Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/86255
Title: On stochastic sensor network scheduling for multiple processes
Authors: Han, Duo
Wu, Junfeng
Mo, Yilin
Xie, Lihua
Keywords: Optimal Scheduling
Schedules
Issue Date: 2016
Source: Han, D., Wu, J., Mo, Y., & Xie, L. On stochastic sensor network scheduling for multiple processes. IEEE Transactions on Automatic Control, 62(12), 6633-6640. doi:10.1109/TAC.2017.2717193
Series/Report no.: IEEE Transactions on Automatic Control
Abstract: We consider the problem of multiple sensor scheduling for remote state estimation of multiple process over a shared link. In this problem, a set of sensors monitor mutually independent dynamical systems in parallel but only one sensor can access the shared channel at each time to transmit the data packet to the estimator. We propose a stochastic event-based sensor scheduling in which each sensor makes transmission decisions based on both channel accessibility and distributed event-triggering conditions. The corresponding minimum mean squared error (MMSE) estimator is explicitly given. Considering information patterns accessed by sensor schedulers, time-based ones can be treated as a special case of the proposed one. By ultilizing realtime information, the proposed schedule outperforms the time-based ones in terms of the estimation quality. Resorting to solving an Markov decision process (MDP) problem with average cost criterion, we can find optimal parameters for the proposed schedule. As for practical use, a greedy algorithm is devised for parameter design, which has rather low computational complexity. We also provide a method to quantify the performance gap between the schedule optimized via MDP and any other schedules.
URI: https://hdl.handle.net/10356/86255
http://hdl.handle.net/10220/44021
ISSN: 0018-9286
DOI: 10.1109/TAC.2017.2717193
Rights: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/TAC.2017.2717193].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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