Fuzzy enabled congestion control by cross layer protocol utilizing OABC in WSN: combining MAC, routing, non-similar clustering and efficient data delivery

摘要

Congestion control in networks turns to be a crucial and challenging issue. This paper presents the cross layer mechanism for the management of congestion using Fuzzy based Cross layer mechanism using Oppostional Artificial Bee Colony (FCOABC) protocol. The proposed protocol integrates the notion of media accessibility and energy proficient hierarchical based cluster routing for increasing the network lifespan and energy efficiency. This proposed cross layer protocol uses fuzzy logic by means of considering the fuzzy descriptors such as, communication link reliability, number of neighboring nodes and precipitant (residual energy) for CH selection. Then, the network is organized into non-similar sized clusters (i.e. the clusters that are much closer to the MS holding smaller sizes and the clusters that are far away from the MS having larger sizes) for addressing the hot spots problem in WSN. The smaller sized cluster are considered because, the CHs of the smaller sized clusters are largely closer to the master station; thus, they experienced only small amount of intra-cluster congestion and forwards the relay traffic effectively using their preserved energy. Finally, the proposed FCOABC protocol employed the Oppositional Artificial Bee Colony optimization algorithm for performing inter cluster multi-hop routing from CHs to master station; therefore, an energy efficient and reliable data transfer is achieved to the master station. The operation of FCOABC protocol is primarily divided into three stages namely, Network-Association stage, nearest node detection stage and consistent-state stage. The results of conventional clustering protocols such as, ULCA, UCR, IFUC, and EAUCF is used for comparing the performance of proposed FCOABC protocol. The simulation results proved the efficiency of proposed FCOABC protocol on other clustering protocols in terms of the evaluation metrics such as, network lifespan, energy consumption, and scalability.