Determination of Mobile Sink Path in Wireless Sensor Networks using Learning Techniques
P Varaprasada Rao1, S Govinda Rao2, Y Manoj Kumar3, G Anil Kumar4, B Padma Vijetha Dev5
1Dr P Varaprasada Rao: Professor in CSE, Department of CSE, Gokaraju Rangaraju Institute of Engineering and Technology (GRIET). Hyderabad (Telangana), India.
2Dr S Govinda Rao: Professor in CSE, Department of CSE, Gokaraju Rangaraju Institute of Engineering and Technology (GRIET). Hyderabad (Telangana), India.
3Y Manoj Kumar: Assistant Professor in CSE, Department of CSE, Gokaraju Rangaraju Institute of Engineering and Technology (GRIET). Hyderabad (Telangana), India.
4G.Anil Kumar: Assistant Professor in CSE, Department of CSE, Gokaraju Rangaraju Institute of Engineering and Technology (GRIET). Hyderabad (Telangana), India.
5B Padma Vijetha Dev: Assistant Professor in CSE, Department of CSE, Gokaraju Rangaraju Institute of Engineering and Technology (GRIET). Hyderabad (Telangana), India.
Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 2412-2419 | Volume-8 Issue-5, June 2019 | Retrieval Number: E7726068519/19©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: This paper describes the network data transmission path finding techniques with mobile sink in wireless sensor networks. It also explores the ways to minimize energy consumption with sink due to communication with other sensor nodes. Congestion prevails in the Sensor nodes near to sink due to enormous data transfers from neighboring sensor nodes. Due to the heavy forwarding of data packets may led to a hotspot problem. By using mobile sink, it assimilates data by moving within the sensing region and balance the load of traffic to all sensor nodes in the network. To recede the delay due to the visit of more number of sensor nodes, some sensor nodes are considered as rendezvous points (RPs) and Mobile sink visits these points only. Source nodes forward their data to adjacent RPs. But it is more difficult to find the finest set of RPs and travelling path of mobile sink. This paper showcases the attempt to explore the ways to discover RPs and getting optimization in network data communication of sensor nodes through mobile sink. Social algorithms and Machine Learning techniques are the highly established efficient approaches to solve many complex optimization problems. The aim of this survey is to present a comprehensive study of applying Social algorithms in selecting finest set of RPs, to mitigate the challenges in Mobile sink path determination to extend network lifetime and exploring Machine Learning towards the energy conservation in WSN.
Keywords: Wireless Sensor Networks, Mobile sink, Rendezvous points,Social algorithms, Machine Learning.
Scope of the Article: WSN