MURAL - Maynooth University Research Archive Library



    Optimal Scheduling of Multiple Sensors with Packet Length Constraint


    Wu, Shuang and Ren, Xiaoqiang and Dey, Subhrakanti and Shi, Ling (2017) Optimal Scheduling of Multiple Sensors with Packet Length Constraint. IFAC-PapersOnLine, 50 (1). pp. 14430-14435. ISSN 2405-8963

    [img]
    Preview
    Download (443kB) | Preview


    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...



    Add this article to your Mendeley library


    Abstract

    This paper considers the problem of sensory data scheduling of multiple processes. There are n independent linear time-invariant processes and a remote estimator monitoring all the processes. Each process is measured by a sensor, which sends its local state estimate to the remote estimator. The sizes of the packets are different due to different dimensions of each process, and thus it may take different lengths of time steps for the sensors to send their data. Because of bandwidth limitation, only a portion of all the sensors are allowed to transmit in each time step. Our goal is to minimize the average of estimation error covariance of the whole system at the remote estimator. The problem is formulated as a Markov decision process (MDP) with average cost over an infinite time horizon. We prove the existence of a deterministic and stationary policy for the problem. We also find that the optimal policy has a consistent behavior and threshold type structure. A numerical example is provided to illustrate our main results.

    Item Type: Article
    Additional Information: This paper was presented at IFAC 2017 World Congress - The 20th World Congress of the International Federation of Automatic Control, 9-14 Jul 2017, Toulouse, France.
    Keywords: Sensor scheduling; Markov decision process; packet length; threshold policy;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 11895
    Identification Number: https://doi.org/10.1016/j.ifacol.2017.08.2283
    Depositing User: Subhrakanti Dey
    Date Deposited: 28 Nov 2019 11:56
    Journal or Publication Title: IFAC-PapersOnLine
    Publisher: Elsevier
    Refereed: Yes
    URI:
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

    Repository Staff Only(login required)

    View Item Item control page

    Downloads

    Downloads per month over past year

    Origin of downloads