In a Wireless Sensor Network, Fuzzy Clustering-Based Data Collection

Abstract

A collection of sensor nodes that are spatially dispersed for the purpose of monitoring physical or environmental conditions like temperature, sound, pressure, etc. is known as a wireless sensor network (WSN) and to transmit their data to a base station in concert over the network. The primary difficulty lies in reducing the amount of energy required for data collection and transmission from sensor nodes to the sink. One of the most popular communication protocols in this field is cluster-based data aggregation. In wireless sensor networks, clustering is an important method for extending the lifetime of the network. Data from relevant cluster nodes is aggregated and sent to the base station by Cluster Heads (CH). The selection of suitable CHs is a major obstacle in WSNs. A tree structure serves as the foundation for another communication protocol. Because there are short paths between the sensors in this protocol, energy consumption is low. Data aggregation using Dynamic Fuzzy Clustering is discussed in this paper. Minimum spanning tree and clustering are the foundations of this strategy. For the initial selection of CHs, the proposed method employs a fuzzy decision-making approach. After that, CHs are used to construct a minimum spanning tree. CHs are chosen effectively and precisely. The advantages of the previous structures are being recovered by combining clustering and tree structure. In terms of energy consumption and residual energy over the lifetime of each sensor network, our method is compared to well-known data aggregation techniques. Each node's energy consumption is reduced by our method. The remaining energy of the nodes will be preserved when the best CHs are chosen and the minimum spanning tree is constructed from the best CHs. In WSN, node lifetime plays a crucial role. The network's survival is enhanced by employing our proposed data aggregation algorithm