An intelligent strategy for tactical movements of UAVs in disaster scenarios

Research Article An Intelligent Strategy for Tactical Movements of UAVs in Disaster Scenarios J. Sánchez-García, J. M. García-Campos, S. L. Toral, D. G. Reina, and F. Barrero Electronic Engineering Department, University of Seville, Camino de los Descubrimientos, S/N, 41092 Seville, Spain Correspondence should be addressed to S. L. Toral; stora[email protected] Received 25 September 2015; Revised 15 January 2016; Accepted 11 February 2016 Academic Editor: Pascal Lorenz Copyright © 2016 J. S´ anchez-Garc´ ıa et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Unmanned Aerial Vehicles (UAVs) are envisioned as flexible and fast-deploying communication network for disaster scenarios, where the typical communication infrastructure is likely to be malfunctioning. A few works propose UAVs for building communication links autonomously between rescue team’s members in disaster scenarios. The techniques used are usually based on navigation, positioning, and signal strength processing. However, these techniques may not be enough if the objective is to provide communication services to the maximum number of victims and rescuers and not only to a few rescuers. In this situation, dissimilarity metrics, like the Jaccard distance, can provide information about whether the communication service provided to victims is efficient or not (e.g., providing a better distribution of the victims assigned to each UAV acting as service provider). We propose an intelligent strategy that allows UAVs to perform tactical movements in a disaster scenario, combining the Jaccard distance and artificial intelligence algorithms like hill climbing and simulated annealing. Our strategy maximizes the number of victims that are serviced by the UAVs while avoiding network disconnections. Also, a mobility model specifically developed for modelling the victims’ movements within the incident site of a disaster scenario is proposed. 1. Introduction UAVs are envisioned to be used in tactical missions, of either rescue or military nature, that typically involve search for victims or other targets [1–4]. Specifically, in disaster scenarios, emergency crew members that arrive first at the disaster area, also known as first responders, perform rescue tasks against theclock.Thereasonisthatthefirst12–24hoursafterthedis- aster occurs is the most critical [5] and consequently a rapid and efficient rescue mission could save an important number of lives or reduce damage. When a group of UAVs is used as a communication network, it usually receives the name of Flying Ad Hoc Networks (FANET) [6] or Aerial Ad Hoc Network(AANET)[2]orsimplyUAVnetworks[7]. Several works present mobility models for victims and first responders in disaster areas [8, 9]. These works not only model the incident site, which is the region where the disaster actually occurred. They also model other regions such as patient’s treatment area, transport area, and others. These mobility models are focused mainly on the transport of patients from the incident site to other regions but do not model in detail the incident site. Victims’ movements within the incident site usually are modelled by using the random waypoint mobility model [10, 11]. We propose a more complete and detailed victims’ mobility model for the incident site, which is composed of different elements found in urban disaster scenarios, such as roads, buildings, wreckage, and others. The proposed victims’ mobility model in this paper specificallyfocusesonurbanscenarios.Theproposedmobility model for victims will serve us for simulating a FANET self-deployment based on the Jaccard distance and artificial intelligence algorithms. In disaster scenarios and specifically in the incident site, UAVs networks can be of paramount help for victims and crew members involved in rescue operations. FANET could provide different types of services to victims and first responders. One possible service is to perform search and discovery tasks to collect information about the victims’ locations, thescenariofeaturesafterthedisaster,andthepotential risks that may arise during the rescue missions [12–14]. This Hindawi Publishing Corporation International Journal of Distributed Sensor Networks Volume 2016, Article ID 8132812, 20 pages http://dx.doi.org/10.1155/2016/8132812 2 International Journal of Distributed Sensor Networks information is very important for the first responders to plan the rescue missions and to select the tactical movements to follow. Another service that UAVs may provide is to deploy communication links between emergency crew members [15]. In this situation, the UAVs play the role of a flexible and rapidly deployed communication infrastructure, which becomes crucial as the normal communications infrastructures may be damaged and nonworking because of the disaster. In order to provide these services, the UAVs composing FANET are usually equipped with hardware and software elements that make them capable of sensing and acting autonomously. It is also possible for the UAVs to include some artificial intelligence algorithms to plan an intelligent strategy about the best actions to be taken at each time. Often, these actions are related to decide whether an UAV should hover at its current position or to calculate a better location to fly to for providing a better service. The most common technologies that UAVs are equipped with are navigation and positioning systems, signal processing, and computer vision. Navigation and positioning systems are extensively used for defining the UAVs positions [2, 16,17].Forexample,itispossibletoforcetheUAVstomaintain a specific Euclidean distance between each other, thus covering a regular area. Although navigation and positioning systems are a must for any standard UAV application, working only on the basis of the UAVs positions and the Euclidean distance might not be very efficient. The main reason is that disaster scenario features may change rapidly over time (in many cases victims are moving continuously) and the only consideration of positioning techniques does not provide an adaptive response to this situation. Signal strength processing combined with positioning techniquesmightyieldgoodresultswhenusedinUAVsselfdeployment problems [3]. This technique could allow the UAVs to adapt to some scenario conditions and other UAVs proximity, adapting their position to the place where the signal strength is maximized. However, by using only this technique it is not possible to infer information about whether someUAVsareservicingthesamevictimsofthescenarioand if other UAVs positions would be allowed to reach a better distribution of victims serviced per drones, thus maximizing the total number of victims serviced. Imaging and computer vision techniques are usually very accurate for analyzing different types of scenarios and their features [18, 19]. These techniques can be suitable for analyzing the victims’ presence and their movements in disaster scenarios; however computer vision techniques usually are resource-consuming (both hardware and software) and might affect considerably the flying autonomy of small UAVs. We propose an innovative approach to accomplish the self-deployment of a group of UAVs by using the Jaccard dissimilarity metric [20] and artificial intelligence algorithms. The Jaccard distance between two UAVs is calculated from the number of victims that they share, and it is proposed in this paper as a useful parameter to consider when calculating the tactical movements of the UAVs in a FANET. As a difference from other methods based only on the Euclidean distance, the Jaccard distance contains updated information about the scenario and the victims’ movements. Depending on its value, two UAVs can make decisions about approaching or moving away from each other, and these decisions may vary over time as victims are also changing their position. For instance, the FANET may detect whether its UAVs are sharing a large number of victims and whether there are other positions that they could occupy to service the maximum number of victims while maintaining their connectivity. However, it is not easy to set a specific value for the Jaccard distance for the UAVs to decide about the best movement to make. SelectingonlyonevalueasthetargetJaccarddistanceto be used for the UAVs at the beginning of a simulation is not efficient at all. Moreover, with the changing conditions of the scenario,theoptimalJaccardvaluecouldbevarying.Forthis reason we implement artificial intelligence algorithms to provide an autonomous selection of the target Jaccard distance to be used for the tactical movement of the UAVs along the entire simulation. The objectives of the proposed approach are twofold: (i) to service the largest number of victims being adaptive to the victims’ movements and (ii) to maintain the network connectivity. The main contributions of this paper are (i) a more realistic mobility model for victims’ movements within the incident site of an urban disaster scenario, (ii) Jaccard-based movement rules to define the UAVs best positions for providing the best communication servicetothevictims, (iii) a comparison among several local search computational intelligence algorithms implemented such as simulated annealing, hill climbing, and random walk for deciding the best tactical UAV movements. Thispaperisorganizedasfollows.Section2reviewsthe related work victims’ mobility models for disaster scenarios andonFlyingAdHocNetworks.Section3presentsthe proposed victims’ mobility model simulating the incident site of a disaster area scenario. Section 4 describes the tactical movement rules that allow the UAVs self-deployment based on the Jaccard distance and artificial intelligence algorithms. Section 5 describes the artificial intelligence algorithms implemented in the software simulator tool developed. Section 6 describes the simulation settings and the main results obtained. Finally, Section 7 concludes the paper. 2. Related Work 2.1. Modelling Victims’ Mobility in Disaster Scenarios. Mobility models for disaster scenarios have been a common research topic in the last decade. There are several works focusing on mobility models to simulate the victims and rescue teams movements in disaster scenarios [8, 9, 21]. The authors in [8] model the disaster scenarios dividing the simulation area according to several sites, namely, the incident site or incident location (where the catastrophe actually occurred and the victims should be rescued), the treatment area (where victims receive first aid care), transport International Journal of Distributed Sensor Networks 3 zone (including ambulance parking point and helicopter landing site), and hospital zone. The focus in [8] is put on civil protection units and therefore it presents a mobility model reproducing their movements in search of victims operations. Two types of nodes are modelled, pedestrians and transport nodes. Regarding the incident site, the mobility model proposed in [8] considers only the presence of transport nodes.Thereasonisthatitisonlymodellingthebehavior of a transport vehicle picking up a patient from the incident location and carrying him or her to another area of the model called patients waiting for treatment area. In fact, the locations to which transport nodes pick up patients within the incident location are selected randomly. Thus, there is not a real mobility model representing the victims’ behavior within the incident site. BonnMotion [21] is a software tool for generating and analyzing mobility models. BonnMotion allows the generation of disaster scenarios, under the name of Disaster Area Model, mobility models according to the description provided in [8]. In this model, the incident site is modelled by following either the random waypoint model, which was first introduced in [22], or a slight variation of it. The authors in [9] present the Human Behavior for Disaster Areas (HBDA) mobility model. HBDA simulates the behavior of rescue teams performing search-for-victims operations. This behavior is represented by a set of algorithms that mimics the operation of a group of people scouting unexplored areas, spreading out over the scenario but maintaining the line of sight with other members involved in the search operation. The Composite Mobility Model (CoM) is presented in [23] as a mobility model for disaster scenarios that consists of the combination of more basic mobility models. Its main characteristic is the possibility of representing group mobility byusingtheReferencePointGroupMobilityModel[24]. Also, it replaces the random waypoint mobility model by the Levy-Walk model proposed in [25] for obtaining more realistic simulations without the nodes concentration usually showedbytherandomwaypointmodel.Althoughmorerealistic than other mobility models, the CoM bases its behavior onthefactofknowingthescenariocharacteristicsinadvance, which may be available in some situations but not in disaster areas where the presence of building wreckage and debris might be present. Although there are several mobility models for disaster scenarios, it is clear that they mainly focus on modelling the mobility of the rescue workers; they do not model the victims’ movements within the incident site in a detailed manner. In the works that also include the victims’ mobility, they model it in a very simple way, such as the Disaster Area Model [8], where the victims’ mobility is represented by one squared area (the incident site) where victims move using the random waypoint mobility model. Moreover, in those works in which victims’ mobility is simulated within the incident location, the models used are the random waypoint model or the Levy- Walk model, which provide a distribution of victims all over the incident location (more distributed in the case of the Levy-Walk model). Current research trends on modelling generic human mobility are based on analyzing real traces of human movements in order to infer new mobility models [26]. Other research efforts are put in validating existing mobility models through the usage of such real mobility traces [27, 28]. These trends are both promising research approaches for designing andanalyzingnewmobilitymodelsrepresentingnormal mobility situations (e.g., the mobility of persons during a normal working day and a normal weekend day) and also for studying the routing protocols performance with these mobility models [29, 30]. However, it is important to notice that there are neither other works nor information about real movements of victims in a disaster-like situation. It is unfeasible to make preparations to record the mobility of humans in a disaster scenario because it is not possible to predict in advance when and where a disaster will occur. Also, some of the techniques used for acquiring data of human movements’ real traces may not function in a disaster scenario, due to damage in the technologies used and the corresponding infrastructure, making the real traces more difficult to gather. The focus of this paper is the area defined as the incident site in [15], because this is the area where the UAVs are going to be deployed for providing communication services to victims. Therefore, a victim’s mobility model more complete than the ones previously mentioned is needed. For this reason,amobilitymodelforvictimswithintheincidentsiteofan urbandisasterscenariowillbefirstproposed. 2.2. UAVs Applications in Disaster Scenarios. Regarding UAVs, there are several works in the area which presents results and applications that are directly or indirectly applicable to disaster scenarios. UAVNet [3] proposes a mesh network using UAVs with the aim of providing communication services between two ground nodes which are not able to communicate by themselves. Although it is not presented for disaster scenarios itcouldbeapplicabletosomeemergencyscenarios.The algorithm developed for UAVNet firstly makes the UAVs localize the first end system position of the entities that want to communicate; then it searches the second end system and calculates the best position for the UAV to be located in order to communicate the end systems. The algorithm bases the best position calculation on Euclidean distance and signal strength processing. It also presents the possibility to use morethanoneUAV,withsomeofthemplayingtheroleof relaying nodes in the communication bridge between the two end systems. Despite the UAVs having a search operation mode, UAVNet worksonthebasisthattheUAVsknowin advance one of the end systems positions, and usually the first end system provides the position of the second end system to the UAVs. This approach addresses the problem of building one communication link with UAVs between two end systems. Therefore, the problem of maximizing the number of victims that are provided with communication services is not addressed. AirShield [17] proposes an aerial network, consisting of a swarm of micro-UAVs (MUAVs), to establish a communication network in situations of emergencies of gases, explosions, and chemical clouds. AirShield is provided with components 4 International Journal of Distributed Sensor Networks that act as an intelligent agent; these are a Decision Support System that measures the chemical cloud trend values and a cloud propagation simulation on which the UAVs based the calculation of their next positions to occupy. Although the AirShield application presented is on chemical clouds, the evaluation focus of the authors in [17] is on the performance of several wireless access technologies implemented on the MUAVsnetwork(mainlyevaluatingtheRoundTripTime of messages exchanged). The solution proposed by AirShield is applicable to disaster scenarios but is mainly focused on tropospheric telemetry for providing this information to the missioncontrolcenter,whichislocatedontheground.Providing communication services to the victims on the ground that might be affected by the catastrophe is not addressed. The authors in [15] propose a swarm of microair vehicles, called SMAVNET, for creating a communication network on a disaster area. The SMAVNET uses fixed-wing UAVs (not quadcopters-like UAVs) which must be moving constantly; therefore, rather than calculating the best positions to serve as a communication network, the UAVs have to calculate flying routes. The technology used in SMAVNET for steering the UAVs only uses communication hardware (WiFi or radio modules) and magnetic compass instead of GPS-like positioning systems. The fixed-wing UAVs in SMAVNET are launched at regular intervals and self-organize themselves to search for other rescuers positions. Rescuers send “hello” messages at a given rate and, when the UAVs detect the reception of these messages at a rate close to the one used by the rescuer, the discovery of a new rescuer is finished. Then the UAVs maintain the communication links between the rescuers by creating a chain between the rescuers’ positions. Two artificial intelligence algorithms were implemented on SMAVNET simulation, an artificial evolution controller and asecondonebasedonantcolonyforagingandpheromone trails. The approach described in [15] uses artificial intelligence for the self-organization of a FANET on a disaster area providing communication links between rescuers. However, the presence of many victims in this approach is not considered and mainly the SMAVNET has been simulated only with afewrescuers. Search-for-target missions have been found in the literature together with the use of artificial intelligence techniques for controlling the UAVs behavior [13, 14]. In [13] an agentbased simulation tool is developed for collaborative search task of a group of UAVs. The search controller is based on a controller mixing central control and pheromone-based techniques. The scenario is divided into cells on which the UAVs leave pheromones when they visited them or are marked as “visited” by the central controller when the cells are under the coverage of the UAVs. The aim is to perform a search task of a target, not to provide coverage to it. In [14], two types of controllers are designed for a nonhelicopters model of UAV groups. The mission for the UAVs is to navigate through an unknown environment, reach a target, and detonate it. The first controller proposed uses a neural network consisting of 7 input neurons (1 encoding for the distance to the target, 3 for the angle, and 3 for the distance to the closest obstacle); additionally, 15 neurons are forming the neural network core, and 2 output neurons are used. The second controller implements a genetic algorithm using a fitness formula depending on the target position and movement. Both controllers implement a multiagent system for four UAVs that collaborate in the search and destroy mission. Although the use of UAVs and FANET has been proposed recently in the literature, there are no other works addressing the problem of providing communication services to victims during the rescue mission, but only to rescue crew members. Moreover,therearenootherworksfollowingtheapproachof using dissimilarity metrics, such as the Jaccard distance, and artificial intelligence algorithms for the calculation of UAVs tactical movements. Then we propose intelligent strategy based on the Jaccard distance and artificial intelligence algorithms for enabling the UAVs self-deployment on a disaster scenario. The main objectives sought with the strategy proposed are (i) to maximize the number of victims and rescue workers that are serviced by the UAVs and (ii) to maintain the network always connected. 3. Disaster Scenario and Victims Mobility Model This paper is focused on the incident site within an urban disaster scenario [8]. The victims’ movements within an incident site can be very heterogeneous and difficult to categorize. Random movements are very likely to be found on victims within the incident site. However, architectural features of the disaster area affect the victims’ movements. As an example, victims could move along a road trying to escape from the danger but they cannot jump over a building or they might find a blocked street due to building wreckage. There is not much information in the literature about victims’ movement traces within the incident site. Previous worksondisastermobilitymodelsaremainlyfocusingonrescueworkersmobility[8,9,21,23].Thefewofthemmodelling victims’ mobility, as, for example, [8], describe the incident site as a squared scenario in which victims moved all over the space following the random waypoint mobility model. We propose a more detailed and complete urban disaster scenario model, with more features than previous models. The victims’ mobility model proposed in this paper has the following main features: (i) The disaster scenario modelled has two dimensions (2D) and is composed of different regions. (ii) We defined several regions types, each of these representing typical elements that are found in common urban scenarios, such as roads and building blocks. Each region type is characterized by specific constraints associated with its shape and the movement that the victims are allowed to perform within the region. The main region types are (i) streets, (ii) building blocks, (iii) open areas such as parks, and (iv) prohibited areas, in which the victims movement is not allowed due to structural features (e.g., fences and walls) or dangers (e.g., fires and wreckage). Other specific regions typical of disaster scenarios are defined in the next bullet. International Journal of Distributed Sensor Networks 5 (iii) Building wreckage or debris usually appears in disaster scenarios. Thus, we define other regions such as (i) blocked roads and (ii) buildings traps. Blocked roads are roads with specific points in which victims only have two possible movements, to stop at that point or to move away from it in the opposite direction. Buildings traps are buildings or other architectural structures that keep some victims trapped and they cannot escape from them without the help of rescue teams. Intheproposedmobilitymodel,victimsaremovingwithin the aforementioned regions according to the following considerations: (i) Victims may move randomly, changing their speed and direction at any time, but only within a region. The mobility model used to model victims movements is based on the random waypoint mobility model improved with some movement constraints thatareassociatedwitheachregiondefined(e.g., victims move in a street region using only a linear movement). (ii) Victims’ speed varies between 0.5 ms−1 and 3 ms−1. Also, victims may be static during a specific time frame, that is, not moving at all. The highest speed represents victims running around, looking for help, escaping from a potential danger, or acting under the influence of nervous breakdown. Static victims and those moving at the lowest speeds represent victims that might have difficulties to move, probably because of the injuries suffered during the disaster. (iii) Sudden changes are also likely to occur in disaster scenarios, such as a building collapse or an explosion. We have modelled these types of changes on the scenario by making some victims groups disappear from the disaster area. An example of the disaster scenario proposed with the different regions (marked with the colored rectangles) is depicted in Figure 1. The different regions defined before are described in Figure 1 legend. This is the urban disaster scenario model that we have used for running our simulations. The situation of several victims groups disappearing from the disaster scenario is shown in Figure 2. We present in Figure 3 the urban disaster scenario proposed in this paper against a digital map picture of a real city (Madrid, Spain; coordinates: 40.39, −3.69). In Figure 3 we can appreciate that though both are not exactly equal, there are important similarities between the model proposed and a real city scenario. 4. UAVs Self-Deployment The UAVs self-deployment is given by two main elements: (i) the tactical movement generation rules based on the Jaccard distance and (ii) artificial intelligence algorithms that maximize the number of serviced victims without losing network connectivity. Also, being adaptive to scenario variable conditions, that is, the movement of victims, is considered a goal. Victims moving around a building block Victims moving along a street that is blocked at the right side Victims moving in an open area, for example, a park Victims trapped within a building-like wreckage Victims moving along a street which connects two building blocks Prohibited areas for victims (e.g., dangers and structural elements that cannot be crossed) Figure 1: Victims moving on the disaster scenario model proposed and regions descriptions. 4.1. Main Assumptions. Intheproposedapproachthefollowing assumptions have been done: (i) We consider helicopter-like UAVs that can hover over a specific position the time that is needed for providing communication services to the victims that are located within their range. (ii) We consider two types of wireless links: (i) UAVUAV when the wireless link is stablished between two UAVs and (ii) UAV-victim when the wireless links are set between an UAV and a victim. (iii) The UAVs composing the FANET are able to detect the victims and other UAVs within their range by using any wireless access technologies. Potential candidates for these technologies could be anyone allowing ad hoc communications mode such as the common 802.11b/g/n or 802.11p which is used in Vehicular Ad Hoc Networks (VANETs). We have developed our work assuming that the UAVs of the FANET are equipped with these technologies. However the proposed approach is not limited to them and could be adaptedtoothers. (iv) Several works present potential solutions that could be used to know the victims that are within range [3, 15]. Also, Mobile Ad Hoc Networks approaches as theonesproposedin[31–33]couldbeusedamong 6 International Journal of Distributed Sensor Networks (a) Disaster scenario with all victims (b) Two groups of victims disappeared (marked with thebluerectangle) Figure 2: Disaster scenario: (a) before a sudden change and (b) after the change that occurred. (a) Disaster scenario proposed 6.9607 cm Map data © 2016 Google (b) Real city scenario Figure 3: (a) Disaster scenario model proposed against (b) a real city urban scenario with the layout of our disaster scenario model on top. the UAVs and the victims so the UAVs could “sense” the victims’ mobile phones. It is assumed that the UAVsareawareofthevictimsthatarelocatedwithin theirrangebecauseoftheusageofanyofthese network techniques. For the sake of clarity, other considerations have been made in order to reduce the number of variables affecting the problem modelled. With these considerations we reduce complexity but not validity of the solution proposed: (i) The UAVs are ideally flying at a specific height that allows them to service the victims that are located within a communication range of 250 m (centered in the UAV). The coverage area of each UAV is considered to be circular as it has been usually modelled in otherworks,asitismentionedin[34]. (ii) The UAVs maximum speed is selected at 10 ms−1.This is a value which highly depends on the type of the UAV and its flying equipment and for this reason has been set to the average speed of a standard UAV. (iii) The UAVs dynamics has been simplified and is supposed to move only in a 2-dimensional plane. This consideration has been done in order to avoid complexities (such as UAV’s acceleration, attitude, twists, and vertical oscillations) which may overload the software simulator developed. International Journal of Distributed Sensor Networks 7 Victims serviced by both UAVs Victims serviced only by the green UAV Victims serviced only by the blue UAV Victims with no service from any of the UAVs j i a2 a1a3 UAVs “i” and “j” Figure 4: Jaccard distance between two UAVs. (iv) Each UAV shares a list of the victims that are within range with its UAVs neighbors in a distributed manner,sotheentireFANETisawareoftheserviced victims. 4.2. Jaccard-Based Tactical Movement Rules. The Jaccardbased tactical movement rules are an innovative approach for generating UAVs’ movements that takes as inputs the current situation on the scenario, that is, the victims’ locations and the current positions of the UAVs. As previously mentioned, it is assumed that the UAVs are aware of the victims that are on range through the usage of any wireless technology working on ad hoc mode. Although the Jaccard distance and the Euclidean distance are different concepts, it has been shown that there exists a correlation between the two of them [20]. The main reason for selecting the Jaccard distance as a parameter to generate mobility is that it provides information not only about the physical proximity of a pair of UAVs, but also about whether a pair of UAVs have many victims located on the region where their coverage areas overlap; that is, they are sharing many victims. Two UAVs that service almost the same victims are not a desired solution as there might be other areas and victims unattended. This is likely to occur in the case of a FANET thatdecidestheUAVspositionsonlybasedonpositioning systems, that is, only using the Euclidean distance between the UAVs. This situation is depicted in Figure 4, where the groupofvictimsontheleftofthefigurecouldnotbeserviced if the UAVs are configured to maintain a fixed Euclidean distance between them. By using the Jaccard-based tactical movement rules proposed in our work, we make the UAVs notice when they are sharing many victims with other UAVs, and thus they calculate new positions to service more victims, share fewer, and maintain connectivity. 4.2.1. Jaccard Distance. The Jaccard distance is a metric which measures the dissimilarity between sample sets. In the Jaccard-based movement rules proposed, each pair of UAVs has a Jaccard distance based on the distribution of victims under them. We define the Jaccard coefficient for a pairofUAVsasthenumberofvictimslocatedwithinthe region where the UAVs coverage areas overlap, divided by the number of victims within the range provided by both of them. BasedonthesituationdepictedinFigure2,theJaccard coefficient (represented by 𝐽𝑖𝑗) may be calculated with the following expression: 𝐽𝑖𝑗 =𝑎1 𝑎1+𝑎2+𝑎3,(1) where 𝐽𝑖𝑗 ∈ [0,1]and the terms 𝑎1,𝑎2,and𝑎3are defined as follows: (i) 𝑎1isthenumberofsharedvictimsfortwodifferent UAVs 𝑖and 𝑗. This is represented by the red dots in Figure 4. (ii) 𝑎2isthenumberofneighbors(victims)oftheUAV𝑖 but not neighbors of the UAV 𝑗. This is represented by thebluedotsinFigure4. (iii) 𝑎3represents the number of neighbors (victims) which are neighbors of the UAV 𝑗but are not neighbors of the UAV 𝑖. This is represented by the green dots in Figure 4. Based on the previous definition of the Jaccard coefficient, the Jaccarddistance(representedalsoby𝐽𝑑)canbecalculated from (1) with the following expression: 𝐽𝑑𝑖𝑗 =1−𝐽𝑖𝑗.(2) 4.2.2. Movement Rules. We describe in this section the UAVs movements calculations based on a given Jaccard distance value that the UAVs should maintain among them. This Jaccard distance value, which we name after target Jaccard, is given by artificial intelligence algorithms. The algorithm implementing these movement rules calculates the difference between the existing Jaccard distances of each pair of UAVs and the target Jaccard and decides for each pair of UAVs if theseshouldmovewithrespecttoeachotherasfollows: (i) In the case where two UAVs have at a specific time a Jaccard distance between them which is bigger than the target Jaccard, they are affected by an attraction force; thus they will move closer to each other. (ii)InthecasewheretwoUAVshaveaJaccarddistance smaller than the target Jaccard, they are affected by a repulsionforce;thustheywillmoveawayfromeach other. The terms “attraction” and “repulsion forces” are used to represent the influence of one UAV on others. These forces are equivalent to velocity vectors that will be applied in the next movement to be performed by each UAV. The intensity of these attraction and repulsion forces is givenbyarelationshipbetweentheJaccarddistanceand 8 International Journal of Distributed Sensor Networks Speed Max. speed w: different values of the target Jaccard Max. speed: maximum speed at which UAVs can move 10 Jd x max. speed w = 0.2 w = 0.5 w = 0.7 Jd (1 − Jd)xmax. speed Figure 5: Relationship between the Jaccard distance of two UAVs and the speed of their next move. the speed at which the UAVs will perform the next move. This relationship is shown in Figure 5, where 𝐽𝑑is the Jaccard distance between the UAVs and 𝑤represent the different values of the target Jaccard. As shown in Figure 5, a specific Jaccard-speed function is defined in our algorithm for each value of the target Jaccard. The red and green lines are examples to represent the cases when the target Jaccard is close to its minimum or maximum value; in these cases the relationship between the Jaccard distances and the speed is modulated by a factor in order to diminish the maximum speed at which the UAVs could move. ThisavoidshavingtheUAVsmovewithhighspeedwhenthey aretooclosetoeachotherortoofar. WhenapairofUAVshasaJaccarddistanceofaspecific value (𝐽𝑑𝑖𝑗), this value is selected from the 𝑥-axis of Figure 5; then the intersection with the line defined by the target Jaccard provides the 𝑦-axis value which corresponds to the speed tobeappliedinthenextmovement.Itisclearthatthebigger the difference between the Jaccard distance and the target Jaccard is, the bigger the magnitude of the UAVs velocity will be. The movement direction of a pair of UAVs will be determined by a virtual line that connects their positions. In the case of a network with more than two UAVs, every UAV creates attraction or repulsion forces on the rest of the UAVs. As an example, in a network with 5 UAVs, each UAV will be influenced by 4 forces from the rest of the UAVs, that is, 4 velocity vectors. Consequently, the velocity vector resulting from the addition of all the velocity vectors affecting UAV will determine its next movement. ThissituationisdepictedinFigure6for3UAVsanda target Jaccard of 0.5. In this example, the UAVs 𝑑1and 𝑑2have a Jaccard distance with respect to the UAV 𝑑0of values 0.2 and 0.4, respectively. The effect of 𝑑1and 𝑑2over 𝑑0will be repulsion forces, which are represented by the red vectors V01 and V02. The resulting vector of adding V01 and V02 will be the velocity applied to 𝑑0.Thus,𝑑0next position will be the result of applying this velocity during one unit of the simulation time step. The movement rules as they have been described so far will not allow the UAVs to spread enough during the selfdeployment. The reason is that each UAV has an attraction Case: target Jaccard w = 0.5 di: UAVs Jdij : Jaccard distance between UAVs iand j ij : velocity calculated from a pair of UAVs’ Jaccard distance Jdij V i,t=T+1:velocity calculated from the addition of velocity ij vectors for each pair of UAVs V0,t=T+1 01 02 d0 Jd01,t=T = 0.2 Jd02,t=T =0.4 d1 d2 Figure 6: Velocity vectors addition: the effect of several UAVs Jaccard distances. or repulsion force from every UAV of the network (except by itself). Then, any UAV will have lower Jaccard distances with closer UAVs and higher ones with further UAVs. This will create a set of repulsion forces (from the closer UAVs) and other sets of attraction forces (from the further UAVs), then resulting in velocity vectors cancelation and thus a network that does not spread enough. This situation is depicted in Figure 7. Velocity vectors cancelation is an undesired effect. In order to overcome this limitation we add another parameter to our movement rules. We define the term Jaccard neighbors for each UAV of the network. The Jaccard neighbors of UAV are the UAVs on the network that have the lowest Jaccard distances with respect to it. This is a parameter that can be set to one value at the beginning of the simulation and remains constant for the entire simulation. As an example, for International Journal of Distributed Sensor Networks 9  01 02 03 Jd01 = 0.2 Jd 02 = 0.4 Jd03 =0.8 d1d0 d2 d3 Case: target Jaccard w = 0.5 Attraction: v03 Repulsion: v02 and v01 Figure 7: Attraction and repulsion velocities vectors cancelation.  01 02 Jd 01 = 0.2 Jd02 = 0.4 Jd 03 =0.8 d1d0 d2 d3 Case: target Jaccard w = 0.5 Attraction: none Repulsion: v02 and v01 Figure 8: UAV 𝑑0Jaccard neighbors (red) and their velocity vectors; UAV 𝑑3(green) is not a Jaccard neighbor of 𝑑0and it does not affect it. a simulation with Jaccard neighbors set to the value 2, each UAV will be only affected by the forces created by the 2 UAVs that have the lowest Jaccard distances with respect to it. Consequently, each UAV will have a couple of Jaccard neighbors. We can consider the Jaccard neighbors parameter a similar concept to the degree of the nodes of the network, so in our example if the Jaccard neighbors parameter is set to 2, every UAV in the network will have degree 2. As a result of this new consideration each UAV will focus on the most similar UAVs to it (equivalent to the UAVs with the lowest Jaccard distances) to calculate its attraction or repulsion forces. This will allow the FANET to spread more and provide communication services to more victims. This situation is depicted in Figure 8. As the last addition to the movement rules, we include a disconnection avoidance procedure. We consider that the UAVs network is disconnected when the network is split in twoormoregroupsofUAVsthathavenowirelessconnectivity among them. Examples of connected and disconnected UAVsnetworksareprovidedinFigure9,wherethebluelines represent the wireless connections between two UAVs. We define the disconnection security distance as the distance (in meters) from the coverage area perimeter of each UAV and measured towards the center of the coverage area. The disconnection security distance then defines a ring area for each UAV, which is shown in Figure 10. When one UAV has one of its Jaccard neighbors within the security distance ring area, then this Jaccard neighbor is considered to be at risk of disconnection in the next movement. For this reason, a UAV at risk of disconnection is affected by additional attraction force towards its Jaccard neighbors, which keeps them away from being disconnected in the next movement. In Figure 10 we can notice that with the absence of the velocity vector 𝑉𝑑02,whichisgeneratedbythe disconnection avoidance procedure, the UAV 𝑑0would have a resulting velocity vector pointing in the opposite direction from the UAV 𝑑2and it would likely lose connectivity with this UAV in the next movement. With the disconnection avoidance procedure the UAV 𝑑0will not be out of the coverage area of UAV 𝑑2. 5. Artificial Intelligence Algorithms for Optimizing the Number of Serviced Victims 5.1. Algorithm Execution Procedure. A disaster scenario has a dynamic and varying nature. One of the main components of the changing nature of disaster scenarios is that victims are not motionless. Victims may move following very different trajectories, at different speeds, may hold still for a few seconds, and then resume their movements, and so on. We represented some of these features in the proposed scenario of Section 3. Then the Jaccard-based movement rules that we propose for the UAVs have to be adaptive to those scenario dynamics. For this reason, we implement several artificial intelligence algorithms in our model. The algorithms implemented are simulatedannealingandhillclimbing.Wealsoimplemented an algorithm based on the random walk concept for comparison purposes. The aim of the artificial intelligence algorithms is to maximize the number of victims serviced by the UAVs and then calculate the target Jaccard and the target UAVs positions associated with this optimal behavior. The algorithms are not executed continuously but periodically. As an example, for a simulation with a period for the algorithms of 30 seconds, an entire execution of the algorithms is carried out every 30 seconds. During the time when the artificial intelligence algorithms are not being executed, the UAVs keep moving to the target positions calculated by the algorithms. This procedure is shown in Figure 11. One reason for following this procedure is that during the time when the artificial intelligence algorithms are not executed, the UAVs have time to share information about thevictimstheyareservicingandthuscalculatetheirJaccard distances for the next execution of the artificial intelligence algorithms. These algorithms may be time-consuming; however, we have implemented them with a maximum number of cycles 16 International Journal of Distributed Sensor Networks Sim. annealing 200 180 160 140 Serviced nodes 120 100 80 60 40 Hill climbing Random walk 200 300 Time (s) 4000 100 500 600 Figure 17: Serviced victims by the different computational intelligence algorithms. software simulates the UAVs self-deployment in the disaster scenario and generates as outputs a text file with the mobility traces of the UAVs and related information about the network conditions such as distance between the UAVs and Jaccard distances. (iv) The simulations carried out have a duration of 600 seconds. (v) The problem modelled has a random nature due to the victims’ movements. Also the artificial intelligence algorithms implemented are probabilistic heuristics, with random behaviors. Thus, the results presented in this section are averaged from sets of simulations runs. 6.2. Serviced Victims. InFigure17theservicedvictimsduring the entire simulation are shown for each of the algorithms described in Section 5. As we can observe, the random walkbased algorithm is the one producing the worst performance. Thisisobviousasthisalgorithmdoesnotactuallysearchfor the best solution for servicing as many victims as possible. By comparing the simulated annealing and the hill climbingalgorithmsshowninFigure17,wecanobservethat there is a slight difference between both of them (for the particularcaseshowninthefigure).Atsomepointsofthe simulation, the simulated annealing algorithm shows a better performance than the hill climbing and at some other points the situation is the opposite. Both of them perform similarly with a small difference: the hill climbing algorithm is servicing 10 victims more than the simulated annealing algorithm after the first stage of the simulation. As it is known for the hill climbing algorithm, this could get stuck in local maxima. The behaviorshownintheresultsbythehillclimbingalgorithm makes us think that there are no local maxima in our fitness function and so the hill climbing algorithm performs similar to the simulated annealing algorithm and slightly better for thisparticularcase.Thefactofthesimulatedannealing algorithm being slightly below the hill climbing algorithm could be due to the fact that the simulated annealing, per definition, accepts worse candidate solutions in order to escape local maxima. As we can see in Figure 17, the scenario has two sudden changes where groups of victims disappear from the scenario. These changes occur at 𝑡=200seconds and 𝑡=400seconds. As we can observe, both algorithms show a decrease of the number of serviced victims. Also, the random walk algorithm is affected by the first group of victims that disappear considerably; however the second one affects it slightly. This is due to the fact that the UAVs network is less spread with the random walk algorithm and the second group of victims disappearing is not serviced entirely at the time they disappear. There is also a common behavior shown by both the simulatedannealingandthehillclimbingthatisworth mentioning. Both algorithms actually spread out the UAVs network and in this process they present two main behaviors: (i) a tendency for increasing the number of serviced victims and (ii) slight oscillations around some values. We notice that in Figure 17 in the time frame between 0 seconds and 200 seconds there is a clear tendency to increase the serviced nodes, as a result of the FANET initial self-deployment. Within this time frame the network reaches its highest value of serviced nodes which is between 160 and 180 (between 54% and 61% of victims are serviced with respect to the total number of victims in the scenario, which is 295). From the instant of 200 seconds up to the instant of 400 seconds the algorithms have slight oscillations around the numbers of 150–160 serviced nodes (around 56% of victims are serviced, when the total numberofvictimsis275,afterthefirst20victimsdisappeared fromthescenario).Finally,fromtheinstantof400seconds up to the end of the simulation there is an increase tendency that ends up in servicing between 150 and 160 victims (around 60%ofvictimsareservicedfromatotalamountof255 victims, after the second group of 20 victims disappeared). As we have described in previous sections, the disaster scenario has a random nature and the artificial intelligence algorithmsimplementedaswell.Itislogicalthentofind oscillations around specific values, which may be due to the fact of victims entering and going out of the UAVs coverage area. Despite these oscillations, the strategy proposed shows that the UAVs network actually spreads out and responds to thescenariochanges. 6.3. Network Connectivity. The second objective for the tactical movement rules proposed is to keep the UAVs network always connected. As described in Section 5.2 the fitness function has a component that penalizes the network disconnections by giving this function a value of −1 when there is a network disconnection. In Figure 18 we show the artificial intelligence algorithms together with their respective fitness functions values. As we can observe in Figure 18 at no point of the simulation the fitness function takes the value −1. This is a signal that the UAVs network does not suffer from disconnections. Although the random walk-based algorithm does not need the fitness function in order to optimize it, we have also International Journal of Distributed Sensor Networks 17 Sim. annealing 180 160 140 Serviced nodes 120 100 80 60 40 200 300 Time (s) 4000 100 500 600 Fitness (a) 200 180 160 140 Serviced nodes 120 100 80 60 40 Hill climbing 200 300 Time (s) 4000 100 500 600 Fitness (b) Serviced nodes 200 300 Time (s) 400 0 100 500 600 85 80 75 70 65 60 55 50 45 40 Random walk Fitness (c) Figure 18: Computational intelligence algorithms and fitness (blue dashed line). evaluatedittoseeiftherearedisconnectionsforthisalgorithm. There are not disconnections for this algorithm as the disconnection avoidance procedure also applies to it. 6.4. UAVs Locations. In Figures 19, 20, and 21, the UAVs positions are shown for different time steps of the simulation where the dashed red lines represent the UAVs coverage areas. These figures correspond to a specific simulation using the simulated annealing algorithm. In Figure 19, we can see how the UAVs are close to each other at the beginning of the simulation.InFigure20wecanobservehowthenetworkhas been spreading out during the first part of the simulation and thus the area covered by the UAVs network is bigger. This corresponds to a larger number of victims serviced by the FANET. Finally, in Figure 21 we can see the UAVs positions at the end of the simulation. We can notice that the changes from Figures 20 and 21 are slighter than the ones observed fromFigures19and20.ThereasonisbecausewhentheUAVs arestartingtospreadouttheUAVsmovefasterandchange their positions to the optimal locations in order to service the maximumnumberofvictimswhilemaintainingthenetwork connected. When the UAVs are already spread out, they change their positions more slightly in order to avoid having UAVsdisconnections,astheymaybelocatedalreadycloserto the limit of their Jaccard neighbor’s coverage areas. Although themovementsaresmaller,wecannoticefromFigures20and 21 that there are changes in the locations of the UAVs black andpinkwithrespecttothescenariolayout.Wecanalsosee these small changes in the coverage areas of Figure 21, which are displaced towards the lower part of the scenario where 18 International Journal of Distributed Sensor Networks Figure 19: UAVs positions at simulation time equal to 2 seconds. Figure 20: UAVs positions at simulation time equal to 43 seconds. the major accumulation of victims is located (the bottom-left corner). As we can see in Figures 19, 20, and 21, there are victims that have not been discovered during the AANET selfdeployment. With this scenario and the selected simulation settings (number of UAVs, coverage area, etc.), it is not feasibletocovertheentirescenarioareawithoutdisconnecting theAANET.Asafuturework,weplantoincludenewbehaviors to the UAVs in order to allow them to explore unknown areas. This could be implemented as an additional algorithm for selecting periodically some “explorer UAVs” from those flying close to the AANET border. An “explorer UAV” would have the role of periodically leaving the AANET in order to explore unknown areas. This type of UAVs would remember the latest position when they were part of the AANET in order to return to it. These UAVs could bring valuable information to the AANET about new victims discovered and their positions and feed the artificial intelligence algorithms for the self-deployment. Such idea has been used in other works such as exploring behavior of the UAVs described in [38]. Figure 21: UAVs positions at simulation time equal to 589 seconds. 7. Conclusions We have addressed the problem of modelling a UAVs network self-deployment on a disaster scenario. The main aim of theUAVsnetworkistoprovidecommunicationservicesto thevictimsandrescueworkerswithintheincidentsiteof a disaster area. This is translated in more specific objectives which are to maximize the number of victims serviced by the FANET and maintain the network connectivity. We have proposed an innovative approach to perform the selfdeployment calculating the UAVs movements as a basis of the Jaccard distance among the UAVs and artificial intelligence algorithms that optimize the number of victims serviced by the network. As there are no realistic mobility models for representing the victims’ movements within the incident site of a disaster area, we have proposed our own model as a combination of regions with different mobility features. The UAVs network self-deployment and the victims’ mobility model were simulated jointly on software built specifically for this purpose.Wehaveanalyzedtheresultsprovidedbythedifferent artificial intelligence algorithms implemented, showing that the simulated annealing and hill climbing algorithms really carry out the UAVs self-deployment and aim to maximize the totalnumberofservicedvictims.Theresultsobtainedshowed that for a network of 6 UAVs and 295 victims the percentage of victims serviced is around 60% of the total. As future work, we plan to make improvements in the disaster scenario model by including more detailed urban features and also creating more complex victims’ mobility models. Also, we plan to include additional behaviors to the UAVs that allow them to be more efficient in their self-deployment, for example, including an exploring behavior so they are able to discover unknown areas of the scenario. 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