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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
Conference or Workshop Item, Bibliographie

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.

Item Type: Conference or Workshop Item
Erschienen: 2022
Creators: Dongare, Sumedh ; Ortiz Jimenez, Andrea Patricia ; Klein, Anja
Type of entry: Bibliographie
Title: Deep Reinforcement Learning for Task Allocation in Energy Harvesting Mobile Crowdsensing
Language: English
Date: 9 December 2022
Publisher: IEEE
Book Title: 2022 IEEE Global Communications Conference (GLOBECOM): Proceedings
Event Title: GLOBECOM 2022 - 2022 IEEE Global Communications Conference
Event Location: Rio de Janeiro, Brazil
Event Dates: 04.-08.12.2022
DOI: 10.1109/GLOBECOM48099.2022.10001204
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.

Uncontrolled Keywords: emergenCITY, emergenCITY_KOM
Additional Information:

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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
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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 > 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 C1: Network-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: 01 Feb 2023 12:26
Last Modified: 02 May 2023 13:55
PPN: 507398203
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