Title: Data classification and scheduling for sensor virtualisation scheme in public healthcare system

Authors: Md. Motaharul Islam; Mohammad Mehedi Hassan; Atif Alamri; Eui-Nam Huh

Addresses: Department of Computer Science and Engineering, Islamic University of Technology, Board Bazar, Gazipur-1704, Bangladesh ' College of Computer and Information Sciences, King Saud University, Riyadh 11543, Kingdom of Saudi Arabia ' College of Computer and Information Sciences, King Saud University, Riyadh 11543, Kingdom of Saudi Arabia ' Department of Computer Engineering, Kyung Hee University, 1 Seochon-dong, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, South Korea

Abstract: It is really surprising that most of the sensor nodes in wireless sensor network (WSN) remain idle for maximum period of its lifetime resulting underutilisation of resources. There are many ongoing researches to utilise WSN resources in an efficient way. Virtualisation of sensor network (VSN) is one of the novel approaches to utilise physical infrastructure of WSN. VSN can be simply defined as the virtual version of WSN over the physical sensor infrastructure. By allowing sensor nodes to coexist on a shared physical substrate, VSN may provide flexibility, cost effectiveness and manageability. This paper proposes quality of service (QoS) aware data classification and scheduling framework for VSN in the healthcare sector. We develop a tiny virtual machine called VSNware for healthcare application which facilitates QoS aware data classification and scheduling ensuring reliability, delay guarantee and speed. Simulation results also show that proposed scheme over performs conventional WSN approaches.

Keywords: virtualisation; WSNs; wireless sensor networks; data classification; data scheduling; public healthcare; overlay networks; virtual networks; quality of service; QoS; virtual machines; simulation; healthcare technology.

DOI: 10.1504/IJSNET.2016.080374

International Journal of Sensor Networks, 2016 Vol.22 No.4, pp.259 - 273

Available online: 18 Nov 2016 *

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