On improving the lifespan of wireless sensor networks with fuzzy based clustering and machine learning based data reduction

• Published in: Applied soft computing Vol. 83; p. 105610
• Main Authors: Radhika, S., Rangarajan, P.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2019
• Subjects: Clustering; Energy efficiency; Fuzzy; Machine learning; Network lifetime; Wireless sensor networks; Wireless sensor networks; Network lifetime; Energy efficiency; Fuzzy; Clustering; Machine learning
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2019.105610

A useful approach to increase the lifetime of wireless sensor networks is clustering. Exchange of messages due to successive and recurrent reclustering burdens the sensor nodes and causes power loss. This paper presents a modified clustering methodology that diminishes the overhead in clustering and message exchanges thereby effectively scheduling the clustering task. The network is clustered subject to the remaining energy of sensor nodes. Energy based parameters decide cluster head nodes and ancillary nodes and the member nodes are linked with them. The roles of the head nodes of the cluster are interchanged depending on the nodes’ states. Reclustering of nodes is accomplished to achieve minimum energy consumption by calculating the update cycle using a fuzzy inference system. The average sensed data rate of cluster members, the distance at which the member nodes are from the sink and the power of cluster head nodes are counted to achieve better energy saving. Cluster member nodes apply machine learning at regular intervals to classify data based on their similarity. The classified data are transmitted to the cluster head after a reduction in the number of message transfers. The proposed method improves the energy usage of clustering and data transmission. •Wireless sensor networks are constrained in battery power.•Energy efficiency is an important factor in network operations.•The paper proposes a clustering approach to improve energy efficiency using fuzzy logic and machine learning.•It aims to improve the network lifetime and reduce energy consumption.