Evaluation of Coordination Strategies for Heterogeneous Sensor Networks Aiming at Surveillance Applications

Transcription

1 Evaluaton of Coordnaton Strateges for Heterogeneous Sensor Networs Amng at Survellance Applcatons Edson Pgnaton de Fretas, *, Tales Hemfarth*, Carlos Eduardo Perera*, Armando Morado Ferrera, Flávo Rech Wagner*, Tony Larsson IDE, Halmstad Unversty, Halmstad, Sweden. {edson.pgnaton, *Insttute of Informatcs, Federal Unversty of Ro Grande do Sul, Porto Alegre, Brazl. {themfarth, Electrcal Engneerng Department, Mltary Insttute of Engneerng, Ro de Janero, Brazl. Abstract A new challenge n the sensor networ area s the coordnaton of heterogeneous sensors (wth dfferent sensng, moblty and computng capabltes) n an ntegrated networ. Ths nd of sensor networs have clearly hgh relevance n survellance systems, n whch both low-end statc ground sensor nodes and more sophstcated sensors carred by moble platforms, such as Unmanned Aeral Vehcles (UAVs), cooperate. Ths paper provdes an analyss of two dfferent strateges to gude the collaboraton among the sensor nodes mentoned above, appled to area survellance systems. The frst analyzed problem s related to the choce of the UAV nstance that wll respond to a gven alarm ssued by a ground sensor node. The second ssue s the estmaton of the response tme untl any UAV can be engaged n handlng an alarm and effectvely handles t. Two strateges are ntroduced and compared: one based on a pheromone nspred approach and another based on utlty functons nspred on rs profles that models decsons of nvestors n the stoc maret. I. INTRODUCTION The use of UAVs n mltary mssons such as Suppresson of Enemy Ar Defense (SEAD) s beng studed by several research groups [1][2]. In addton, the advance of the Wreless Sensor Networ (WSN) technologes allows the utlzaton of UAVs equpped wth sophstcated sensors (e.g. radar, nfra-red cameras, etc) n conjuncton wth WSN nodes to perform survellance mssons [3]. However, the desgn of coordnaton strateges to mae these heterogeneous sensor networs wor effcently brngs new challenges to researchers. The coordnaton among these survellance nodes presents a hgher complexty f compared to tradtonal approaches, as for nstance, the ones used n SEAD mssons. Ths ncreasng complexty comes from concerns that range from energy consumpton of the low-end ground nodes to WSN communcaton ssues. The study of these dfferences and the adapton of strateges used to solve these coordnaton problems motvate ths wor, as descrbed further n the text. A frst motvaton for usng low-end ground sensor nodes n cooperaton wth more sophstcated sensors embedded n UAV platforms s to reduce the overall cost of the system, as the prce of ground sensor nodes s several tmes lower than the prce of UAVs, even tang nto account small UAV platforms such as MLB models [4]. Another motvaton s related to the ncrease n the system effcency, as the low-end nodes can provde nformaton about the surroundng envronment n advance to the UAVs, before they decde to move to a gven locaton. In a system n whch the UAVs have dfferent capabltes, ths nformaton can be used to support the decson about whch UAV s more sutable to a specfc stuaton, due to the types of sensors they carry, for nstance. The man contrbuton of ths paper s the ntroducton and comparson of two dfferent strateges to coordnate heterogeneous sensor networs composed by low-end wreless sensor nodes deployed on the ground and more sophstcated sensors embedded n UAV platforms, appled to area survellance systems. The frst approach uses a pheromone metaphor to drve the selecton of a UAV needed to respond an alarm ssued by a ground sensor node. The second approach uses a utlty-based functon, whch drves the negotaton among the UAVs to decde whch one s more sutable to respond a gven alarm, accordng to a rs analyss. These two approaches are then compared accordng to relevant effcency metrcs. The remander of ths paper s organzed as follows. Secton II presents the descrpton of the scenaro and the UAV model adopted n ths wor. Secton III ntroduces the pheromone-based coordnaton strategy, whle Secton IV provdes the descrpton of the utlty-based one. Secton V presents the evaluaton of the obtaned results and a dscusson on pros and cons of each strategy. Secton VI dscusses related wors, whle Secton VII concludes the paper, gvng the drectons of the future wors. Ths study s partally founded by the Brazlan CNPq (Natonal Research Councl), under a PNPD project, and by the KK- Foundaton n Sweden /09/$ IEEE 591 IEEE SENSORS 2009 Conference

2 II. SCENARIO AND MODEL DESCRIPTION A. Scenaro Descrpton The operatonal scenaro studed n ths wor s composed by a large square area, dvded n equal number of contguous columns and rows, formng a grd. Each cell of ths grd s dentfed by ts Cartesan coordnates, x and y, coverng a untary part of the entre area. All elements of the scenaro (targets and sensors) are supposed to occupy only one cell of the grd at a tme. However, one cell may be occuped by more than one element at a tme. Targets are consdered as non-authorzed ndvdual vehcles or persons, or groups of vehcles or persons, whch appear n a non-determnstc way (modeled as a Posson dstrbuton P(r), where r s the number of new targets that enter n the survellance area at a gven nstant n tme t). A gven target τ s sad to be of nd and havng an dentfer, whch represents ts entrance order n the survellance area. There are K possble nds of targets that may appear n the survellance area, so = 1,, K. The movement of the targets s consdered to be performed wth a constant speed v τ, but dfferent targets may move wth dfferent speeds. Targets may randomly change the drecton of ther movement. The survellance system s composed by heterogeneous sensors, whch have dfferent sensng and movement capabltes. There are G statc sensors on the ground (sn, = 1,, G) and N UAVs flyng over the area (u, = 1,, N). It s assumed that a statc sensor node on the ground s capable of detectng a target when t passes n ts coverage area, also called sensor range. Ths range s tunable, dependng on the type of sensor used, but for smplcty t s assumed that the range s wthn the sensor cell. When a detecton s made, an alarm s ssued, and sent to all sensors nodes (statc or carred by UAVs) that are postoned wthn the communcaton range of the alarm ssuer node. Ths range s also a tunable parameter. An alarm s ept ssued untl at least one UAV receves t. B. UAV Model The UAVs move autonomously over the survellance area, accordng to a gven movement pattern descrbed by the user when establshng the msson. The focus of ths wor s not on the movement pattern tself; for more detals about dfferent approaches that may be applcable, nterested readers are referred to [5]. The dea s that UAVs move accordng to a predefned movement pattern and then send and respond to coordnaton events needed to handle targets, accordng to the adopted strategy. 1) Internal State: The nternal state S (t) of a UAV u at a gvng tme t s composed by three elements: Physcal State, Current Tas State and Alarm Lst. The Physcal State ncludes nformaton about ts current poston p (t)= (x (t), y (t)), speed (v (t)), headng angle (ψ (t)), sensor devce types and status (ς j (t)), whch descrbes the accuracy level provded by the sensor, and energy resources (e (t)). As for Current Tas State, UAVs can perform one of the followng tass: search, analyze or trac. The search tas mplements the sequence of actons that the UAV s performng to detect a new target. The analyze tas gathers detaled nformaton about the detected target, whle the trac tas maes the UAV capable of eepng trac of a target movement. When an UAV s engaged n a msson, the Current Tas State also ncludes nformaton about whch target ths tas s referred to. The Alarm Lst contans the alarms that an UAV has nowledge about, possbly wth the nformaton about whch UAV s handlng the target subject of that alarm. 2) Knematc Model: The UAV nematc model adopted n ths wor s almost the same as adopted by several others, such as [1] and [2], n whch the UAVs move on contnuous trajectores wth constant speed and wth a constraned varaton n the headng angle. In the present wor, the assumpton of a constant speed does not hold n order to mae possble the speed adjustment to allow target tracng. Assumng ths, the followng formulas descrbe the model: dx dt dy dt = v cosψ ; v snψ dψ = ; η. (1) dt where (x, y ) denotes the poston of the UAV u, v represents ts speed, and ψ s ts headng angle, whch has η as a constrant to ts varaton. A fourth assumpton s added to the model, namely that the UAVs maxmum speed may be hgher then the targets maxmum speed (targets of any nd ), dependng on the partcular characterstcs of a gven UAV. Ths assumpton allows the system to address the needs related to survellance of large areas wth a hgh-level of responsveness to handle new targets. v MAX > v MAX. (2) However, due to constrants le energy consumpton mnmzaton, the UAVs are assumed to mantan constant speeds almost all the tme. Varatons n speed only occur when a UAV engages n a tracng tas and has to tune ts speed to trac a target movng on the ground. 3) UAV Sensng Capablty Model: The sensors that equp the UAVs are supposed to detect members of the set of possble targets and precsely analyze and trac, only a subset of the varous types of targets. Whch and how many depends on the types of sensors and targets under concern. In case that a sensor, needed for analyss or tracng of a target, s mssng or does not match well, poor results wll be generated. Fgure 1. Sensor coverage area and ranges. 592

3 The range of the detecton, analyze and trac tas capabltes are tunable, accordng to the types of sensors that equp the UAVs n the fleet. Ths tunng s done by the adjustment of the number of cells around the UAV that are able to be detected and/or analyzed/traced. It s assumed that the ranges to detect and analyze/trac are defned by an actuaton range, whch s called r D for detecton and r A for analyss/tracng. Fgure 1 llustrates ths dea. It s mportant to hghlght that t s assumed that the cells nsde the range are only consdered f ther center ponts are nsde the respectve radus. In Fgure 1, cells 1, 2, 3 and 4 are out of the range (r D ). III. PHEROMONE-BASED COORDINATION As mentoned, ths paper descrbes two approaches to coordnate a networ composed by low-end ground sensor nodes and UAVs. In ths secton, the pheromone-based approach s presented. Artfcal pheromones are usually appled to dstrbuted coordnaton by means of stgmergy, the ndrect communcaton usng envronment cues [6]. A pheromone tral s deposted n the envronment when the enttes are movng. The pheromone provdes nformaton to other enttes when they pass over t. Artfcal pheromone also looses ts strength along the tme, modelng the evaporaton of the real pheromones. In the UAV research feld, pheromones are used to gude the movement of UAV swarms, for nstance n survellance and patrollng applcatons [5] [7]. Dfferently from the exstng approaches, n the present one, pheromones are used to gude the assgnment of an UAV to a gven target. When an alarm s ssued by the detecton of a target, the networ s responsble for selectng an approprate UAV to respond to the alarm. Ths s performed by routng a gven alarm to the UAV that has the strongest pheromone trace over the area. Havng ths nformaton, the UAVs would base ther movement decsons n a way to respond to the receved alarms. Ths strategy s called here heurstc-p. Followng the above outlned deas, the UAVs that are not engaged n the handlng of any target leave pheromone traces over the area whch they cross. Ths pheromone trace s represented by a pece of nformaton that s taen by the ground sensor nodes that are deployed n the area where the UAVs have passed. When a target s detected by a ground sensor node, t ssues an alarm, as already mentoned n the problem descrpton. The decson about whch UAV wll handle the target that refers to the ssued alarm wll be taen by the ground sensor nodes, by routng the alarm n the drecton that ponts to the UAV whch has the strongest pheromone trace over that area of the networ. Ths process does not consder any other condton, just the pheromone trace left by the UAVs. Ths means that the only parameter taen nto account s how long tme an UAV passed by that specfc locaton, and the strategy s to route the alarm nto the drecton that ponts to the UAVs that passed by that locaton more recently. Heurstc-P s nspred n [8], whch presents a pheromone-based strategy to mgrate servces n a sensor networ, n whch the pheromone concentraton determnes the places where the servces are requred. In heurstc-p, nstead of servces, alarms are moved through the networ followng the pheromone concentraton. When an alarm reaches the UAV ndcated by the pheromone trace, f t s not engaged n the handlng of another alarm t engages ths receved one and sends a confrmaton message to the node n the networ that had delvered the alarm. If the suggested UAV s already engaged n another alarm, the current alarm follows the second strongest pheromone trace to fnd another UAV to engage. When an dle UAV detects a new target, t taes the responsblty for handlng t. In case that the UAV s already busy wth another target, t ssues an alarm that wll be routed to another UAV, accordng to the pheromone-based heurstc- P strategy explaned above. In order to ncrease the robustness of the proposal, n case an alarm s ssued by a node that has no pheromone trace, a drecton s randomly chosen and the alarm s sent n that drecton untl t fnds a pheromone trace. When the trace s found, t follows the trace as explaned above. Ths stuaton s more lely to occur n the ntalzaton of the system, especally n cases n whch the number of UAVs deployed n the system s very low. IV. UTILITY-BASED COORDINATION The utlty-based coordnaton approach, called heurstc- U, totally transfers the decson mang responsblty related to the handlng of alarms to the UAVs nstead of leavng ths decson to the ground sensor nodes. Ths approach consders a larger range of varables f compared to the prevously presented heurstc-p based on pheromones. The decson concernng whch UAV that wll engage n a tas over a gven target s modeled by a maxmzaton of a mult-attrbute utlty functon, whch consders both the applcablty of the sensor carred by the UAV n order to engage n that tas, and a energy consumpton factor, whch s based on the dstance between the UAV and the target under concern. Ths modelng of the problem ncludes the three elements of a decson mang theory presented n [9], whch vews the problem: 1) as a game n whch the decson maer gathers nformaton about the envronment state, and 2) based on that nowledge then proposes an acton to be performed, whch fnally 3) s assessed n terms of the utlty,.e., the gans that are orgnated from the consequences of the decson maer s choce of acton. Ths s done by means of the assessment of the mult-attrbute utlty functon. Smlarly to heurstc-p, when an alarm s ssued by a ground sensor node, t s retransmtted untl t arrves to the frst UAV that s not engaged n the handlng of any target. The frst dfference s that n ths approach (U), there s no nformaton drvng the spread of the alarm over the networ le n heurstc-p. Moreover, when the alarm s receved by the UAV, t wll share ths alarm wth all UAVs n ts communcaton range, as well as ts capablty n handlng that alarm. Le ths, a negotaton taes place, n whch the best UAV to handle the alarm taes the responsblty. The two ey concepts of heurstc-u are thus the sensor applcablty factor and the utlty functon that uses t. 593

4 A. Sensor Applcablty The sensor applcablty s a number calculated by the UAV u that estmates the value of applyng a gven type of sensor (type j) to the handlng of a gven type of target () nformed by an alarm, n a specfc tme nstant. j ς ( t) We, j ( t), f κ j κ θ, ( t) =. (3) 0, otherwse where We,j (t) s a functon that estmates the degree of degradaton n the measurements offered by a sensor of type j, due to the weather condtons at tme t, and κ j s the subset of all types of targets contanng those that match the sensor type j. In ths model of the heurstc-u, poor results offered by sensors that do not match the type of target, are mapped to a value zero. B. Utlty Functon The utlty functon s used to evaluate the tas to be performed over a target subject of an alarm, whch s defned by the commands establshed n the msson drectons. Then the results provded by the utlty functon calculated by each UAV are compared. Tang the one that has the maxmum value, t s decded whch UAV that wll engage n the alarm and perform the requred tas. In the long run, ths wll maxmze the usage of the entre system. Ths reduces the problem to the maxmzaton problem: U max ( U ( θ ( t), C( e ( t), p ( t), p ))) ( t) = max, j. (4) where C s the cost n terms of energy consumpton requred to tae the UAV from the current poston p (t) to the target poston p j reported n the alarm, based on the current energy resource status (provded by e (t)). It s mportant to hghlght that the computaton of (3) carres a certan degree of uncertanty due to the possble mprecson or ncomplete nformaton about the weather condtons. Ths s also true for the computaton of C used n (4), whch carres an uncertanty about the locaton of the target, due to ts unpredctable (unnown) movement pattern. The uncertanty n the proposed approach s translated nto a rs of a wrong estmaton accordng the model of rs profles presented n [10]. Accordng to the referred wor n [10], whch models the behavour of nvestors n the stoc maret usng utlty functons, the nvestors can be classfed n dfferent rs profles. These profles can represent nvestors more or less prone to rs when performng ther trades. Dfferent types of functons are used to represent these profles, as a mappng of the how prone to rs the nvestor s. The complete theory ncludes addtonal detals, such as coeffcents to tune the degree of rs averson and concerns about the most sutable types of functons dependng on other factors. However, n the present approach a smplfed model s adopted wthout all the elements presented n the orgnal theory. The metaphor used to defne heurstc-u s to assocate the dea of rs profles of the nvestors to profles that can be assgned to the UAVs n the sense that they can be more or 594 less prone to tae rss when estmatng ther utlty to handle a gven target. The UAVs that have better resource condtons and more powerful capabltes are more lely to tae rs n computng ther utlty, whle consderng the uncertanty of the nput data, as they expect to have good results,.e. to be really useful n handlng a gven target. On the other hand, UAVs that are weaer n the sense of havng less capabltes and lower resources are more lely to use a more conservatve utlty functon. The choce of whch type to use s based on threshold values of the consdered capabltes. Ths study consders the use of two functons to express the profles for the UAVs, a logarthmc one for the rs tolerant UAVs and a quadratc one for the conservatve ones. Moreover, dfferent weghts can be attrbuted to the two components of the utlty functon used n (4), namely sensor applcablty and the energy cost, by means of a parameter α that tunes whch part wll contrbute more wth the utlty computaton. Equaton (5) shows the verson for the more rs tolerant UAVs, whle (6) presents the one for those less rs tolerant. The assumed vald ntervals for θ, and C s [0, 1], whle for α t s (0, 1). U U, C) = ( θ (e-1))+1) α ln + f 0, otherwse, ( ) ( C ) ( θ, 1 α ln (e-1))+1, θ, 2 ( θ ) + ( 1 α ) > 0. (5) 2 α C f C = >, j,, θ, 0 ( θ,, ). (6) 0, otherwse V. EVALUATION AND COMPARISON The evaluaton and comparson s based on smulatons conducted usng ShoX [11], whch s a powerful wreless networ smulator mplemented n Java that provdes easy extenson mechansms. The metrcs evaluated n the smulatons were the mean response tme to the alarms generated n the system, the tme to handle a target, and the relatve utlty, n terms of how sutable the assgned UAV s to handle a gven target. The followng subsectons detals the setup of the smulaton and provdes a dscusson about the obtaned results for the two coordnaton approaches. A. Smulaton Setup The survellance area has dmensons 10 Km x 10 Km, n whch ground sensor nodes are randomly deployed wth ndependent unform probablty (homogeneous Posson pont process n two dmensons, whch generates a geometrcal random graph). Ths dstrbuton gves around 70% of probablty that the nodes n the networ form a connected graph [12], for a communcaton range of 500 meters. Sx UAVs of three dfferent types, equally dstrbuted, patrol the area, havng a communcaton range of 1,5 Km and are flyng at speeds from 100 Km/h up to 120 Km/h. Four dfferent runs were smulated, wth one, three, and fve targets for both heurstcs. The targets can be of fve dfferent types, randomly chosen. The energy resources start randomly dstrbuted between 90% and 100% for all UAVs. These resources are consumed accordng to a decreasng lnear functon per part, havng the

5 tme as parameter and weghted by the current speed of the UAV n each of ts parts. Sensor status s randomly started for each UAV, startng from values between 70% and 90%, and may decrease after the utlzaton of the UAV n handlng a target. Ths decrease s randomly chosen, smulatng some possble damage due to hostle target attacs, for nstance. For the smulatons of heurstc-u, t was consdered that UAVs wth less than 30% of remanng energy resources or less then 30% of sensor capablty use the utlty functon presented n (6), whle the others use the one presented n (5). The value of α n (5) and (6) s randomly chosen between 0.1 and 0.5 f the sensor status s lower than the percentage of remanng energy and between 0.5 and 0.9 n the other case. Table I presents the summary of the man setup parameters. targets) and the mean values are plotted n the fgure. It s possble to observe that the results provded by the pheromone-based strategy, heurstc-p, are slghtly better than the ones provded by heurstc-u. An explanaton for ths behavor s that the pheromone approach provdes an easer way to the sensor networ to fnd an UAV and delver an alarm to be handled, by mang the alarm follow the pheromone traces, wthout requrng a negotaton. On the other hand, n the utlty-based approach the sensor nodes have no ndcaton regardng to where t s better to retransmt the alarms n order to fnd a sutable UAV, so a controlled floodng s done. TABLE I. SIMULATION PARAMETERS Parameter Value Scenaro Area 10Km x 10Km Number of UAVs 6 Types of UAVs 3 UAV Speed 100Km/h 120Km/h UAV Communcaton Range 1,5Km Number Ground Statc Nodes Ground Nodes Communcaton Range 500m Number of targets 1, 3 and 5 Types of targets 5 Target speed 50 Km/h 80Km/h B. Results and Dscusson The results of the three metrcs provded n ths secton present the assessment of the effcency of each strategy ntroduced before. The frst metrc provdes a comparson between the two heurstcs n terms of tme to assgn a UAV to handle a target that s the subject of an ssued alarm. The second measures the tme to handle a gven target. These two metrcs are mportant assessment parameters, as t s desred that the system respond to the alarms and handle the targets as faster as possble. The sutablty n usng an adequate UAV to handle an alarm s also an mportant parameter, as t represents the effcency n the resource allocaton n the system and the qualty of the nformaton provded to the users. Fgure 3. Tme to handle targets for the two consdered approaches. Fgure 3 presents the values obtaned for the tme to handle targets metrc. It s possble to see that heurstc-p presents better values, whch can be explaned because t just follows the pheromone traces to the nearest UAV, wthout consderng ts utlty, whle n heurstc-u the utlty s the bass upon whch t s decded whch UAV wll handle the target. Even consderng that the utlty functon taes the dstance between UAV and target nto account, t happens that a UAV that s far from the target may wn the negotaton and go to handle the target, snce t can be better than other UAV n the other crtera evaluated by the utlty functon. Fgure 4. Normalzed utlty for each approach. Fgure 2. Tme to respond the alarms for the two consdered approaches. Fgure 2 presents the smulaton results for the two approaches n terms of tme to respond to the alarms. Both raw data from each run (total of 20 runs for each number of Fgure 4 presents the results of the measurements of the utlty values, whch are normalzed by the optmum soluton (the best assgnment UAV-target that could be done). As expected, heurstc-u provdes the best results. Ths s explaned frstly because t consders addtonal parameters when compared to the pheromone approach used n heurstc- P, whch mplctly taes nto account only the postons of the 595

6 UAVs and targets. Moreover, even dscardng UAVs that are completely unable to handle the type of target of a gven alarm, the heurstc-p soluton looses because the negotaton among the UAVs, comparng ther utltes n the heurstc-u soluton avods poor assgnments, whch s a feature that does not exst n the pheromone-based soluton used n heurstc-p. The results presented express a tradeoff between tme and utlty n selectng one of the proposed solutons. The pheromone-based approach was more effectve n terms of tme to respond to an alarm and to handle a target. However, the utlty evaluaton of the second approach provdes a better ft UAV-target. Moreover, the pheromone approach uses sgnfcantly less messages than the utlty-based one, whch uses a controlled floodng that s more costly n terms of networ resource usage. VI. RELATED WORKS Jn et al. [1] provde a very consstent proposal to handle the problem of balance between target search and response by a team of UAVs. The wor evaluates the tradeoff between search and response wthn the framewor, presentng a predctve algorthm that provdes a good balance between these tass. The frst dfference between our approach and ths wor s that we handle only the alarm response, abstractng the UAVs movement plannng to perform the search for new targets. Ths dfference s due to the pecularty of the dstnct msson addressed by each one. We focus on area survellance, whle they focus on target acquston. In our case, the whole area must be covered, wthout the assumpton of preferred locatons to move to, whch s true n the target acquston they address. Another dfference s that we use the UAVs n coordnaton wth ground sensor nodes. Besdes, the assumpton of a centralzed nformaton base consdered n that wor s not used n our proposal. The ntal centralzed off-lne tas assgnment s another premse that s not vald n our wor but holds n ther proposal. The AWARE project s presented n [3]. Ths project ams at ntegratng a sensor networ of resource constraned ground nodes wth moble sensors, both on the ground and carred by UAVs. In the large sense ths wor s closely related to ours. The common dea presented n both wors s to use ground sensors and UAVs tang part of the same sensor networ, cooperatng n order to acheve the msson goals. In our paper, the focus s to descrbe how the UAVs cooperate among them, after the alert emtted by the ground nodes usng ad hoc communcaton. Compared wth our approach, AWARE does not provde the same flexblty n relaton to the coordnaton among nodes. In AWARE, the nodes mae part of a group that s lned to a gven tas, or set of tass, and to change ths formaton, new commands must be sent explctly from a central control base. Instead, n our approach, the coordnaton s done autonomously by the nodes. VII. CONCLUSION AND FUTURE WORKS Ths paper presented two dfferent strateges to drve the coordnaton among heterogeneous sensor networs appled n survellance systems. A detaled problem formulaton was also provded to support the assumptons done n each of the proposed strateges. An assessment of the effcency of both 596 approaches was done, by means of three metrcs of nterest and a crtcal analyss of the resultng data was performed. Our study ponted out the strengths and weanesses of each strategy. A combnaton of both strateges, n whch a pheromone-based alarm routng s used to search for UAVs accordng ther capabltes, transferrng part of the negotaton to the pheromone trals n the ground sensors, wll be developed. The selecton of most sutable tral to route an alarm n ts search for an UAV wll reduce dramatcally the amount of messages when compared to the controlled floodng used n heurstc-u, and at the same tme, wll select a hghly sutable UAV to a gven target, by means of ts utlty. ACKNOWLEDGMENT E. P. Fretas thans the Brazlan Army for the grant to follow the PhD program n Embedded Real-tme Systems at Halmstad Unversty n Sweden, n cooperaton wth UFRGS n Brazl. T. Hemfarth thans the Brazlan CNPq (Natonal Research Councl) for the fundng provded to hs research, whch s developed at UFRGS under a PNPD project. REFERENCES [1] Y. Jn, Y. Lao, A.A. Mna, M.M. Polycarpou, Balancng Search and Target Response n Cooperatve Unmanned Aeral Vehcle (UAV) Teams, IEEE Transactons on System, Man, Cybernetcs-Part B: Cybernetcs, vol. 36, nr. 3, p , [2] Y. Ben-Asher, S. Feldman, P. Gurfl, M. Feldman, "Dstrbuted Decson and Control for Cooperatve UAVs Usng Ad Hoc Communcaton", IEEE Transactons on Control Systems Technology, vol. 16, nr. 3, p , [3] A.T. Erman, L. Hoesel, P. Havnga, Enablng Moblty n Heterogeneous Wreless Sensor Networs Cooperatng wth UAVs for Msson-Crtcal Management, IEEE Wreless Communcatons, vol. 15, s. 6, p , [4] MLB Co. web ste. Submeter-scale arcraft, [5] P. Gaudno, B. Schargel, E. 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