An obstacle-aware human mobility model for ad hoc networks

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1 An obstacle-aware human mobility model for ad hoc networks Christos Papageorgiou, Konstantinos Birkos, Tasos Dagiuklas, Stavros Kotsopoulos Dept. of Electrical and Computer Engineering University of Patras, Greece Dept. of Telecommunications, Systems and Networks TEI of Mesolonghi Nafpaktos, Greece Abstract In this work we present an obstacle-aware human mobility model for ad hoc networks. Typical examples where the nodes of mobile ad hoc networks are human-operated are natural or man-made disasters, military activities or healthcare services. In these scenarios, obstacles are an integral part of the areas where such networks are deployed in order to facilitate communication among the firemen, policemen, medics, soldiers, etc. In the proposed mobility model, the nodes of the network move around the obstacles in a natural and realistic way. A recursive procedure is followed by each node according to which every time an obstacle is encountered between the node s current position and the final destination point, the node moves to the obstacle s vertex that is closest to the destination. This process is repeated until the destination is reached. The obstacles are also taken into account in modeling the signal propagation. When a packet is transmitted through an obstacle, the power at which it is received is attenuated by a certain value representing the physical layer phenomena suffered by the signal. The model is implemented as an add-on module in Network Simulator ns-2. A thorough simulation study conducted highlights the differences of the proposed model with other mobility models, by investigating the properties of the resulting network topologies and their impact on network performance. I. INTRODUCTION Mobile ad hoc networks constitute a well established research field that in the recent years has drawn lots of attention. Simulation is a primary tool for studying these networks and their protocols, since it provides researchers with a series of advantages over the real-world testing such as repeatability of scenarios, large scale experiments at far smaller cost and time duration, evaluation of a wide range of metrics and parameterspecificstudy.asaresult,thevastmajorityofpapersrelatedto ad hoc networks includes some kind of performance evaluation by means of simulation, with however often questionable results [19]. An important part of any simulation study regarding mobile ad hoc networks is the model according to which the nodes move inside the network area. Mobility is an innate characteristic of these networks and thus the way it is modeled has a decisive impact on the simulation s reliability. A series of mobility models has been proposed in the literature ranging from straightforward purely random-based models to specific models targeting various scenarios like nodes moving in groups, mobility in city environments, etc. Moreover, the cases of mobile ad hoc networks consisting of human-operated nodes like rescue operations in emergency situations and military activities, are of high importance since they are among the main application fields of such networks. Due to their infra-structureless nature, ad hoc networks are often the only option for the urgent communicational needs in situations like the above. In such scenarios, the nodes comprising the mobile ad hoc network are firemen, medics, policemen, soldiers, etc, operating in an area where obstacles are indispensable part of the scenery. Examples of the obstacles encountered in the aforementioned situations can be fallen buildings, trees, hills, etc. In this work we present an obstacle-aware human mobility model for ad hoc networks, to be referred to as Human Mobility Obstacle (HUMO) model. In the proposed HUMO model, the nodes of the network move around the obstacles in a natural and realistic way. A recursive procedure is followed by each node according to which every time an obstacle is encountered between the node s current position and the final destination point, the node moves to the obstacle s vertex that is closest to the destination. This process is repeated all over again until the destination is reached. The obstacles are taken into account not only in the node movement level, but also in the signal propagation modeling. When a packet is transmitted and there is an obstacle between the transmitter and the receiver, the power at which it is received is attenuated by a certain value representing the physical layer phenomena suffered by the signal. The HUMO model is implemented as an add-on module in Network Simulator ns-2[23] and is freely available for download [15]. A thorough simulation study conducted highlights the differences of the proposed model with other obstacle mobility models and the Random Waypoint model, by investigating their properties in terms of the resulting network connectivity versus time and their impact on network performance under a well-known routing algorithm. The rest of the paper is organized as follows. In Section 2 we discuss the related work in the field of mobility models.

2 Section 3 presents in detail the proposed human obstacle mobility model. In Sections 4 and 5 the simulations setting is outlined and the results are shown, respectively. Finally, Section 6 concludes the paper with the conclusions drawn and the possible extensions of the current work. II. RELATED WORK Several models have been developed to provide the research community with solutions for realistic simulations on mobile ad hoc networks. Recent surveys summarizing the related work in the field can be found in [1],[4],[10]. According to the Random Waypoint (RWP) mobility model [17], nodes select a random destination in the simulation area and they move towards it with a random uniformly distributed speed. After a certain pause time they repeat the same process all over again. The properties of the RWP model have been studied extensively in the literature [8],[29], while several variations have also been proposed [7],[12],[14],[22]. In the Random Walk (RW) mobility model, each node randomly chooses speed and direction in constant time intervals without setting destination. The Random Direction mobility model (RD) [25] differs from the aforementioned models in that nodes determine speed and direction and then move until they reach the boundary of the area. In the Realistic Mobility Model (RRM) [18] speed and direction follow distributions that yield more realistic node movement. In the Boundless Simulation Area mobility model [13] there is a relationship between the previous speed and direction of a node and its current ones. Furthermore, when a node reaches the boundary of the area it reappears from the opposite one resulting this way in toroid network areas instead of the regular rectangular ones. The Gauss-Markov[20] mobility model can offer different levels of randomness via one tuning parameter. Temporal dependency of mobility is a key characteristic in Smooth Random mobility model [6] that produces more realistic mobility patterns. Apart from the above models, where each node moves independently from the rest, a series of models that follow a group-based mobility pattern has also been proposed. The most representative model in this category is the Reference Point Group Mobility (RPGM) [14] model, in which the nodes of each group move around the group s leader that acts as a reference point. In [26] different group mobility models applicable to a variety of situations are presented. The Column mobility model imitates the movement of a group of nodes moving in a straight line that periodically changes direction. The Nomadic Community Mobility Model allows each node to use one of the entity mobility models discussed in the previous paragraph as it moves around the common reference point. The Pursue Mobility Model is suitable for simulating a group of nodes that chase a particular target. All the above mobility models assume a free-space area where the mobile ad hoc network nodes move. This assumption is, according to our opinion, a very strong one since obstacles are an integral part of nearly all kind of scenarios where mobile ad hoc networks operate. Therefore, obstacle mobility models have also been proposed. In [16] the Obstacle Mobility (OM) model is introduced that apart from movement constraints it incorporates propagation impairments due to the presence of obstacles. This model supports movement in campus-like environments with nodes following predefined paths connecting a limited set of points in the network area. These paths and points are defined from a Voronoi diagram constructed based on the obstacles positions in the simulation area. Extending this work,[3] presents a model in which nodes select destinations based on their activity type. Likewise, in the Pathway mobility model [27] nodes are allowed to move along predefined edges that represent streets and pathways. In this context, the City Section mobility model is appropriate for simulating mobility in the street network of a city and includes safe driving characteristics, while according to the Manhattan mobility model [5] node movement in freeways is emulated. In the Environment-Aware Mobility (EAM) model [2] the area is divided into accessible and non-accessible areas with different mobility factors. In [28] some principles of the social network theory are applied to produce mobility patterns in the presence of obstacles. Apart from specific mobility models, frameworks that simulate mobility have been implemented. TRAILS [11] is an ns-2 extension with advanced mobility features, dynamic obstacles and node failure simulation characteristics. MobiSim [21] supports a series of both entity and group mobility models, while MOMOSE [9] is a Java-based simulation environment with even wider range of supported mobility models and higher flexibility in the design and placement of the obstacles. The aforementioned frameworks can produce ns-2 compatible trace files but they cannot be integrated directly into the simulator core. III. THE HUMAN MOBILITY OBSTACLE MODEL The proposed Human Mobility Obstacle (HUMO) model targets to realistically simulate mobile ad hoc networks that consist of human-operated nodes and are deployed in areas where obstacles are present. Primary examples of such scenarios are emergency situations like earthquakes, fires, etc, and military operations, where the nodes consisting the mobile ad hoc network are firemen, medics, policemen, soldiers, etc, operating in an area where obstacles are indispensable part of the scenery. The HUMO model is implemented as an add-on module to Network Simulator (ns-2) [23], which is the de facto standard for the networking research community, and is available for free download and use [15]. The integration of the HUMO model in the operation of ns-2 is straightforward, providing thus an advantage over solutions that require separate software platforms to be run on and then import the produced traces in ns-2. The user can design the obstacles by using a graphical user interface which are then imported in the ns-2 operation by copying the produced coordinates text file to predefined locations within the directory hierarchy. Then, the movement scenario generator is run and the resulting file is imported to the final script. Furthermore, a visualization tool is provided depicting the produced scenario s node movement within the

3 Fig. 2. An example of how a node moves towards its destination point around the obstacles in the network area according to the HUMO mobility model. Fig. 1. An example of a visualization of a mobility scenario. network area in the presence of obstacles. An example of such a visualization is given in Figure 1. The operation of the HUMO model, just like every mobility model, can be divided in two distinct functions: the destination selection mechanism and the node movement process. Apart from incorporating the presence of obstacles in each of these functions, the propagation model is also included in the HUMO mobility model. In order to obtain more reliable simulation results, obstacles should not only restrict node movement but also affect the signals propagated through them. In what follows, the aforementioned building blocks of the proposed HUMO model are presented in detail. A. Destination selection In the HUMO model, the destination points are selected by the nodes randomly based on a uniform distribution. Each node can move to every point in the network area as long as it does not reside within the boundaries of an obstacle. In doing so, all the points in the network area take part in the random selection, but the process is repeated until a point outside the obstacles limits is drawn. When a destination is selected, each node moves towards it according to the movement mechanism described below with a randomly taken speed until the destination is reached. Then, it waits for a certain predefined time interval and starts over. The randomness of the selection process as well as the destination selection-movement-pause operational cycle described above, resemble to the Random Waypoint Model [17]. The HUMO model however, differs in that the obstacles are taken into account. In contrast to the majority of the obstacle mobility models that is application-specific (campus [16], city, freeway [5] environments), the proposed HUMO model dictates the nodes to select among all points in the network area as long as they do not reside within an obstacle. This is a much more generic approach suitable for studying humanoperated mobile ad hoc networks in a wide range of scenarios. Given that the destination selection mechanism is entirely independent of the mobility model s movement mechanism, the HUMO model as described in the present paper can be further extended with more sophisticated destination selection mechanisms resembling specific real-life situations. For instance aspects of how emergency workers move in disaster areas can be incorporated with notions like hierarchical or urgent event-driven movement, rescue and evacuation tactics, etc. We plan to investigate these directions in future work. B. Movement In the HUMO model, when a destination point is set according to the mechanism described above, each node moves around the obstacles following a recursive process in order to reach it. If there is an unobstructed line of sight connecting the node with the destination point, the node follows this direct line to get to the desired destination. If there is an obstacle in the way, the node sets as its next intermediate destination the vertex of the obstacle s edge directly visible that is closest to the destination and repeats the same process all over again with starting point its initial position and destination the chosen vertex. This is repeated until an unobstructed direct line until the current destination is found. The whole process is recursively executed until the destination is reached. An example depicting this movement process is shown in Figure 2. The node u located in point s has set as it destination point d. Checking if the direct line connecting s to d is unobstructed, realizes that obstacle P is in the way. From the vertices of the edge (P 1,P 2 ) that is the first one obstructing node u s way to the point d, the one closest to d is P 1. Therefore, P 1 is set as the next intermediate destination. In

4 order to reach P 1 node u repeats the same process with s as its starting point and P 1 as destination. Obstacle Q obstructs node u to reach P 1 immediately, so the closest to P 1 vertex between Q 1 and Q 2 is selected, which is Q 2. Then, the same process dictates that node u can move directly to Q 2, since there are no obstacles between s and Q 2. From Q 2 node u following the same process reaches d through points P 1 and P 3. The above node movement mechanism imitates in a realistic way how people move their way around obstacles restricting their movement. When a person moves towards a certain point, it is rational to assume that he/she will try to go around the first obstacle in front of him/her over the vertex closest to the desired destination. Even if eventually this will not be the most efficient choice in terms of the total distance covered until the destination, given that the overall obstacle placement is in most cases unknown, making this decision is the best a person moving in such areas can do. C. Propagation model In order to make the integration of the obstacles complete, the propagation model in use is also modified so that the effect of the physical layer phenomena like fading, multipath propagation, diffraction, attenuation, etc, caused by the presence of obstacles is also taken into account. More specifically, the default propagation model is modified to ensure that when a signal is propagated through an obstacle, it suffers an attenuation randomly taken from a uniform distribution between fixed values. These values range between 6 and 50 db and are experimentally verified. This obstacle-aware propagation model is a simplified version of the propagation model proposed in [16] for the Obstacle Mobility model. It is incorporated in the proposed HUMO model in order to further enhance its reliability. A. Setting IV. SIMULATIONS The purpose of the simulation study conducted in the context of the present work is twofold. On the one hand, to identify the properties and characteristics of the proposed HUMO model and on the other, to evaluate the impact on the network performance under a well known routing algorithm. The comparative analysis is made with the Random Waypoint model [17] and the Obstacle Mobility model [16], to be referred to as RWP and OM respectively. The RWP mobility model is the most widely used model in the literature given that is simple and straightforward. OM is a model that takes into account obstacles in a complete way hindering both the node movement and the radio communication. In fact, the proposed HUMO model extends OM by enabling nodes to move towards all the network area in a natural way around the obstacles, rather than following predefined pathways connecting a limited set of location points, which is the case for the OM model. The mobile ad hoc network we are running the simulations on, consists of a number of N nodes that move according Fig. 3. The network area where the main part of the simulations results is taken. The obstacles cover approximately the 34% of the total area. According to the HUMO model the nodes move in all the available area, while under the OM model the nodes move along the pathways shown. Fig. 4. The network area where the obstacle covers approximately the 6% of the total area. According to the HUMO model the nodes move in all the available area, while under the OM model the nodes move along the pathways shown. to each one of the RWP, OM and HUMO mobility models within an area of 1000m 1000m dimensions. When the RWP model is used no obstacles are taken into account. On the other hand, the OM and HUMO obstacle mobility models are evaluated in scenarios where there are obstacles in the network area covering that are placed as shown in Figures 3 and 4. The choice of the these placements is completely arbitrary,

5 since the simulations conducted in this paper target only to identifying the generic differences between the proposed HUMO and other mobility models like RWP and OM. In the first case (Figure 3), where the main part of the results presented below (Sections IV-B1 and IV-B2) refers to, a large percentage O (approximately 34%) of the network area is covered by obstacles, while in the second (Figure 4) this percentage is significantly lower (around 6%). We study both cases in order to to investigate the behavior of the network in areas of variable obstacle coverage. Placements of obstacles resembling real-world scenarios can be made in order for the simulation studies to be more realistic. This is another possible extension for the present work that we plan for the future. The nodes under the HUMO mobility model move freely towards all the available, outside the obstacles boundaries that is, network area, while in the OM model the nodes move only between the so-called location points(marked with circles in Figures 3 and 4). Some of the points lie within obstacles since OM targets in resembling the movement in campus-like environments where obstacles are buildings. The routes that a node can follow under the OM model that comprise a Voronoi diagram, are drawn as lines in both Figures. As far as the the propagation model is concerned, the Two Ray Ground model is used with the modification according to which, every time a signal is transmitted through an obstacle, the power with which it is received is attenuated by a certain value randomly taken from a experimentally verified range. The transmission range of the nodes is set at 250m, while at the MAC layer the IEEE b protocol is used. The nodes in all mobility models select their speed randomly from a uniform distribution with minimum value 0 and maximum value u that in our experiments is taken to be 1, 2, 5 and 7 meters per second. The choice of speeds is made having in mind that the proposed HUMO mobility model is mainly targeted to human-comprised mobile ad hoc networks. As far as the pause time p of the nodes is concerned, it was fixed at 0.5 seconds in all the experiments so that the node mobility rate is defined by the node maximum speed. The simulation time of each experiment is 2000 seconds, while each value depicted in the figures below is taken as an average of 10 executions with different seeds. B. Results 1) Network topology: In this first set of experiments we are interested in the differences between the the proposed HUMO model and the OM obstacle mobility model regarding the network topologies that both result to. The two obstacle mobility models are simulated in a network area with obstacles placed as shown in Figure 3. As discussed above, even though both models take into account obstacles, the way each of them dictates nodes how to move around them varies considerably. In order to evaluate how the network topologies created by each of the HUMO and OM models compare to each other, the following metrics are used: the network density ND, defined as the average number of neighbors per node in the network. Fig. 5. Illustrates the average number of neighbors per node through time for the HUMO and the OM obstacle mobility models, for the scenario of N=50 nodes moving with maximum speed u=5 meters/second and pause time p=0.5 seconds in the area of Figure 3. the average total link lifetime L, defined as the averaged time for which a link between two nodes is active. L is taken as the average of the total time each of the possible N (N 1), where N is the number of nodes, links is active during each experiment. Two nodes are assumed to be neighbors if they are within the vicinity of each other. In this case the link between the two nodes is also taken to be active. The above definitions mean that the effect of obstacles only regarding the node movement can be investigated using these metrics. Hindering the radio communication is not taken into account since the transmission range of each node is taken to be the disk with fixed radius, regardless of the obstacles that lie within it. Therefore, in this set of simulation experiments the RWP model does not take part, since only the differences between the two obstacle mobility models are evaluated. In Figure 5 the results regarding the network density ND are depicted. The OM model has throughout the entire duration oftheexperimenthigheraveragenumberofneighbors ND per node than HUMO, since the nodes, instead of moving within all the available network area, only follow the predefined pathways (Figure 3). Thus in the latter case, the spatial dispersion of the nodes is significantly lower and therefore the average number of neighbors per node is higher. This is a generic difference between the HUMO and the OM mobility models that can be observed for all mobility rates. The increased node density of the OM mobility model is also verified by the higher average total link lifetime L it achieves in comparison to the HUMO model (Figure 6). For all cases of the number of nodes comprising the mobile ad hoc network, OM results in longer periods of active links. For both obstacle mobility models, L grows with the number of nodes, since the node density in the network area increases. This means that the probability two nodes can communicate with each other is also higher. However, when the OM model is used the network achieves always greater L due to the fact

6 Fig. 6. Illustrates the average link lifetime in seconds as a function of the number of nodes moving in the area of Figure 3 with maximum speed u=7 meters/second and pause time p=0.5 seconds, for the HUMO and the OM obstacle mobility models. that it dictates the nodes to move in a far more restricted area than HUMO. The nodes under the HUMO mobility model move within all the available, outside the obstacles boundaries that is, network area, while in the OM model the nodes move only along the predefined pathways connecting the so-called location points. 2) Network performance: In the second part of our simulation study, we evaluate RWP, OM and HUMO in terms of their impact on network performance. Constant Bit Rate (CBR) traffic is inserted in the network with 50 sessions of 100 packets, 64 bytes each, sent with an interval of 10 seconds between randomly chosen, by a uniform distribution, sources and destinations. The AODV protocol [24] is used by the mobile ad hoc network in order for the packets to be routed to their destinations. The network performance when nodes move according to the RWP, OM and the HUMO mobility models is evaluated using the metrics below. theaveragenumberofhops hperpacket,whichisdefined as the number of transmission required for each packet to be transmitted to its destination. the delivery ratio r, taken to be the ratio of the number of received packets at their destinations over the number of the packets originally sent. the number of collisions C occurred between transmitted packets. the average delay D, defined as the time that elapses from the instant that a packet is sent by the node it originates from, until it reaches its destination. the average number CP of control packets sent by the routing protocol per data packet. In Figure 7 it can be observed that the RWP model achieves far smaller average number of hops than both obstacle mobility models, OM and HUMO. The presence of the obstacles makes the packets follow longer routes towards to their destinations since the connectivity of the resulting topologies, is limited compared to the obstacle-free case that the RWP model assumes. Given that the majority of works on mobile ad hoc Fig. 7. Illustrates the average number of hops followed by each packet in order to arrive to its destination as a function of the maximum node speed, in the scenario where N=50 nodes move according to the RWP, OM and HUMO mobility models in the area of Figure 3 (without the obstacles for the RWP) with pause time p=0.5 seconds. Fig. 8. Illustrates the number of packet collisions as a function of the maximum node speed, in the scenario where N=50 nodes move according to the RWP, OM and HUMO mobility models in the area of Figure 3 (without the obstacles for the RWP) with pause time p=0.5 seconds. networksisbasedontherwpmodel,figure7indicatesthatin real-life scenarios where obstacles are present in the network deployment area, a more realistic simulation study has to be performed. Asfarasthenumberofcollisions C isconcerned,theresults depicted in Figure 8 show that again the network performance is overestimated by the RWP model compared to the other two obstacle mobility models. According to the RWP model, the nodes move randomly in all the network area and therefore the probability of concurrent transmission in close proximity between nodes is lower than in the cases of OM and HUMO models, where nodes move within a restricted network area due to the presence of the obstacles. Since the restriction in the node movement posed by the OM model is bigger than in the case of the HUMO model, OM results in higher C than HUMO. Figure 9 shows the average per data packet number of control packets CP sent when the mobile ad hoc network

7 Fig. 9. Illustrates the total number of control packets transmitted as a function of the maximum node speed, in the scenario where N=50 nodes move according to the RWP, OM and HUMO mobility models in the area of Figure 3 (without the obstacles for the RWP) with pause time p=0.5 seconds. Fig. 11. Illustrates the packet delivery ratio achieved by the network as a function of the maximum node speed, in the scenario where N=50 nodes move according to the RWP, OM and HUMO mobility models in the area of Figure 3 (without the obstacles for the RWP) with pause time p=0.5 seconds. Fig. 10. Illustrates the average packet delivery delay as a function of the maximum node speed, in the scenario where N=50 nodes move according to the RWP, OM and HUMO mobility models in the area of Figure 3 (without the obstacles for the RWP) with pause time p=0.5 seconds. nodes move according to each of the RWP, OM and HUMO mobility models. In the same context with the above remarks, the network performance concerning the CP metric is overestimated when the presence of obstacles is ignored (RWP model). When the RWP model is used the CP is far smaller than the respective values for the OM and HUMO models. When the network nodes move under the OM model the CP is the highest since the nodes are concentrated in a far smaller area than the other two models and therefore the retransmissions caused by collisions are more frequent. This effect was also discussed above regarding the results of the number of collisions. The effect of the presence of obstacles in network performance regarding the average packet delivery delay D, as Figure 10 shows, is similar. Again the network under the RWP model performs significantly better than in the cases where obstacles are taken into account. When no obstacles are assumed to hinder the node movement and communication, the packets are modeled to arrive at their destination more easily and quickly. On the other hand, the resulting D, when the proposed HUMO mobility model is used, is lower than the respective value of when the network nodes move according to the OM model, despite the fact that both take into account the obstacles. The fact that the nodes in the HUMO model move within all the available network area instead of following the predefined pathways, which is the case for the OM model, increases the node spatial dispersion and thus the number of available routing options for each node. Furthermore, in the HUMO model the collisions are less frequent than in the OM model. Therefore, the total time it takes a packet to reach its destination is lower under the HUMO model than the OM model. In Figure 11 the packet delivery ratio R of the network under all the mobility models is illustrated. Clearly, when the RWP model is used, the percentage of the delivered packets is higher than in the cases where obstacles are taken into account. Modeling the simulation study of a mobile ad hoc network area without taking into account obstacles does not lead to reliable results. As shown in Figure 11 while the network under the RWP model delivers for all mobility rates nearly all the packets sent (R=85% in the worst case), when obstacles are concerned the achieved delivery ratio falls at values around 70%. The presence of obstacles increases the number of packets dropped either due to collisions or lack of a valid path. Between the two obstacle mobility models HUMO seems to lead to network topologies that exhibit better performance than the OM model. This is because of the higher spatial diversity of the nodes under the HUMO model compared to the OM model that dictates the nodes to move only along predefined pathways, rather than ranging in all the available network area. The effect of mobility is more or less equal to all the models, with the resulting R falling as the mobility rate increases, since as nodes move faster the probability that a route remains valid until a packet reaches its destination falls. 3) The impact of the obstacle area: In order to investigate the impact of the obstacle area in the two obstacle mobility

8 which the achieved delivery ratio R approaches the respective value under the RWP model. In the case of Figure 4 the node movement under the HUMO model resembles the RWP model since the obstacle covered area O is too small to produce significant changes. Fig. 12. Illustrates the average number of neighbors per node through time for the HUMO and the OM obstacle mobility models, for the scenario of N=50 nodes moving with maximum speed u=5 meters/second and pause time p=0.5 seconds in the area of Figure 4. Fig. 13. Illustrates the packet delivery ratio achieved by the network as a function of the maximum node speed, in the scenario where N=50 nodes move according to the RWP, OM and HUMO mobility models in the area of Figure 4 (without the obstacles for the RWP) with pause time p=0.5 seconds. models, an extra set of simulations is run with the network area of Figure 4. In the case of Figure 3, where all the above discussed results were based, the network area O covered by obstacles is equal to the 34% of the total network, while in the case of Figure 4 this percentage falls at 6%. Figure 12 depicts the results for this case regarding the average node density. The OM model again seems to result in network topologies of higher node density than the HUMO model. The difference between them however is broadened compared to the obstacle placement of Figure 3. The Voronoi diagram constructed based on the coordinates of the only obstacle in Figure 4 has fewer edges than in the case of Figure 3, which means that the node dispersion is even denser in the former case than in the latter. On the other hand the HUMO model also benefits by resulting in slightly larger ND from the more free space, but at a smaller scale. In the network performance, the effect of the obstacle covered area is more obvious in the HUMO model according to V. CONCLUSIONS AND FUTURE WORK In this paper a human mobility model, Human Mobility Obstacle (HUMO), was proposed for mobile ad hoc networks in the presence of obstacles. Primary examples of such scenarios are emergency situations like earthquakes, fires, etc, and military operations, where the nodes consisting the mobile ad hoc network are firemen, medics, policemen, soldiers, etc, operating in an area where obstacles are indispensable part of the scenery. In the HUMO model, each node can move to every point in the network area as long as it does not reside within the boundaries of an obstacle. If there is an unobstructed line of sight connecting the node with the destination point, the node follows this direct line to get to the desired destination. If there is an obstacle in the way, the node sets as its next intermediate destination the vertex of the obstacle s edge directly visible that is closest to the destination and repeats the same process all over again with starting point its initial position and destination the chosen vertex. This is repeated until an unobstructed direct line until the current destination is found. The whole process is recursively executed until the destination is reached. Furthermore, the default propagation model is also modified and incorporated to the HUMO model to ensure that when a signal is propagated through an obstacle, it suffers an attenuation randomly taken from a uniform distribution between fixed values. The HUMO model is implemented as an add-on module to Network Simulator and is available for free download and use [15]. A comparative simulation study was undertaken that highlighted the differences between the proposed HUMO model with other obstacle mobility models. Furthermore, the need for reliable mobility models was also verified given that the most commonly used in the literature Random Waypoint model was shown to clearly overestimate the network performance under all the metrics considered. The proposed HUMO can be further extended and improved by incorporating aspects of how human-operated nodes move in certain situations. For example the nodes can be modeled to be organized in hierarchical groups in order to resemble reallife situations. Another possible direction we plan to follow is to extend the HUMO model by incorporating various importance classes in the events each node or group answers to. In this way, when an event is of higher complexity, reinforcements of other nodes or groups will be called. Furthermore, several real-life movement patterns can be included in order for the model to become more realistic. For example, to imitate how the medical stuff or the firemen move and operate in an emergency area. Finally, by studying the placement of the obstacles so that it reflects real-life scenarios the model can produce even more realistic results.

9 ACKNOWLEDGEMENTS The authors wish to acknowledge the support of the ICT European Research Programme and all the partners in PEACE: PDMF&C, Instituto de Telecomunicaciones, FhG Fokus, Kingston University, Thales, Telefonica, Pale Blue. REFERENCES [1] E. Atsan and Ö. Özkasap. A classification and performance comparison of mobility models for ad hoc networks. In ADHOC-NOW, pages , [2] H. Babaei, M. Fathi, and M. Romoozi. A novel environment-aware mobility model for mobile ad hoc networks. In MSN, LNCS 3794, pages Springer, [3] H. Babaei, M. Fathi, and M. Romoozi. Obstacle mobility model based on activity area in ad hoc networks. In ICCSA, LNCS 4706, pages Springer, [4] F. Bai and A. Helmy. A survey of mobility models in wireless ad hoc networks, Book Chapter in Wireless Ad Hoc and Sensor Networks, Kluwer Academic Publishers, [5] F. Bai, N. Sadagopan, and A. Helmy. The important framework for analyzing the impact of mobility on performance of routing for ad hoc networks. 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