Performance Analysis for Node Recovery and Forward Node Move Algorithm on Channel Models
Deepak v Biradar1, Nataraj K.R2
1Deepak v Biradar, Ph.D Research Scholar, Jain University Bangalore (Karnataka), India.
2Dr. Nataraj K. R, Professor, Department of Electronics and Communications Engineering, (SJB) Institute of Technology Bangalore, Bangalore (Karnataka), India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 1250-1257 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6569048419/19©BEIESP
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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: in the current world, the sensor nodes are of a minimized size and are available at a cost-effective value. The WSN contains a set of sensors that can be traced in the format of Linear, Grid or Random deployment patterns. The applications of WSN spans a large base varying from enemy detection, sensing for weather forecast and is having constraints like bandwidth, energy, and coverage. When the nodes are involved in route discovery or packet delivery process then energy is consumed by the nodes and eventually after a certain period of time dead node occurs in the network. This lead to packets dropped. In this paper, a method is proposed which categorizes nodes into upthreshold and under-threshold nodes and picks the best node during the routing process. The proposed Node Recovery and Forward Node Move (NRFNM) method makes use of sink relocation and restoration of dead nodes using genetic so that network lifetime is improved and throughput is increased. The proposed method is then analyzed on various channel models like HATA Model Sub Urban Channel Model, HATA Model Urban Channel Model, Clutter Factor Channel Model, Free Space Channel Model, and Walfish Channel Model
Keywords: Sink Relocation, Node Recovery, Propagation Models
Scope of the Article: High Performance Computing