MOBILITY MODELS FOR DELAY TOLERANT NETWORK : A SURVEY <s> Random Walk (RW) Model <s> This paper presents a mobile tracking scheme that exploits the predictability of user mobility patterns in wireless PCS networks. Instead of the constant velocity fluid-flow or the random-walk mobility model, a more realistic Gauss-Markov model is introduced, where a mobile's velocity is correlated in time to a various degree. Based on the Gauss-Markov model, a mobile's future location is predicted by the network based on the information gathered from the mobile's last report of location and velocity. When a call is made, the network pages the destination mobile at and around the predicted location of the mobile and in the order of descending probability until the mobile is found. A mobile shares the same prediction information with the network and reports its new location whenever it reaches some threshold distance away from the predicted location. We describe an analytical framework to evaluate the cost of mobility management for the proposed predictive distance-based scheme. We then compare this cost against that of the regular, non-predictive distance-based scheme, which is obtained through simulations. Performance advantage of the proposed scheme is demonstrated under various mobility and call patterns, update cost, page cost, and frequencies of mobile location inspections. <s> BIB001 </s> MOBILITY MODELS FOR DELAY TOLERANT NETWORK : A SURVEY <s> Random Walk (RW) Model <s> 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. <s> BIB002 </s> MOBILITY MODELS FOR DELAY TOLERANT NETWORK : A SURVEY <s> Random Walk (RW) Model <s> Realistic mobility models are crucial for the simulation of Delay Tolerant and Opportunistic Networks. The long standing benchmark of reproducing realistic pairwise statistics (e.g., contact and inter-contact time distributions) is today mastered by state-of-the-art models. However, mobility models should also reflect the macroscopic community structure of who meets whom. While some existing models reproduce realistic community structure - reflecting groups of nodes who work or live together - they fail in correctly capturing what happens between such communities: they are often connected by few bridging links between nodes who socialize outside of the context and location of their home communities. In a first step, we analyze the bridging behavior in mobility traces and show how it differs to that of mobility models. By analyzing the context and location of contacts, we then show that it is the social nature of bridges which makes them differ from intra-community links. Based on these insights, we propose a Hypergraph to model time-synchronized meetings of nodes from different communities as a social overlay. Applying this as an extension to two existing mobility models we show that it reproduces correct bridging behavior while keeping other features of the original models intact. <s> BIB003
Random walk mobility model is based on the argument that entities naturally move around in unpredictable ways. In this model, every node moves towards a new randomly chosen location. A random direction and speed is assigned to each node from a predefined range and nodes of a network are independent from one another BIB003 . A new direction is again assigned from predefined ranges whenever any node reaches the destination location. In this model, the distributions of mobility parameters are a function of time. As the mobility parameters achieve the stable state of distributions, the simulation produces consistent results. This has kept its popularity to evaluate DTN protocols. However, the variations of direction and speed in this model are limited in a certain ranges that are defined beforehand. Besides, the RW Models produce memory-less mobility pattern because it cannot keep records of previous patterns formed by the locations and speed values of mobile nodes BIB001 . The current speed and direction of a node is independent of its past speed and direction as well. These characteristics can generate unrealistic movements such as sudden stops and sharp turns. A variant of this model, namely Gauss-Markov Mobility Model, has been introduced to resolve this discrepancy BIB002 .