Modified sailfish optimization for energy efficient data transmission in IOT based sensor network

Data transmission in IoT-based sensor networks is perceived major consideration due to the emergent implication of IoT applications. In a sensor network, a large number of sensors are grouped together with limited energy, necessitating the creation of a long-lasting network to overcome the energy limitation. Furthermore, the shortest path selection for sensor communication is an NP-Complete problem. The swarm-based metaheuristic approach proves to solve the NP-complete problem by providing the optimal solution. Moreover, IoT applications extent to diverse network requirements, same network topology can’t fulfill the needs of every IoT application. The sensor-enabled smart network supports heterogeneous applications that require different network topology and optimal paths for data transmission for energy optimization. In this way, the hexagon is the largest regular polygon that is considered the ideal shape for clustering the sensors. In this perspective, this article proposed a swarm optimization-centric optimal energy-efficient route selection for data transmission in a large network. Specifically, instead of a standard network area, the proposed architecture divides the entire area into hexagonal clusters. Consequently, sailfish optimization is a good problem solver in the literature, a modified chaos-based sailfish optimization algorithm (CSFO) is presented due to its imbalanced search behavior and low convergence. Finally, CSFO is used to find out optimal cluster head (CH) and data transmission routes in the proposed model. To validate the proposed algorithm’s experimental results have been evaluated on various parameters. The simulation results have been compared with the baseline algorithms and proved to be better in this regard.