Quantifying the Effects of Homogeneous Interference on Coverage Quality in Wireless Sensor Networks

This study develops a coverage perception interference model for Wireless Sensor Networks, focusing on the challenges of homogeneous interference within Regions of Interest. Traditional perception models often overlook areas that, while covered, do not meet the required coverage standards for accurate classification. This model addresses both uncovered areas and those inadequately covered, which are susceptible to classification errors. A propositional space for the coverage model is defined to assess the impact of homogeneous interference on sensor nodes, with the aim of quantifying its effects on network coverage quality and stability in complex environments. The study emphasizes the generation of Basic Probability Assignments using Dempster-Shafer theory, a robust framework for managing uncertain information in sensory data. Probability Density Functions derived from historical and real-time data are utilized to facilitate precise BPA calculations by integrating over specific attribute ranges, thereby enhancing the accuracy and reliability of target detection. Algorithms are also developed to calculate the interference effect BPA, which are integrated with perception coverage models to improve the assessment and optimization of coverage quality. The research enhances the methodological understanding of managing interference in WSNs and offers practical strategies for improving sensor network operations in environments affected by significant interference, boosting the reliability and effectiveness of critical surveillance and monitoring applications.

@article{Liu2024,
  title     = {Quantifying the Effects of Homogeneous Interference on Coverage Quality in Wireless Sensor Networks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {8},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Qingmiao Liu and Qiang Liu and Minhuan Wang},
  doi       = {10.14569/IJACSA.2024.0150823},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150823}
}