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

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.

Automated summary

Coverage is crucial for the performance and proper functioning of wireless sensor networks, but it faces challenges due to limited sensing range, communication range, and energy of the sensors. This paper proposes an approach based on the teaching-learning based optimization algorithm to solve the network coverage problem and compares it with other methods through experimental results, showing significant improvements in different metrics.