Energy-Efficient Cluster-Based Routing Protocol for WSN Based on Hybrid BSO–TLBO Optimization Model

Abstract

The most famous wireless sensor networks is one of the cheapest and rapidly evolving networks in modern communication. It can be used to sense various substantial and environmental specifications by providing cost-effective sensor devices. The development of these sensor networks is exploited to provide an energy-efficient weighted clustering method to increase the lifespan of the network. We propose a novel energy-efficient method, which utilizes the brainstorm algorithm in order to adopt the ideal cluster head (CH) to reduce energy draining. Furthermore, the effectiveness of the BrainStorm Optimization (BSO) algorithm is enhanced with the incorporation of the modified teacher–learner optimized (MTLBO) algorithm with it. The modified BSO–MTLBO algorithm can be used to attain an improved throughput, network lifetime, and to reduce the energy consumption by nodes and CH, death of sensor nodes, routing overhead. The performance of our proposed work is analyzed with other existing approaches and inferred that our approach performs better than all the other approaches.

Multi‐objective‐derived energy efficient routing in wireless sensor networks using hybrid African vultures‐cuckoo search optimization

“…The analysis like alive node analysis, cluster head analysis, delay analysis, energy analysis, fault tolerance analysis, load analysis, normalized energy, temperature analysis, throughput analysis, and transmission load was performed over the developed model in WSN. Different existing approaches utilized for the analysis are C‐SSA, 24 BSO‐MTLBO, 31 AVOA, 44 and CSO 45 . The simulation parameter utilized in developed energy‐efficient routing is listed in Table 2.…”

“…Krishnan et al 31 BSO-MTLBO • It shows the competitive performance of other algorithms. • It reduces routing overhead, death of sensor nodes, and the energy consumed by CH and nodes.…”

“…In 2021, Krishnan et al 31 have recommended a novel energy‐efficient method with a brainstorming approach to attain an ideal CH for minimizing energy draining. The efficiency of the “Brain Storm Optimization (BSO)” approach was improved by utilizing the “Modified Teacher–Learner Optimized (MTLBO)” approach.…”

“…The performance degradation is observed at initial times. BSO–MTLBO 31 shows competitive performance over other algorithms and reduces routing overhead, death of sensor nodes, and the energy consumption by nodes and CH. On the other hand, the convergence problem of BSO affects the performance of routing.…”

Assessing High and Low-Risk Bank Loan Approvals

“…The Random Forest objective is to figure out advance acknowledgment forecast techniques and, at last, valuing systems for Jordanian microfinance items by concentrating on the client behaviour [21]. Our group has broad information and exploration experience that has convert into great distributions [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36]. Accordingly, the review results produce better execution in both test and factual investigation, yet it has a few constraints to the proposed work.…”