Solving of the maximal exposure path problem in heterogeneous directional wireless sensor networks using an improved genetic algorithm

Barrier coverage is a well-known model within the Internet of Things Wireless Sensor Networks (WSNs) domain, playing a vital role in various military and security applications. It is particularly important for monitoring and detecting moving objects across a sensor field. This research paper examines the fundamental aspect of barrier coverage in WSNs, with a specific focus on the maximal exposure path (MaEP) problem, which is classified as NP-Hard. The MaEP problem involves identifying an optimal coverage path that either conserves energy or minimizes energy usage while maintaining a short traversal distance. Previous studies in this area primarily relied on problem formulations based on Euclidean distance metrics and were often addressed using computational geometry techniques. However, these methods encounter significant difficulties when applied to large-scale, complex, and highly sophisticated WSNs. To address this, our research reinterprets the MaEP problem through the lens of the integral of sensing field intensity. We then introduce an improved genetic algorithm, named MIGA, specifically tailored to efficiently solve the MaEP problem. The polynomial complexity and convergence of the proposed MIGA are mathematically obtained. Moreover, to evaluate the effectiveness of this algorithm, we conduct a comprehensive series of experiments and provide detailed experimental results.