network sensors consist of sensor nodes in which every node covers a limited area. The most common use of these networks is in unreachable fields.Sink is a node that collects data from other nodes. One of the main challenges in these networks is the limitation of nodes battery (power supply).Therefore, the use of topology control is required to decrease power consumption and increase network accessibility.In this paper, a network is modeled by a graph. Each vertex in the graph indicates a sensor node and the edges display the communication links between them. Changes in the graph show changes in network topology and a different path to the sink.In this research, “fuzzy logic” is used for topology control. As the fuzzy logic utilizes optimized sensing radius comparing with minimum-maximum sensing radius, we expect less dead nodes, more mean residual energy and relatively more load balance in the network. At first, 2-input fuzzy algorithm was chosen. However 3-input fuzzy algorithm was also observed due to reasons explained in the main text.In both algorithms, we have load balance in network and prolong network lifetime. Unreachable paths are less encountered with higher rates of packet delivery. The final standard deviation (STD) reaches to its minimum level, while the residual energy in sensors remains close to each other.

Background and Objectives: The quick response time and the coverage range are the crucial factors by which the quality service of a Wireless sensor network can be acknowledged. In some cases, even networks possess sufficient available bandwidth but due to coverage tribulations, the customer satisfaction gets down suddenly. The increasing number of nodes directly is neither a canny solution to overcome the coverage problem nor a cost-effective. In fact, by changing the positions of the deployed node sagaciously can resolve the coverage issue and seems a cost-effective solution. Therefore, keeping all circumstances, a Depuration based Efficient Coverage Mechanism (DECM) has been developed. This algorithm suggests the new shifting positions for previously deployed sensor nodes to fill the coverage gap. Methods: It is a redeployment process and accomplished in two rounds. The first round avails the Dissimilitude Enhancement Scheme (DES), which searches the node to be shifted at new positions. The second round controls the unnecessary movement of the sensor nodes by the Depuration mechanism thereby the distance between previous and new positions is reduced. Results: The factors like loudness, pulse emission rate, maximum frequency, and sensing radius are meticulously explored during simulation rounds conducted by MATLAB. The performance of DECM has been compared with superlative algorithms i. e., Fruit Fly Optimization Algorithm (FOA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) in terms of mean coverage range, computation time, standard deviation, and network energy diminution. Conclusion: According to the simulation results, the DECM has achieved more than 98% coverage range, with a trivial computation time of nearly 0. 016 seconds as compared to FOA, PSO, and ACO.