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Wireless Sensor Network MCDS Construction Algorithms With Energy Consideration for Extreme Environments Healthcare

Tang, Qiang and Yang, Kun and Wang, Jin and Luo, Yuansheng and Li, Keqin and Yu, Fei (2019) 'Wireless Sensor Network MCDS Construction Algorithms With Energy Consideration for Extreme Environments Healthcare.' IEEE Access, 7. pp. 33130-33144. ISSN 2169-3536

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Abstract

With the enhancement of people's health awareness, more and more users are willing to wear portable micro-health monitoring equipment and communicate with remote medicine center for real-time diagnosis. Although, under normal circumstances, users' health status can be detected at any time, in extreme circumstances, such as earthquakes, how to make the medical center monitor user data for a long time for rescue will be of great significance. In this paper, we will study the networking of portable wearable devices based on wireless sensor networks. We mainly use minimal connected dominating sets (MCDSs) to organize nodes in extreme environments effectively, form virtual backbone networks, send data to the rescue or medical personnel, and maximize network lifetime. Specifically, we propose an adverse dominator selection procedure (ADSP), where the dominators are selected by their children-independent nodes. The ADSP has two versions, which are Independent node degree-based Adverse Dominator Selection Procedure (IADSP) and residual Energy-based Adverse Dominator Selection Procedure (EADSP). Based on IADSP and EADSP, two approximation MCDS construction algorithms named Independent node degree based MCDS (IMCDS) and Energy-efficient Independent neighbor-based MCDS (EIMCDS) are proposed, respectively. Both of them have the message complexity as O($N\Delta $ ). The performance ratio of IMCDS has an upper bound as O($\sqrt {N}$ ). The simulation results show that IMCDS and EIMCDS perform well in terms of CDS size, and the routing algorithm based on EIMCDS has better energy efficiency performance than that of IMCDS and classical routing protocol.

Item Type: Article
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 06 Aug 2021 13:59
Last Modified: 15 Jan 2022 01:27
URI: http://repository.essex.ac.uk/id/eprint/26949

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