Simulation is a cost effective, fast and flexible alternative to test-beds or practical deployment for evaluating the characteristics and potential of mobile ad hoc networks. Since environmental context and mobility have a great impact on the accuracy and efficacy of performance measurement, it is of paramount importance how closely the mobility of a node resembles its movement pattern in a real-world scenario. The existing mobility models mostly assume either free space for deployment and random node movement or the movement pattern does not emulate real-world situation properly in the presence of obstacles because of their generation of restricted paths. This demands for the development of a node movement pattern with accurately representing any obstacle and existing path in a complex and realistic deployment scenario. In this paper, we propose a general mobility model capable of creating a more realistic node movement pattern by exploiting the concept of flexible positioning of anchors. Since the model places anchors depending upon the context of the environment through which nodes are guided to move towards the destination, it is capable of representing any terrain realistically. Furthermore, obstacles of arbitrary shapes with or without doorways and any existing pathways in full or part of the terrain can be incorporated which makes the simulation environment more realistic. A detailed computational complexity has been analyzed and the characteristics of the proposed mobility model in the presence of obstacles in a university campus map with and without signal attenuation are presented which illustrates its significant impact on performance evaluation of wireless ad hoc networks.
The Suburban Ad Hoc Network (SAHN) is a cooperative ad hoc wireless mesh network. Nodes are owned and operated by end-users without reliance on central infrastructure. It provides symmetrical bandwidth allowing peer-to-peer services and distributed servers. We minimize the use of scarce unlicensed RF spectrum supported by Smart Antenna technology. RF interference in such networks and techniques and strategies to reduce it are examined. Traffic is spread across multiple frequency channels, and multiple directional beams to achieve improved spatial re-use. We focus on the control of smart antennas rather than their design. By dynamically adjusting our network topology using Smart Antennas and dynamically re-routing current communications we optimize the network for its current traffic needs.