Fault autonomous model handling through integrated adaptive-filters for eliminating deployment faults in wireless sensor networks

Abstract

Wireless Sensor Networks (WSNs) are exposed to various data-deployment faults during the communication action. These faults may impact the behaviour of the sensors that degrade its performance and cuts its life. Therefore, we tend to implement the integration of two independent trends are self-awareness and self-adaptation capabilities along with two integrated adaptive filters, FIR and RLS. The proposed Autonomous Fault-Awareness and Adaptive (AFAA) model composed of three adaptive two-stage executed self-awareness approach to limit the impact of such faults during the propagation process. In this paper, we introduce the operational mechanism of AFAA that manages to identify the failure and aware of the lost signal values autonomously, then filter the perceptive-signals for eliminating the accompanied interference and gaining convergent values. It executed the incorporated autonomous model at the level of Cluster Head (CH) for independent fault-correction using an adaptive feedback model. Compared to the state-of-the-art methods, the proposed model achieved speed in fault diagnosis; also high-accuracy rate in the prediction of the lost signal values as much as 98.63%, thus improving the percentage of performance efficiency to 3:1 times along of duty cycle. Hence, it enhanced the overall network lifetime.