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Delay- and Incentive-Aware Crowdsensing: A Stable Matching Approach for Coverage Maximization

Simon, Bernd ; Dongare, Sumedh ; Mahn, Tobias ; Ortiz Jimenez, Andrea Patricia ; Klein, Anja (2022)
Delay- and Incentive-Aware Crowdsensing: A Stable Matching Approach for Coverage Maximization.
2022 IEEE International Conference on Communications. Seoul, South Korea (16.-20.05.2022)
doi: 10.1109/ICC45855.2022.9838603
Conference or Workshop Item, Bibliographie

Abstract

Mobile crowdsensing (MCS) is a novel approach to increase the coverage, lower the costs, and increase the accuracy of sensing data. Its main idea is to collect sensor data using mobile units (MUs). The sensing is controlled by a mobile crowdsensing platform (MCSP) through the assignment of delay-sensitive sensing tasks to the MUs. Although promising, research effort in MCS is still needed to find task assignment solutions that maximize the coverage while considering the cost incurred by the MCSPs, the preferences of the MUs and the limited communication resources available. Specifically, we identify two main challenges: (i) A task assignment problem which incorporates the MCSP’s utility and the preferences of the MUs. (ii) An underlying communication resource allocation problem formulating the requirement of the timely transmission of sensing results given the limited communication resources. To address these challenges, we propose a novel two-stage matching algorithm. In the first stage, potential MU-task pairs are constructed considering the preferences of the MUs and the utility of the MCSP. In the second stage, the communication resource allocation is done based on potential MU-task pairs from the first stage. Through numerical simulations, we show that our proposed approach outperforms state-of-the-art methods in terms of the MCSP’s utility, coverage and MU’s satisfaction.

Item Type: Conference or Workshop Item
Erschienen: 2022
Creators: Simon, Bernd ; Dongare, Sumedh ; Mahn, Tobias ; Ortiz Jimenez, Andrea Patricia ; Klein, Anja
Type of entry: Bibliographie
Title: Delay- and Incentive-Aware Crowdsensing: A Stable Matching Approach for Coverage Maximization
Language: English
Date: 11 August 2022
Publisher: IEEE
Book Title: ICC 2022 - IEEE International Conference on Communications
Event Title: 2022 IEEE International Conference on Communications
Event Location: Seoul, South Korea
Event Dates: 16.-20.05.2022
DOI: 10.1109/ICC45855.2022.9838603
Abstract:

Mobile crowdsensing (MCS) is a novel approach to increase the coverage, lower the costs, and increase the accuracy of sensing data. Its main idea is to collect sensor data using mobile units (MUs). The sensing is controlled by a mobile crowdsensing platform (MCSP) through the assignment of delay-sensitive sensing tasks to the MUs. Although promising, research effort in MCS is still needed to find task assignment solutions that maximize the coverage while considering the cost incurred by the MCSPs, the preferences of the MUs and the limited communication resources available. Specifically, we identify two main challenges: (i) A task assignment problem which incorporates the MCSP’s utility and the preferences of the MUs. (ii) An underlying communication resource allocation problem formulating the requirement of the timely transmission of sensing results given the limited communication resources. To address these challenges, we propose a novel two-stage matching algorithm. In the first stage, potential MU-task pairs are constructed considering the preferences of the MUs and the utility of the MCSP. In the second stage, the communication resource allocation is done based on potential MU-task pairs from the first stage. Through numerical simulations, we show that our proposed approach outperforms state-of-the-art methods in terms of the MCSP’s utility, coverage and MU’s satisfaction.

Uncontrolled Keywords: Open6GHub, emergenCITY_KOM
Divisions: 18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Communications Engineering
DFG-Collaborative Research Centres (incl. Transregio)
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres
LOEWE
LOEWE > LOEWE-Zentren
LOEWE > LOEWE-Zentren > emergenCITY
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > B: Adaptation Mechanisms
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > B: Adaptation Mechanisms > Subproject B3: Economics of Adaption
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > C: Communication Mechanisms
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > C: Communication Mechanisms > Subproject C2: Information-centred perspective
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > Transfer projects
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > Transfer projects > Transfer project T2: Prediction of network load
Date Deposited: 02 Aug 2022 07:11
Last Modified: 27 Oct 2022 09:21
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