Adaptive Energy Efficient Load Distribution Using Fuzzy Approach
Varun Kumar Sharma and Mahesh Kumar
Mobile Ad-hoc Networks (MANETs) are self-configuring and decentralized networks of wireless nodes (mobile), which communicate through radio waves. The core predicaments associated with MANETs encompass limited bandwidth availability, quick topology changes due to nodes’ mobility within the network, network partitioning, link-layer contentions, dynamic wireless channel characteristics and inadequate energy resources. Consequently, traditional routing strategies of MANETs should consider the exceedingly random nature (e.g., dynamic network and node status changes) into the routing scheme design. However, in the past, several authors have expressed concern towards deprived forwarding schemes in MANETs, which contributes to the consumption of excess energy in network. Consequently, it causes high risk of network disconnections as well. Hence, considering higher energy consumption in MANETs as a problem, many researchers have presented numerous smart routing paradigms in past. Still, many of these suggested schemes drastically failed in achieving the desired quality of service (QoS) in network. Further, the pattern of interest shifts towards the cross-layer energy optimization schemes. These ideas did use of lower layers’ special information explicitly in order to run the network efficiently. Furthermore, considering the sleep and idle energy consumption, the researchers have suggested many more solutions in the past. Nevertheless, these methods require complex synchronization and efficient coordination which is too inefficient for extremely variable networks (MANETs). To address these issues, we propose an efficient fuzzy based energy efficient load distribution scheme considering congestion as a major concern. This paper compares the performance of the proposed method, ELBRP and AODV via simulation. The simulation results show that the proposed method offers better Throughput performance, lesser Energy Consumption, lesser number of Dead nodes, elongate Network’s Lifetime than other approaches. Additionally, the simulation results confirm that the proposed method suggests better Normalized Routing and Normalized MAC Load (Total Network Overhead) than other approaches as well.
Keywords: Ad-hoc networks, routing, energy efficiency, energy optimization, fuzzy system