Energy Reckoning Distance Based Clustering for Spectrum Aware Cognitive Radio Wireless Sensor Networks
1T.Shankar, Department of Communication Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
2V.Srividhya, Department of Communication Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Manuscript received on November 14, 2019. | Revised Manuscript received on 23 November, 2019. | Manuscript published on December 10, 2019. | PP: 842-850 | Volume-9 Issue-2, December 2019. | Retrieval Number: B6717129219/2019©BEIESP | DOI: 10.35940/ijitee.B6717.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Implementing cognitive radio sensor nodes in wireless sensor networks introduced a smart combination called cognitive radio sensor network (CRSN) which creates new challenges in the design of network topology. Conserving the nodes energy helps to extend the lifetime of the network. This stands as an important criterion while designing any algorithm. In order to achieve the same, two important criteria are to be considered – the communicating distance between the nodes or node to base station and proper spectrum sharing technique. In the proposed work, Energy Reckoning Distance-Based Clustering (ERDBC) algorithm, both the criterion is taken into consideration and designed in order to increase the lifetime of a cognitive radio sensor network. In the ERDBC algorithm, the whole network area is divided into three regions according to the distance and the cluster heads are elected based on energy, distance and common channel creates a greater impact on retaining the nodes energy. Also, implementing multi-hop routing using proper spectrum sharing technique helps to avoid data collision and retransmission thereby; the energy consumption of the nodes are reduced to a greater extent. The performance of the proposed ERDBC algorithm is measured on the basis of residual energy, throughput, channel usage, first node death, last node death, and network lifetime, and compared with the already existing LEACH, CogLEACH, LEAUCH and CEED algorithms. Thus the network lifetime of the proposed ERDBC algorithm is 78.18% more than LEACH, 73.6% more than CogLEACH, 29.88% more than CEED and 17.98% more than LEAUCH algorithms.
Keywords: Cognitive LEACH, Cognitive radio sensor Network, Low Energy Clustering, Spectrum aware Clustering.
Scope of the Article: Clustering