Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/82497
Title: Optimal power allocation for diffusion-type sensor networks with wireless information and power transfer
Authors: Yang, Gang
Tay, Wee Peng
Guan, Yong Liang
Liang, Ying-Chang
Keywords: Diffusion
Least-Mean-Sqaures (LMS)
DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Yang, G., Tay, W. P., Guan, Y. L. & Liang, Y.-C. (2019). Optimal power allocation for diffusion-type sensor networks with wireless information and power transfer. IEEE Access, 32408–32422. doi:10.1109/ACCESS.2019.2904084
Series/Report no.: IEEE Access
Abstract: This paper investigates the problem of power allocation for distributed estimation via diffusion in wireless sensor networks (WSNs) with simultaneous wireless information and power transfer (SWIPT). We consider a WSN consisting of smart sensor nodes (SNs) and common sensor nodes (CNs), and each SN is capable of performing SWIPT via multi-antenna beamforming to its neighboring (i.e., near-tier) CNs. In each diffusion iteration, all nodes collect measurements and exchange intermediate estimates with their neighbors. We first analyze the effect of each SN’s beamforming design and each near-tier CN’s harvested power allocation on the steady-state network-wide mean square deviation (MSD) of the diffusion least-mean-squares (LMSs) strategy. Then, we formulate a problem to minimize an upper bound MSD by jointly optimizing the global power allocation weights for each SN to perform beamforming, and the local power allocation proportion for each CN to perform measurement collection. We further show that the formulated non-convex problem is decomposable and propose a gradient-based iterative algorithm to find the optimal solution. In addition, for practical implementation, we propose adaptive online approaches to estimate some parameters required for system optimization. Finally, extensive simulation results demonstrate that with optimal power allocation, our proposed scheme improves the MSD performance significantly, compared to the conventional diffusion LMS strategy without wireless power transfer (WPT).
URI: https://hdl.handle.net/10356/82497
http://hdl.handle.net/10220/48024
DOI: 10.1109/ACCESS.2019.2904084
Rights: © 2019 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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