A Proactive Risk-Aware Robotic Sensor Network for Critical Infrastructure Protection

Transcription

1 A Proactve Rsk-Aware Robotc Sensor etwork for Crtcal Infrastructure Protecton Jameson McCausland, George D ardo 2, Rafael Falcon,2, Ram Abelmona,2, Vocu Groza, and Eml Petru School of Electrcal Engneerng and Computer Scence, Unversty of Ottawa, Ottawa, Canada 2 Research & Engneerng, Larus Technologes Corporaton, Ottawa, Canada E-mal: jmcca080@uottawa.ca; george.dnardo@larus.com; rafael.falcon@larus.com; ram.abelmona@larus.com; groza@ste.uottawa.ca; petru@eecs.uottawa.ca Abstract In ths paper, a rsk-aware robotc sensor network (RS) s proposed n the context of Crtcal Infrastructure Protecton. Such a network wll be comprsed of moble sensor nodes that perceve varous aspects of ther envronment and topologcally reconfgure n order to secure a strategc area of nterest. Rsk awareness s provded through the applcaton of a recently developed Rsk Management Framework to the RS. The rsk level of each node s assessed n terms of ther degree of dstress, proxmty factor, and terran maneuverablty. Rsk montorng alerts are ssued whenever any gven sensor node s quanttatve rsk metrc exceeds a user-defned threshold value. At ths pont, a node-n-dstress (ID) has been dentfed as the weak pont of the securng structure around whch the RS s deployed. The ID can no longer be used wth confdence and the effectve permeter coverage of the RS has been reduced, thus creatng potental securty breaches n the area of nterest. In response, the remanng nodes wll self-organze to maxmze the permeter coverage whle mnmzng the cost of dong so. A lmted set of contngency network topologes s produced va evolutonary mult-objectve optmzaton usng the on- Domnated Sortng Genetc Algorthm (SGA-II) and then ranked accordng to a human-guded alternatve selecton algorthm. The securty operator pcks the most sutable topology, whch s then effectuated upon the envronment. Results ndcate that SGA-II s capable of producng feasble network topologes to satsfy maxmum permeter coverage, whle reducng the energy requred for topology reconfguraton. As far as we are concerned, ths s the frst tme a RS appled to a CIP scenaro s self-organzed n response to a rsk analyss conducted on every sensor node on the bass of multple rsk features. Keywords robotc sensor networks; rsk management; selforganzaton; crtcal nfrastructure protecton; terrtoral securty I. ITRODUCTIO Robot Sensor etworks (RSs) [] can be appled n the doman of Crtcal Infrastructure Protecton (CIP). In such an applcaton, a RS s deployed to safe-guard some crtcal nfrastructure (e.g., buldng, ppelne, etc.) n a secure and relable fashon. The network would consst of a collecton of moble sensor nodes capable of percevng dfferent aspects about ther envronment. Unlke typcal wreless sensor network nodes, RS nodes are capable of movng whch n turn allows the network to dynamcally self-confgure by adoptng a dfferent topology. A self-organzng network s very approprate n CIP. Deployed sensor networks are subject to varous forms of unavodable rsk, thus ncreasng the probablty of sensor node falure and coverage gaps. Rsk can arse n many fashons such as: low battery power, harsh envronmental condtons, malcous attacks, terran hostlty, etc. Although a large body of research focuses on how to avod these rsks, an alternatve proposed n [2] features how rsk can be assessed, montored and mtgated. A rsk-aware RS node can utlze all ts sensng nstruments and evaluate ts total rsk at any pont n tme. Raw sensor data feeds are turned nto useful rsk features, specfcally degree of dstress, ntruder proxmty rsk, and terran maneuverablty rsk, all enablng the sensor network to montor the rsk feature space. A noden-dstress (ID) s dentfed (.e., a sensor node whose rsk value exceeds a certan threshold) and the network must explore a possble soluton to assst such a node n mmnent danger, as t may cause a coverage gap that ncreases the probablty of undetected ntrusons. In a RS, a new topology can be computatonally derved to meet the operatonal goals of the network. We model the dscovery of a new network topology as a mult-objectve optmzaton problem over a combnatoral search space. Good solutons are sought va the on-domnated Sortng Genetc Algorthm II (SGA-II) [3], whch provdes a set of mutually non-domnated canddate network topologes. To the best of our knowledge, ths s the frst paper that addresses self-reconfgurablty ssues n a RS from a rsk-aware perspectve n a CIP scenaro. The remander of ths paper has been structured as follows. Secton II brefly touches on relevant works n the lterature. Secton III outlnes the applcaton of the rsk management framework (RMF) to a self-organzng RS n the CIP realm. Secton IV elaborates on the SGA-II confguraton as part of the RMF s response selecton module. Secton V sheds lght on the emprcal study whereas Secton VI concludes the work. II. RELATED WORK Ths secton brefly touches on some relevant works regardng CIP usng wreless sensors technology wth and wthout rsk analyss. Coppolno et al [4] put forth a hybrd ntruson detecton system (IDS) to protect crtcal nformaton structures. The IDS regards sensors as the source and target of potental attacks and develops a two-ter soluton to prevent snkhole and sleep deprvaton securty breaches. Gomez and Ulmer [5] ntroduce a system prototype for stadum survellance. Sensors detect dangerous crowd actvtes or stuatons and report alerts to a Command and Control (C2) centre, where a decson maker may choose to assgn frst responders and dspatch them to the

2 ncdent scene. These works nether nclude rsk analyss nor optmze the set of potental responses that wll mtgate the rsk condton. Aubert et al [6] and Schaberreter et al [7] propose rsk modelng approaches for crtcal nfrastructures. The former ams at modelng the securty propertes of nterdependent systems and measures ther rsk levels and assurances. The latter constructs the servce decomposton graph for rsk assessment and showcases an onlne montorng tool of three rsk parameters. Despte performng rsk feature extracton and assessment, these frameworks do not embrace consderng a set of prospectve responses to be actuated upon the envronment. Another loosely related group of studes optmze the sensor dstrbuton ether pror to or after deployment. Jn et al [8] employ a mult-objectve dfferental evoluton algorthm to derve sensor dstrbutons over the montorng regon wth maxmum coverage and mnmum overlap. Self-organzaton n cluster sensor networks after sensor falure s pursued va a local scheme n [9]. Intruson detecton n a moble sensor network (MS) s tackled n [0] by provdng k-barrer coverage. Our work touches on the three aspects mentoned above and, to the best of our knowledge, s yet novel n tself. We pursue self-organzaton n a RS that protects a crtcal nfrastructure n a proactve and rsk-aware fashon. The proposed approach s an extenson of the work presented n [2] n whch rsk analyss drves the entre operaton of a sensor and robot network for CIP. However, [2] was not concerned wth elctng a set of promsng responses to counter the perceved threat n the network. The authors recently augmented ther RMF n [] wth a response selecton module that utlzes SGA-II as a mult-objectve optmzaton method to evolve a group of promsng responses that could be actuated upon the envronment. The framework was successfully tested n the context of martme Search and Rescue operatons. Ths paper apples the RMF to a CIP scenaro smlar to the one n [2]. As far as we are concerned, ths s the frst tme a RS s self-organzed n response to a rsk analyss conducted on every sensor node on the bass of multple rsk features. III. RISK-AWARE ROBOTIC SESOR ETWORKS The proposed RS s rsk-aware, meanng that t s capable of evaluatng the rsk of each ndvdual node and flag some of them as IDs. Raw sensor data are transformed nto rsk features through the Rsk Feature Extracton module of the RMF n []. The three rsk features selected for ths applcaton are the followng: degree of dstress, ntruder proxmty rsk and terran maneuverablty rsk. Degree of Dstress: Ths rsk feature models the node s current battery level as a fuzzy set μ DD (x battery ). Wth the followng trangular membershp functon: A=0, B=0, C=00. Intruder Proxmty Rsk: The proxmty of detectons by the sensor node can contrbute to the overall rsk of the sensor unt. An equpped laser range fnder (LRF) provdes depth percepton to the sensor node. If we consder x detecton to be the dstance (n m) to the nearest LRF-detected ntruder, then μ proxmty (x detecton ) s the fuzzy set modelng the object proxmty rsk. Ths fuzzy set uses a trapezodal membershp functon wth parameters A=0, B=0, C=, and D=3.5; Terran Maneuverablty Rsk: Ths rsk feature s a nomnal rsk feature, whch provdes a terran maneuverablty metrc gven some localzaton context. The rsk metrc can be confgured manually as approprate for the deployment envronment, or n the case of ths paper, a random real value between 0.0 and.0 Future work wll allow sensor nodes to update the KB from sensor percepts. That beng sad, the terran nformaton can be quered from the KB by provdng the sensor s Cartesan coordnates, P node (p x, p y, p z ). Localzaton nformaton can be provded by ether a Global Postonng System (GPS) module or any other localzaton algorthm. The terran maneuverablty rsk values are normalzed between 0.0 (no terran rsk) and.0 (hghest terran rsk). The RMF s Rsk Assessment module consders these rsk features to produce an overall rsk metrc for the sensor unt. We have followed the same evaluaton scheme used n [2]. A user-defned rsk threshold s compared aganst the overall rsk of each sensor node n the network. Unts exceedng the rsk threshold are marked as IDs, whch represent a network vulnerabltes. Ths trggers the nvocaton of the RMF s Response Selecton module so as to determne a new feasble network topology to mtgate the threat. The response selecton process s explaned n the next secton. IV. RISK-DRIVE SELF-ORGAIZATIO I A RS Each canddate response topology s evaluated accordng to two dfferent (and conflctng) objectves: F = Total Permeter Coverage: The total area (n %) of the crtcal nfrastructure covered by the RS. F2 = Total Moblzaton Cost: The total cost (n %) of moblzng the nodes to ther target locatons. An optmal soluton s one that maxmzes F and mnmzes F2. Often, n a mult-objectve optmzaton problem we run nto a set of mutually non-domnated solutons (meanng that none s superor to the others). SGA- II [3] s a well-known algorthm that effcently produces a good spread of Pareto-optmal (.e. non-domnated) solutons. The SGA-II optmzaton algorthm, wll mantan a Pareto Archve Set (PAS) over each generaton. In the followng, the SGA-II s confguraton for the problem under consderaton s unfolded. A. Algorthm Confguraton Once a ID s elcted by the RMF s Rsk Assessment module, a snapshot of the network s current state s acqured and becomes the startng pont for the self-organzaton phase. To begn explorng the soluton space for new network

3 topologes, the followng nformaton must be retreved/derved from the RS:. Sensor ode State, Φ node (t), =.. a 2. Sensor ode Response Regons, Ω response 3. Securty Permeter Contour, c securty ) Sensor ode State The a sensor nodes are represented at tme t by a smple model: to as a decay rate for the exponental functon (shown n Fgure ), whch produced desrable results. R response = R max e d ID β (4) So, we can defne the response vector as n (5): Ω response = [P response R response ] (5) Φ node (t) = [ x battery (t) P node (t) x a battery(t) a Pnode ] () (t) The smulated battery level on board each node s provded as a percentage quantty. A constant dscharge rate γ dscharge (n %) occurs for a deployed sensor node. A power consumpton rate of γ movement (n % per meter travelled) s used durng sensor locomoton. Let Φ network (t) be the state of the network at tme t. Φ network (t) = [Φ node (t) Φ a node(t)] (2) A detected ID wll trgger a snapshot, whch captures the ID network state Φ network (t ID ) = Φ network. Ths state wll smply contan the set of battery levels and the set of locatons of each sensor node requred for algorthm confguraton. The state of each sensor node s crucal for the next steps of the algorthm confguraton. 2) Sensor ode Response Regon A response regon s assgned to each sensor node potentally nvolved n the response (new topology). A response regon s defned as the area that contans a possble target locaton for a sensor node. The regon tself s crcular and defned by a center and a radus. The center of the regon s set to the sensor node s current locaton whereas the radus (r response ) s a functon of battery level (x battery ), dstance to the ID (d ID ), and battery level threshold (λ battery ). The avalable battery power on the sensor node s a constrant on the maxmum dstance travelled. The battery level threshold s the mnmum battery level necessary to engage the robotc platforms that are carryng the sensors n dfferental drve operatons. Thus, the maxmum response rng radus s: = { R max x battery γ movement 0 x battery < λ battery λ battery otherwse (3) Ths s the maxmum change n poston possble wthout depletng the battery below the λ battery level. The response radus s vald on the nterval 0 R response R max. The radus of the response regon s defned n (4) usng a combnaton of the maxmum response radus and a monotoncally decreasng exponental functon of the dstance from the ID. A value of β=0.45 was expermentally chosen Fg. Response regon radus calculaton. An exponental relatonshp (e d ID β ) s defned between R response and d ID. β = 0.45 Each sensor node s equpped wth multple sensors mounted on a dfferental drve robotc platform. Gven a target locaton, a dsplacement vector for the sensor node can be calculated. A set of target locatons are generated for each sensor node f ( R response > 0). Let the set of target locatons be: S targe = {(r cos (θ ), r sn (θ )),, (r t cos (θ t ), r t sn (θ t )) } Where r ~U(0, R response ) and θ ~U(0, 2π). U(a, b) s a unform dstrbuton between a and b. Let C represent the algorthm s ntalzaton matrx defned as n (6): C = [ Ω response P node S target ] (6) Ω response P node S target 3) Sensor etwork Coverage Objectve Sensor network coverage represents the extent of the securty permeter that can be surveyed by the sensor nodes. The permeter s represented by a contour csecurty around the crtcal nfrastructure. The RS must have near-complete coverage of ths contour to succeed n detectng any ntruson attempts. Each sensor node n the RS wll contrbute to the permeter coverage by ntersectng the sensor node s feld of vew wth the entre contour or a segment of the contour. For computatonal purposes, let the contour be a set of permeter

4 ponts unformly dstrbuted on the contour, P s k (x s k, y s k, z s k ), where k=..k represents the ndex of the permeter pont. Let the feld of vew radus for the th sensor be r fov. By evaluatng the dstance between the sensor node s poston and each dscrete contour pont, a node wll have a contour pont surveyed f and only f (P k s P node ) f fov. The coverage of the securty permeter s the rato of the number of permeter ponts covered to the total number of permeter ponts. SGA- II wll seek topology solutons whch produce large permeter coverage values. Coverage gaps (.e. lower permeter coverage values) must be avoded to reduce undetected ntrusons. 4) Energy Cost Objectve The power requred to execute a topologcal change must be mnmzed when explorng the search space for canddate solutons. Gven γ movement (power consumpton % per meter travelled), the total power requred by the RS to selforganze nto the new topology can be estmated by (9): deployed n an outdoor CIP scenaro n a secluded area. Sensor nodes are shown as whte rectangular unts formng a permeter around the buldng (.e., crtcal nfrastructure). A total of seven sensor nodes are used n ths experment to form a secure permeter around the buldng. The sensor node resdes on a smple dfferental drve robotc platform and produces raw sensor data from a GPS, an electronc compass, and a laser range fnder. The laser range fnder equps the node wth a sensor feld of vew (FOV) capable of detectng ntrusons on the securty permeter. The FOV s modeled as a crcular regon wth a sensng radus of 3.5 m. Sensor Feld of Vew Robotc Moble ode Securty Permeter Crtcal Infrastructure Cost = = d response γ movement (8) 5) Algorthm Stop Crtera The stoppng crteron for the optmzaton algorthm s based on the algorthm s runtme ξ runtme (n sec.). B. Chromosome Desgn A chromosome n our SGA-II mplementaton represents a possble soluton to the RS self-organzaton problem. Each node s response regon wll consst of t possble target locatons. Let α be the ndex of the j th target pont for the th node n the RS. α Z and can be a value on the nterval of - to ( t ). The ndex value of - ndcates that the asset s not used n the soluton. Each chromosome can then be represented by the followng vector: 2 Chromosome = [α α α a ] (9) A chromosome s a set of target locaton ndces, one for each RS node. The ntal chromosome populaton n SGA- II s randomly ntalzed usng a unform dstrbuton n the specfed range. Each node nvolved n the soluton wll be assgned a random ndex. C. Crossover and Mutaton We mplement unform crossover wth probablty p crossover. When two parent chromosomes are selected from the populaton to crossover, genes are randomly chosen from ether parent. Durng a mutaton operaton, all chromosomes n SGA- II s extended populaton are nvestgated. For a gven chromosome, the probablty of a gene value beng mutated s p mutaton. The algorthm wll terate through each gene value f the gene s to be mutated then a new random value n the range [0; t ] s selected to replace the exstng one. V. EXPERIMETAL RESULTS A set of experments usng Mcrosoft Robotcs Developer Studo (MRDS)[2] have been created to smulate a RS Fg. 2 A 2-D graphcal representaton of the 20.0 m (along x) by 22.5 m (along y) smulaton envronment. The red rectangle s the crtcal nfrastructure. The dashed contour around the crtcal nfrastructure represents the securty permeter. Dots encapsulated by crcular regons are the sensor nodes and sensor FOVs. The shape of the securty permeter s defned by an ellpse centered at the poston of the buldng. The sem-major axs (along x-axs) s 5.42 m and the sem-mnor axs (along z-axs) s 9.0 m. The ellptcal contour s dscretzed nto 200 ponts. Fgure 2 shows a 2-dmensonal representaton of the scenaro ncludng addtonal vsual enttes to help vsualze sensor FOVs and the ellptcal securty permeter. Table I outlnes the ntal condtons for the smulaton. depcted n Fgure 2. TABLE I. SIMULATIO IITIAL STATE Asset, Smulaton Intal State, S[t 0] Sensor odes Pntal(x, z)[m] xbattery (%) f fov [m] (2.8, -2.4) (0.8, -7.8) (.9, -24.2) (6.8, -26.6) (20.4, -22.4)

5 Asset, Smulaton Intal State, S[t 0] Sensor odes Pntal(x, z)[m] xbattery (%) f fov [m] 6 (2.2, -7.) (8.5, -2.2) To detect a ID n the RS, raw data streams from sensor nodes are used to extract the three rsk features outlned n Secton III. Rsk Assessment. Durng the course of the smulaton, moble sensor becomes the ID as ts overall rsk s 0.73; ths s attrbuted to the degree of dstress rsk feature as ths node was deployed n the montorng regon wth a battery level of 30%. After the dentfcaton of the ID, the SGA-II optmzaton algorthm can proceed to ntalze the populaton once the followng nformaton s calculated: the dstances to the ID from each node; the set of response regons for each node; the target locatons for each node The set of dstances of each sensor node s locaton to the locaton of the ID can be quckly computed by: d ID = (P x node P x ID ) 2 + (P y node P y ID ) 2 (0) The response regon s a functon of both the battery power avalable on the node and the dstance from the node to the ID. Evaluatng equaton (4), gven that β = 0.45, λ battery = 0.30, and γ movement = 0.05 for each node, produces a set of response regons. The center of each response regon P response s set to the locaton of the assets from the ntal condtons (Table ). The response regon rad are descrbed n Table IV. TABLE II. RESPOSE REGIO RADII Response Regon Rad, R response (m) ode Value A set of target ponts are generated S targe for 0 j t, t = 200. Wth target locatons for each node, the chromosome populaton can be ntalzed. A populaton sze of 00 chromosomes, p crossover = 0.8, and p mutaton = 0. were used. The optmzaton runs untl the stop crteron s satsfed, whch n ths case s a runtme of 20 seconds. The SGA-II parameters of [] were used as startng pont, but were adjusted through expermentaton to acheve desrable results. Fg. 3 Plots of the frst three Pareto Fronts are dsplayed n ascendng order from green, red, and blue. The remanng solutons (front > 4) are plotted as blue scatter ponts. The Pareto Fronts from the PAS are dsplayed n Fgure 3 to pant a clear pcture of the non-domnated solutons dscovered n the soluton space. The frst front ndcates the truly non-domnated solutons dscovered n the search space. The maxmum coverage and mnmum energy objectve functons share equal weghtng n the optmzaton by SGA- II. It s due to ths that extreme solutons are presented wth poor coverage but wth mnmal energy cost along wth others that present excellent coverage combned wth very hgh energy cost values. Fgure 4 shows a subset of the total set of optmzed solutons. It can be observed that solutons are well spread across the Pareto front whch confrms SGA-II s ablty to obtan such a unform dstrbuton of the solutons. Fg. 4 Response Selecton form dsplayng a lst of optmzed solutons. It s up to a decson maker to select a feasble response for ths RS. Fgure 5 depcts the resultng topology when selectng network response 5, whch provdes an approprate tradeoff between coverage and energy usage. Conversely, network response 7 provdes the maxmum permeter coverage (95.5%), but wth the use of sgnfcant energy (39.7%). ew sensor locaton Orgnal sensor locaton Asset 3 Asset 4 Asset 5 Asset 6 ew sensor FOV Asset 2 Asset ID

6 Fg. 5 (left) A 2-D graphcal representaton of the network response 5. The orgnal and fnal sensor locatons are shown. ew sensor FOVs are dsplayed as the red crcular regons. (rght) A 3-D graphcal vew of the response network. Red dots denote the new node locatons. Whte dots ndcate the orgnal node locaton. etwork response 5 provdes a coverage metrc of 92.0% (a loss of -3.5% from response 7); however t can be acheved usng 27.5% (savngs of 3.8% from response 7) energy collectvely from the network. A decson maker wll lkely choose a response that leans towards hgh coverage but wth the energy cost mnmzed (.e., select network response 5 nstead of 7). The algorthm was also tested on a smulated RS of 47 robotc nodes, protectng a large L-shaped permeter. After an optmzaton tme of 20 seconds, the PAS contans four solutons. The ntal coverage for the network s 99% and the ID causes a coverage gap of 2.23 m thus reducng the permeter coverage to 97%. Fgure 6 depcts the optmzed soluton wth maxmum permeter coverage of 99.2% and a collectve energy consumpton of.58%. Fg. 6 (left) A 2-D graphcal representaton of the network response for maxmum coverage. The orgnal and fnal sensor locatons are shown. ew sensor FOVs are shown as the red crcular regons. (rght) A 3-D graphcal vew of the response network. Red dots denote the new node locatons. Whte dots ndcate the orgnal node locatons. VI. ID COCLUSIOS It s mpossble to escape the varous rsks assocated wth the operaton of a RS n any envronment. The use of a rskaware RS grants a new level of percepton to antcpate the falure of any gven sensor node. In ths paper, a smulated RS was appled to crtcal nfrastructure protecton. The deployment goals of the network are: to mantan maxmum permeter coverage and to stay operatonal for as long as possble. In the event of the presence of a ID, the network s subject to a coverage gap; dramatcally ncreasng the rsk of undetected ntruson of the secure permeter. Through multobjectve optmzaton wth the SGA-II t s possble to obtan a new network topology for the RS that maxmzes sensor coverage whle balancng energy cost. The current work s lmted to a sngle response for mtgatng rsk. Future work wll ntroduce multple detected IDs and the mtgaton of the nduced rsk usng smultaneous network responses. In ths research we hope to develop a more robust rsk-aware RS. VII. REFERECES [] R. Falcon: Towards Fault Reactveness n Wreless Sensor etworks wth Moble Carrer Robots, PhD Dssertaton, Unversty of Ottawa, Aprl 202. [2] R. Falcon, R. Abelmona and, A. ayak: "An Evolvng Rsk Management Framework for Wreless Sensor etworks," n 20 Int l Conference on Computatonal Intellgence for Measurement Systems and Applcatons (CIMSA), pp.-6, Sept. 9-2, 20. [3] K. Deb, A. Pratap, S. Agarwal and T. Meyarvan: A Fast and Eltst Mult-objectve Genetc Algorthm: SGA-II, IEEE Trans. on Evolutonary Computaton, vol 6(2), pp , [4] L. Coppolno, S. D Antono, L. Romano and G. Spagnuolo: An Intruson Detecton System for Crtcal Informaton Inffrastructures Usng Wreless Sensor etwork Technologes, th Int l Conference on Crtcal Infrastructure (CRIS), pp. -8, Sept 20-22, 200. [5] L. Gomez and C. Ulmer: Secure Sensor etworks for Crtcal Infrastructure Protecton, n th Int l Conference on Sensor Technologes and Applcatons (SESORCOMM), pp , July 8-25, 200. [6] J. Aubert, T. Schaberreter, C. Incoul, D. Khadraou, and B. Gateau. Rsk-Based Methodology for Real-Tme Securty Montorng of Interdependent Servces n Crtcal Infrastructures. In Int l Conference on Avalablty, Relablty and Securty, pages , February 200. [7] T. Schaberreter, C. Bonhomme, J. Aubert, C. Incoul, and D. Khadraou. Support Tool Development for Real-Tme Rsk Predcton n Interdependent Crtcal Infrastructures. In IEEE Int l Symposum on Sofware Relablty Engneerng [8] L. Jn, J. Ja and D. Sun: ode Dstrbuton Optmzaton n Moble Sensor etwork based on Mult-Objectve Dfferental Evoluton Algorth, n 4 th Int l Conference on Genetc and Evolutonary Computng (ICGEC), pp. 5-54, Dec 3-5, 200 [9] R. Msra and C. Mandal: Self-Healng for Self-Organzng Cluster Sensor etworks, n 2006 Annual IEEE Inda Conference, pp. -6, Sept 5-7, 2006 [0] G.Y. Keung, B. L and Q. Zhang: The Intruson Detecton n Moble Sensor etwork, IEEE/ACM Trans. on etworkng, vol 20(4), pp. 52-6, August 202. [] R. Falcon and R. Abelmona: A Response-Aware Rsk Management Framework for Search-and-Rescue Operatons, IEEE Congress on Evolutonary Computaton (CEC), pp , June 0-5, 202. [2] Mcrosoft: Mcrosoft Robotcs Developer Studo (Verson 4). Avalable from