A QoS aware optimal node deployment in wireless sensor network using Grey wolf optimization approach for IoT applications

The growth of Wireless Sensor Networks (WSN) becomes the backbone of all smart IoT applications. Deploying reliable WSNs is particularly significant for critical Internet of Things (IoT) applications, such as health monitoring, industrial and military applications. In such applications, the WSN's inability to perform its necessary tasks and degrading QoS can have profound consequences and can not be tolerated. Thus, deploying reliable WSNs to achieve better Quality of Service (QoS) support is a relatively new topic gaining more interest. Consequently, deploying a large number of nodes while simultaneously optimizing various measures is regarded as an NP-hard problem. In this paper, a Grey wolf-based optimization technique is used for node deployment that guarantees a given set of QoS metrics, namely maximizing coverage, connectivity and minimizing the overall cost of the network. The aim is to find the optimum number of appropriate positions for sensor nodes deployment under various p-coverage and q-connectivity configurations. The proposed approach offers an efficient wolf representation scheme and formulates a novel multi-objective fitness function. A rigorous simulation and statistical analysis are performed to prove the proposed scheme's efficiency. Also, a comparative analysis is being carried with existing state-of-the-art algorithms, namely PSO, GA, and Greedy approach, and the efficiency of the proposed method improved by more than 11%, 14%, and 20%, respectively, in selecting appropriate positions with desired coverage and connectivity.