AN OPTIMAL DATA AGGREGATION SCHEME FOR WIRELESS SENSOR NETWORK USING QOS PARAMETERS WITH EFFICIENT FAILURE DETECTION AND LOSS RECOVERY TECHNIQUE

Abstract

WSN: Wireless Sensor Networks play a significant part in its modern era but its limited power supply acts as a blocking stone in it growth. In order to save energy in WSN the concept of aggregator node is introduced, where the aggregator node would act as a mid-point between the source and destination node during the data transmission. The data aggregation process creates major problems like excess energy expenditure, and delay. In the process of eliminating or reducing the delay and energy expenditure, the researchers have been handled in different ways. Applications like environment monitoring, target tracking, military surveillance and health care require reliable and accurate information. Many researchers have proposed data aggregation techniques to enhance the latency, average energy consumption and average network lifetime. However, these techniques are not sufficient to address situations like node failure and loss recovery. This paper proposes to build a solid wireless sensor system which concentrate on efficient optimal data aggregation along with additional QoS metrics such as failure detection and loss recovery. The first contribution of this paper is to propose an Improved Wolf Optimization (IWO) algorithm for clustering. The clustering process includes an efficient cluster formation like, Cluster Head (CH), and Sub Head (SH) selection. The second contribution of this paper is inclusion of failure detection and loss recovery. The former is developed based on Multi-criteria Moths-Flame Decisionmaking (MMFD) model and the latter is achieved through SH. SH node will act as the backup node for cluster head when failure instances are detection. CH recovers the lost data through SH, which minimize the additional delay of backup node selection process and save much more energy. The results are simulated using network simulator 2 tool and it is compared with existing techniques. The Network Simulator – 2 results disclose that the findings are better than the available existing methodologies.