A Novel Nearest Neighbor Algorithm to Low Energy Data Aggregation in Sensor Networks

This paper introduces a singular nearest neighbor set of rules for information aggregation in Wi-Fi sensor networks. This set of rules is based on the nearest neighbor precept, which reduces electricity consumption by minimizing transmission and verbal exchange delays. The proposed algorithm utilizes an allotted cooperative search that enables nodes within the community to perform distributed statistics aggregation, focusing on the nearest neighbor inside the community instead of broadcasting statistics to all the nodes in the community. The proposed algorithm can aggregate records from more than one sensor node in a single transmission and offers efficient statistics aggregation for low-strength programs. The paper additionally evaluates the performance of the proposed set of rules using simulations and compares it with existing algorithms. The consequences show that the proposed set of rules provides dependable information aggregation with decreased transmission strength consumption compared to existing algorithms. This paper demonstrates a fantastic energy-green information aggregation method for wireless sensor networks. This paper introduces a novel nearest-neighbor rule for low-power statistics aggregation in sensor networks. The set of rules utilizes an ok-Nearest Neighbor (kNN) method to accumulate and aggregate low-energy information from multiple sensor nodes effectively. The method uses the geographical coordinates of the sensor nodes to construct the highest-quality kNN configuration. The kNN parameters are optimized using a Genetic algorithm (GA) to reap a higher usual aggregation efficiency. Furthermore, an experimental performance analysis is performed to research the proposed rules' energy efficiency and information aggregation accuracy. The outcomes show that the proposed algorithm considerably reduces strength consumption even as presenting an advanced accuracy of statistics aggregation.