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INTRODUCTION

A hurricane comes ashore near Galveston, Texasin 2008. Hundreds of thousands of people aredisplaced as high winds, torrential rains, andflooding force them from their homes. A fewthousand stay behind. Buildings are submergedor swept away as public utilities fail. Roads areblocked by debris and flood waters. Survivorsfind themselves trapped with no water, electrici-ty, or communications.

In the aftermath of the storm, the first 36hours are critical. A command post is assembledin a large field to manage disaster response.Police, fire, and medical personnel converge onthe scene from scores of jurisdictions. Assignedareas to search, they spread throughout the dis-aster to assess the situation, assist survivors, andlocate casualties. A medical triage facility treatsvictims as a complex logistics infrastructure isassembled. Teams of specialists work feverishlyto restore electricity and water. At the peak ofthe response effort, the total number of respon-ders exceeds 65,000.

Management of resources in a disaster is dif-ficult. Rescue personnel spread across wideswaths of the disaster area. Situational aware-ness is maintained using hand-written tables andcharts. Updates arrive in the form of verbal mes-sages via radio or courier. In this environmentwireless ad hoc and sensor networks (WASNs)can significantly enhance situational awarenessby improving and automating updates, monitor-ing and reacting to status changes, and extendingdata communications across the entire disaster.However, while WASNs have made significantcontributions in surveillance [1], target tracking[2], and healthcare [3], they have not achievedbroad application in disaster response. Severalchallenges make their integration into this fielddifficult.

First, disasters are infrequent, and the loca-tion, communications requirements, and sensingneeds of the next disaster cannot be predicted.Existing WASN systems often fill niche needs,like specialized chemical detection, that are illsuited for general deployment. Other solutions,like building monitoring systems, require pre-dis-aster installation and maintenance. To succeed,WASNs supporting disaster response must beextensible, flexible, adaptive, and designed toleverage and incorporate emerging technologies.

A second challenge involves scale and stan-dardization. Small disasters, like a localizedflood, normally involve resources from a singlejurisdiction and are organized using simple adhoc command and control (C2). Larger disas-ters, like a hurricane, may incorporate thousandsof resources from scores of jurisdictions. Tomanage complexity at this scale, respondersoften organize following Incident Command Sys-tem (ICS) guidelines [4]. To operate in this diffi-cult environment, WASNs must provide standardarchitectures and composable, scalable networksstructured to conform to ICS standards.

Lack of communications infrastructure is athird challenge faced in larger disasters. Entireregions suffer from degraded communications,

IEEE Communications Magazine • March 2010128 0163-6804/10/$25.00 © 2010 IEEE

ABSTRACT

Situational awareness in a disaster is criticalto effective response. Disaster respondersrequire timely delivery of high volumes of accu-rate data to make correct decisions. To meetthese needs, we present DistressNet, an ad hocwireless architecture that supports disasterresponse with distributed collaborative sensing,topology-aware routing using a multichannelprotocol, and accurate resource localization.Sensing suites use collaborative and distributedmechanisms to optimize data collection and min-imize total energy use. Message delivery is aidedby novel topology management, while congestionis minimized through the use of mediated multi-channel radio protocols. Estimation techniquesimprove localization accuracy in difficult envi-ronments.

TOPICS IN SITUATION MANAGEMENT

Stephen M. George, Wei Zhou, Harshavardhan Chenji, MyoungGyu Won, Yong Oh Lee,

Andria Pazarloglou, and Radu Stoleru, Texas A&M University

Prabir Barooah, University of Florida

DistressNet: A Wireless Ad Hoc andSensor Network Architecture forSituation Management in Disaster Response

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IEEE Communications Magazine • March 2010 129

and remaining capacity is exhausted by thedemands of victims. Disaster responders mustarrive with their own communications. WASNsdesigned for disaster response must providestandalone, robust, and pervasive communica-tions sufficient to support the geographic cover-age and mobility requirements of its users.

A final challenge involves the occasionallycongested but normally dispersed and discon-nected nature of disaster response activities.Responders are highly mobile and often scat-tered across long distances. Groups coalesce tosolve manpower-intensive problems but quicklyseparate to continue search and rescue (SAR).Sensors are deployed densely to monitor unsta-ble structures or scattered widely to track disper-sion of gaseous chemicals. Since all elementsperiodically report status and location, WASNsoperating in this environment must be preparedto communicate opportunistically as conditionspermit, and tolerate high mobility, long discon-nections, and infrequent updates.

As a solution to these challenges, we proposeDistressNet, a WASN architecture for disasterresponse. Contributions and novelties of thiswork include:• An extensible, scalable, heterogeneous net-

work addressing the challenges of disasterresponse by providing area-wide situationalawareness through large-scale integrationof static and mobile elements with commonprotocols and a robust support infra-structure

• Novel network management and configura-tion strategies that handle arrival anddeparture of elements, minimize congestionthrough the use of multi-channel communi-cations, maximize message delivery withopportunistic and delay-tolerant protocols,and offer guaranteed levels of service forsafety-critical messages

• Distributed collaborative sensing and robust,adaptive localization and location-basedservices that provide accurate, detailed datathat enhances decision support, situationalawareness, and general C2 activitiesDistressNet is being implemented using cus-

tom and commodity sensors, mobile and staticgateways capable of cross-protocol routing, anda variety of servers providing network services,analysis, and decision support. This article givesan overview of the DistressNet architectureincluding principal network elements and keysoftware components. The second section pre-sents related work. The third and fourth sectionsdetail the system architecture and software com-ponents, respectively. The final section offersconclusions.

RELATED WORKICS [4] is a set of guidelines for organizing disas-ter response. It provides standardized but flexi-ble mechanisms to guide the formation ofcollaborative teams capable of cross-jurisdiction-al coordination. ICS also provides a frameworkof common processes supporting integration ofresources from different organizations into cohe-sive teams.

Sensor networks have been deployed to a

variety of challenging environments. ExScal [1]and VigilNet [2] are two such systems. ExScaldeployed over 1200 elements in an outdoormonitoring application with a static preplannedtopology. VigilNet, with approximately 200nodes, used a static but unplanned topology in asimilar effort. Both projects used mostly homo-geneous hardware. DistressNet requires greaterscalability, more heterogeneity, and higherdegrees of mobility. AlarmNet [3], deployed inan assisted living environment, offers a heteroge-neous network of static and mobile elementsdeployed across a confined area. Unlike Dis-tressNet, AlarmNet relies on fixed infrastructurecovering bounded areas. Code Blue [5], anothermedical system, relies on fixed infrastructure inmostly indoor areas. DieselNet [6], a bus-baseddelay-tolerant network (DTN) with a high degreeof mobility along predictable routes, relies onthe presence of a large amount of 802.11 infra-structure. In contrast, DistressNet assumes avail-ability of no external infrastructure along itsvaried unpredictable routes.

DISTRESSNETSYSTEM ARCHITECTURE

DistressNet is a complex multilayer multiproto-col architecture displaying high degrees of mobil-ity and varied connectivity. Figure 1 offers insightinto the scope of the problems presented by adisaster. The inset map demonstrates how teamswith distinct missions operate at a distance fromthe C2 area. Periodic reports to the C2, contain-ing the location and status of personnel andequipment, are required to maintain situationalawareness. As shown in Fig. 1a, there is often nodirect network path between a team and the C2area. Voice communications are used, but chan-nels may be congested due to the number oftransmitters. Data communications are enabledthrough the use of DTN protocols. Figure 1boffers a different problem. Although teams arestill isolated from the C2, the presence of threeSAR teams, each with a separate audio sensingnetwork, causes local congestion. Multichannelmedium access control (MAC) protocols allowthe teams to coexist without interference whiledata communications with C2 are enabled usingDTN protocols. The C2 area in Fig. 1c faces adifferent set of problems. C2 elements managedata from hundreds of workers and dozens ofsensing networks. High-bandwidth connectionsare critical, but the network must incorporateDTN protocols in order to communicate withoutstations.

In spite of the apparent complexity of itsrequirements, DistressNet uses simple compo-nents to form integrated networks. The composi-tion of these elements is guided by a set of coreprinciples.

Extensibility through the use of common proto-cols: Disaster response is composed of manysmall specialized elements assembled to meet therequirements of the situation. DistressNet is simi-larly designed to enable incorporation of newelements and networks through the use of com-mon protocols like 802.11, 802.15.4, and IPv6,and simple network applications.

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Cross-layer optimization of MAC and routing:Communications within DistressNet are com-plex. Some segments of the network may be con-gested while others are frequently disconnected.Management of these extremes requires robustcross-layer MAC optimizations that leverageavailable spectrum and transport mechanisms foroptimal communication with every node. Com-plex routing schemes involving multiple frequen-cies and delay-tolerant segments must becreated, tracked, and updated with enough fre-quency to guarantee communications.

Composable localization that manages uncer-tainty: Every node in DistressNet is localized.Localization is critical to the situational aware-ness required by C2 elements. DistressNet incor-porates composable localization using varioustechniques to reduce uncertainty and provideaccurate locations.

Data aggregation and cooperative sensing tominimize energy use and network congestion:Sensing and localization consume energy. Thecommunications required for these activities areeven more energy-intensive. Within DistressNet,networks collaborate in their sensing and local-

ization tasks to minimize individual energy con-sumption and maximize network lifespan. Theydistribute sensing duties where possible andcooperatively communicate complete answersinstead of raw data.

PRINCIPLE COMPONENTSAt its most basic level, DistressNet is composedof sensors that know or can determine theirlocation, name, and network address. These sen-sors, often elements of more complex systems,organize into networks to perform sensing andsupport tasks. Divided by function, these net-works represent individual sensors and people,static and mobile sensing suites, multi-elementteams, and large-scale distributed systems. Theyrange from minimally capable tracking devices tomultiprocessor, multichannel, multiprotocolrouters. DistressNet integrates these networks asshown in Fig. 1 and provides a common softwarearchitecture to enable sensing, communications,and localization (Fig. 2).

BodyNet is a body-worn wireless sensor net-work designed to monitor the health and statusof its host (Figs. 1a and 2a). A BodyNet also

Figure 1. DistressNet, superimposed on an image of the aftermath of Hurricane Ike, Galveston, Texas, 2008: a) utility workers restoreelectrical and water service (BodyNet/TeamNet); b) search and rescue teams (BodyNet/TeamNet) look for victims in damaged buildingsusing sophisticated audio sensing suites (SenseNet); c) command and control, medical triage, and logistics work under an AreaNetumbrella. (Photo courtesy of the National Weather Service.)



provides localization for its hosts and sendsupdates to C2. First responders, support staff,and C2 elements within a disaster area are out-fitted with BodyNets. Typically covering a rangeof meters using 802.15.4, a BodyNet may includeadditional sensors like specialized monitoringsystems. Data from BodyNet nodes is transmit-ted through higher-level networks to C2 ele-ments to support situational awarenessapplications.

TeamNet is a network composed of theBodyNets of the members of a single team, oneor more VehicleNet nodes, and collocated equip-ment (Fig. 1a). A TeamNet normally representsa group performing a single role like SAR ormedical support. Managed by a central node onthe team’s vehicle, TeamNet operates in a fullmesh to track and monitor team member status.TeamNet nodes provide each other with local-ization and communications support. Data with-in a TeamNet is consolidated and summarizedbefore being forwarded to C2 elements.

VehicleNet is one of two backbone networkswithin DistressNet (Fig. 1a). Composed of nodeslocated on every vehicle deployed to the disas-ter, VehicleNet offers mobile routing and DTNto elements throughout DistressNet. VehicleNetnodes bridge 802.15.4 and 802.11 segments ofDistressNet (Fig. 2c). In more densely populatedareas VehicleNet nodes function as routers. Insparser areas they provide delay-tolerant relayservices. Since every TeamNet includes at leastone vehicle, mobile VehicleNet nodes providethe most common path between TeamNets andthe network core.

AreaNet is the other backbone network withinDistressNet (Fig. 1c). Composed of static nodesdeployed to localized positions, AreaNet pro-vides high-bandwidth 802.11-based mesh net-working. Also capable of bridging 802.15.4 and802.11 network segments, AreaNet has thecapacity to carry large volumes of data and sup-port bandwidth-intensive rescue operationsinvolving imagery, audio, and video (Fig. 2c).AreaNet grows as responders take control ofincreasingly larger sectors of the disaster area.

SenseNet describes a variety of specializednetworks within DistressNet (Fig. 1b). These

networks transmit sensed data and status updatesto C2 elements via VehicleNet or AreaNet, andmay be grossly divided into three categories: spe-cialized sensing, monitoring, and tracking (Fig.2b). Specialized sensing networks are employedin a variety of niche applications including audiomonitoring, structural stability tracking, and dis-tributed chemical sensing. Monitoring networksmay be used to monitor the status of physicalobjects or people (e.g., victims in a triage area orwater levels behind a levee). Tracking networksmay be used to maintain visibility of itemsthroughout the disaster area (i.e., specializedtools, radios, and generators).

DISTRESSNET SOFTWARECOMPONENTS

The integration of the elements discussed in theprevious section and depicted in Fig. 1 form Dis-tressNet. However, in order to sense, localize,and communicate, these elements rely on com-mon software components. The software compo-nents shown with darker shading in Fig. 2 aredescribed in detail in the next section.

SENSINGThe primary purpose of any wireless sensor net-work is sensing. In DistressNet, collaborative dis-tributed sensing provides local nodes and C2elements with information required to maintainsituational awareness. Sensing systems withinDistressNet cover areas as small as two meters,in the case of a single human body, and as largeas hundreds of meters, in the case of distributedchemical detection.

We propose a novel form of collaborative dis-tributed sensing to enhance SAR. In order todetect trapped victims, simple wireless audiosensors are deployed around damaged buildingsto cover areas inaccessible to rescuers. The sen-sors form a multihop mesh network and monitortheir local audio spectrum. Upon detectingaudio in the range of a typical human voice, 300Hz–4 kHz, the sensors collaborate to record thesound. Since high-resolution sampling is not sus-tainable due to constraints on power consump-

Figure 2. DistressNet software architecture (darker blocks represent contributions). a) BodyNet; b) SenseNet; c) VehicleNet and AreaNet.

Other appsas needed Health monitor

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tion and bandwidth, each sensor samples at asub-Nyquist rate of 1 kHz and streams the col-lected audio over multiple routes to a centralnode for processing (Fig. 3a).

Figure 3b illustrates the sampling and recon-struction. The original audio source is denotedx(t). Discrete-time signals si(k), i = 0, …, n – 1are noisy sub-Nyquist versions of x(t) producedby each node after sampling (k denotes the timeindices at sensor node sampling rate fs and k′denotes the time indices for the higher-resolu-tion signals at sampling rate fs′ = fs · r afterupsampling). To optimize si(k) for transmission,1-byte encoding provides a 2:1 compressionratio. For reconstruction, the received streamsare decoded by the base station and upsampledto a common rate through interpolation to com-pensate for variable time delays. Time delay esti-mation using the resulting signals sn[k′] alignsthem properly on the time domain. The outputsignal is formed by superimposing the alignedsignals and applying a denoising filter.

Analysis indicates that when multiple nodescooperate to sample audio at low resolution, theresults can approximate audio sampled by a sin-gle sensor at much higher resolution. Figure 3cuses the normalized root mean square error(NRMSE) to demonstrate that even at very lowsampling rates, audio reconstruction can be suc-cessful when enough nodes participate. In fact,when sufficient sensors participate, typically fouror more, the result is an intelligible reproductionof human voice [7].

MEDIA ACCESS CONTROL AND ROUTINGIn DistressNet, communications are critical.Information produced by teams and sensorsenables accurate situational awareness permit-ting reactive precise responses to developing sit-uations. Decisions made by C2 elements must berapidly disseminated to affected systems andareas. However, due to its complex, highlymobile nature, DistressNet suffers from two sig-nificant problems: congestion in core regionsand frequently disconnected groups on the

periphery. The solutions to these problems areadaptive spectrum-aware MAC [8] and intelli-gent multipath routing [9].

Multichannel MAC — In heavily populatedregions of DistressNet, the network may experi-ence congestion. Traditional single-channelMAC protocols, designed to handle low-duty-cycle traffic, are insufficient. DistressNet requiresan energy-efficient solution capable of distribut-ing users and groups intelligently across avail-able spectrum and guaranteeing throughput withlow delay. Existing multichannel MAC protocolsdisplay poor energy performance, largely due torigid scheduling and high volumes of coordina-tion traffic between individual nodes. Complex,fragile rendezvous protocols also impact flexibili-ty and scalability in the network, wasting energyand increasing delay, especially when traffic vol-umes are lighter.

To address such issues, we propose a negotia-tor-based multichannel MAC protocol able tomanage radio spectrum access in dense net-works. The protocol, depicted in Fig. 4a, usesdesignated negotiators to manage scheduleinformation and coordinate on behalf of individ-ual nodes. Negotiators monitor a default channelcontinuously and coordinate agreements thatallow nodes to communicate.

Simulations analyzing the use of three, four,and six channels reveal good congestion manage-ment and low packet delivery latency, even athigh data rates. Figure 4b considers the impact ofmultiple channels on latency, and notes a directcorrelation between the number of channels andthe delay suffered at high data rates. This multi-channel MAC also offers other benefits, includ-ing overall traffic reduction, due to a reducedneed for internode coordination, and reducedstorage requirements on individual nodes.

Multipath Energy-Efficient Routing withDelay Guarantees — Routing within Distress-Net is difficult due to its highly mobile natureand varied topology. While routing in the core is

Figure 3. Distributed audio sensing: a) multiple sub-Nyquist streams carry sensed audio; b) sampling, transport, and reconstruction; c)audio reconstruction using various numbers of audio sensors and transmission rates.

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complex due to high degrees of connectivity andrapid arrivals and departures of nodes, entiregroups on the periphery may be disconnected forextended periods of time. Energy efficiencyrequirements and safety-critical messaging guar-antees complicate solutions. To meet this chal-lenge, DistressNet uses combinations of twotypes of routing protocols.

For connected parts of the network, Distress-Net uses an on-demand routing scheme to adap-tively balance energy efficiency and end-to-enddelay based on message prioritization. Safety-critical messages are routed with minimal delay.Less critical messages are routed with a goal ofmaximizing energy efficiency.

In sparser segments of the network Distress-Net relies on DTN protocols to transfer multiplecopies of messages between the network coreand disconnected groups using mobile Vehi-cleNet nodes. Acting as data ferries with storeand forward capabilities, these mobile nodestransfer data in both directions as their routespermit. Safety-critical messages are flooded fromthe network core to disconnected networks usingall available resources.

LOCALIZATIONLocalization is critical to the proper functioningof DistressNet and common to every element.Once localized, every node provides its locationto centralized situational awareness applications.However, robust accurate localization is difficultdue to the sparsely populated nature of manyareas within DistressNet. Sparseness impactslocalization accuracy and renders most multihopalgorithms ineffective. A combination of indoorand outdoor environments increases the com-plexity of any solution.

Reference [10] reviews commonly used local-ization techniques and proposes a novel compos-ability mechanism. DistressNet extends this ideato include external localization support andenhancements using estimation techniques.Composable localization orders available local-ization protocols into a library and allows the

execution of multiple localization schemes.Results obtained are compared, and the mostaccurate location possible is selected. WithinDistressNet, localization mechanisms varydepending on the constraints affecting a node.

Mobile VehicleNet and static AreaNet nodesare localized using GPS. These and other simi-larly localized nodes act as beacons within Dis-tressNet. Nodes near these beacons localizethemselves using ranging protocols. However,various difficult environments within DistressNetrequire special techniques, including frequentlydisconnected nodes, localization inside buildingsor where radio signals are degraded, and nodeswith insufficient data to self-localize. We pro-pose two solutions to resolve these cases: fuzzylocalization and centralized localization support.

Indoors, a node may be unable to find enoughbeacons to localize itself. Distances to unlocal-ized and possibly mobile neighbors must be cal-culated to compensate for the lack of beacons.Normal ranging techniques infer distance basedon received signal strength (RSS) but multipathpropagation and channel fading, exacerbated inindoor environments, greatly affect signalstrength. Fuzzy logic overcomes this by trainingthe system to accurately map signal strength todistance, even in cases where RSS is altered bythe environment. Beacons cooperate to generatea set of rules relating signal strength to distancein the fuzzy domain. The results are used todeduce distance using a center-average defuzzifi-cation technique triggered by hearing a messagebroadcast by a node desiring localization. Usingdistances to localized beacons, a set of nonlinearequations in the fuzzy domain is solved to obtainan estimate of the node’s location. Simulationsreveal that mobility and a dynamically changingtopology do not significantly affect accuracy.

Another solution to some difficult scenariosis external localization support, where robustservers leverage current and historical datasources to assist an unlocalized node. When thelocalization support server has enough data toestimate a location, it informs the node of its

Figure 4. Multi-channel media access control: a) multichannel multipath routing with end-to-end delay guarantees; negotiators, N1–N4coordinate the use of three different radio channels to avoid congestion; b) average delay experienced at various packet rates using 3, 4,and 6 channels.

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location along with a degree of confidence.Nodes localized in this way become less reliablebeacons for their neighbors.

TOPOLOGY MANAGEMENTRapid and frequent topology changes areendemic to DistressNet. High degrees of mobili-ty, frequent arrivals and departures, sparse nodedeployments, and a typically urban setting allconspire to cause frequent network reconfigura-tion. Critical to the proper functioning of Dis-tressNet is an ability to detect and compensatefor topology changes. In some cases, like thoseinvolving VehicleNet, the topology change isannounced. As the vehicle travels, it periodicallybeacons its current location. A node hearing anew beacon can assume that local topology haschanged. Other changes, like the simple disap-pearance of a peer, may be more difficult todetect. In these cases prompt, reliable detectionof this silent topology change, or cut, is impor-tant. The technique of cut detection providesnotification of topology changes and supportstopology management.

The state-of-the-art cut detection algorithm,Distributed Source Separation Detection(DSSD) [11], recognizes cuts by maintaining ascalar state that is updated periodically throughcommunication with its immediate neighbors. Aspecially designated source node provides the ini-tial state and periodic updates. During normaloperations, the state converges to a steady value,as shown in the first phase of Figs. 5a and 5c.However, when a cut occurs, the states of thedisconnected nodes quickly decay to zero, andstill connected nodes converge to a new steadystate value. Hence, a node can independentlydetect a cut by monitoring its own state.

The DSSD algorithm can detect cuts thatseparate the network into multiple componentsof arbitrary shapes. It displays good energy effi-ciency and causes little congestion due to itsreliance on nearest-neighbor communications.However, the geographic scale and density varia-tions of DistressNet affect its execution. In thedensely populated core, where nodes have a highdegree of connectivity, frequent exchange ofstate messages can cause congestion. On theperiphery, frequently disconnected network seg-ments miss state updates. To solve these prob-lems, DistressNet uses the robust energy-efficientcut detection technique (RE2-CD) [12].

Assuming that nodes are properly localized,as indicated in Fig. 5b, RE2-CD divides the net-work into clusters based on group membershipand location. In each cluster a leader is electedand made responsible for tracking state for thegroup. DSSD is executed on the resulting simpli-fied network of leaders. If a network separationoccurs and any leader is disconnected, the statesof the other leaders change. Upon detecting theinconsistency in the state, a leader reports thisevent with its locations to the C2, where theinformation is consolidated and the disconnectedregion and groups are noted.

Critical to this cut detection technique is theaccuracy of information received from neighbor-ing leaders. Wrong information can lead to afalse decision on a network separation, resultingin wasted time and energy of the operation

Figure 5. Cut detection using a virtual superimposed grid with a base stationserving as a “source node”: a) the state of a node connected to the sourcenode; note the early convergence before a cut at iteration 65; b) a network witha superimposed virtual grid; a cut is depicted by dotted lines; c) the state of anode disconnected from the source; note the early convergence before a cut atiteration 65.

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team. Through the use of a robust outlier rejec-tion scheme, RE2-CD provides leaders with thecapability to determine if received information isreliable enough to be used in the decision pro-cess. The level of accuracy is a tunable systemparameter, and higher accuracy requires moreprocessing power and a greater number of itera-tions of the algorithm.

Simulations have analyzed the performanceof RE2-CD and compared it to DSSD in a virtu-al network of 264 uniformly distributed nodes.Figure 6a compares convergence speeds andshows that RE2-CD has an almost constant con-vergence speed thanks to its grid topology. Fig-ures 6b and 6c compare the message overheadof the two algorithms for a single iteration andthe total number of packets required to reachconvergence, respectively. RE2-CD displays goodenergy efficiency at both low and high networkdensities.

CONCLUSIONSWe propose an architecture, a set of protocols,and required applications to support disasterresponse. The architecture, DistressNet, isdesigned expressly for the alternately congestedand sparse environments typical of a disasterarea where conventional infrastructure is notavailable. Energy-efficient by nature, the networkincorporates innovative distributed sensingenhanced with collaborative features designed tomaximize coverage and minimize communica-tions. A spectrum-aware multichannel MAC pro-tocol adds another element of energy efficiencyby minimizing congestion through intelligent useof available frequencies. On-demand and delay-tolerant routing serves both connected and dis-connected segments of the network. Composablelocalization, coupled with fuzzy estimation tech-niques, provides accurate locations and supportssituational awareness. Industry-standard network-ing allows hardware- and operating-system-agnostic messaging, supports heterogeneity at alllevels, and ensures extensibility. All of these ele-ments combine to provide first responders withdetailed situational awareness suitable for mis-sion-critical decision support.

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[5] K. Lorincz et al., “Sensor Networks for EmergencyResponse: Challenges and Opportunities,” IEEE Perva-sive Comp., vol. 3, no. 4, Oct. 2005, pp. 16–23.

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[8] W. Zhou and R. Stoleru, “Towards Higher Throughputand Energy Efficiency in Dense Wireless Ad Hoc andSensor Networks,” Proc. ACM Symp. Applied Comp.,2010.

Figure 6. Cut detection simulation results in networks of various densities: a)the number of iterations required to achieve convergence; b) the number ofpackets transmitted in a single iteration of the algorithm; c) the number ofpackets required to achieve convergence.

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[9] M. Won et al., “Towards Energy-Efficiency with DelayGuarantees in Ad Hoc Networks,” tech. rep., TexasA&M Univ., 2009.

[10] R. Stoleru, J. Stankovic, and S. H. Son, “On Compos-ability of Localization Protocols for Wireless Sensor Net-works,” IEEE Network, vol. 22, no. 4, July 2008, pp.21–25.

[11] P. Barooah, “Distributed Cut Detection in Sensor Net-works,” Proc. IEEE Conf. Decision and Control, 2008,pp. 1097–1102.

[12] M. Won, S. M. George, and R. Stoleru, “RE2-CD:Robust and Energy Efficient Cut Detection in WirelessSensor Networks.” Proc. 4th Int’l. Conf. Wireless Algo-rithms, Sys., and Applications, LNCS, Springer-Verlag,Berlin, vol. 5682, pp. 80–93.

BIOGRAPHIESSTEPHEN M. GEORGE ([email protected]) is pursuing aPh.D. in the Department of Computer Science and Engi-neering at Texas A&M University. His research interestsinclude security and adaptive behavior in large-scale wire-less sensor networks, particularly in disaster response andmilitary applications. He received an M.S. in software engi-neering from Southern Methodist University.

WEI ZHOU ([email protected]) received his B.S. degreefrom Peking University in electrical engineering in 2007.Since then he has been a Ph.D. candidate in the Depart-ment of Computer Science and Engineering at Texas A&MUniversity. He works on networking protocols and applica-tions in ad hoc and sensor networks. His research interestsinclude MAC protocols in multi-channel environments,routing protocols for mobile networks, and cognitive radioapplications.

HARSHAVARDHAN CHENJI ([email protected]) obtained hisM.S. degree in computer engineering from Texas A&M Uni-versity in 2009 and his B.Tech. degree from the NationalInstitute of Technology Karnataka, India, in 2007 specializ-ing in electrical and electronics engineering. He is currentlya Ph.D. candidate at Texas A&M University under Dr. RaduStoleru. His research interests include wireless sensor net-works and localization techniques for resource constraineddevices.

MYOUNGGYU WON ([email protected]) received his B.E.degree with honors from Sogang University, Seoul, Korea.He is currently pursuing his Ph.D. degree in the Depart-ment of Computer Science and Engineering at Texas A&MUniversity. His research interests include topology control,energy-efficient routing protocols, and distributed comput-ing in wireless ad hoc and sensor networks.

YONG OH LEE ([email protected]) received B.S. and M.S.degrees from Yonsei University, Seoul, Korea, in 2005 and2007, respectively. Since fall 2007 he has been at TexasA&M University where he is a research assistant workingtoward a Ph.D. degree in electrical and computer engineer-ing. His research interest is QoS routing protocols in wiredand wireless networks.

ANDRIA PAZARLOGLOU ([email protected]) received aDiploma in computer and communication engineering in2005 from the University of Thessaly, Volos, Greece. Shereceived an M.C.S. from Texas A&M University in 2009,where she was a research assistant working on parallel sys-tems and wireless sensor networks. Her research interestsare in the field of wireless sensor networks with emphasison high-data-rate transmission and data compression.

RADU STOLERU ([email protected]) is an assistant profes-sor in the Department of Computer Science and Engineer-ing at Texas A&M University. His research interests are indeeply embedded wireless sensor systems, distributed sys-tems, embedded computing, and computer networking. Hehas authored over 35 papers and won the OutstandingGraduate Student Research Award from the Department ofComputer Science, University of Virginia in 2007. Hereceived a Ph.D. in computer science from the University ofVirginia in 2007.

PRABIR BAROOAH ([email protected]) is an assistant profes-sor in the Department of Mechanical and Aerospace Engi-neering at the University of Florida. He received his Ph.D.degree in electrical and computer engineering in 2007from the University of California, Santa Barbara. From 1999to 2002 he was a research engineer at United TechnologiesResearch Center, East Hartford, Connecticut. He receivedhis M.S. degree in mechanical engineering from the Univer-sity of Delaware in 1999.


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