angle of signal arrival As comparedto range-free methods these methods are expensive becauseof their pricey hardware [2] Range-based approaches havealso utilized time of arrival received signal strength timedifference of arrival of two different signals (TDOA) and theangle of arrival Though they can reach the fine resolutioneither the required hardware is expensive or the resultsdepend on impractical assumptions about signal propagation[33] While range-free methods use local and hop counttechniques for range-based approaches these methods arevery cost effective Many localization algorithms have beenproposed so far such as elastic localization algorithm (ELA)and mobile geographic distributed localization algorithmboth of these algorithms assume nonlimited storage in sensornodes [2] For sensor networks [33] two types of range-freealgorithms have been proposed local techniques and hopcount techniques Local techniques rely on high speed on ahigh density of seeds so that every node can hear several seedsand hop count techniques rely on a flooding network Eachnode in centroid method estimates its location by calculatingthe center of the seeds locations it hears Location errorcan be reduced if seeds are well positioned but in ad hocdeployments this is impossible The APIT method separatesthe environment into triangular regions between beaconingnodes and to calculate the maximum area it uses the grid


Figure 7 Coordination measurement and location estimation phase

algorithm in which a node will likely reside [33] Hop counttechniques propagate the location estimation throughout thenetwork where the seed density is low In mobility-basedmethods to improve accuracy and precision of localizationmethod sequential Monte Carlo localization (SML) was pro-posed [27] without additional hardware except for GPS [2]Andwithout decreasing the nonlimited computational abilitymany techniques using SML are also being proposed In orderto achieve the accurate localization researchers proposedmany algorithms using the principles of Doppler shift andradio interferometry to achieve the accurate localizationhas also been used [2] The three phases typically used inlocalization are (1) Coordination (2) measurement and (3)position estimation [30] (Figure 7)

To initiate the localization a group of nodes coordinatefirst a signal is then emitted by some nodes and then someproperty of the signal is observed by some other nodesBy transforming the signal measurements into positionestimates node position is then determined To find thepositions of sensors in order to reduce the frequency oflocalization three techniques were proposed static fixed rate(SFR) dynamic velocity monotonic (DVM) and mobilityaware dead reckoning driven (MADRD) [35]

(1) Static fixed rate (SFR) the performance of this pro-tocol varies with the mobility of sensors In thisbase protocol each sensor invokes its localizationperiodically with a fixed time period 119905

119904119891

119903 In this

technique the error will be high if the sensor ismoving quickly and if it is moving slowly the errorwill be low [36]

119890

119904119891119903=

119899 times sin 120579sin120572 (1)

(2) Dynamic velocity monotonic this is an adaptiveprotocol with the mobility of sensors localization iscalled adaptively in DVM the higher the observedvelocity is the faster the node should localize tomaintain the same level of error It computes the nodevelocity when it localizes by dividing the distance ithasmoved since the last localization point by the timethat elapsed since localization The next localizationpoint is scheduled based on the velocity at the timewhen a prespecified distance will be travelled if thenode continues with the same velocity [37]

(3) Mobility aware dead reckoning driven (MADRD)to predict the future mobility this protocol com-putes the mobility pattern of the sensors When theexpected difference between the predicted mobilityand expected mobility reaches the error threshold thelocalization should be triggered [38]

Errmadrd = int119879

0119864 [(119909 minus119909

1015840

)

2+ (119910+119910

1015840

)

2] 119889119905

(2)

Mobility aware interpolation (MAINT) was proposed toestimate the current position with better trade-off betweenenergy consumption and accuracy [37] Their method usesinterpolation which gives better estimation in most cases

Errmaint = int119879

0119864 [(119909 minus119909

10158401015840

)

2+ (119910+119910

10158401015840

)

2] 119889119905

(3)

23 Positioning Systems Many methods have been usedfor the problem of localization Positioning systems willuse positioning technology to determine the position andorientation of an object or person in a room [39]

231 Xbee Technology It is a brand of radios that support avariety of communication protocols It uses Zigbee protocolZigbee is a wireless communication protocol like Wi-Fi andBluetooth These modules use the IEEE 802154 network-ing protocol for fast point-to-multipoint or peer-to-peernetworking Its low-power consumption limits transmissiondistances to 10ndash100-meter line of sight though depending onpower output and environmental characteristics [40] It oper-ates in unlicensed ISM bands so it is prone to interferencefrom a wide range of signal types using the same frequencywhich can disrupt the radio frequency From RSSI values thedistance between twoZigbee nodes is calculated based on thedistance calculated from Zigbee modules many researchersfound it suitable for indoor localization It comes withmodules though orientation is the problem inmanymodulesbecause most of its modules are without omnidirectionalantenna (Figure 8)

232 Wi-Fi Based Indoor Localization Wi-Fi or wirelessnetworking is one of the biggest changes to the way we usecomputers since the PC was introduced Wi-Fi also allowscommunications directly from one computer to another


Figure 8 Xbee

without an access point intermediary This is called ad hocWi-Fi transmission A typical wireless access point using80211b or 80211g with a stock antenna might have a rangeof 35m (115 ft) indoors and 100m (330 ft) outdoors [41]The widespread availability of wireless networks (Wi-Fi) hascreated an increased interest in harnessing them for otherpurposes such as localizing mobile devices There has longbeen interest in the ability to determine the physical locationof a device given only Wi-Fi signal strength This problemis called Wi-Fi localization and has important applicationsin activity recognition robotics and surveillance The keychallenge of localization is overcoming the unpredictability ofWi-Fi signal propagation through indoor environments Thedata distribution may vary based on changes in temperatureand humidity as well as position of moving obstacles suchas people walking throughout the building The uncertaintymakes it difficult to generate accurate estimates of signalstrength measurements Wi-Fi based systems have numberof issues including high power consumption being limitedto coverage and being prone to interference [42]

233 Ultrawideband and FM Radio Based Technique Toachieve high bandwidth connections with low-power con-sumption ultrawideband is the best communication methodUltrawideband wireless radios send short signal pulses overbroad spectrum [43]This technology has been used in a vari-ety of localization tasks requiring higher accuracy 20ndash30 cmthan achievable through conventional wireless technologiesfor example radio frequency identification (RFID) WLANand so forth [41] The limitation with this technique is that itrequires specialized hardware and dedicated infrastructureresulting in high costs for wide adoption [42] FM radioscan be used for indoor localization while providing longerbattery life thanWi-Fi making FM an alternative to considerfor positioning FM radio signals are less affected by weather

conditions such as rain and fog in comparison to Wi-Fi orGSMThese signals penetrate walls easily as compared toWi-Fi this makes sure high availability of FM positioning signalsin indoor environments FM radio uses frequency divisionmultiple access (FDMA) approach which splits the band intonumber of frequency channels that are used by stationsThereare only few papers dedicated to FM radio based positioning

234 Bluetooth Positioning System By executing the inquiryprotocol Bluetooth device detects other devices Deviceswithin its range that are set to ldquodiscoverablerdquo will respond byidentifying themselves Bluetooth communicates using radiowaves with frequencies between 2402GHz and 2480GHzwhich is within the 24GHz ISM frequency band a fre-quency band that has been set aside for industrial scientificand medical devices by international agreement The mainadvantage of using Bluetooth is that this technology is ofhigh security low power low cost and small size Manyresearchers have used Bluetooth for indoor positioning andthis reference used Bluetooth [44]

235 Radio Frequency Identification An RFID (radio fre-quency identification) system consists of a reader with anantenna which interrogates nearby active transceivers orpassive tags Using RFID technology data can be transmittedfrom RFID tags to the reader via radio waves The dataconsists of the tags unique ID (ie its serial number)which can be related to available position information of theRFID tag These are used in localization because of theiradvantages radio waves can pass through walls obstaclesand human bodies easily This technique needs less hardwareand has large coverage area By using advanced identificationtechnology and noncontact this technology uses one wayone wireless communication that uses radio signals to putan RFID tag on objects and people to track them andautomatically identify them [41]

236 Hybrid Positioning System These systems use severaldifferent technologies to find the location of a mobile deviceby using many positioning technologies To overcome thelimitations of GPS these systems are mainly developedbecause GPS does not work well in indoors This system isbeing highly investigated for commercial and civilian basedlocation services like Google maps for mobile devicescapeand so forth [41 45]

237 Quick Response Code It is a matrix code that keeps acomparative huge amount of location information comparedto standard barcode It can be attached to some key areas ofthe buildings to wait for scanning to provide its positionalinformation from database [46]

In this section we present a brief overview of the localiza-tion methods used by some researchers In localization usingRSSI based on Xbee modules in wireless sensor networks[47] proposed a localization technique using RSSI and thetechnique is based on decision tree obtained from a setof empirical experiments By applying the Cramerrsquos ruleapproach they used the decision tree to select the best three


neighbor reference nodes that are involved in the estimationof the position of target sensor node The results obtainedbased on empirical data and gathered from the experimentsindicate accuracy less than 2 meters By using Zigbee CC2431modules [48] proposed closer tracking algorithm for indoorwireless sensor localizationTheproposed algorithm can suit-ably select an adaptive mode to obtain precise locationsTheyalso improved the fingerprinting algorithm inmean timeTheproposed technique CTA can determine the position witherror less than 1 meter Based on artificial neural networks[49] presented location estimation system in the indoorenvironment This architecture provides robust mechanismfor coping with unavailable information in real life situationsas they employ modular multilayer perceptron (MMLP)approach to effectively reduce the uncertainty in the locationestimation system Moreover their system does not requireruntime searching of nearest neighbors in huge backenddatabase Yu proposed a measurement and simulation basedwork of a fingerprinting technique based on neural networksand ultrawideband signalsTheir proposed technique is basedon the construction of a fingerprinting database of LDPsextracted fromanUWBmeasurement campaign To learn thedatabase and to locate the targeted positions the feed forwardneural network with incremental back backpropagation isused In order to evaluate the positioning performancedifferent types of fingerprinting database and different sizesare considered [50] To perform indoor localization [51]evaluated several ANN designs by exploiting RSS finger-prints collected in an office environment They relied onWLAN infrastructure to minimize the deployment costThe proposed cRBF algorithm can be a good solution tothe location estimation problem in indoor environmentsMoreover based on their experimental results it is found thatthe proposed algorithm achieves more accuracy compared tosRBF MLP and GRNN designs and KNN algorithm Ref-erence [52] used RSS fingerprinting technique and artificialneural network for mobile station location in the indoorenvironmentThe proposed system learns offline the locationldquosignaturesrdquo from extracted location-dependent features ofthe measured data for LOS and NLOS situations and thenit matches online the observation received from a mobilestation against the learned set of ldquosignaturesrdquo to accuratelydetermine its position It was found that location precision ofthe proposed system is 05 meters for 90 of trained data and5 meters for 45 of untrained data By using RSSI values ofanchor node beacons researchers presented an artificial feedforward neural network based approach for node localizationin sensor networks They evaluated five different trainingalgorithms to obtain the algorithm that gives best resultsThe multilayer perceptron (MLP) neural network has beenobtained using the Matlab software and implemented usingthe Arduino programming language on the mobile node toevaluate its performance in real time environment By using12-12-2 feed forward neural network structure an averageerror of 30 cm is obtained [53] To infer the clients position inthe wireless local area network (LAN) researchers presenteda novel localization algorithm namely discriminant adap-tive neural network (DANN) which takes received signalstrength (RSS) from access points (APs) as an input For

network learning they extracted the useful information intodiscriminative components (DCs) This approach incremen-tally inserts DCs and recursively updates the weightingsin the network until no further improvement is requiredTraditional approaches were implemented on the same testbed including weighted-nearest neighbor (WKNN) max-imum likelihood (ML) and multilayer perceptron (MLP)and then results were compared The results showed thatthe proposed technique has better results as compared toother examined techniques [54] Ali used neural networks tosolve the problem of localization in sensor networks Theycompared the performances of three different families ofneural networks multilayer perceptron (MLP) radial basisfunction (RBF) and recurrent neural networks (RNN) Theycompared these networks with two variants of Kalman filterwhich are also used for localization Resource requirementsin terms of computational and memory resources were alsocompared The experimental results in [55] show that RBFneural network has the best performance in terms of accuracyand MLP neural networks has best computational and mem-ory resource requirement Another neural network basedapproach was used by Laslo For the processing receivedsignal strength indicator (RSSI) was used and its also used forlearning of neural network and preprocessed (mean medianand standard deviation) in order to increase the accuracy ofthe system Fingerprint (FP) localization methodology wasalso applied in the indoor experimental environment whichis also presented The RSSI values used for the learning ofthe neural network are preprocessed (mean median andstandard deviation) in order to increase the accuracy of thesystem To determine the accuracy of the neural networkmean square error of Euclidean distance between calculatedand real coordinates and the histogram was used in [56]Wi-Fi based technique was proposed for detecting usersposition in an indoor environment They implemented thetrilateration technique for localization They used mobilephone to obtain RSSI from the access points and then theRSSI datawas converted into distance between users and eachAPThey also proposed to determine the users position basedon trilateration technique [57]

distance 119889119894= 119901 (1 minus119898

119894) (4)

where 119898 is the percentage of signal strength 119901 is themaximum coverage of signal strength and 119868 = 1 2 3 Byanalyzing a large number of experimental data it is foundthat the variance of RSSI value changes along with thedistance regularly They proposed the relationship functionof the variance of RSSI and distance and establish the log-normal shadowing model with dynamic variance LNSM-DVbased on the result analysis Results show that LNSM-DVcan further reduce error and have strong self-adaptabilityto various environments compared with LNSM [58] Inthis study the problem of indoor localization using wirelessEthernet IEEE 80211 (Wireless Fidelity Wi-Fi) was analyzedThemain purpose of this workwas to examine several aspectsof location fingerprinting based on indoor localization thataffects positioning accuracy The results showed that theyachieved the accuracy of 2ndash25meters [59] In the mobile


node localization a system was proposed for real environ-ment when both anchors and unknown nodes aremoving Tofigure out the current position history of anchor informationwas used Userrsquos movement was modeled by the archivedinformation and for discovering new positions movementmodels were also used In complex situations where anchorsand nodes are mobile researchers presented three methodsto resolve localization problem [38] The proposed methodstake into account the capability of nodes nodes which cancalculate either distances or angles with their neighbors ornone of both When the sensor has at least two anchorsin the neighborhood the proposed methods determine theexact position else it gives a fairly accurate position and inthis case it can compute the generated maximal error Theproposed method also defines periods when a node has toinvoke its localization A GPS free localization algorithm wasin MWSNs [60] In order to build the coordinate systemthe proposed algorithm uses the distance between the nodesand also nodes positions are computed in two dimensionsBased on dead reckoning Tilak et al [36] proposed anumber of techniques for tracking mobile sensors Amongall the proposed techniques mobility aware dead reckoningestimates the position of the sensor instead of localizing thesensor every time it moves Error in the estimated positionis calculated every time the localization is called and withthe time error in the estimation grows And also the nextlocalization time is fixed depending on the value of thiserror For a given level of accuracy in position estimationfastmobile sensors trigger localizationwith higher frequencyInstead of localizing sensor a technique was proposed toestimate the positions of a mobile sensor [37] It gives higheraccuracy for particular energy cost and vice versa Whensensor has some data to be sent the position of the sensoris required then onlyThe information of an inactive sensor isceased to be communicated In order to reduce the arithmeticcomplexity of sensors most calculations are carried out atbase station To solve the set of equations [31] proposed thenovel algorithm for MSN localization in which they tookthree nodes which are neighbors to each other and if thesolutions are unique they are the node positions And forsearching the position of the final node a scan algorithmwas also introduced called a metric average localizationerror (ALE) (which is the root mean square error of nodelocations divided by the total number of nodes) to evaluatethe localization error

ALE =sum

119873

119894=11003817

1003817

1003817

1003817

119901

119894minus 119870

119894

1003817

1003817

1003817

1003817

119873

(5)

3 Security Issues in Mobile Ad HocSensor Networks

Because of the vulnerability of wireless links nodes limitedphysical protection nonexistence of certification authorityand lack of management point or a centralized monitoringit is difficult to achieve security in mobile ad hoc networks[61] Sensor networks have many applications they areused in ecological military and health related areas Theseapplications often examine some sensitive information such

as location detection or enemy movement on the battlefieldsTherefore security is very important inWSNWSN has manylimitations such as small memory limited energy resourcesand use of insecure channels in communication these prob-lems make security in WSN a challenge [62] Designing thesecurity schemes of wireless sensor networks is not an easytask sensor networks have many constraints compared tocomputer networks [63] The main objective of the securityservice in WSN is to protect the valuable information andresources from misbehavior and attacks requirements inwireless sensor network security include availability autho-rization confidentiality authentication integrity nonrepu-diation and freshness Attacks in WSN can be categorizedas follows attacks on network availability these are oftenreferred to as DOS (denial of service attacks) these attackscan target any layer of a sensor network Attacks on secrecyand authentication include packet replay attacks eavesdrop-ping or spoofing of attacks There are also stealthy attacksagainst service integrity In this type of attack the motiveof the attacker is to make network accept false data valueSecurity threats and issues in WSN can be classified into twocategories active and passive attacks [63]

31 Active Attacks It implies the disruption of the normalfunctionality of the network meaning information inter-ruption modification or fabrication [64] Impersonatingmodification fabrication message replay and jamming arethe examples of active attacksThe following attacks are activein nature

32 Routing Attacks in Sensor Networks Routing attacks arethe attacks which act on the network layer While routingthe messages many attacks can happen some of themare as follows attacks on information in transit selectiveforwarding and BlackholeSinkhole attack

33 Wormhole Attacks In this type of attack the attackerrecords packets at one location in the network and thentunnels them to another location and retransmits them thereinto the network [65]

34 HELLO Flood Attacks In this type of attack the attackeruses HELLO packets to convince the sensors in WSN Theattacker sends routing protocols HELLO packets from onenode to another with more energy [63]

35 Denial of Service (DOS) These attacks can target anylayer of a sensor network This type of service is producedby unintentional failure of nodes or malicious action

36 Node Subversion Capture of a node may disclose itsinformation and thus compromise the whole sensor networkIn this attack the information stored on sensor might beobtained by attacking it [66]

37 Node Outage When a node stops its function this kindof situation occurs In the case where a cluster leader stops


functioning the sensor network protocols should be robustenough to mitigate the effects of node outages by providingan alternate route

38 Node Malfunction It can expose the integrity of a sensornetwork if the node starts malfunctioning

39 Physical Attacks These types of attacks can destroysensors permanently These attacks on sensor networks typi-cally operate in hostile outdoor environments Attackers canextract cryptographic secrets modify programming in thesensors tamper with associated circuitry and so forth

310 False Node This can lead to injection of malicious databy the additional node this can spread to all the nodespotentially destroying the whole network In this case anintruder adds a node to the system that feeds false data orit can also prevent the passage of true data

311 Massage Corruption Any change in the content of amessage by an attacker compromises its integrity [63]

312 Node Replication Attacks In this attack attacker adds anadditional node to the existing network by copying the nodeIDThis can result in a disconnected sensor network and falsesensor readings

313 Passive Information Gathering Strong encryption tech-niques need to be used in order to minimize the threats ofpassive information gathering [63]

314 Passive Attacks In passive attacks the attack obtainsdata exchange in the network without interrupting the com-munication [64] Some of the most common attacks againstsensor privacy are as the following

3141 Monitoring and Eavesdropping The most commonattack to the privacy is the monitor and eavesdroppingEavesdropping is the intercepting and reading of messagesand conversations by unintended users [64]

3142 Traffic Analysis There is a high possibility analysis ofcommunication patterns even when themessages transferredare encrypted The activities of sensor can disclose a lot ofinformation to enable an adversary to cause malicious harmto the network

3143 Camouflage Adversaries In the network one cancomprise the nodes to hide or can insert their node Afterinserting their node that node can work as a normal nodeto attract packets and then misroute them which can affectprivacy analysis

4 Robotic Sensor Network Applications

Most of the problems in traditional sensor networks may beaddressed by incorporating intelligent mobile robots directly

into it Mobile robots provide the means to explore andinteractwith the environment in a dynamic anddecentralizedway In addition to enabling mission capabilities well beyondthose provided by sensor networks these new systems ofnetworked sensors and robots allow for the development ofnew solutions to classical problems such as localization andnavigation [1] Many problems in sensor networks can besolved when putting robotics into use problems like nodepositioning and localization acting as data mule detectingand reacting to sensor failure for nodes mobile batterychargers and so forth And also wireless sensor networkscan help solve many problems in robotics such as robot pathplanning localization mapping and sensing in robots [9]Mobile nodes can be implemented as autonomous perceptivemobile robots or as perceptive robots whose sensor systemsaddress environmental task and navigational task Roboticsensor networks are particular mobile sensor networks or wecan say robotic sensor networks are distributed systems inwhich mobile robots carry sensors around an environmentto sense phenomena and to produce in depth environmentalassessments There are many applications of wireless sen-sor networks in robotics like robotics advanced sensingcoordination in robots robot path planning and robotlocalization robot navigation network coverage proper datacommunication data collection and so forth Using WSNhelps emergency response robots to be conscious of theconditions such as electromagnetic field monitoring andforest fire detection These networks improve the sensingcapability and can also help robot in finding the way tothe area of interest WSNs can be helpful for coordinatingmultiple robots and swarm robotics because the network canassist the swarm to share sensor data tracking its membersand so forth To perform the coordinated tasks it sends robotsto the different locations and also a swarm takes decisionsbased on the localization of events allowing path planningand coordination for multiple robots to happen efficientlyand optimally and direct the swarm members to the area ofinterest In the localization part there are many techniquesfor localizing robots within a sensor network Cameras havebeen put into use to identify the sensors equipped withinfrared light to triangulate themselves based on distancesderived from pixel size A modified SLAM algorithm hasbeen utilized by some methods which uses robots to localizeitself within the environment and then compensates forSLAM sensor error by fusing the estimated location withthe estimated location in WSN based on RSSI triangulation[9] An intruder detection system was presented in [59]which uses both wireless sensor networks and robots Inorder to learn and detect intruders in previously unknownenvironment sensor network uses an unsupervised fuzzyadaptive resonance theory (ART) neural network A mobilerobot travels upon the detection of an intruder to the positionwhere the intruder is detected The wireless sensor networkuses a hierarchical communicationlearning structure wheremobile robot is root node of the tree

In wireless sensor networks robotics can also play acrucial role They can be used for replacing broken nodesrepositioning nodes recharging batteries and so forth Toincrease the feasibility of WSNs [67] used robots because


they have actuation but limited coverage in sensing whilesensor networks lack actuation but they can acquire dataIn servicing WSNs robot task allocation and robot taskfulfillment was examined [68] Problems are examined inrobot task allocation such as using multitask or single-taskrobots in a network and how to organize their behavior tooptimally service the network The route which a robot takesto service nodes is examined in robot task fulfillment Toimprove the robot localization [69] adapts sensor networkmodels with informationmaps and then checks the capabilityof such maps to improve the localization The node replace-ment application was developed by [70] in which a robotwould navigate a sensor network based on RSSI (receivedsignal strength indication) from nearby nodes and it wouldthen send a help signal if a mote will begin to run lowon power And then through the network the help signalwould be passed to direct the robot to replace the nodeRobots can also be used to recharge batteriesThe problem oflocalization can also be solved using robots they can be usedto localize the nodes in the networkThey can also be used indata aggregation In a network they can also serve as datamules data mules are robots that move around the sensornetwork to collect data from the nodes and then transportthat data back to sink node or perform the aggregation oper-ations operation on data [9] The use of multirobot systemsfor carrying sensors around the environment represents asolution that has received a considerable attention and canprovide some remarkable advantages as well A number ofapplications have been addressed so far by robotic sensornetworks including environmental monitoring and searchand rescue When put into use robotics sensor networkscan be used for effective search and rescue monitoringelectromagnetic fields and so forth Search and rescuesystems should quickly and accurately locate victims andmap search space and locations of victims and with humanresponders it should maintain communication For a searchand rescue system to fulfill its mission the system shouldbe proficient to rapidly and reliably trace its victims withinthe search space and should also be well capable to handlea dynamic and potentially hostile environment For utilizingad hoc networks [7] presented an algorithmic frameworkconsisting of a large number of robots and small cheapsimple wireless sensors to perform proficient and robusttarget tracking Without dependence on magnetic compassor GPS localization service they described a robotic sensornetwork for target tracking focusing on algorithmswhich aresimple for information propagation and distributed decisionmakingThey presented a robotic sensor network system thatautonomously conducts target tracking without componentpossessing localization capabilities The approach providesa way out with minimal hardware assumptions (in termsof sensing localization broadcast and memoryprocessingcapabilities) while subject to a dynamic changing envi-ronment Moreover the framework adjusts dynamically toboth target movement and additiondeletion of networkcomponents The network gradient algorithm provides anadvantageous trade-off between power consumption andperformance and requires relatively bandwidth [71] Themonitoring of EMF phenomena is extremely important in

practice especially to guarantee the protection of the peopleliving and working where these phenomena are significantA specific robotic sensor network oriented to monitor elec-tromagnetic fields (EMFs) was presented [8]The activities ofthe system are being supervised by a coordinator computerwhile a number of explorers (mobile robots equipped withEMF sensors) navigate in the environment and performEMF measurement tasks The system is a robotic sensornetwork that can autonomously deploy its explorers in anenvironment to cope with events like a moving EMF sourceThe system architecture is hierarchical The activities of thesystem are being supervised by the computer and to performtheEMF tasks a number of explorers (mobile robots equippedwith EMF sensors navigate through the environment) Thegrid map of the environment is maintained by the system inwhich each cell can be either free or occupied by an obstacleor by a robot The map is supposed to be known by thecoordinator and the explorers The environment is assumedto be static and themap is used by the explorers to navigate inthe environment and by the coordinator to localize the EMFsource [72]

5 Coverage for Multirobots

The use of multirobots holds numerous advantages over asingle robot system Their potential of doing work is way farbetter than that of single robot system [73] Multiple robotscan increase the robustness and the flexibility of the systemby taking benefits of redundancy and inherent parallelismThey can also cover an area more quickly than a singlerobot and they have also potential to accomplish a singletask faster than a single robot Multiple robots can alsolocalize themselves more efficiently when they have differentsensor capabilities Coverage for multirobot systems is animportant field and is vital for many tasks like search andrescue intrusion detection sensor deployment harvestingand mine clearing and so forth [74] To get the coveragethe robots must be capable of spotting the obstacles in theenvironment and they should also exchange their knowledgeof environment and have a mechanism to assign the coveragetasks among themselves [75] The problem of deploying amobile senor network into an environment was addressed in[76] with the task of maximizing sensor coverage and alsotwo behavior based techniques for solving the 2D coverageproblems using multiple robots were proposed Informativeand molecular techniques are the techniques proposed forsolving coverage problems and both of these techniques havethe same architecture When robots are within the sensorrange of each other the informative approach is to assignlocal identities to them This approach allows robots tospread out in a coordinated manner because it is based onephemeral identification where temporary local identities areassigned andmutual local information is exchanged No localidentification is made in molecular approach and also robotsdo not perform any directed communication Each robotmoves in a direction without communicating its neighborsbecause it selects its direction away from all its immediatesensed neighborsThen these algorithmswere comparedwithanother approach known as basic approach which only seeks


to maximize each individual robotrsquos sensor coverage [74]Both these approaches perform significantly better than basicapproach and with the addition of few robots the coveragearea quickly maximizes An algorithm named (StiCo) whichis an coverage algorithmwas proposed formultirobot systemsin [77]This algorithm is based on the principle of stigmergic(pheromone-type) coordination known from the ant soci-etieswhere a groupof robots coordinate indirectly via ant-likestigmergic communication This algorithm does not requireany prior information about the environment and also nodirect robot-robot communication is required Similar kindof approach was used by Wagner et al [78] for coverage inmultirobot in which a robot deposits a pheromone whichcould then be detected by other robots these pheromonescomeupwith a decay rate allowing continuous coverage of anarea via implicit coordination [75] For multirobot coverage[75] proposed boustrophedon decomposition algorithm inwhich the robots are initially distributed through space andeach robot is allocated at virtually bounded area to coverthe area and is then decomposed into cells with the fixedcell width By using the adjacency graph the decomposedarea is represented which is incrementally constructed andshared among all robots and without any restriction robotcommunication is also available By sharing information reg-ularly and task selection protocol performance is improvedBy planting laser beacons in environment the problem oflocalization in the hardware experiment was overthrown andusing the laser range finder to localize the robots as thiswas the major problem to guarantee accurate and consistentcoverage Based on spanning-tree coverage of approximatecell decomposition robustness and efficiency in a family ofmultirobot coverage algorithms was addressed [79] Theirapproach is based on offline coverage it is assumed that therobots have a map of the area a priori The algorithm theyproposed decomposes the work area into cells where eachcell is a square of size 4D and each cell is then further brokendown into quadrants of size D

6 Localization for Robots

In mobile robotics localization is a key component [80]The process to determine the robots position within theenvironment is called localization or we can say that it is aprocess that takes amap as an input and estimates the currentpose of the robot a set of sensor readings and then outputsthe robotrsquos current pose as a new estimate [81] There aremany technologies available for robot localization includingGPS activepassive beacons odometer (dead reckoning) andsonar For robot localization and map count an algorithmwas presented using data from a range based sonar sensor[82] For localization the robots position is determined bythe algorithm by correlating a local map with a globalmap Actually no prior knowledge of the environment isassumed it uses sensor data to construct the global mapdynamically The algorithm estimates robots location bycomputing positions called feasible poses where the expectedview of robot matches approximately the observed rangesensor data The algorithm then selects the best fit from thefeasible poses It requires robots orientation information to

make sure that the algorithm identifies the feasible posesFor location information Vassilis also used dead reckoningas a secondary source when combined with range sensorbased localization algorithm it can provide a close real timelocation estimate A Monte Carlo localization algorithm wasintroduced using (MHL) was used for mobile robot positionestimation [83] They used the Monte Carlo type methodsand then combined the advantages of their previous workin which grid based Markov localization with efficiency andaccuracy of Kalman filter based techniques was used MCLmethod is able to deal with ambiguities and thus can globallylocalize the robot As compared to their previous grid basedmethod MCL method has significantly reduced memoryrequirements while at the same time incorporating sensormeasurements at a considerably higher frequency Basedon condensation algorithm the Monte Carlo localizationmethod was proposed in [84] It localizes the robot globallyusing a scalar brightness measurement when given a visualmap of the ceiling Sensor information of low feature is usedby these probabilistic methods specifically in 2D plane andneeds the robot to move around for probabilities to graduallyconverge toward a pack The pose of the robots was alsocomputed by some researchers based on the appearancePanoramic image-based model for robot localization wasused by Cobzas and Zhang [55] with the depth and 3Dplanarity information the panoramicmodel was constructedwhile thematching is based on planar patches For probabilis-tic appearance based robot localization [56] used panoramicimages For extracting the 15-dimensional feature vectors forMarkov localization PCA is applied to hundreds of trainingimages In urban environments the problem of mobile robotlocalization was addressed by Talluri and Aggarwal [85] byusing feature correspondence between images taken by cam-era on robot and a CAD or similar model of its environmentFor localization of car in urban environments [86] usedan inertial measurement unit and a sensor suite consists offour GPS antennas Humanoid robots are getting popularas research tools as they offer new viewpoint compared towheeled vehicle A lot of work has been done so far on thelocalization for humanoid robots In order to estimate thelocation of the robot [87] applied a vision based approachand then compared the current image to previously recordedreference images In the local environment of the humanoid[88] detects objects with given colors and shapes and thendetermines its pose relative to these objects With respect toa close object [89] localizes the robot to track the 6D pose ofa manually initialized object relative to camera by applying amodel based approach

7 Conclusion

In this paper we reviewed MSN issues sensor networkapplications in robotics and vice versa robot localization andalso coverage for multiple robots This is certainly not theextent that robotics is used in wireless sensor networks andalso wireless sensor networks in robotics However we foundthat integrating static nodes with mobile robots enhancesthe capabilities of both types of devices and also enablesnew applications The possibilities of robotics and wireless


sensor networks being used together seem endless and if usedtogether in future also will help to solve many problems

Conflict of Interests

The authors declare no conflict of interests

Acknowledgment

This work was supported by the Brain Korea 21 PLUSProject National Research Foundation of Korea (NRF)grant funded by the Korean government (MEST) (no2013R1A2A2A01068127 and no 2013R1A1A2A10009458)

References

[1] S Basagni A Carosi and C Petrioli ldquoMobility in wirelesssensor networksrdquo Journal ofWireless Networks vol 14 no 6 pp831ndash858

[2] C Zhu L Shu T Hara LWang and S Nishio ldquoResearch issueson mobile sensor networksrdquo in Proceedings of the 5th Interna-tional ICST Conference on Communications and Networking inChina (ChinaCom rsquo10) pp 1ndash6 IEEE Beijing China August2010

[3] G Song Y Zhou F Ding and A Song ldquoA mobile sensornetwork system for monitoring of unfriendly environmentsrdquoSensors vol 8 no 11 pp 7259ndash7274 2008

[4] httpwwwwikipediacom[5] C Flanagin ldquoA survey on robotics system and performance

analysisrdquo httpwwwcsewustledusimjaincse567-11ftprobots[6] M A Goodrich and A C Schultz ldquoHuman-robot interaction

a surveyrdquo Foundations and Trends in Human-Computer Interac-tion vol 1 no 3 pp 203ndash275 2007

[7] J Reich and E Sklar ldquoRobot-sensor Networks for search andrescuerdquo in Proceedings of the IEEE International Workshop onSafety Security and Rescue Robotics Gaithersburg Md USAAugust 2006

[8] F Amigoni G Fontana and S Mazzuca ldquoRobotic sensor net-works an application to monitoring electro-magnetic fieldsrdquoin Proceedings of the 2007 Conference on Emerging ArtificialIntelligence Applications in Computer Engineering pp 384ndash3932007

[9] S Shue and J M Conrad ldquoA survey of robotic applications inwireless sensor networksrdquo in Proceedings of the IEEE Southeast-con pp 1ndash5 Jacksonville Fla USA April 2013

[10] httpenwikipediaorgwikiSensor node[11] httpwwwmerriam-webstercomdictionarysensor[12] httpwebscsberkeleyedutos[13] httpwwwpagesdrexeledusimkws23tutorialsmotesmotes

html[14] E Stavrou and A Pitsillides ldquoSecurity evaluation methodology

for intrusion recovery protocols in wireless sensor networksrdquoin Proceedings of the 15th ACM International Conference onModeling Analysis and Simulation of Wireless and MobileSystems pp 167ndash170 Paphos Cyprus 2012

[15] S Meguerdichian F Koushanfar M Potkonjak and M BSrivastava ldquoCoverage problems in wireless ad-hoc sensor net-worksrdquo in Proceedings of the 20th Annual Joint Conference of theIEEE Computer and Communications Societies (INFOCOM rsquo01)vol 3 pp 1380ndash1387 April 2001

[16] B Liu O Dousse P Nain and D Towsley ldquoDynamic coverageof mobile sensor networksrdquo IEEE Transactions on Parallel andDistributed Systems vol 24 no 2 pp 301ndash311 2013

[17] J Luo and Q Zhang ldquoProbabilistic coverage map for mobilesensor networksrdquo in Proceedings of the IEEE Global Telecommu-nications Conference (GLOBECOM rsquo08) pp 1ndash5 New OrleansLa USA December 2008

[18] O Khatib ldquoReal-time obstacle avoidance for manipulatorsand mobile robotsrdquo in Proceedings of the IEEE InternationalConference on Robotics and Automation vol 2 IEEE 1985

[19] R C Arkin ldquoMotor schema-based mobile robot navigationrdquoThe International Journal of Robotics Research vol 8 no 4 pp92ndash112 1989

[20] A Howard M J Mataric and G S Sukhatme ldquoMobilesensor network deployment using potential fields a dis-tributed scalable solution to the area coverage problemrdquo inDistributed Autonomous Robotic Systems 5 chapter 8 pp 299ndash308 Springer Tokyo Japan 2002

[21] S Poduri andG S Sukhatme ldquoConstrained coverage formobilesensor networksrdquo in Proceedings of the IEEE InternationalConference on Robotics and Automation (ICRA rsquo04) vol 1 pp165ndash171 IEEE May 2004

[22] A Howard M J Matarıc and G S Sukhatme ldquoAn incre-mental self-deployment algorithm for mobile sensor networksrdquoAutonomous Robots vol 13 no 2 pp 113ndash126 2002

[23] GWang G Cao and T F La Porta ldquoMovement-assisted sensordeploymentrdquo IEEE Transactions on Mobile Computing vol 5no 6 pp 640ndash652 2006

[24] G Wang G Cao and T la Porta ldquoMovement-assisted sensordeploymentrdquo in Proceedings of the 23rd Annual Joint Conferenceof the IEEE Computer and Communications Societies (INFO-COM rsquo04) pp 2469ndash2479 March 2004

[25] Y Zou and K Chakrabarty ldquoSensor deployment and targetlocalization based on virtual forcesrdquo in Proceedings of the22nd Annual Joint Conference on the IEEE Computer andCommunications Societies pp 1293ndash1303 April 2003

[26] M A Batalin M Rahimi Y Yu et al ldquoCall and responseexperiments in sampling the environmentrdquo in Proceedings of the2nd International Conference on Embedded Networked SensorSystems (SenSys rsquo04) pp 25ndash38 2004

[27] B Liu P Brass and O Doussie ldquoMobility improves coverageof sensor networksrdquo in Proceedings of the 6th InternationalSymposium on Mobile adhoc Networking (MobiHoc rsquo05) pp300ndash308 2005

[28] AMaxim andG S Batalin ldquoCoverage exploration and deploy-ment by a mobile robot and a communication networkrdquo inProceedings of InternationalWorkshop on Information Processingin Sensor Networks pp 376ndash391 PaloAlto Research Center(PARC) Palo Alto Calif USA April 2003

[29] GWang G Cao T La Porta andW Zhang ldquoSensor relocationin mobile sensor networksrdquo in Proceedings of the 24th AnnualJoint Conference of the IEEE Computer and CommunicationsSocieties (INFOCOM rsquo05) vol 4 pp 2302ndash2312 Miami FlaUSA March 2005

[30] I Amundson and X D Koutsoukos ldquoMobile sensor local-ization and navigation using RF doppler shiftsrdquo in MobileEntity Localization and Tracking in GPS-less EnvironnmentsSecond International Workshop MELT 2009 Orlando FL USASeptember 30 2009 Proceedings vol 5801 of Lecture Notesin Computer Science pp 235ndash254 Springer Berlin Germany2009


[31] Y Ganggang and Y Fengqi ldquoA localization algorithm formobile wireless sensor networksrdquo in Proceedings of the IEEEInternational Conference on Integration Technology (ICIT rsquo07)pp 623ndash627 March 2007

[32] J Yi J Koo and H Cha ldquoA localization technique formobile sensor networks using archived anchor informationrdquoin Proceedings of the 5th Annual IEEE Communications SocietyConference on Sensor Mesh and Ad Hoc Communications andNetworks (SECON rsquo08) pp 64ndash72 San Francisco Calif USAJune 2008

[33] L Hu and D Evans ldquoLocalization for mobile sensor networksrdquoin Proceedings of the 10th Annual International Conference onMobile Computing and Networking (MobiCom rsquo04) pp 45ndash57October 2004

[34] B Dil S Dulman and P Havinga ldquoRange-based localizationin mobile sensor networksrdquo in Wireless Sensor Networks ThirdEuropeanWorkshop EWSN 2006 Zurich Switzerland February13ndash15 2006 Proceedings vol 3868 of Lecture Notes in ComputerScience pp 164ndash179 Springer Berlin Germany 2006

[35] S Tilak V Kolar N B Abu-Ghazaleh and K-D KangldquoDynamic localization control for mobile sensor networksrdquoin Proceedings of the 24th IEEE International PerformanceComputing and Communications Conference (IPCCC rsquo05) pp587ndash592 IEEE April 2005

[36] S Tilak V Kolar N B Abu Ghazaleh and K-D KangldquoDynamic localization protocols for mobile sensor networksrdquohttparxivorgabscs0408042

[37] B Sau S Mukhopadhyaya and K Mukhopadhyaya ldquoLocaliza-tion control to locate mobile sensorsrdquo inDistributed Computingand Internet Technology Proceedings of the 3rd InternationalConference (ICDCIT rsquo06) Bhubaneswar India December 20ndash232006 vol 4317 of Lecture Notes in Computer Science pp 81ndash88Springer Berlin Germany 2006

[38] C Saad A R Benslimane and J-C Koing ldquoA distributedmethod to localization for mobile sensor networksrdquo in Pro-ceedings of the IEEE Wireless Communications and NetworkingConference (WCNC rsquo07) 2007

[39] httpenwikipediaorgwikiPositioning technology[40] httpenwikipediaorgwikiZigBee[41] Z Farid R Nordin and M Ismail ldquoRecent advances in

wireless indoor localization techniques and systemrdquo Journal ofComputer Networks and Communications vol 2013 Article ID185138 12 pages 2013

[42] A Popleteev V Osmani and O Mayora ldquoInvestigation ofindoor localizationwith ambient FM radio stationsrdquo inProceed-ings of PerCom-2012 Lugano Switzerland March 2012

[43] httpcompnetworkingaboutcomodnetworkprotocolsgul-tra wide bandhtm

[44] C Frost C S Jensen K S Luckow BThomsen and R HansenldquoBluetooth indoor positioning system using fingerprintingrdquo inMobile Lightweight Wireless Systems vol 81 of Lecture Notesof the Institute for Computer Sciences Social Informatics andTelecommunications Engineering pp 136ndash150 Springer BerlinGermany 2012

[45] httpenwikipediaorgwikiHybrid positioning system[46] L Niu ldquoA survey of wireless indoor positioning technology for

fire emergency routingrdquo IOP Conference Series Earth and Envi-ronmental Science vol 18 Article ID 012127 2014 Proceedingsof the 8th International Symposium of the Digital Earth (ISDErsquo08)

[47] A Cheriet M Ouslim and K Aizi ldquoLocalization in a wirelesssensor network based on RSSI and a decision treerdquo PrzegladElektrotechniczny vol 89 no 12 pp 121ndash125 2013

[48] Y-T Chen C-L Yang Y-K Chang and C-P Chu ldquoA RSSI-based algorithm for indoor localization using zigbee in wirelesssensor networkrdquo in Proceedings of the 15th International Confer-ence on Distributed Multimedia Systems (DMS rsquo09) pp 70ndash752009

[49] U Ahmad A Gavrilov U Nasir M Iqbal S J Cho and S LeeldquoIn-building localization using neural networksrdquo in Proceedingsof the IEEE International Conference on Engineering of IntelligentSystems (ICEIS rsquo06) April 2006

[50] L Yu ldquoFingerprinting localization based on neural networksand ultra wideband signalsrdquo in Proceedings of the IEEE Inter-national Symposium on Signal Processing and Information Tech-nology (ISSPIT rsquo11) pp 184ndash189 2011

[51] C Laoudias D G Eliades P Kemppi C G Panayiotou andMM Polycarpou ldquoIndoor localization using neural networkswithlocation fingerprintsrdquo in Artificial Neural NetworksmdashICANN2009 vol 5769 of Lecture Notes in Computer Science pp 954ndash963 Springer Berlin Germany 2009

[52] C Nerguizian C Despins and S Affes ldquoIndoor geolocationwith received signal strength fingerprinting technique andneural networks rdquo in Telecommunications and NetworkingmdashICT 2004 11th International Conference on TelecommunicationsFortaleza Brazil August 1ndash6 2004 Proceedings vol 3124 ofLecture Notes in Computer Science pp 866ndash875 SpringerBerlin Germany 2004

[53] S Kumar and S-R Lee ldquoLocalization with RSSI values for wire-less sensor networks an artificial neural network approachrdquo inProceedings of the International Electronic Conference on Sensorsand Applications 2014 Paper d007

[54] S-H Fang and T-N Lin ldquoIndoor location system based ondiscriminant-adaptive neural network in IEEE 80211 environ-mentsrdquo IEEETransactions onNeural Networks vol 19 no 11 pp1973ndash1978 2008

[55] D Cobzas and H Zhang ldquoCylindrical panoramic image-basedmodel for robot localizationrdquo in Proceedings of the IEEERSJInternational Conference on Intelligent Robots and Systems(IROS rsquo01) pp 1924ndash1930 November 2001

[56] B J A Krose N Vlassis and R Bunschoten ldquoOmni directionalvision for appearance-based robot localizationrdquo in Sensor BasedIntelligent Robots vol 2238 of Lecture Notes in ComputerScience pp 39ndash50 Springer 2002

[57] N AidaMahiddin E NadiaMadi S Dhalila E Fadzli Hasan SSafie and N Safie ldquoUser position detection in an indoor envi-ronmentrdquo International Journal of Multimedia and UbiquitousEngineering vol 8 no 5 pp 303ndash312 2013

[58] J Xu W Liu F Lang Y Zhang and C Wang ldquoDistancemeasurement model based on RSSI in WSNrdquo Wireless SensorNetwork vol 2 pp 606ndash611 2010

[59] G Jekabsons V Kairish and V Zuravlyov ldquoAn analysis of Wi-Fi based indoor positioning accuracyrdquo Scientific Journal of RigaTechnical University Computer Sciences vol 44 no 1 pp 131ndash137 2012

[60] S Capkun M Hamdi and J P Hubaux ldquoGPS free positioningin mobile ad hoc networksrdquo Journal Cluster Computing vol 5no 2 pp 157ndash167 2002

[61] D Djenouri L Khelladi and N Badache ldquoA survey of securityissues in mobile ad hoc and sensor networksrdquo IEEE Communi-cations Surveys and Tutorials vol 7 no 4 pp 2ndash28 2005


[62] YWang G Attebury and B Ramamurthy ldquoA survey of securityissues in wireless sensor networksrdquo IEEE CommunicationsSurveys amp Tutorials vol 8 no 2 pp 2ndash23 2006

[63] V Kumar A Jain and P N Barwa ldquoWireless sensor networkssecurity issues challenges and solutionsrdquo International Journalof Information and Computation Technology vol 4 no 8 pp859ndash868 2014

[64] H Kaur ldquoAttacks in wireless sensor networksrdquo Research Cell Inpress

[65] Y-C Hu A Perrig and D B Johnson ldquoWormhole attacks inwireless networksrdquo IEEE Journal on Selected Areas in Commu-nications vol 24 no 2 pp 370ndash380 2006

[66] G Padmavathi and D Shanmugapriya ldquoA survey of attackssecurity mechanisms and challenges in WSNrdquo InternationalJournal of Computer Science and Information Security vol 4 no1-2 2009

[67] A LaMarca W Brunette D Koizumi et al ldquoPlantCare aninvestigation in practical ubiquitous systemsrdquo in UbiComp2002 Ubiquitous Computing 4th International ConferenceGoteborg Sweden September 29-October 1 2002 Proceedingsvol 2498 of Lecture Notes in Computer Science pp 316ndash332Springer Berlin Germany 2002

[68] X Li I Lille R FalconANayak and I Stojmenovic ldquoServicingwireless sensor networks by mobile robotsrdquo IEEE Communica-tions Magazine vol 50 no 7 pp 147ndash154 2012

[69] S M Schaffert Closing the loop control and robot navigationin wireless sensor networks [PhD thesis] EECS DepartmentUniversity of California Berkeley Calif USA 2006

[70] J-P Sheu K-Y Hsieh and P-W Cheng ldquoDesign and imple-mentation of mobile robot for nodes replacement in wirelesssensor networksrdquo Journal of Information Science and Engineer-ing vol 24 no 2 pp 393ndash410 2008

[71] J Reich and E Sklar ldquoRobotic sensor networks for search andrescuerdquo in Proceedings of the IEEE International Workshop onSafety Security and Rescue Robotics (SSRR rsquo06) 2006

[72] F Amigoni G Fontana and S Mazzuca ldquoRobotic sensor net-works an application to monitoring electro-magnetic fieldsrdquo inProceedings of the Conference on Emerging Artificial IntelligenceApplications in Computer Engineering Real Word AI Systemswith Applications in eHealth HCI Information Retrieval andPervasive Technologies pp 384ndash393 IOSPress AmsterdamTheNetherlands 2007

[73] L Iocchi D Nardi and M Salerno ldquoReactivity and delibera-tion a survey on multi-robot systemsrdquo in Balancing Reactivityand Social Deliberation in Multi-Agent Systems vol 2103 ofLecture Notes in Computer Science pp 9ndash32 Springer BerlinGermany 2001

[74] B Walenz ldquoMulti robot coverage and exploration a survey ofexisting techniquesrdquo httpbwalenzfileswordpresscom201006csci8486-walenz-paperpdf

[75] S K Chan A P New and I Rekleitis ldquoDistributed coveragewith multi-robot systemrdquo in Proceedings of the IEEE Interna-tional Conference on Robotics and Automation (ICRA rsquo06) pp2423ndash2429 Orlando Fla USA May 2006

[76] M A Batalin and G S Sukhatme ldquoSpreading out a localapproach to multi-robot coveragerdquo in Proceedings of the 6thInternational Symposium on Distributed Autonomous RoboticsSystem pp 373ndash382 Fukuoka Japan June 2002

[77] B Ranjbar-Sahraei G Weiss and A Nakisaee ldquostigmergiccoverage algorithm for multi-robot systems (demonstration)rdquoin Proceedings of the 10th German conference (MATES 12) pp126ndash138 Springer Trier Germany October 2012

[78] I A Wagner M Lindenbaum and A M Bruckstein ldquoDis-tributed covering by ant-robots using evaporating tracesrdquo IEEETransactions on Robotics and Automation vol 15 no 5 pp 918ndash933 1999

[79] N Hazon and G A Kaminka ldquoOn redundancy efficiencyand robustness in coverage for multiple robotsrdquo Robotics andAutonomous Systems vol 56 no 12 pp 1102ndash1114 2008

[80] E RoyerM LhuillierMDhome and J-M Lavest ldquoMonocularvision for mobile robot localization and autonomous naviga-tionrdquo International Journal of Computer Vision vol 74 no 3pp 237ndash260 2007

[81] R G Brown and B R Donald ldquoMobile robot self-localizationwithout explicit landmarksrdquo Algorithmica vol 26 no 3-4 pp515ndash559 2000

[82] V Varveropoulos Robot Localization and Map constructionusing sonar data The Rossum project

[83] F Dellaert D Fox W Burgard and S Thrun ldquoRobust MonteCarlo localization for mobile robotsrdquo Artificial Intelligence vol128 no 1-2 pp 99ndash141 2001

[84] F Dellaert W Burgard D Fox and S Thrun ldquoUsing thecondensation algorithm for robust vision-based mobile robotlocalizationrdquo in Proceedings of the IEEE Computer SocietyConference on Computer Vision and Pattern Recognition (CVPRrsquo99) pp 588ndash594 June 1999

[85] R Talluri and J K Aggarwal ldquoMobile robot self-location usingmodel-image feature correspondencerdquo IEEE Transactions onRobotics and Automation vol 12 no 1 pp 63ndash77 1996

[86] R Nayak ldquoReliable and continuous urban navigation usingmultiple Gps antenna and a low cost IMUrdquo in Proceedings of theION GPS Salt Lake City Utah USA September 2000

[87] J Ido Y Shimizu Y Matsumoto and T Ogasawara ldquoIndoornavigation for a humanoid robot using a view sequencerdquoInternational Journal of Robotics Research vol 28 no 2 pp 315ndash325 2009

[88] R Cupec G Schmidt and O Lorch ldquoExperiments in vision-guided robot walking in a structured scenariordquo in Proceedingsof the IEEE International Symposium on Industrial Electronics(ISIE rsquo05) pp 1581ndash1586 Dubrovnik Croatia June 2005

[89] P Michel J Chestnutt S Kagami K Nishiwaki J Kuffnerand T Kanade ldquoGPU-accelerated real-time 3D tracking forhumanoid locomotion and stair climbingrdquo in Proceedings ofthe IEEERSJ International Conference on Intelligent Robots andSystems (IROS rsquo07) pp 463ndash469 IEEE San Diego Calif USANovember 2007

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Active and Passive Electronic Components

Control Scienceand Engineering

Journal of



RotatingMachinery


Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design



Shock and Vibration


Civil EngineeringAdvances in

Acoustics and VibrationAdvances in



Electrical and Computer Engineering

Journal of

Advances inOptoElectronics


Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of


Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Chemical EngineeringInternational Journal of Antennas and

Propagation




Navigation and Observation



DistributedSensor Networks



nodes can be implemented as autonomous perceptive mobilerobots or as perceptive robots whose sensor systems addressenvironmental and navigational tasks So we can say roboticsensor networks are the distributed systems in which mobilerobots carry sensors around an area to sense phenomenaand to produce detailed environmental assessments [8]The use of multirobot systems for carrying sensors aroundthe environment represents a solution that has received asignificant attention and can also provide some extraordinaryadvantages A number of applications have been addressedso far by robotic sensor networks such as rescue searchand environmental monitoring In wireless sensor networksrobotics can also be used to solve many problems to advanceperformances such as responding to a particular sensorfailure node distribution and data aggregation Similarlyfor solving the problems that exist in the field of roboticswireless sensor networks can play a crucial role Problems likelocalization path planning coordination for multiple robotand sensing can be solved by using wireless sensor networks[9] Today we have many applications of sensor networkson ground air underwater and underground In mobileUWSNsensormobility can bring twomajor benefits Floatingsensors can increase system reusability and can also help toenable dynamicmonitoring and coverageMobile sensors canhelp to track changes in water masses thus providing 4D(space and time) environmental monitoring As comparedto ground based sensor networks mobile UWSNs have toemploy acoustic communications because in hard waterenvironments radio does not work Similarly undergroundsensor network can be used tomonitor a variety of conditionssuch as properties of soil and environmental monitoringfor toxic substances They are buried completely under-ground and do not require any wired connections On theground they can be used for target tracking environmentalmonitoring detecting forest fire industrial monitoring andmachine health monitoring Wireless sensor nodes are intothe service from a long time and were being used for differentapplications such as earthquake measurements and warfareThe recent growth of small sensor nodes dates back to theyear 1998 NASA SensorWebs project and smart dust projectTo make autonomous sensing and communication possiblewithin a cubic millimeter of space was the main purposeof the smart dust project This project led to many moreresearch projects including major research centers in CENSand Berkeley NESTThe termmotewas coined by researchersworking in these projects to refer to a sensor node pod isthe same term used in the NASA Sensor Webs project for aphysical sensor node although in a Sensor Web the sensornode can be another Sensor Web itself [10]

The main components of a sensor node are as followstransceiver a microcontroller external memory one or moresensors and power source [10] The controller processes thedata and controls functionality of other components in thesensor nodes The feasible option of wireless transmissionmedia is infrared radio frequency (RF) and optical commu-nication As far as external memory is concerned the mostrelevant kinds of memory are the flash memory and theon-chip memory of a microcontroller The most importantfeature in the development of a wireless sensor node is to

Internet

User

Sink node

Sensor node

Target

Wireless sensor network

Figure 1 Wireless sensor network architecture

Figure 2 Temperature humidity sensor module

make sure that there is always sufficient energy available tothe power system Sensor nodes consume power for dataprocessing sensing and communication power is stored incapacitors or batteries Batteries can be both rechargeableand nonrechargeable and for sensor nodes they are the mainresource of power supply And sensor is a device that detectsor senses heat light sound motion and so forth and thenresponds to it in a particular way [11] (Figure 2)

The crossbow radioprocessor boards usually known asmotes permit many sensors scattered over a large area towirelessly transmit their data back to the base station whichis attached to the computer (Figure 3) These motes runTinyOS operating system which is an open source operatingsystemdesigned for low-powerwireless devices such as thoseused in PANs smart meters ubiquitous computing sensornetworks and smart buildings [12] It controls power radiotransmission and networking transparent to the user and thenetwork which is formed as an ad hoc network [13]

TheMICA2Mote is a third generationmote module with512 Kbytes of measurement (serial) flash memory 128 Kbytesof program flash memory and 4Kbytes of programmableread-only memory (Figure 4)

Stargate is a 400MHZ Intel PXA255 Xscale processorwith 32Mbytes of flash memory and 64Mbytes of syn-chronous dynamic random access memory A number ofclasses of sensors are available these include barometricpressure acceleration seismic acoustic radar magneticcamera light temperature relative humidity magnetic cam-era and global positioning system (GPS) Usually sensorsare classified into 3 types passive omnidirectional passivenarrow-beam and active sensors Passive sensors are self-powered they sense the data without actually manipulatingthe environment by active probing while active sensorsactively probe the environment Narrow beam sensors havea well-defined notion of direction of measurement Omnidi-rectional sensors have no notion of direction involved in theirmeasurements [10] (Figure 5)



Battery

Io interface









Client

Internet

Client

LAN

Server

Client

RS-232USB

Sensor node












119904119891

119903 In this


119890

119904119891119903=

119899 times sin 120579sin120572 (1)




0119864 [(119909 minus119909

1015840

)

2+ (119910+119910

1015840

)

2] 119889119905

(2)



0119864 [(119909 minus119909

10158401015840

)

2+ (119910+119910

10158401015840

)

2] 119889119905

(3)





Figure 8 Xbee












distance 119889119894= 119901 (1 minus119898

119894) (4)




ALE =sum

119873

119894=11003817

1003817

1003817

1003817

119901

119894minus 119870

119894

1003817

1003817

1003817

1003817

119873

(5)





































7 Conclusion






Acknowledgment


References



























































































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











Battery

Io interface









Client

Internet

Client

LAN

Server

Client

RS-232USB

Sensor node












119904119891

119903 In this


119890

119904119891119903=

119899 times sin 120579sin120572 (1)




0119864 [(119909 minus119909

1015840

)

2+ (119910+119910

1015840

)

2] 119889119905

(2)



0119864 [(119909 minus119909

10158401015840

)

2+ (119910+119910

10158401015840

)

2] 119889119905

(3)





Figure 8 Xbee












distance 119889119894= 119901 (1 minus119898

119894) (4)




ALE =sum

119873

119894=11003817

1003817

1003817

1003817

119901

119894minus 119870

119894

1003817

1003817

1003817

1003817

119873

(5)





































7 Conclusion






Acknowledgment


References



























































































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










Client

Internet

Client

LAN

Server

Client

RS-232USB

Sensor node












119904119891

119903 In this


119890

119904119891119903=

119899 times sin 120579sin120572 (1)




0119864 [(119909 minus119909

1015840

)

2+ (119910+119910

1015840

)

2] 119889119905

(2)



0119864 [(119909 minus119909

10158401015840

)

2+ (119910+119910

10158401015840

)

2] 119889119905

(3)





Figure 8 Xbee












distance 119889119894= 119901 (1 minus119898

119894) (4)




ALE =sum

119873

119894=11003817

1003817

1003817

1003817

119901

119894minus 119870

119894

1003817

1003817

1003817

1003817

119873

(5)





































7 Conclusion






Acknowledgment


References



























































































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation














119904119891

119903 In this


119890

119904119891119903=

119899 times sin 120579sin120572 (1)




0119864 [(119909 minus119909

1015840

)

2+ (119910+119910

1015840

)

2] 119889119905

(2)



0119864 [(119909 minus119909

10158401015840

)

2+ (119910+119910

10158401015840

)

2] 119889119905

(3)





Figure 8 Xbee












distance 119889119894= 119901 (1 minus119898

119894) (4)




ALE =sum

119873

119894=11003817

1003817

1003817

1003817

119901

119894minus 119870

119894

1003817

1003817

1003817

1003817

119873

(5)





































7 Conclusion






Acknowledgment


References



























































































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










Figure 8 Xbee












distance 119889119894= 119901 (1 minus119898

119894) (4)




ALE =sum

119873

119894=11003817

1003817

1003817

1003817

119901

119894minus 119870

119894

1003817

1003817

1003817

1003817

119873

(5)





































7 Conclusion






Acknowledgment


References



























































































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation












distance 119889119894= 119901 (1 minus119898

119894) (4)




ALE =sum

119873

119894=11003817

1003817

1003817

1003817

119901

119894minus 119870

119894

1003817

1003817

1003817

1003817

119873

(5)





































7 Conclusion






Acknowledgment


References



























































































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











ALE =sum

119873

119894=11003817

1003817

1003817

1003817

119901

119894minus 119870

119894

1003817

1003817

1003817

1003817

119873

(5)





































7 Conclusion






Acknowledgment


References



























































































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation



































7 Conclusion






Acknowledgment


References



























































































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation













Acknowledgment


References



























































































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation







































































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation









Review Article A Review on Sensor Network Issues...

Documents

Transcript of Review Article A Review on Sensor Network Issues...