Sangdae Kim, Cheonyong Kim, Hyunchong Cho, Kwansoo Jung, Seungmin Oh

KIPS Transactions on Computer and Communication Systems, Vol. 9, No. 11, pp. 256-264, Nov. 2020
https://doi.org/10.3745/KTCCS.2020.9.11.256,   PDF Download:
Keywords: Wireless Sensor Networks (IWSNs), Linear Regression Algorithm, Mobility Support, Graph Construction, Resource Allocation
Abstract

Industrial Wireless Sensor Networks (IWSNs) is exploited to achieve various objectives such as improving productivity and reducing cost in the diversity of industrial application, and it has requirements such as low-delay and high reliability packet transmission. To accomplish the requirement, the network manager performs graph construction and resource allocation about network topology, and determines the transmission cycle and path of each node in advance. However, this network management scheme cannot treat mobile devices that cause continuous topology changes because graph reconstruction and resource reallocation should be performed as network topology changes. That is, despite the growing need of mobile devices in many industries, existing scheme cannot adequately respond to path failure caused by movement of mobile device and packet loss in the process of path recovery. To solve this problem, a network management scheme is required to prevent packet loss caused by mobile devices. Thus, we analyse the location and movement cycle of mobile devices over time using machine learning for predicting the mobility pattern. In the proposed scheme, the network manager could prevent the problems caused by mobile devices through performing graph construction and resource allocation for the predicted network topology based on the movement pattern. Performance evaluation results show a prediction rate of about 86% compared with actual movement pattern, and a higher packet delivery ratio and a lower resource share compared to existing scheme.

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