Optimal coverage of directional sensor networks with an improved artificial bee colony algorithm

Directional Sensor Networks (DSNs) are subset of Wireless Sensor Networks (WSNs), consisting sensor nodes with limited angle of view. Deployment of DSNs with connection ensuring as well as optimal coverage of a predefined target set is more difficult and remains a key concern in the network operation. Depending on the applications and deployment fields, there are three common coverage models including area, barrier and target coverage. It is challenging to avoid suboptimal distribution with coverage holes or idle sensors in all these models. Therefore, this paper proposes an effective solution to the coverage maximization problem with connectivity constraint (CC-CM) in DSNs that can be applied on all the coverage models of interest. The proposed algorithm Improved Artificial Bee Colony (IABC) is introduced to enhance the original one by modifying its local optimization phase to better fit the CC-CM problem. To validate the effectiveness of the method, extensive simulations are conducted under diverse target and sensor configurations such as varying sensing radii, sensing angles and node densities. The performance of the IABC is rigorously evaluated across multiple test cases, comparing it against existing optimization techniques. Simulation results indicate that the IABC significantly improves coverage quality in all cases while maintaining reliable connectivity. In addition, the proposed solution reaches over 99 to 100% coverage in the case of area and target coverage model. On the other hand, it ensures complete success rate in constructing closed barrier and peaks up to 100% coverage in the case of open barrier. • Modeling the Connectivity Constrained – Coverage Maximization problem for Directional Sensor Networks, considering area, barrier and target coverage. • Enhancing and modifying the Artificial Bee Colony algorithm to solve the problem in every coverage models. • The proposed algorithm performance is analyzing under different scenarios to evaluate its quality, stability.