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Title: Two-dimensional swarm formation in time-invariant external potential: Modeling, analysis, and control
Authors: Wang, Yanran
Hikihara, Takashi
Author's alias: 引原, 隆士
Issue Date: 25-Sep-2020
Publisher: American Institute of Physics Inc.
Journal title: Chaos
Volume: 30
Issue: 9
Thesis number: 093145
Abstract: Cluster formation has been observed in many organisms in nature. It has the desirable properties for designing energy efficient protocols for Wireless Sensor Networks (WSNs). In this paper, we present a new approach for energy efficient WSN protocols that investigates how the cluster formation of sensors responds to the external time-invariant energy potential. In this approach, the necessity for data transmission to the Base Station is eliminated, thereby conserving energy for WSNs. We define swarm formation topology and estimate the curvature of an external potential manifold by analyzing the change of the swarm formation in time. We also introduce a dynamic formation control algorithm for maintaining defined swarm formation topology in the external potential. Energy conservation is a crucial challenge in Wireless Sensor Networks (WSNs). As energy for data transmission is most costly, WSNs’ algorithms need to be designed in ways where data transmission, especially to the control center called the Base Station (BS), is minimized. Clustering is a possible mechanism to design energy efficient algorithms for WSNs. In this paper, we combine the idea of swarm intelligence with WSNs and design an algorithm that captures the environmental information through analyzing the change in sensor cluster formations (swarm formation) rather than gathering information directly through individual sensor measurements. In this approach, it is numerically clarified that the necessity for the BS is eliminated, and the formation is controllable based on the obtained information.
Rights: This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Cite as: Chaos 30, 093145 (2020) and may be found at https://doi.org/10.1063/5.0019886.
The full-text file will be made open to the public on 25 September 2021 in accordance with publisher's 'Terms and Conditions for Self-Archiving'.
URI: http://hdl.handle.net/2433/255630
DOI(Published Version): 10.1063/5.0019886
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