Survey of Mobile AD Hoc Networks

In general, a Mobile Ad hoc Network (MANET) is a collection of wireless nodes communicating with each other in the absence of any infrastructure. Due to the availability of small and inexpensive wireless communicating devices, the MANET research field has attracted a lot of attention from academia and industry in the recent years. In the near future, MANETs could potentially be used in various applications such as mobile classrooms, battlefield communication and disaster relief applications. MANET simulation has several key parameters, including mobility model and communicating traffic pattern, among others. In this chapter and mainly focus on the analysis, modeling of mobility models and also studying the impact of mobility on the performance of MANET.

The mobility model is designed to describe the movement pattern of mobile users, and how their location, velocity and acceleration change over time. Since mobility patterns may play a significant role in determining the MANET performance, it is desirable for mobility models to emulate the movement pattern of targeted real life applications in a reasonable way. Each mobile node of a MANET is treated as an autonomous peer, and the random mobility patterns of mobile nodes need to be analyzed to investigate the dependency of performances of the variable topology network. Moreover, a MANET is a resource-constrained communications network with limited energy, computing resources, and memory.

Over the years, many mobility models have been used to analyze the mobile ad hoc network performances. Many mobility models are designed in order to recreate the real world scenarios better for application to MANET. The statistical properties of these mobility models are analyzed designing different mobility metrics and studying the influences of mobility models on performances of networking protocols including routing, service discovery, and mobile peer-to-peer applications. Thus, when evaluating MANET performance, it is necessary to choose the proper underlying mobility model. For example, the nodes in Random Waypoint model behave quite differently as compared to nodes moving in groups. It is not appropriate to evaluate the applications where nodes tend to move together using Random Waypoint model.

Therefore, there is a real need for developing a deeper understanding of mobility models and their impact on MANET performance. General method to create realistic mobility patterns should be constructed with help of trace- based mobility models and provides the information to the users. However, since MANETs have not been implemented and deployed on a wide scale, obtaining real mobility traces becomes a major challenge. Therefore, various researchers proposed different kinds of mobility models and represented in 'realistic' fashion with different style. Much of the current research has focused on the so-called synthetic mobility models that are not trace-driven. In the previous studies on mobility patterns in wireless cellular networks, researchers mainly focus on the movement of users relative to a particular area (i.e., a cell) at a macroscopic level, such as cell change rate, handover traffic and blocking probability. However, to model and analyze the mobility models in MANET, the movement of individual nodes at the microscopic-level, including node location and velocity relative to other nodes, because these factors directly determine when the links are formed and broken since communication is peer-to-peer.

A mobility model attempts to mimic the movement of real mobile nodes that change the speed and direction with time. The mobility model that accurately represents the characteristics of the mobile nodes in an ad hoc network is the key to examine whether a given mobility model is useful in a particular type of mobile scenario. The possible approaches for modeling of the mobility patterns are of two types: traces and syntactic. The traces provide those mobility patterns that are observed in real-life systems. In trace-based models, everything is deterministic. However, mobile ad hoc networks are yet to be deployed widely to know the traces involving a large number of participants and an appropriately long observation period. In absence of traces, the syntactic models that have been proposed to represent the movements of mobile nodes realistically in ad hoc networks are presented. The syntactic mobility models can also be classified based on the description of the mobility patterns in ad hoc networks, individual mobile movements and group mobile movements. In the former case, mobility models attempt to the anticipate mobile’s traversing patterns from one place to another at a given point of time under various network scenarios.

In the latter case, mobility models try to characterize the group’s traversing patterns with individualism averaged. Unlike trace-based mobility models, syntactic mobility models considered here have randomness, and further classifications can be made based on randomness; constrained topology-based models and statistical models. In constrained topology-based mobility models, mobile nodes have only partial randomness where the movement of nodes is restricted by obstacles, pathways, speed limits, and others.

If the nodes are allowed to move anywhere in the area and the speed and direction are allowed to choose, it is termed as total randomness. The model that is based on total randomness is defined as statistical mobility model. Based on specific mobility characteristics, the classification of mobility models is also made primarily into four categories: random models, models with temporal dependency, models with spatial dependency, and models with geographical restrictions. In random models, like statistical models, nodes move randomly and can be classified further based on the statistical properties of randomness, and random waypoint, random direction, and random walk mobility model fall into this category.