Fuzzy logic approach to repair coverage holes in internet of things monitoring applications

Using the internet of things paradigm for monitoring applications requires sufficient coverage of a monitored region to collect accurate data and observe events. The problem of a coverage hole arises when a sensor fails (loses connectivity with its neighbouring sensors) for reasons such as energy exhaustion or physical damage due to deployment in harsh environments. It is vital to detect and repair coverage holes as soon as they appear because they can have debilitating effects on network coverage and connectivity if left unattended. The authors propose a novel approach that employs a fuzzy inference system to select a neighbouring sensor that moves to recover the lost area. All neighbouring sensors communicate with each other to estimate the size and location of the coverage hole. Each sensor considers its own energy level, its distance from the approximated hole, and its redundancy ratio to evaluate its eligibility. The most eligible sensor is then selected to patch the coverage hole. Through extensive simulations, they analysed the performance of the proposed approach and compared it with baseline approaches in terms of coverage recovery effectiveness. The results show that the proposed approach is much more effective in repairing the holes as compared to the baseline approaches.