An energy-aware link fault detection and recovery scheme for QoS enhancement in Internet of Things-enabled wireless sensor network

Abstract

In the era of digitalization, the proliferation of the Internet of Things (IoT) has assisted in transforming the entire world into a smarter globe. Wireless Sensor Networks (WSNs) play an important role in developing many IoT-based real-time applications. Due to energy resource constraints, there are possibilities for hardware, communication links, and software faults occurring in the IoT-enabled WSN (IWSN) environment. These faults affect the data reading and cause serious impairment in the IWSN. To alleviate this scenario, it is imperative to identify a reliable fault diagnosis technique to accurately detect and respond to faults promptly. This work proposes a novel Energy-Aware Hardware and Link Fault Diagnosis (EAHLFD) scheme to enhance the quality of service in IWSN. Initially, the suggested EAHLFD scheme applies the Hardware Fault Detection and Classification method to detect various hardware faults. The Adaptive Communication Link Fault Detection and Recovery method is then invoked in the second stage for detecting the link faults. In this method, the optimized recovery link is determined based on three factors namely: probability value, link cost, and battery energy. The efficacy of the proposed scheme has been tested under two different simulation environments (IWSN#1 and IWSN#2). From the simulation results, it is manifest that the suggested EAHLFD scheme attains a superior fault diagnosis accuracy of 99.04% as compared to existing schemes.