Multi-objective optimized multi-path and multi-hop routing based on hybrid optimization algorithm in wireless sensor networks

Abstract

Multi-path and multi-hop routing are multi-objective optimization problems involving multiple constraints that need to be addressed in the current scenario in wireless sensor networks. The routing process is challenging due to the constrained energy resources and transmission bandwidth. The conventional strategies possess shortcomings, like, as high computing complexity, extensive problem-solving time, complexity in achieving optimal values, and ease of falling into local solutions. Hence, the aim is to propose a hybrid metaheuristic algorithm, known as a multi-objective optimized multi-path and multi-hop routing algorithm (MMMRA). It incorporates the chimp optimization algorithm (COA) for determining the optimal multi-path route based on multi-objective function and ant colony optimization for determining the optimal multi-hop routing. The proposed MMMRA is implemented using NS-2 and to evaluate the performance, nine various scenarios are considered. The MMMRA is validated using different performance measures and compared with other benchmark algorithms. The simulation results indicate that the MMMRA exhibits percentage improvement in terms of residual energy by 1.63%, 4.96%, 6.89%, 7.51%, and 9.67% over IPSMT, BIM2RT, SCP, PSOBS, and RDICMR algorithms respectively. Moreover, the HND and FND of the MMMRA algorithm perform better in all three scenarios (center, corner, and outside positions of sink node), especially when the sink node is placed at the center position, the HND of MMRA shows a percentage improvement by 24% and 12.73% over IPSO–GWO, and COA–HGS algorithms respectively. Similarly, the FND of MMRA shows percentage improvement by 21.05% and 9.5% over IPSO–GWO, and COA–HGS algorithms respectively.