Wireless Sensor Networks (WSNs) have left an indelible mark on the lives of all by aiding in various sectors such as agriculture, education, manufacturing, monitoring of the environment, etc. Nevertheless, because of the wireless existence, the sensor node batteries cannot be replaced when deployed in a remote or unattended area. Several researches are therefore documented to extend the node's survival time. While cluster-based routing has contributed significantly to address this issue, there is still room for improvement in the choice of the cluster head (CH) by integrating critical parameters. Furthermore, primarily the focus had been on either the selection of CH or the data transmission among the nodes. The meta-heuristic methods are the promising approach to acquire the optimal network performance. In this paper, the ‘CH selection’ and ‘sink mobility-based data transmission’, both are optimized through a hybrid approach that consider the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm respectively for each task. The robust behavior of GA helps in the optimized the CH selection, whereas, PSO helps in finding the optimized route for sink mobility. It is observed through the simulation analysis and results statistics that the proposed GAPSO-H (GA and PSO based hybrid) method outperform the state-of-art algorithms at various levels of performance metrics.
- Energy consumption rate (ECR)
- GA-based CH selection
- PSO-based sink mobility
- Sink mobility
- Wireless sensor network