S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
Award Criteria
Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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Wireless Body Area Network Auxiliary Relief Mechanism based on Uneven Population Clustering and Rescue Request Information Diffusion Strategy

Fenglian Jiang, Mingling Wang, Chun-Chi Chen, and Hsien-Wei Tseng

(Received July 20, 2020; Accepted March 17, 2021)

Keywords: wireless body area network, relief mechanism, uneven clustering, propagation strategy, energy consumption balance, network dynamics

We propose an auxiliary relief mechanism (ARM) for a wireless body area network (WBAN). The ARM, as a supplement to the traditional medical assistance provided by a WBAN, aims at solving the problem of the lack of timely relief in the existing WBAN relief mechanism. When an emergency occurs, it can help the rescue seekers find k potential rescuers (PR) nearby. By fully considering the limited energy consumption and dynamic characteristic of auxiliary relief network (ARN) nodes, we propose an ARN dynamic population clustering algorithm for the ARM to cluster people around rescue seekers that uses dynamic uneven clustering to ensure balanced energy consumption between clusters. On the basis of the clustering, a rescue request information (RRI) diffusion strategy is proposed, and appropriate intracluster and intercluster propagation strategies are adopted to spread the RRI, so that the information can be transmitted quickly to k PRs. Our experiment shows that the ARN dynamic population clustering algorithm realizes uneven clustering for a dynamic population. The ARM RRI diffusion strategy is superior to a comparative algorithm in both searching time and energy consumption, and has higher searching efficiency.

Corresponding author: Chun-Chi Chen, Hsien-Wei Tseng




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