Meta-Analysis of Heuristic Approaches for Optimizing Node Localization and Energy Efficiency in Wireless Sensor Networks
Oluwasegun J. Aroba1, Nalindren Naicker2, Timothy T. Adeliyi3, Ropo E. Ogunsakin4

1Oluwasegun. J. Aroba*, Department of Information Systems. A graduate of Information Technology University from prestigious Coventry University United Kingdom
2Dr. N. Naicker Department of Information Systems; Information Technology and Computer Science.
3Dr. Timothy T. Adeliyi Department of Information Technology at the Durban University of Technology.
4Dr. R. E. Ogunsakin, Department of Information Technology at the Durban University

Manuscript received on September 02, 2020. | Revised Manuscript received on September 15, 2020. | Manuscript published on October 30, 2020. | PP: 81-88 | Volume-10 Issue-1, October 2020. | Retrieval Number: 100.1/ijeat.A17171010120 | DOI: 10.35940/ijeat.A1717.1010120
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Abstract: Background: In the literature node localization and energy efficiency are intrinsic problems often experienced in wireless sensor networks (WSNs). Consequently, various heuristic approaches have been proposed to allay the challenges faced by WSNs. However, there is little to nothing in the literature to support which of the heuristic approaches is best in optimizing node localization and energy efficiency problems in WSN. The aim of this paper is to assess the best heuristic approach to date on resolving the node localization and energy efficiency in WSNs. Method: The extraction of the relevant articles was designed following the technique of preferred reporting items for systematic reviews and meta-analyses (PRISMA). All the included research articles were searched from the widely used databases of Google Scholar and Web of Science. All statistical analysis was performed with the fixed-effects model and the random-effects model implementation in RStudio. The overall pooled global estimate and categorization of performance for the heuristic approaches were presented in forest plots. Results: A total of 18 studies were included in this meta-analysis and the overall pooled estimated categorization of the heuristic approaches was 35% (95% CI (13%, 67%)). According to subgroup analysis the pooled estimation of heuristic approach with hyper-heuristic was 71% (95% CI: 6% to 99%), I2 = 100%) while the hybrid heuristic, was 31% (95% CI: 3% to 87%, I2 = 100%) and metaheuristic was 21%(95% CI: 9% to 41%, I2 = 100%). Conclusion: It can be concluded based on the experimental results that hyper-heuristic approach outclassed the hybrid heuristic and metaheuristic approaches in optimizing node localization and energy efficiency in WSNs.
Keywords: Hyper-Heuristic, Hybrid Heuristic, Metaheuristic, Node Localization, Wireless Sensor Network.
Scope of the Article: Wireless Sensor Network