Multi-objective teaching–learning evolutionary algorithm for enhancing sensor network coverage and lifetime

• Published in: Engineering applications of artificial intelligence Vol. 108; p. 104554
• Main Authors: Tam, Nguyen Thi, Hoang, Vu Dinh, Binh, Huynh Thi Thanh, Vinh, Le Trong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2022
• Subjects: Evolutionary algorithms; Load balancing; Network lifetime; Wireless underground sensor networks; Wireless underground sensor networks; Network lifetime; Evolutionary algorithms; Load balancing
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2021.104554

Coverage plays a vital role in the performance and proper functioning of wireless sensor networks. However, ensuring a network’s coverage is met numerous challenges due to sensors having limited sensing range, communication range, and energy. Many coverage problems are NP-hard, one of which is the network coverage with lifetime problem (CTLP). As such, a number of meta-heuristic algorithms have been proposed to solve CTLP in practical scenarios. This paper proposes an approach for CTLP based on the teaching–learning based optimization algorithm (TLBO), which is often employed to address continuous optimization problems. Specifically, a discrete version of multi-objective improved teaching–learning based optimization algorithm (MO-ITLBO) called HTLBO is proposed, employing genetic operators inspired by evolutionary computing methods. Experimental results are extensively compared to those obtained from previous approaches, namely MO-ITLBO, fast elitist non-dominated sorting genetic algorithm (NSGA-II), multi-objective differential evolution (MODE), and multi-objective evolutionary algorithm based on decomposition (MOEA/D). The evaluation shows significant improvements in different metrics, including spacing, hypervolume, non-dominated solutions, and coverage. •We investigate the problem of optimal sensor node placement with three objectives: (i) minimize the number of deployed sensor nodes, (ii) maximize the k-coverage metric, and (iii) maximize the network lifetime.•We propose a hybrid algorithm combining teaching–learning based optimization and evolutionary computing for multi-objective sensor placement. The proposed algorithm introduces multiple teachers to improve the learners’ results in different subjects. Moreover, a learner interacts with other learners through a crossover operator.•We compare the proposed algorithm with existing methods, including MODE, MOEAD, MO-TLBO, and NSGA-II on C-metric, spacing-metric, hypervolume-metric, and non-dominated solutions metric to demonstrate its effectiveness.