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Collaborative Mobile Crowdsensing in Opportunistic D2D Networks: A Graph-based Approach

Published: 17 May 2019 Publication History

Abstract

With the remarkable proliferation of smart mobile devices, mobile crowdsensing has emerged as a compelling paradigm to collect and share sensor data from surrounding environment. In many application scenarios, due to unavailable wireless network or expensive data transfer cost, it is desirable to offload crowdsensing data traffic on opportunistic device-to-device (D2D) networks. However, coupling between mobile crowdsensing and D2D networks, it raises new technical challenges caused by intermittent routing and indeterminate settings. Considering the operations of data sensing, relaying, aggregating, and uploading simultaneously, in this article, we study collaborative mobile crowdsensing in opportunistic D2D networks. Toward the concerns of sensing data quality, network performance and incentive budget, Minimum-Delay-Maximum-Coverage (MDMC) problem and Minimum-Overhead-Maximum-Coverage (MOMC) problem are formalized to optimally search a complete set of crowdsensing task execution schemes over user, temporal, and spatial three dimensions. By exploiting mobility traces of users, we propose an unified graph-based problem representation framework and transform MDMC and MOMC problems to a connection routing searching problem on weighted directed graphs. Greedy-based recursive optimization approaches are proposed to address the two problems with a divide-and-conquer mode. Empirical evaluation on both real-world and synthetic datasets validates the effectiveness and efficiency of our proposed approaches.

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Published In

cover image ACM Transactions on Sensor Networks
ACM Transactions on Sensor Networks  Volume 15, Issue 3
August 2019
324 pages
ISSN:1550-4859
EISSN:1550-4867
DOI:10.1145/3335317
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 17 May 2019
Accepted: 01 March 2019
Revised: 01 December 2018
Received: 01 March 2018
Published in TOSN Volume 15, Issue 3

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Author Tags

  1. Crowdsensing
  2. coverage
  3. greedy search
  4. incentive
  5. transmission

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  • Research-article
  • Research
  • Refereed

Funding Sources

  • National Natural Science Foundation for Distinguished Young Scholars
  • Natural Science Basic Research Plan in Shaanxi Province of China
  • National Key Research and Development Program of China
  • Fundamental Research Funds for the Central Universities

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  • (2024)Group Task Recommendation in Mobile Crowdsensing: An Attention-Based Neural Collaborative ApproachIEEE Transactions on Mobile Computing10.1109/TMC.2023.334586523:8(8066-8076)Online publication date: Aug-2024
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