research-article

Compressive Sensing Based Distributed Data Storage for Mobile Crowdsensing

Authors:

Keqin LiAuthors Info & Claims

ACM Transactions on Sensor Networks (TOSN), Volume 18, Issue 2

Article No.: 25, Pages 1 - 21

https://doi.org/10.1145/3498321

Published: 04 February 2022 Publication History

Abstract

Mobile crowdsensing systems typically operate centralized cloud storage management, and the environment data sensed by the participants are usually uploaded to certain central cloud servers. Instead, this article addresses the decentralized data storage problem in scenarios where cloud servers or network infrastructures do not work as expected and the sensing data have to be temporarily stored on the mobile devices carried by the participants. Considering that the sensing data are generally correlated, this article investigates a compressive distributed storage scheme for mobile crowdsensing. We notice a key observation: when a participant has a random walk in the target sensing area, his walking/sensing process can be considered as a random sampling for the entire area, although the activity of the participant may only have a local scope. We then propose an encoding algorithm based on compressive sensing theory. Each participant encodes the sensing data in their local trajectory, but the encoded CS measurement is capable of roughly reflecting the entire information of the whole area. While a participant stores a blurred global image of the target sensing area, the entire data can then be collaboratively stored by a certain number of participants. We further present a period-based data recovery algorithm to exploit the inter-period correlations, improving the recovery accuracy. Experimental results using real environmental data demonstrate the performance of the proposed compressive storage scheme. The test datasets and our source codes are available at https://github.com/siwangzhou/MCS-Storage.

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Zhao RZhang YWen WLan RXiang Y(2024)E-TPE: Efficient Thumbnail-Preserving Encryption for Privacy Protection in Visual Sensor NetworksACM Transactions on Sensor Networks10.1145/359261120:4(1-26)Online publication date: 11-May-2024
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Zhou SZhang XLiu YJiang HLi K(2024)Decentralized and Compressed Data Storage for Mobile CrowdsensingIEEE Transactions on Mobile Computing10.1109/TMC.2023.329496923:5(4694-4708)Online publication date: May-2024
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Index Terms

Compressive Sensing Based Distributed Data Storage for Mobile Crowdsensing
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing design and evaluation methods
2. Networks
  1. Network algorithms
    1. Data path algorithms
      1. Packet scheduling

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Published In

cover image ACM Transactions on Sensor Networks

ACM Transactions on Sensor Networks Volume 18, Issue 2

May 2022

370 pages

ISSN:1550-4859

EISSN:1550-4867

DOI:10.1145/3494076

Editor:
Yunhao Liu
Tsinghua University, China

Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

Journal Family

ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 04 February 2022

Accepted: 01 November 2021

Revised: 01 August 2021

Received: 01 November 2020

Published in TOSN Volume 18, Issue 2

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National Science Foundation of China
Changsha Municipal Natural Science Foundation

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Zhou SZhang XLiu YJiang HLi K(2024)Decentralized and Compressed Data Storage for Mobile CrowdsensingIEEE Transactions on Mobile Computing10.1109/TMC.2023.329496923:5(4694-4708)Online publication date: May-2024
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Zhao HZhang LZhang YWang Y(2024)Imagery Overlap Block Compressive Sensing With Convex OptimizationIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.337645525:7(8076-8092)Online publication date: Jul-2024
https://doi.org/10.1109/TITS.2024.3376455
Wang HTao JChi DGao YWang ZZou DXu Y(2024)A Preference-Driven Malicious Platform Detection Mechanism for Users in Mobile CrowdsensingIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.335241219(2720-2731)Online publication date: 10-Jan-2024
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Gao WZhou S(2024)Privacy-Preserving for Dynamic Real-Time Published Data Streams Based on Local Differential PrivacyIEEE Internet of Things Journal10.1109/JIOT.2023.333739711:8(13551-13562)Online publication date: 15-Apr-2024
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Liu XZhou SPeng JZhang WTang DLi K(2024)Stopping Criteria for Distributed Data Storage in Compressive CrowdSensing SystemsIEEE Internet of Things Journal10.1109/JIOT.2023.333098311:7(11767-11778)Online publication date: 1-Apr-2024
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Liu XZhou SLuo JYu JZhang W(2024)Region-based compressive distributed storage in Mobile CrowdSensingFuture Generation Computer Systems10.1016/j.future.2024.04.018158(200-209)Online publication date: Sep-2024
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