Automata Based Hybrid PSO–GWO Algorithm for Secured Energy Efficient Optimal Routing in Wireless Sensor Network - Prithi, S.; Sumathi, S. (Wireless personal communications, 2021)
The main objective in wireless sensor networks is to exploit efficiently the sensor nodes and to prolong the lifetime of the network. The discussion of energy is a significant concern to extend the lifetime of the network. Moreover, a nature inspired hybrid optimization approach called hybrid Particle Swarm Optimization–Grey Wolf Optimizer (PSO–GWO) is used in this work to efficiently utilize the energy and to transmit the data securely in an augmented path. A Learning Dynamic Deterministic Finite Automata (LD 2 FA) has been innovated and initiated to learn the dynamic role of the environment. LD 2 FA is mainly used to provide the learned and accepted string to hybrid PSO–GGWO so that the routes are optimized. Hybrid PSO–GWO is used to choose the optimal next node for each path to obtain the optimal route. The simulation results are obtained in MATLAB for 100–700 sensor nodes in a region of 500 × 500 m 2 which demonstrate that the proposed LD 2 FA based Hybrid PSO–GWO algorithm obtains better results when compared with existing algorithms. It is observed that LD 2 FA based Hybrid PSO–GWO has an increase of 18% and 48% betterment in lifetime of the network than PSO and GLBCA, nearly 57% and 75% increase in network lifetime when compared with GA and LDC respectively. It also shows an improvement of 24% increase compared to cluster-based IDS, nearly a rise of 90% throughput when compared with lightweight IDS. The consumption of energy is reduced by 13% and 15% than PSO and GA and an increase of 15% utilization of energy than LDC. Therefore, LD 2 FA based Hybrid PSO–GWO is been considered to efficiently utilize energy in an optimal route.