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Background: Over the last decade, Wireless Sensor Networks (WSNs) have found applications across various domains, such as mines, agricultural sectors, healthcare, etc. These networks consist of multiple sensor nodes responsible for collecting and relaying data to a central gateway. Consequently, the integration of sensor devices bears the potential to influence the operational effectiveness of these systems. Objective: This study concentrates on scrutinizing coverage and connectivity within a WSN deployed for monitoring purposes. This research delves into determining the most efficient deployment pattern requiring the minimum number of sensors. Methods: For attaining thorough coverage and uninterrupted connectivity, adopting a nondeterministic strategy in sensor deployment is crucial. This study utilizes a model inspired by the salp swarm optimization method, a technique rooted in swarm-based optimization principles. In this methodology, clusters are defined as groups of sensor nodes meeting connection criteria and ensuring adequate coverage. Adequacy is achieved when at least one of these nodes transmits monitored data to the central hub. Results: The results offer compelling evidence that the discrete salp swarm optimization algorithm is better than state-of-the-art algorithms. The results are interpreted in three different metrics, namely the number of deployed sensors, total computational time, and the ratio between potential sensors available and the number of sensors deployed to cover the region. As a result, on average, the proposed model achieved an overall 87% of coverage and connectivity that are simulated with different iteration numbers on a 50X50 grid. For the 100X100 grid, a total of 89% of coverage and connectivity among sensors were achieved. Conclusion: The application of the discrete salp swarm optimization model presents a promising approach to addressing the coverage connectivity problem in WSNs. Through the utilization of salp-inspired behaviors, this model effectively optimizes network coverage while ensuring robust connectivity, thus enhancing the overall performance and reliability of WSNs. By harnessing the collective intelligence of salp swarms, the proposed algorithm demonstrates superior convergence speed and solution quality compared to traditional methods.
Sunkavalli et al. (Wed,) studied this question.