Simon, Bernd ; Ortiz Jimenez, Andrea Patricia ; Saad, Walid ; Klein, Anja (2024)
Decentralized Online Learning in Task Assignment Games for Mobile Crowdsensing.
In: IEEE Transactions on Communications, 72 (8)
doi: 10.1109/TCOMM.2024.3381718
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
The problem of coordinated data collection is studied for a mobile crowdsensing (MCS) system. A mobile crowdsensing platform (MCSP) sequentially publishes sensing tasks to the available mobile units (MUs) that signal their willingness to participate in a task by sending sensing offers back to the MCSP. From the received offers, the MCSP decides the task assignment. A stable task assignment must address two challenges: the MCSP’s and MUs’ conflicting goals, and the uncertainty about the MUs’ required efforts and preferences. To overcome these challenges a novel decentralized approach combining matching theory and online learning, called collision-avoidance multi-armed bandit with strategic free sensing (CA-MAB-SFS), is proposed. The task assignment problem is modeled as a matching game considering the MCSP’s and MUs’ individual goals while the MUs learn their efforts online. Our innovative “free-sensing” mechanism significantly improves the MU’s learning process while reducing collisions during task allocation. The stable regret of CA-MAB-SFS, i.e., the loss of learning, is analytically shown to be bounded by a sublinear function, ensuring the convergence to a stable optimal solution. Simulation results show that CA-MAB-SFS increases the MUs’ and the MCSP’s satisfaction compared to state-of-the-art methods while reducing the average task completion time by at least 16%.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Simon, Bernd ; Ortiz Jimenez, Andrea Patricia ; Saad, Walid ; Klein, Anja |
| Art des Eintrags: | Bibliographie |
| Titel: | Decentralized Online Learning in Task Assignment Games for Mobile Crowdsensing |
| Sprache: | Englisch |
| Publikationsjahr: | 8 August 2024 |
| Verlag: | IEEE |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | IEEE Transactions on Communications |
| Jahrgang/Volume einer Zeitschrift: | 72 |
| (Heft-)Nummer: | 8 |
| DOI: | 10.1109/TCOMM.2024.3381718 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | The problem of coordinated data collection is studied for a mobile crowdsensing (MCS) system. A mobile crowdsensing platform (MCSP) sequentially publishes sensing tasks to the available mobile units (MUs) that signal their willingness to participate in a task by sending sensing offers back to the MCSP. From the received offers, the MCSP decides the task assignment. A stable task assignment must address two challenges: the MCSP’s and MUs’ conflicting goals, and the uncertainty about the MUs’ required efforts and preferences. To overcome these challenges a novel decentralized approach combining matching theory and online learning, called collision-avoidance multi-armed bandit with strategic free sensing (CA-MAB-SFS), is proposed. The task assignment problem is modeled as a matching game considering the MCSP’s and MUs’ individual goals while the MUs learn their efforts online. Our innovative “free-sensing” mechanism significantly improves the MU’s learning process while reducing collisions during task allocation. The stable regret of CA-MAB-SFS, i.e., the loss of learning, is analytically shown to be bounded by a sublinear function, ensuring the convergence to a stable optimal solution. Simulation results show that CA-MAB-SFS increases the MUs’ and the MCSP’s satisfaction compared to state-of-the-art methods while reducing the average task completion time by at least 16%. |
| Freie Schlagworte: | BMBF Open6GHub |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Kommunikationstechnik DFG-Sonderforschungsbereiche (inkl. Transregio) DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > B: Adaptionsmechanismen DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > B: Adaptionsmechanismen > Teilprojekt B3: Adaptionsökonomie |
| Hinterlegungsdatum: | 22 Okt 2024 12:49 |
| Letzte Änderung: | 22 Okt 2024 12:50 |
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