Abstract
In the context of wireless sensor networks (WSNs), the utilization of artificial intelligence (AI)-based solutions and systems is on the ascent. These technologies offer significant potential for optimizing services in today's interconnected world. AI and nature-inspired algorithms have emerged as promising approaches to tackle various challenges in WSNs, including enhancing network lifespan, data aggregation, connectivity, and achieving optimal coverage of the targeted area. Coverage optimization poses a significant problem in WSNs, and numerous algorithms have been proposed to address this issue. However, as the number of sensor nodes within the sensor range increases, these algorithms often encounter difficulties in escaping local optima. Hence, exploring alternative global metaheuristic and bio-inspired algorithms that can be adapted and combined to overcome local optima and achieve global optimization in resolving wireless sensor network coverage problems is crucial. This paper provides a comprehensive review of the current state-of-the-art literature on wireless sensor networks, coverage optimization, and the application of machine learning and nature-inspired algorithms to address coverage problems in WSNs. Additionally, we present unresolved research questions and propose new avenues for future investigations. By conducting bibliometric analysis, we have identified that binary and probabilistic sensing model are widely employed, target and k-barrier coverage are the most extensively studied coverage scenarios in WSNs, and genetic algorithm and particle swarm optimization are the most commonly used nature-inspired algorithms for coverage problem analysis. This review aims to assist researchers in exploring coverage problems by harnessing the potential of nature-inspired and machine-learning algorithms. It provides valuable insights into the existing literature, identifies research gaps, and offers guidance for future studies in this field.
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Abbreviations
- ACO:
-
Ant Colony Optimization
- AoI:
-
Area of Interest
- ABC:
-
Artificial Bee Colony
- ANN-PSO:
-
Artificial Neural Network-Particle Swarm Optimization
- ANN:
-
Artificial Neural Networks
- BPNN:
-
Back Propagation Neural Network
- BOA:
-
Bat Optimization Algorithm
- CH:
-
Cluster Head
- CFPA:
-
Chaotic Flower Pollination Algorithm
- DPSO:
-
Democratic Particle Swarm Optimization
- DE:
-
Differential Evolution
- Ex-GWO:
-
Expanded Grey Wolf Optimization
- FOA:
-
Fruit Fly Optimization Algorithm
- GA:
-
Genetic Algorithm
- GPS:
-
Global Positioning System
- GSO:
-
Glowworm Swarm Optimization
- GDMIP:
-
Graph-based Dynamic Multi-Mobile Agent Itinerary Planning approach
- GWO:
-
Grey Wolf Optimization
- I-GWO:
-
Incremental Grey Wolf Optimization
- HMCR:
-
Harmony Memory Consideration Rate
- HMS:
-
Harmony Memory Size
- HAS:
-
Harmony Search Algorithm
- ICS:
-
Improved Cuckoo Search
- IoT:
-
Internet of Things
- KF:
-
Kalman Filter
- LA:
-
Learning Automata
- ML:
-
Machine Learning
- MAC:
-
Medium Access Control
- MADIT:
-
Mobile Agent Distributed Intelligence Tangle-based
- MWSM:
-
Mobile Wireless Sensor Network
- PSO:
-
Particle Swarm Optimization
- PAR:
-
Pitch Adjustment Rate
- PSC:
-
Probabilistic Sensing Coverage
- QoS:
-
Quality of Service
- RoI:
-
Region of Interest
- SOM:
-
Self-Organizing Map
- SIR:
-
Sensor Intelligence Routing
- SN:
-
Sensor Node
- SCP:
-
Smart Car Park
- STCDRR:
-
Spatial and Temporal Correlation-based Data Redundancy Reduction
- SVM:
-
Support Vector Machine
- SI:
-
Swarm Intelligence
- TLBO:
-
Teaching–learning-based optimization
- TCO:
-
Termite Colony Optimization
- TPSMA:
-
Territorial Predator Scent Marking Algorithm
- WOA:
-
Whale Optimization Algorithm
- WSNs:
-
Wireless Sensor Networks
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Egwuche, O.S., Singh, A., Ezugwu, A.E. et al. Machine learning for coverage optimization in wireless sensor networks: a comprehensive review. Ann Oper Res (2023). https://doi.org/10.1007/s10479-023-05657-z
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DOI: https://doi.org/10.1007/s10479-023-05657-z