Supervised learning-based Lifetime
Extension of Wireless Sensor Network Nodes Jaewoong Kang, Jongmo Kim, and Mye Sohn+ Department of Industrial Engineering, Sungkyunkwan University, Suwan,
Korea {kjw1727,
dignityc, myesohn}@skku.edu
Abstract In this paper, we propose a new approach to increase
the sustainability of the Wireless Sensor Network (WSN) nodes by extending
their lifetimes. To do so, we attempt to find the optimal values of the
collection interval and the transmission interval for each task that can
maximize the lifetime of the WSN nodes by applying machine learning
techniques. As a preprocessing for finding the optimal value of two
parameters, we first determine the combination of nodes necessary to perform
each task using the wrapper method. In addition, we applied Simulated
Annealing (SA) to find the values of two parameter that lower power
consumption without being significantly affected by the WSN’s performance. To
prove the superiority of the method, we perform two kinds of experiments.
Finally, we prove the reduction of energy consumption using our framework. Keywords: Sustainability, Wireless Sensor Network, Interval
Determination, Machine Learning +: Corresponding
author: Mye Sohn InfoScience Laboratory, Department of Systems
Management Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 440-746, Republic of
Korea, Tel: +82-(0)31-290-7605 Journal
of Internet Services and Information Security
(JISIS), 9(4): 59-67,
November 2019 DOI: 10.22667/JISIS.2019.11.30.059 [pdf] |