Bandwidth efficient cluster-based data aggregation for Wireless Sensor Network. (January 2015)

• Record Type: Journal Article
• Authors: Mantri, Dnyaneshwar S.; Prasad, Neeli Rashmi; Prasad, Ramjee
• Is Part Of: Computers & electrical engineering. Volume 41(2015)
• Page Start: 256
• Page End: 264
• Publication Date: 2015-01
• Subjects: Bandwidth -- Clustering -- Data aggregation -- Energy consumption -- Throughput -- Wireless Sensor Network; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2014.08.008
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Cluster head is elected according to the highest energy among the CM, number of neighbor nodes with one hop connectivity. Network uses random distribution of heterogeneous nodes with mobile sink. Packets are generated at variable rate by each node and aggregated at CH and then at sink. It uses the perfectly compressible aggregation function on data generated by nodes. The performance is measured in terms of PDR and throughput to show the effective utilization of bandwidth. Abstract: A fundamental challenge in the design of Wireless Sensor Network (WSNs) is the proper utilization of resources that are scarce. The critical challenge is to maximize the bandwidth utilization in data gathering and forwarding from sensor nodes to the sink. The main design objective is to utilize the available bandwidth efficiently. The proposed Bandwidth Efficient Cluster-based Data Aggregation (BECDA) algorithm presents the solution for the effective data gathering with in-network aggregation. It considers the network with heterogeneous nodes in terms of energy and mobile sink to aggregate the data packets. The optimal approach is achieved by intra and inter-cluster aggregation on the randomly distributed nodes with the variable data generation rate. The proposed algorithm uses the correlation of data within the packet for applying the aggregation function on the data generated by nodes. BECDA shows significant improvement in PDR (67.44% and 26.79%) and throughput (41.25% and 26.16%) as compared to the state-of-the-art solutions.