RETRACTED ARTICLE: Soft computing approach based energy and correlation aware cooperative data collection for wireless sensor network

• Published in: Journal of ambient intelligence and humanized computing Vol. 12; no. 5; pp. 5297 - 5308
• Main Authors: Vinodha, R., Durairaj, S.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2021; Springer Nature B.V
• Subjects: Artificial Intelligence; Computational Intelligence; Engineering; Original Research; Robotics and Automation; User Interfaces and Human Computer Interaction; Link aware forwarding; Energy balancing; Energy conservation; Soft computing approach; Data compression; Passive clustering
• ISSN: 1868-5137; 1868-5145
• DOI: 10.1007/s12652-020-02008-9

Energy plays the predominant constraint in wireless sensor network (WSN) hence, an energy and correlation aware cooperative data collection (ECCDC) method of soft computing approach has been discussed in this work. This work is an attempt to maximize the life span of the network by identifying and rectifying the major energy exploiting activities of WSN and it also defines a hierarchical network structure with balanced clusters and energy harvesting sensor nodes. These sensor nodes select the Cluster Heads (CHs) among themselves using a low overhead passive clustering approach. The cluster balancing has been performed by acknowledging only the most deserved sensor nodes as their members. The members co-operatively collect the data from the region of interest. During every time slot, the cluster members are chosen for reporting, based on the mutual correlation, link quality of the member with the CH and the residual energy of the member nodes. The fraction of data collected at the CH is then sent through the optimized inter-cluster paths to the sink, wherein the received data are used for estimation, based on the node’s position and correlation pattern. This proposed system of optimize method reduces the energy consumed, due to the transmission of redundant data and the data loss by preferring optimized data paths.