Event Based Clustering Localized Energy Efficient Ant Colony Optimization for Performance Enhancement of Wireless Sensor Network

The main challenge of a wireless sensor network (WSN) in disaster situations is to discover efficient routing, to improve quality of service (QoS) and to reduce energy consumption. Location awareness of nodes is also useful or even necessary. Without knowing the position of sensor nodes, collected data is insignificant. Ant colony optimization (ACO) is a unique form of optimization method, which is highly suitable for adaptive routing and guaranteed packet delivery. The primary drawbacks of ACO are data flooding, huge overhead of control messages and long convergence time. These drawbacks are overcome by considering location information of sensor nodes. An event-based clustering localized energy efficient ant colony optimization (EBC_LEE-ACO) algorithm is proposed to enhance the performance of WSN. The main focus of the proposed algorithm is to improve QoS and minimize the network energy consumption by cluster formation and selecting the optimal path based on the biological inspired routing-ACO and location information of nodes. In clustering, data is aggregated and sent to the sink (base station) through cluster head (CH) which reduces overheads. EBC_LEE-ACO is a scalable and energy efficient reactive routing algorithm which improves QoS, lifetime and minimizes energy consummation of WSN as compared to other routing algorithms like AODV, ACO, ACO using RSSI. The proposed algorithm reduces energy consumption by approximately 7%, in addition to improvement in throughput, packet delivery ratio and increase in packet drop which has been observed in comparison with other algorithms, i.e. autonomous localization based eligible energetic Path_with_Ant Colony optimization (ALEEP with ACO) of the network. Use of IEEE 802.11 standard in proposed work increased packet drop