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Energy-Aware Routing Algorithms for Maximizing System Lifetime in Dynamic Ad Hoc Networks

Mobile ad hoc networks face challenges from battery-operated nodes, where energy depletion can disrupt connectivity. Traditional approaches tend to optimize for total energy consumption without adapting to dynamic conditions. This paper proposes innovative energy-aware routing algorithms aimed at maximizing network lifetime. We discuss various strategies, including Maxmin, Maxmax, Maxavg, and a traditional shortest path routing algorithm. Through simulations with varying node mobility and topologies, we demonstrate that the Maxmin algorithm yields superior performance, promoting energy efficiency and longevity in mobile computing environments.

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Energy-Aware Routing Algorithms for Maximizing System Lifetime in Dynamic Ad Hoc Networks

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  1. Energy-aware routing algorithms in Ad Hoc Networks • Motivation • Mobile nodes are battery-operated and one or several nodes’ energy exhaustion may cause the disruption of the entire network • A number of approaches focus on minimizing the total consumed power. • Several approaches’ goal was to maximize the life time of the system. However, these are based on static topology and given traffic demands in advance. • Our goal is to propose energy-aware routing algorithms in dynamic ad hoc network environments to achieve max. system lifetime. • Routing Algorithms • Maxmin routing algorithm – choose the path whose minimum energy level node has the maximum value among all paths. • Maxmax routing algorithm – choose the path which has maximal residual energy on the path • Maxavg routing algorithm – choose the path which has maximal average energy level • Shortest path routing algorithm – no energy consideration • Simulation Setup • Single source-destination pair without node mobility • Single source-destination pair with node mobility • Multiple source-destination pairs without node mobility • Multiple source-destination pairs with node mobility • Introduction of energy balance factor which represents the heterogeneity of initial energy level at each node • Results/Lesson Learned • Overall our Maxmin algorithm performs well. • One example of results: • Simulation techniques such as generating topology and random traffic etc. • Obtain a research trend of energy-aware design in mobile computing environments Project team: Jangwon Lee & Xiangying Yang URL: http://www.ece.utexas.edu/~yangxy/395T.htm

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