Table 1.

Summary of literature review.

Reference	Technique	Summary	Limitations
15	Honey Badger algorithm	Enhanced coverage through parameter optimization, improving balance between exploration and exploitation.	Requires fine-tuning of parameters for optimal performance.
16	Genetic algorithm with sensor clouds	Integrates genetic algorithm for clustering and path planning, improving efficiency and coverage.	High computational cost and complex setup required.
17	Ant lion optimizer with NSGA-II	Focuses on network coverage and minimizing sensor node movement, integrating NSGA-II for multi-objective optimization.	May converge to local optima without extensive parameter tuning.
18	Particle swarm optimization (PSO)	Optimizes sensor deployment locations through PSO, addressing minimum exposure path issues.	PSO can be trapped in local optima in complex environments.
19	Improved Sparrow Search Algorithm	Addresses 3D WSN coverage with enhancements like safety threshold attenuation and stagnation update procedures.	Specific to 3D environments, not generally applicable.
20	Army ant search optimizer	Enhances network coverage and lifetime with a multi-objective approach, integrating fast-nondominated sorting and chaotic mapping.	Increased computational complexity with multiple optimization layers.
21	Harris Hawk optimization	Utilizes HHO for optimal router placement for maximum connectivity and coverage.	Performance varies significantly with network size and complexity.
22	Self-adaptive artificial bee colony	Improves node deployment with a strategic pool and adaptive selection mechanism to enhance performance.	Requires constant adaptation to maintain efficiency.
23	NSGA-III	Solves multi-objective optimization problems to maximize connectivity and coverage while minimizing energy consumption.	Complex and resource-intensive, challenging to implement in larger networks.
24	Cuckoo search algorithm (Enhanced)	Improves WSN performance through optimized non-uniform clustering and faster convergence with Cauchy distribution.	Potential for premature convergence if not properly tuned.
25	Whale optimization with Levy flight	Enhances exploration capabilities of the whale optimization algorithm to avoid local optima and improve coverage efficiency.	Specific tuning required to balance exploration and exploitation.
26	Voronoi-Glowworm swarm optimization-K-means	Combines multiple algorithms to optimize the sensing radius and enhance network lifetime through effective node deployment.	Complex integration of multiple algorithms can lead to operational inefficiencies.
27	PSO and chaos optimization	Encodes nodes as particle positions to move towards optimal locations, enhancing coverage with chaos optimization.	Requires careful calibration to ensure effective optimization without overspending energy.
28	Vampire bat algorithm with bipartite graph	Optimizes node moving distance during redeployment, integrating virtual force-based optimization for refined coverage.	Dependent on the initial deployment strategy for effectiveness.
29	Mult objective Dingo optimization	Focuses on dynamic energy decay and adaptive coverage strategies, optimizing node sensing capabilities over time.	Specific to applications requiring high adaptability in sensing and energy management.