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Deep Reinforcement Learning for Task Allocation in Energy Harvesting Mobile Crowdsensing

Dongare, Sumedh ; Ortiz Jimenez, Andrea Patricia ; Klein, Anja (2022)
Deep Reinforcement Learning for Task Allocation in Energy Harvesting Mobile Crowdsensing.
GLOBECOM 2022 - 2022 IEEE Global Communications Conference. Rio de Janeiro, Brazil (04.-08.12.2022)
doi: 10.1109/GLOBECOM48099.2022.10001204
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

Mobile crowd-sensing (MCS) is an upcoming sensing architecture which provides better coverage, accuracy, and requires lower costs than traditional wireless sensor networks. It utilizes a collection of sensors, or crowd, to perform various sensing tasks. As the sensors are battery operated and require a mechanism to recharge them, we consider energy harvesting (EH) sensors to form a sustainable sensing architecture. The execution of the sensing tasks is controlled by the mobile crowd-sensing platform (MCSP) which makes task allocation decisions, i.e., it decides whether or not to perform a task depending on the available resources, and if the task is to be performed, assigns it to suitable sensors. To make optimal allocation decisions, the MCSP requires perfect non-causal knowledge regarding the channel coefficients of the wireless links to the sensors, the amounts of energy the sensors harvest and the sensing tasks to be performed. However, in practical scenarios this non-causal knowledge is not available at the MCSP. To overcome this problem, we propose a novel Deep-Q-Network solution to find the task allocation strategy that maximizes the number of completed tasks using only realistic causal knowledge of the battery statuses of the available sensors. Through numerical evaluations we show that our proposed approach performs only 7.8% lower than the optimal solution. Moreover, it outperforms the myopically optimal and the random task allocation schemes.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2022
Autor(en): Dongare, Sumedh ; Ortiz Jimenez, Andrea Patricia ; Klein, Anja
Art des Eintrags: Bibliographie
Titel: Deep Reinforcement Learning for Task Allocation in Energy Harvesting Mobile Crowdsensing
Sprache: Englisch
Publikationsjahr: 9 Dezember 2022
Verlag: IEEE
Buchtitel: 2022 IEEE Global Communications Conference (GLOBECOM): Proceedings
Veranstaltungstitel: GLOBECOM 2022 - 2022 IEEE Global Communications Conference
Veranstaltungsort: Rio de Janeiro, Brazil
Veranstaltungsdatum: 04.-08.12.2022
DOI: 10.1109/GLOBECOM48099.2022.10001204
Kurzbeschreibung (Abstract):

Mobile crowd-sensing (MCS) is an upcoming sensing architecture which provides better coverage, accuracy, and requires lower costs than traditional wireless sensor networks. It utilizes a collection of sensors, or crowd, to perform various sensing tasks. As the sensors are battery operated and require a mechanism to recharge them, we consider energy harvesting (EH) sensors to form a sustainable sensing architecture. The execution of the sensing tasks is controlled by the mobile crowd-sensing platform (MCSP) which makes task allocation decisions, i.e., it decides whether or not to perform a task depending on the available resources, and if the task is to be performed, assigns it to suitable sensors. To make optimal allocation decisions, the MCSP requires perfect non-causal knowledge regarding the channel coefficients of the wireless links to the sensors, the amounts of energy the sensors harvest and the sensing tasks to be performed. However, in practical scenarios this non-causal knowledge is not available at the MCSP. To overcome this problem, we propose a novel Deep-Q-Network solution to find the task allocation strategy that maximizes the number of completed tasks using only realistic causal knowledge of the battery statuses of the available sensors. Through numerical evaluations we show that our proposed approach performs only 7.8% lower than the optimal solution. Moreover, it outperforms the myopically optimal and the random task allocation schemes.

Freie Schlagworte: emergenCITY, emergenCITY_KOM
Zusätzliche Informationen:

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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
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LOEWE > LOEWE-Zentren
LOEWE > LOEWE-Zentren > emergenCITY
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 > C: Kommunikationsmechanismen
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > C: Kommunikationsmechanismen > Teilprojekt C1 : Netzzentrische Sicht
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > T: Transferprojekte
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > T: Transferprojekte > Transferprojekt T2: Prädiktion Netzauslastung
Hinterlegungsdatum: 01 Feb 2023 12:26
Letzte Änderung: 02 Mai 2023 13:55
PPN: 507398203
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