Mobile multi-robot teams deployed for monitoring or search-and-rescue missions in urban disaster areas can greatly improve the quality of vital data collected on-site. Analysis of such data can identify hazards and save lives. Unfortunately, such real deployments at scale are cost prohibitive and robot failures lead to data loss. Moreover, scaled-down deployments do not capture significant levels of interaction and communication complexity. To tackle this problem, we propose novel mobility and failure generation frameworks that allow realistic simulations of mobile robot networks for large scale disaster scenarios. Furthermore, since data replication techniques can improve the survivability of data collected during the operation, we propose an adaptive, scalable data replication technique that achieves high data survivability with low overhead. Our technique considers the anticipated robot failures and robot heterogeneity to decide how aggressively to replicate data. In addition, it considers survivability priorities, with some data requiring more effort to be saved than others. Using our novel simulation generation frameworks, we compare our adaptive technique with flooding and broadcast-based replication techniques and show that for failure rates of up to 60 it ensures better data survivability with lower communication costs.
In this paper, we present a survey of various mobility models in both cellular networks and multi-hop networks. We show that group motion occurs frequently in ad hoc networks, and introduce a novel group mobility model Reference Point Group Mobility (RPGM) to represent the relationship among mobile hosts. RPGM can be readily applied to many existing applications. Moreover, by proper choice of parameters, RPGM can be used to model several mobility models which were previously proposed. One of the main themes of this paper is to investigate the impact of the mobility model on the performance of a specific network protocol or application. To this end, we have applied our RPGM model to two different network protocol scenarios, clustering and routing, and have evaluated network performance under different mobility patterns and for different protocol implementations. As expected, the results indicate that different mobility patterns affect the various protocols in different ways. In particular, the ranking of routing algorithms is influenced by the choice of mobility pattern. This paper presents an analysis of the behavior of mobile ad hoc networks when group mobility is involved. We propose four different group mobility models and present a mobility pattern generator, called grcmob that we designed to be used with the ns-2 simulator. Using 2k factorial analysis we determine the most representative factors for protocol performance. We then evaluate the performance of a dynamic source routing (DSR) based MANET, using both TCP and UDP data traffic. The results are compared with the classical random waypoint mobility model. It is shown that the number of groups parameter is more important than the number of nodes one and that the impact of the area size is almost negligible. We make also evident that the mix of inter- and intra-group communication has the strongest impact on the performance. Finally, it is evidenced that the presence of groups forces the network topology to be sparser and therefore the probability of network partitions and node disconnections grows. In this paper, we propose a novel group mobility model, called reference region group mobility (RRGM) model, which can be used in the description of group motion behavior as well as individual movement. This model is designed to be applicable to many different scenarios, such as military operation, search and rescue, exhibition hall visiting, building search, etc., where a group may be partitioned into a number of smaller groups and groups may merge whenever necessary. Moreover, by using the density-based approach, our model can control the size of the region to be covered by a group. Our main contribution is on the effectiveness of modeling group mobility scenarios with group partitioning and merging, which are most likely to be found in ad hoc networks This paper provides a model that realistically represents the movements in a disaster area scenario. The model is based on an analysis of tactical issues of civil protection. This analysis provides characteristics influencing network performance in public safety communication networks like heterogeneous area-based movement, obstacles, and joining leaving of nodes. As these characteristics cannot be modeled with existing mobility models, we introduce a new disaster area mobility model. To examine the impact of our more realistic modeling, we compare it to existing ones (modeling the same scenario) using different pure movement and link-based metrics. The new model shows specific characteristics like heterogeneous node density. Finally, the impact of the new model is evaluated in an exemplary simulative network performance analysis. The simulations show that the new model discloses new information and has a significant impact on performance analysis. In wireless ad-hoc networks, network partitioning occurs when the mobile nodes move with diverse patterns and cause the network to separate into completely disconnected portions. Network partitioning is a wide-scale topology change that can cause sudden and severe disruptions to ongoing network routing and upper layer applications. Its occurrence can be attributed to the aggregate group motion exhibited in the movements of the mobile nodes. By exploiting the group mobility pattern, we can predict the future network partitioning, and thus minimize the amount of disruption. We propose a new characterization of group mobility, based on existing group mobility models, which provides parameters that are sufficient for network partition prediction. We then demonstrate how partition prediction can be made using the mobility model parameters and illustrate the applicability of the prediction information. Furthermore, we use a simple but effective data clustering algorithm that, given the velocities of the mobile nodes in an ad-hoc network, can accurately determine the mobility groups and estimate the characteristic parameters of each group.