MOBILITY MODELS FOR DELAY TOLERANT NETWORK : A SURVEY <s> INTRODUCTION <s> Mobile wireless delay-tolerant networks (DTNs) are wireless networks that suffer from intermittent connectivity, but enjoy the benefit of mobile nodes that can store, carry, and forward packets or messages, bringing them closer to their destinations through a selective forwarding policy. The evaluation of DTN routing protocols has primarily relied on simulation because most theoretical mobility models are unable to represent the mobility patterns that such protocols seek to take advantage of. In this paper we present and analyze a mobility model that we call localized random walk. This model is simple enough that it can be incorporated into mathematical models, but is spatially localized, which unlike other common mobility models, will make it possible to showcase the properties of heuristic-based DTN routing protocols. We derive the stationary spatial distribution of the mobility model, approximate what we call its spatial cross section, approximate the properties of its interaction with nodes following other mobility models, and use it to model some relatively simple DTN scenarios. <s> BIB001 </s> MOBILITY MODELS FOR DELAY TOLERANT NETWORK : A SURVEY <s> INTRODUCTION <s> Delay Tolerant Networking (DTN) considers how to provide communication in contexts where it is unreasonable to assume end-to-end connectivity. Network devices exchange buffered messages when they come into communication range; messages may be buffered and carried physically several times before ultimately being received. Service characteristics of a DTN depend intimately on the underlying movement of devices through physical space; correspondingly, an assessment of DTN technology (e.g. routing protocols, message exchange policies, etc.) depends on that same movement. Existing mobility models provided in simulators lack characteristics one expects in post-disaster communication. We propose a mobility model that includes the impact of the disaster on the transportation network, and that models population and relief vehicle movement. We augment the "Opportunistic Network Environment" (ONE) simulator of DTNs with required extensions and show that characteristics of the DTN are very different using the new model than it is under models that ONE currently provides. <s> BIB002 </s> MOBILITY MODELS FOR DELAY TOLERANT NETWORK : A SURVEY <s> INTRODUCTION <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
Delay tolerant network (DTN) is increasingly becoming popular due to its capability to be deployed with low cost in the environments where end-to-end connectivity is not normal. In DTNs, the messages are delivered from source to destination via intermediate mobile nodes that store, carry and forward data as the contact opportunity arises. Therefore, the underlying mobility patterns of the nodes in such a network will determine the performance of the network. However, it is difficult to evaluate DTN protocols and applications in real world environment. Thus, the performances of DTN protocols are usually evaluated using simulator based on different mobility models. These mobility models are expected to reflect the real mobility patterns of nodes BIB002 - BIB001 . In DTN applications, message transmission occurs only when appropriate relays meet each other. The time elapsed between such two meetings is the basic metric for characterizing mobility in DTNs. The time taken by a node to first meet other node is defined as the meeting time. Once two nodes come into communication range of each other, they start processes to exchange data. The time duration they will exchange data depends on the time they remain in the communication range of each other. This time duration is defined as the contact time. If they do not finish exchanging the packet during the contact time (due to contention in the network), then they require waiting till they come into communication range again. This time duration is termed as the inter-meeting time. Therefore, a realistic performance analysis of any mobility assisted routing scheme requires a knowledge of statistics of these three quantities. There have been a number of studies regarding these microscopic mobility properties and its characteristics. As a result, today's mobility models are perfectly capable to assume these properties BIB003 . Mobility models are developed using practical traces and some synthetic theories that try to achieve realism. Many traditional mobility models are still widely used to support easy DTN protocol evaluation. They usually cover only selected mobility characteristics whereas synthetically generated node mobility models allow for fine-tuning in many respects. We have grouped simplest traditional models as Random Mobility Models. In the literature, there are a number of such mobility models. Among them, the following popular models will be discussed in this paper. Applications of the synthetic models on the map derived another mobility models class where random pattern is constrained within the map. In these models, map data is included using a subset of the Well Known Text (WKT) format. In fact, WKT is based on ASCII format that is frequently used in Geographic Information System (GIS) programs  . We have grouped these models as Map-Constrained Mobility Models. The following are some of the popular map-based mobility models. 1. Map-Based Mobility Models (MBM): A model that moves nodes with random speed and directions following a map. 2. Shortest Path-Based Map Mobility Model (SPBMM): A model that is similar to MBM, but selects a certain destination in the map for all nodes and finds the shortest path to reach the destination. 3. Route-Based Map Mobility Model (RBM): A model that have predetermined routes on the map that they travel. 4. Manhattan Mobility Model (MMM): A model that formulates a grid like structure of horizontal and vertical lines on map to drive nodes with predefined probability. In many DTN applications, wireless nodes are carried by humans, animals, vehicles, or planets which possess their own movement patterns. DTN routing protocols often exploit these patterns to implement applications  . In the human community, people meet strangers by chance or familiar strangers due to having similar mobility patterns while family, friends and colleagues by intention BIB003 . Therefore, human mobility pattern also has characteristic like macroscopic structure along with microscopic mobility properties. Thus, many researchers have developed mobility models based on human behaviour. We have grouped these models as Social Mobility Models. We have analysed the following popular models. Since mobility models can be configured on per node basis, it is possible to combine multiple mobility models in one simulation. Consequently, researchers have motivated to create mobility models where, for example, in a shopping centre or a park some nodes move following map along roads and others who walk around randomly within. We have included such mobility models into Composite Mobility Models group. The following are the very familiar and successful mobility models of this type. In our study, we have used different notions and terminology to represent the properties of DTN. That is described in this paragraph. A node is here denoted as a wireless enabled device integrating at least one interface. Node position describes the specific location of a node in relation to a grid. The mobility features that capture a node movement in mobility models are a node's speed and also a direction. The pause time of a node relates to the time period that a node is steady in a specific position, i.e., the interval of time when the node's speed is zero or close to zero. Inter-contact time indicates the time duration between two successive meetings of the same two nodes. As for contact duration, this refers to the time period two nodes attain while within the same radio range. The purpose of this study is to provide an overview about the available mobility models used in simulator and a comparison of their performance as it affects the performance of DTN routing protocols significantly. We have presented different characteristics of each model. These characteristics are frequently applicable when one selects mobility models to evaluate their designed DTN protocols. In addition, we have focused to find out what are the potential future research issues to develop more realistic mobility models. We have organized the papers as follows. In the section 2, we have presented descriptions of each mobility model characteristics. The comparisons of performance of model have been described in the section 3. Future research directions have been provided in the section 4. Finally, we have concluded our observation in section 5.