RETRACTED ARTICLE: K-Partitioned Smallest Distance Mining Tree for Path Optimation in Wireless Sensor Network

Abstract

In wireless sensor networks (WSN), energy efficiency is an important issue, since the sensor nodes act as both data originator and data router. The sink functionality typically includes mining and sensing data from sensors in the network via multihop relays performing data processing. The nodes which are closer to the sink have to take more substantial traffic load consequently reduce their energy quickly leading to energy hole around the sink. Then the energy hole may cause failure in the sensor network and create large coverage hole. Sink repositioning can be performed using multiple sink deployment and sink mobility. Relocating or repositioning the sink is very challenging during the regular network operation. It is challenging in a multi-hop network environment to find an optimal location. Most of the existing localization solutions had low accuracy in congested situations and did not consider the resource limitations of WSN. To rectify the issues of sink repositioning, this work presents a K-partitioned smallest distance tree (k-PSDT) utilizing the ideal scan for setting an ideal number of sinks in sensor networks. At first, the quantity of sinks is resolved utilizing the ideal sink algorithm fulfilling the h-jump requirement. k-PSDT is developed for situating numerous sink nodes and setting up the courses. In the wake of deciding the ideal number of sink positions and directing, best sink reposition is chosen by ideal pursuit technique. Sink development is finished by utilizing the canny development, and it confines the sinks developments while keeping up their bearing to the ideal positions. The performance of the new techniques is implemented using network simulator (NS2). Simulation results show that the proposed technology do the better performance as compared to the existing method regarding the metrics such as average packet delivery ratio, delay, and energy consumption.