Computer Science Review 39 (2021) 100342 Contents lists available at ScienceDirect Computer Science Review journal homepage: www.elsevier.com/locate/cosrev Review article Nature-inspired algorithms for Wireless Sensor Networks: A comprehensive survey Abhilash Singh a , Sandeep Sharma b,∗ , Jitendra Singh c a Fluvial Geomorphology and Remote Sensing Laboratory, Indian Institute of Science Education and Research Bhopal, India b School of ICT, Gautam Buddha University, Greater Noida, India c Department of Electrical Engineering, Indian Institute of Technology Kanpur, India article info Article history: Received 3 July 2020 Received in revised form 20 November 2020 Accepted 1 December 2020 Available online 23 December 2020 Keywords: Optimal coverage Bio-inspired algorithm Lion Optimization WSNs abstract In order to solve the critical issues in Wireless Sensor Networks (WSNs), with concern for limited sensor lifetime, nature-inspired algorithms are emerging as a suitable method. Getting optimal network coverage is one of those challenging issues that need to be examined critically before any network setup. Optimal network coverage not only minimizes the consumption of limited energy of battery- driven sensors but also reduce the sensing of redundant information. In this paper, we focus on nature-inspired optimization algorithms concerning the optimal coverage in WSNs. In the first half of the paper, we have briefly discussed the taxonomy of the optimization algorithms along with the problem domains in WSNs. In the second half of the paper, we have compared the performance of two nature-inspired algorithms for getting optimal coverage in WSNs. The first one is a combined Improved Genetic Algorithm and Binary Ant Colony Algorithm (IGA-BACA), and the second one is Lion Optimization (LO). The simulation results confirm that LO gives better network coverage, and the convergence rate of LO is faster than that of IGA-BACA. Further, we observed that the optimal coverage is achieved at a lesser number of generations in LO as compared to IGA-BACA. This review will help researchers to explore the applications in this field as well as beyond this area. © 2020 Elsevier Inc. All rights reserved. Contents 1. Introduction......................................................................................................................................................................................................................... 2 2. WSNs and optimizations ................................................................................................................................................................................................... 2 2.1. Problem domains in WSNs ................................................................................................................................................................................... 3 2.1.1. Optimal coverage in WSNs ................................................................................................................................................................... 4 2.1.2. Data aggregation in WSNs .................................................................................................................................................................... 4 2.1.3. Energy efficient clustering and routing in WSNs ............................................................................................................................... 4 2.1.4. Sensor localization in WSNs ................................................................................................................................................................. 4 2.2. Optimization in WSNs ........................................................................................................................................................................................... 5 3. Theoretical background of the leading algorithms in WSNs arena.............................................................................................................................. 6 3.1. Mathematical foundation of the nature-inspired algorithms ........................................................................................................................... 6 3.2. Particle swarm optimization (PSO) ...................................................................................................................................................................... 7 3.3. GA and adaptive GA (or IGA) ............................................................................................................................................................................... 8 3.4. ACO and BACA........................................................................................................................................................................................................ 8 3.5. IGA with BACA ....................................................................................................................................................................................................... 9 3.6. LO............................................................................................................................................................................................................................. 9 4. Solution to the problem domains and present status ................................................................................................................................................... 9 4.1. Applications of PSO in WSNs ............................................................................................................................................................................... 9 4.1.1. For optimal coverage using PSO........................................................................................................................................................... 9 4.1.2. For sensor localization using PSO ........................................................................................................................................................ 11 4.1.3. For energy efficient clustering and routing using PSO ...................................................................................................................... 11 4.1.4. For data aggregation using PSO............................................................................................................................................................ 11 ∗ Corresponding author. E-mail address: sandeepsharma@gbu.ac.in (S. Sharma). https://doi.org/10.1016/j.cosrev.2020.100342 1574-0137/© 2020 Elsevier Inc. All rights reserved.