Abstract

In wireless networks, coverage is a critical parameter that determines the efficiency of the network. Several factors affect coverage and, consequently, the overall performance of the network. Researchers have explored coverage from various perspectives, such as using different sensing range models, assessing boundary impacts, and considering sensor failures, often employing analytical approaches. Some researchers have also employed heuristic methods, though often neglecting either boundary conditions or sensor failure scenarios. However, no study has specifically explored the use of soft computing techniques to assess coverage and coverage reliability in the presence of sensor failures and boundary conditions. Coverage reliability refers to the duration during which a network maintains efficient coverage, ensuring consistent and reliable performance. In this paper, we compute Fuzzy Coverage (FC) and Fuzzy Coverage Reliability (FCR) using a Fuzzy Inference System (FIS) model under two distinct scenarios: Sensor Node (SN) failure and boundary condition. The analytical findings derived are verified using FIS model and shown to align with existing results, confirming the accuracy and applicability of the fuzzy approach. Additionally, we investigate the influence of various parameters, including the number of SNs, Sensing Range (SR), and the Sensor’s Failure Rate (FR), on both FC and FCR. This analysis provides valuable insights into how these factors impact the network's coverage and coverage reliability, offering a more comprehensive understanding of network performance in real-world scenarios.

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