Energy-aware scheduling protocol-based hybrid metaheuristic technique to optimize the lifespan in WSNs

Topology control is a significant research problem when it comes to designing energy-efficient wireless sensor networks (WSNs). It is a metric for evaluating network service quality. To ensure the quality of network services, it is critical to provide energy-efficient sensor node scheduling in order to extend the network's lifespan. In this paper, we propose an energy-aware scheduling protocol (ESP)-based hybrid metaheuristic technique for minimizing the amount of energy consumption and ensuring sufficient coverage for the monitored area while maximizing the network lifespan for WSNs. The ESP consists of two phases: distributed clustering and sensor node scheduling. The clustering phase implements the DBSCAN clustering algorithm with slide modification to cluster the sensor nodes in the area of interest into clusters of nodes, and the efficient periodic cluster head election approach is proposed based on criteria such as remaining energy, number of neighbors, and distance for each node in the cluster. This clustering is only performed at the beginning to group the nodes into clusters. Then, only the cluster head election will be done every period. In the scheduling phase, we model the scheduling optimization problem using three objectives: reducing the number of uncovered zones, minimizing the number of active sensors, and selecting the active sensor nodes with the maximum remaining energy. This scheduling optimization model is solved using a hybrid metaheuristic algorithm. When the genetic algorithm's operators were added to the regular Cuckoo algorithm, the balance between exploration and exploitation abilities was improved even more. The algorithms were able to search a larger area. Extensive simulation experiments were conducted using the OMNeT++ network simulator. The results show that the proposed ESP protocol introduces better performance in terms of coverage ratio, active sensor ratio, energy consumption, network lifespan, and execution time compared to other existing techniques.