Enhanced Routing for Low Power and Lossy Network Based on Link Quality Estimation
S.Nirmal Kumar1, T. Suresh2
1S. Nirmal Kumar, Research Scholar, Department of Computer and Information Sciences, Annamalai University. Chidambaram.
2Dr. T. Suresh, Associate Professor, Department of Computer Science and Engineering, Annamalai University. Chidambaram.
Manuscript received on 1 August 2019. | Revised Manuscript received on 7 August 2019. | Manuscript published on 30 September 2019. | PP: 3401-3406 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5039098319/2019©BEIESP | DOI: 10.35940/ijrte.C5039.098319
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: Internet of Things (IoT) network is designed using a set of wireless sensor nodes connected together through a Base station. The sensor nodes capture the data about the surrounding environment and forward it to the base station (BS) along with the geotag and timestamp. For a better quality of service in a IoT network the intelligent routing becomes essential factor. The routing protocol must be energy efficient to prevent packet loss or packet drop, and early dying of certain nodes. Hence it also becomes necessary to balance the energy spending in the network by implementing optimal routing decisions derived from intelligent machine learning techniques. Many researchers have provided solutions for energy efficient routing in IoT network. However the solutions provided need to be enhanced or redesigned to address other challenges and issues in an IoT network. This paper proposes a link quality estimation mechanism when a node is considering it neighboring node as a parent node. Based upon the experiments conducted in this research by implementing the proposed routing protocol it is observed that the routing algorithm exhibits better performance with respect to the following performance metrics including average energy consumption, packet drop rate, overall network life time, and average end to end delay.
Keywords: IoT, Energy Efficient, Machine Learning, Link Quality Estimation, Energy Consumption
Scope of the Article: Routing and Transport Protocols