Bodacious-Instance Coverage Mechanism for Wireless Sensor Network

Table 1

Comparative analysis among various algorithms with the proposed BiCM.

Algorithm	Working ground	Expediency	Impairments	Comparison with proposed BiCM
Genetic algorithm (GA)	Stochastic search methodology through generic system: within a population, it impels the recombination and mutation.	It is faster and has the ability to find the best quality solution in trivial time, possesses parallel capabilities, and easily discovers the global optimum.	It never guarantees an optimal solution. It is hard to choose parameters like number of generations and population size. It is expensive.	It functions in a hybrid environment and ensures relocation of the intended instance position within the coverage area; therefore, energy consumption remains confined.
Particle swarm optimization (PSO)	Inspired by bird flocking and fish schooling: the particles move in a multidimensional search space, and the single intersection of all dimensions forms a particle.	It can overcome the unconstrained minimization issue. Providing the derivative-free technique, it is less sensitive and less dependent on a set of initial points. It can generate high-quality solutions.	It can easily fall into the local optimum in high-dimensional space and has a low convergence rate in the iterative process. It is difficult to adopt the best topology.	At the beginning, it rummages where the sensor nodes should be moved; therefore, local minima can easily be avoided.
Tabu search (TS)	It works on the principle of adaptive memory and responsive exploration.	It has simple implementation and provides robust solution for complex issues.	It vanishes in a local minimum, requires large computing time, and cannot give an upper bound for the computation time	Within a trivial period, it maintains the network coverage range.
Bacterial foraging algorithm (BFA)	It works on search and optimal foraging decision-making capabilities; problems and movement take place either in clockwise or counterclockwise direction.	It is used for unconstrained numerical optimization, having dual movement, i.e., swimming and tumbling called chemotaxis.	It has a weak ability to perceive the environment and is vulnerable to perception of the local extreme; it is hard to deal with complex optimization problems.	As it operates in two stages, thereupon, no vulnerabilities can slow down the performance, and each stage performs independently.
Ant colony optimization (ACO)	Based on social behaviour of the insects, the optimization process is initialized by random solutions.	It allows rapid discovery of good solutions with guaranteed convergence.	It has dependent sequences of random decisions, a complicated theoretical analysis, and uncertain time to convergence.	The depuration technique in second stage reduces the moving distance, and there exists no uncertainty.
Harmony search (HS)	It is based on musical instrument harmony and is a process for better harmony movement.	No setting value is required; it can deal with discrete and continuous variables and can ignore the local optima.	It encounters a high-dimensional multimodal issue, causes unproductive iterations, and has poor local search.	Due to the hybrid environment, the local search is free of being followed by factors; thus, there are no impeaching hurdles.
Artificial bee colony (ABC)	Search optimization consists of three essential components: employed and unemployed foraging bees and food sources.	It minimizes the expense of deploying nodes inside the monitoring region, deals with local solution, and has broad applicability and complex functions.	It has a low process and a higher number of objective function evaluations; number of dimensions might change.	It maintains the network dimension by reducing the moving distance between instance nodes.
Jenga-inspired optimization algorithm (JOA)	Based on greedy fast convergence, it selects the minimum cost node subset through the roulette method and is a bridge between the optimal solution and a short computation time.	It addresses the energy-efficient coverage issues, having stochastic approach to conduct random exploration; if a sensor node cannot cover an area, the other node will avail of the chance.	The detection probability decreases exponentially as the distance becomes greater.	It has shrewd control over the moving distance; therefore, no uncouth movement can degrade the overall communication.