Memetic Algorithm for Maximizing K-coverage and K-Connectivity in Wireless Sensor Network

Abstract

The rapid growth of IoT has enabled diverse applications using Wireless Sensor Networks across various fields. A significant challenge in Wireless Sensor Networks is the efficient deployment of sensors to ensure coverage and connectivity. Effective coverage allows continuous target tracking and data collection, while connectivity ensures data transmission to the base station. In this paper, we address the challenge of maximizing the number of targets satisfying K-coverage and K-connectivity, where each target is tracked by K sensors and has K transmission paths to the base station. We propose a two-phase methodology to tackle this challenge. The first phase enhances the Greedy algorithm to solve the K-coverage problem. The second phase addresses the K-connectivity problem using Memetic algorithms augmented by an efficient local search mechanism called PMA. We evaluate the algorithm on various datasets and compare it with baseline methods, including Greedy and Prim-based with the withdrawal strategy (PWS). Our results show that the proposed PMA with a robust local search outperforms alternative algorithms, with improvements exceeding 10% to 15% compared to the baseline methods. Additionally, we validate the performance of the proposed method using a real-world dataset and outline plans for further enhancements in the near future.