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Sensors 2018, 18(12), 4258; https://doi.org/10.3390/s18124258

Energy-Balancing Unequal Clustering Approach to Reduce the Blind Spot Problem in Wireless Sensor Networks (WSNs)

Faculty of Computer Science, Dalhousie University, 6050 University Ave., PO Box 15000, Halifax, NS B3H 4R2, Canada
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Received: 31 October 2018 / Revised: 20 November 2018 / Accepted: 21 November 2018 / Published: 4 December 2018
(This article belongs to the Section Sensor Networks)
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Abstract

Wireless Sensor Networks (WSNs) have become a significant part of surveillance techniques. With unequal clustering approaches and multi-hop communication, WSNs can balance energy among the clusters and serve a wide monitoring area. Recent research has shown significant improvements in unequal clustering approaches by forming clusters prior to the selection of cluster heads. These improvements adopt different geometric fractals, such as the Sierpinski triangle, to divide the monitoring area into multiple clusters. However, performance of such approaches can be improved further by cognitive partitioning of the monitoring area instead of adopting random fractals. This paper proposes a novel clustering approach that partitions the monitoring area in a cognitive way for balancing the energy consumption. In addition, the proposed approach adopts a two-layered scrutinization process for the selection of cluster heads that ensures minimum energy consumption from the network. Furthermore, it reduces the blind spot problem that escalates once the nodes start dying. The proposed approach has been tested in terms of number of alive nodes per round, energy consumption of nodes and clusters, and distribution of alive nodes in the network. Results show a significant improvement in balancing the energy consumption among clusters and a reduction in the blind spot problem. View Full-Text
Keywords: WSN; unequal clusters; blind spot; Sierpinski triangle; cognitive partitioning; energy balancing WSN; unequal clusters; blind spot; Sierpinski triangle; cognitive partitioning; energy balancing
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Islam, N.; Dey, S.; Sampalli, S. Energy-Balancing Unequal Clustering Approach to Reduce the Blind Spot Problem in Wireless Sensor Networks (WSNs). Sensors 2018, 18, 4258.

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