Genetic Algorithm-Based Coverage Maximization and Connectivity Preserving Approach using Computational Geometry for 3-Dimensional Directional Sensor Networks

Directional Sensor Networks (DSNs) are a vital advancement in sensor technology, offering targeted, efficient and precise data collection as compared to traditional omnidirectional sensors. Sensing area overlapping problem in DSNs arises due to factors such as improper sensor placement, environmental changes, fixed orientations and high sensor density. This problem results in inefficiencies in data collection, energy consumption and communication. Optimizing sensor orientation can help mitigate these issues and improve overall network performance. This paper presents a three-dimensional mathematical model designed to detect various overlapping types among sensor nodes in the DSNs using Computational Geometry. The proposed model aims to optimize the sensing coverage area, mainly applied in fields where coverage is a primary concern, such as with cameras or infrared detectors. Coverage overlap can lead to redundant data collection, unnecessary energy depletion and potential communication interference; all these factors can significantly affect the network’s overall efficiency. A Genetic Algorithm (GA)-based approach has been introduced for maximizing area coverage. By selecting the most effective cover set from numerous possible combinations, the approach emphasizes directional sensing while minimizing coverage overlap. A fitness function is developed to assess the degree of overlap and maximize the covered area to reduce coverage overlap. Comprehensive simulations have been carried out to validate the efficiency of our proposed method. Results indicate that the degree of overlap decreases and the coverage fraction improves as the algorithm iterates through more generations. Moreover, the proposed GA-based optimization method outperforms existing state-of-the-art approaches.