1Research Department for Underwater Acoustics and Marine Geophysics, Bundeswehr Technical Centre for Ships and Naval Weapons, Naval Technology and Research (WTD 71), Klausdorfer Weg 2, 24148 Kiel, Germany 2Navy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory, 4555 Overlook Avenue S.W., Washington, DC 20375, USA 3Acoustic Research Laboratory, National University of Singapore, 21 Lower Kent Ridge Road, Singapore 119077 4 Systems Technology Department, NATO Undersea Research Centre (NURC), Viale S. Bartolomeo 400, 19126 La Spezia, Ital

Don Sofge

Frank Ehlers

John Potter

Mandar Chitre

International Journal of Distributed Sensor Networks

English

10.1155/2012/970831International Journal of Distributed Sensor Networks2012

Ehlers, F.

Sofge, D.

Chitre, M.

Potter, J.

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Distributed mobile sensor networks for hazardous applications

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Hindawi Publishing CorporationInternational Journal of Distributed Sensor NetworksVolume 2012, Article ID 970831, 3 pagesdoi:10.1155/2012/970831EditorialDistributed Mobile Sensor Networks for Hazardous ApplicationsFrank Ehlers,1 Don Sofge,2 Mandar Chitre,3 and John Potter41 Research Department for Underwater Acoustics and Marine Geophysics, Bundeswehr Technical Centre for Ships andNaval Weapons, Naval Technology and Research (WTD 71), Klausdorfer Weg 2, 24148 Kiel, Germany2 Navy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory, 4555 Overlook Avenue S.W.,Washington, DC 20375, USA3 Acoustic Research Laboratory, National University of Singapore, 21 Lower Kent Ridge Road, Singapore 1190774 Systems Technology Department, NATO Undersea Research Centre (NURC), Viale S. Bartolomeo 400,19126 La Spezia, ItalyCorrespondence should be addressed to Frank Ehlers, drfrank.ehlers@gmail.comReceived 8 February 2012; Accepted 8 February 2012Copyright © 2012 Frank Ehlers et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.1. IntroductionA key justification for the use of robotic systems is forapplications that are dull, dirty, or dangerous, where the useof robotic systems may reduce or eliminate the danger tohumans working in an environment known to be hazardous.Hazards may include nuclear, biological, or chemicalcontaminants, flood, fire, or earthquake damage as well asunderwater, under-ice, down mines, and space exploration.Hazardous applications include rescue missions, dealingwith hostile targets, addressing potential terrorist threats, orany operation performed in a hazardous environment.Distributed networks of robots may be deployed in suchhazardous environments to effectively perform missionssuch as reconnaissance, surveillance, search and recovery,or resource harvesting. When mobile robots are used tohost sensor and communication nodes for a distributedsensing network, the sensor network may achieve improvedcapabilities for detection and classification of potentialtargets and other objects of interest.Intervention begins with gathering information aboutthe nature of the problem. Sensor fusion in the context ofdistributed sensor networks has emerged as a leading methodfor implementing robust and efficient surveillance.The “designer” of such distributed sensing systems hassimple rules for the behavior of individual team members onone end and the overall objective function of the entire teamat the other end. Observed in biological swarms, emergentbehaviors are able to link these two ends, as also engineeringapproaches for the design of “Systems of Systems” can do.For hazardous environments and applications, this spe-cial issue contains papers describing concepts, simulations,and real applications. The focus lies on sensor networks thathave the additional features of control parameters in order tooptimize the sensor and fused data quality. Implementing theresulting adaptive behavior into the robots lets the networkadvance towards its high-level goal.2. Fusion and Performance EvaluationS. Jiang et al. propose a new Linear Decision Fusion Algo-rithm under the Control of Constrained-PSO for WSNs.They state that a major application of a distributed WSN(Wireless Sensor Network) is to monitor a specific area fordetecting some events such as disasters and enemies. Inorder to achieve this objective, each sensor in the networkis required to collect local observations which are probablycorrupted by noise, make a local decision regarding the pres-ence or absence of an event, and then send its local decisionto a fusion center. After that, the fusion center makes the finaldecision depending on these local decisions and a decisionfusion rule, so an efficient decision fusion rule is extremelycritical. It is obvious that the decision-making capability ofeach node is different owing to the dissimilar signal noiseratios and some other factors, so it is easy to understand thata specific sensor’s contribution to the global decision shouldbe constrained by this sensor’s decision-making capability,and based on this idea, we establish a novel linear decisionfusion model for WSNs. The authors employ the constrained2 International Journal of Distributed Sensor Networksparticle swarm optimization (constrained-PSO) algorithmto control the parameters of this model. They also applythe typical penalty function to solve the constrained-PSOproblem. The emulation results indicate that their design iscapable to achieve a very high accuracy.A. Skvortsov and B. Ristic discuss the modelling andperformance analysis of a network of chemical sensors withdynamic collaboration. The problem of environmental mon-itoring using a wireless network of chemical sensors with alimited energy supply is considered. Since the conventionalchemical sensors in active mode consume vast amounts ofenergy, an optimisation problem arises in the context of abalance between the energy consumption and the detectioncapabilities of such a network. A protocol based on “dynamicsensor collaboration” is employed: in the absence of anypollutant, majority of sensors are in the sleep (passive) mode;a sensor is invoked (activated) by wakeup messages fromits neighbors only when more information is required. Theauthors propose a mathematical model of a network ofchemical sensors using this protocol. The model providesvaluable insights into the network behavior and near optimalcapacity design (energy consumption against detection).An analytical model of the environment, using turbulentmixing to capture chaotic fluctuations, intermittency andnonhomogeneity of the pollutant distribution, is employedin their study. A binary model of a chemical sensor isassumed (a device with threshold detection). The outcomeof their study is a set of simple analytical tools for sensornetwork design, optimisation, and performance analysis.X. Sun and E. J. Coyle address the effects of motionon distributed detection in mobile ad hoc sensor networks.A large set of mobile wireless sensors observe their envi-ronment as they move about. The authors consider thesubset of these sensors that each made observations abouta brief, localized event at the time when near that location.As the sensors continue to move, one of them eventuallyfinishes processing its observations, decides that an event ofinterest occurred, and wants to determine if other sensorsconfirm its results. This sensor thus assumes the role ofa Cluster-Head (CH) and requests that all other sensorsthat collected observations at that time/location reply to itwith their decisions. The motion of the sensors since theobservation time determines how many wireless hops theirdecision must cross to reach the CH. The authors analyzethe effect of this motion in the 1D case by modeling eachsensor’s motion as a Correlated Random Walk (CRW), whichcan account for realistic transient behavior, geographicalrestrictions, and nonzero drift. The authors also account forobservation errors and errors in each hop in the wirelesschannel. Quantities, such as the error probability of the finaldecision at the CH and the minimum energy required tocollect the local decisions from all relevant sensors, can thenbe directly calculated as functions of time and the parametersof the CRW, the measurement noise, and the channel noise.These results thus allow a rapid characterization of thetime dependence of distributed detection algorithms that arebeing executed in realistic mobile sensor networks.3. Energy and Topology OptimizationJ. Jia et al. present theoretical analysis and simulationsof energy balanced density control to avoid energy holefor wireless sensor networks. Density control is of greatrelevance for wireless sensor networks monitoring hazardousapplications where sensors are deployed with high density.Due to the multihop relay communication and many-to-one traffic characters in wireless sensor networks, the nodescloser to the sink tend to die faster, causing a bottleneckfor improving the network lifetime. The authors investigatesystematically the theoretical aspects of the network load andthe node density. Furthermore, the authors prove the accessi-bility condition to satisfy that all the working sensors exhausttheir energy with the same ratio. By introducing the conceptof the equivalent sensing radius, a novel algorithm for densitycontrol to achieve balanced energy consumption per node isthus proposed. Different from other methods in the litera-tures, a new pixel-based transmission mechanism is adopted,to reduce the duplication of the same messages. Combinedwith the accessibility condition, nodes on different energylayers are activated with a nonuniform distribution, so asto balance the energy depletion and enhance the survival ofthe network effectively. Extensive simulation results are pre-sented to demonstrate the effectiveness of the new algorithm.A. Jawahar et al. present a new capacity-preserved,energy-enhanced hybrid topology management scheme inwireless sensor networks for hazardous applications. A wire-less sensor network is composed of large number of sensornodes which are densely deployed in the field. These nodesmonitor the environment, collect the data, and route it to asink. The main constraint is that the nodes in such a networkhave a battery of limited stored energy, and if the nodes startto die, the network lifetime gets reduced. There are varioustopology management schemes such as SPAN, STEM, GAF,and BEES, for improving network parameters such as capac-ity, lifetime, coverage, and latency. None of these schemeswill improve all the mentioned network parameters. In Sus-tainable Physical Activity in Neighbourhood (SPAN), someof the nodes become coordinators to form the backbonepath and can only forward messages. A noncoordinator willcheck periodically whether it should become coordinator.SPAN preserves network capacity, decreases latency butprovides less energy savings. Sparse Topology and EnergyManagement scheme (STEM) improves network lifetimeby putting the sensor nodes either in monitoring state orin transfer state. STEM does not try to preserve capacityresulting in great energy savings and high latency. In thescheme proposed by the authors, a new coordinator rule isimplemented in SPAN, and then integrated with STEM. Theauthors observe that the energy conserved increases by about3.18% to 4.17% without sacrificing network capacity. Due todefinite path in the proposed scheme the latency is reducedby almost half the latency of STEM scheme.4. ApplicationsX. Dai et al. apply techniques for wireless communicationnetworks for gas turbine engine testing. A new trendInternational Journal of Distributed Sensor Networks 3in the field of aeronautical engine health monitoring isthe implementation of wireless sensor networks (WSNs)for data acquisition and condition monitoring to partiallyreplace heavy and complex wiring harnesses, which limitthe versatility of the monitoring process as well as creat-ing practical deployment issues. Augmenting wired withwireless technologies will fuel opportunities for reducedcabling, faster sensor and network deployment, increaseddata acquisition flexibility, and reduced cable maintenancecosts. However, embedding wireless technology into an aeroengine (even in the ground testing application consideredhere) presents some very significant challenges, for example,a harsh environment with a complex RF transmissionchannel, high sensor density, and high data rate. The authorsdiscuss the results of the Wireless Data Acquisition in GasTurbine Engine Testing (WIDAGATE) project, which aimedto design and simulate such a network to estimate networkperformance and de risk the wireless techniques before thedeployment.S. Petillo et al. investigate the construction of a dis-tributed AUV network for underwater plume-tracking oper-ations. In recent years, there has been significant concernabout the impacts of offshore oil spill plumes and harmfulalgal blooms on the coastal ocean environment and biologyas well as on the human populations adjacent to these coastalregions. Thus, it has become increasingly important todetermine the 3D extent of these ocean features (“plumes”)and how they evolve over time. The ocean environment islargely inaccessible to sensing directly by humans, motivatingthe need for robots to intelligently sense the ocean for us.The authors propose the use of an autonomous under-water vehicle (AUV) network to track and predict plumeshape and motion, discussing solutions to the challengesof spatiotemporal data aliasing (coverage versus resolution),underwater communication, AUV autonomy, data fusion,and coordination of multiple AUVs. A plume simulationis also developed as the first step toward implementingbehaviors for autonomous, adaptive plume tracking withAUVs, modeling a plume as a sum of Fourier orders, andexamining the resulting errors. This is then extended toinclude plume forecasting based on time variations, andfuture improvements and implementation are discussed.AcknowledgmentsThe guest editors of this special issue are much indebted totheir authors and reviewers for their effort and dedication tomake this issue a reality.Frank EhlersDon SofgeMandar ChitreJohn PotterCopyright of International Journal of Distributed Sensor Networks is the property of Hindawi PublishingCorporation and its content may not be copied or emailed to multiple sites or posted to a listserv without thecopyright holder's express written permission. However, users may print, download, or email articles forindividual use.

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