期刊
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
卷 16, 期 3, 页码 501-515出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTSP.2022.3144874
关键词
Monitoring; Optimization; Internet of Things; Energy consumption; Nonlinear dynamical systems; Vehicle dynamics; Real-time systems; Physical process; sampling frequency; age of information; distributed reinforcement learning
资金
- Beijing Natural Science Foundation-Haidian Original Innovation Foundation [L192003]
- National Natural Science Foundation of China [61871041, 61671086, 61629101]
- 111 Project [B17007]
- National Key R&D Program of China [2018YFB1800800]
- Hetao Shenzhen-HK S&T Cooperation Zone through Basic Research Project [HZQB-KCZYZ-2021067]
- Shenzhen Outstanding Talents Training Fund [202002]
- Office of Naval Research (ONR) under MURI [N00014-19-1-2621]
- EPSRC [EP/T015985/1]
- [2017ZT07X152]
- [2019CX01X104]
This paper studies the problem of minimizing the weighted sum of age of information (AoI) and total energy consumption of Internet of Things (IoT) devices. In the considered model, each IoT device monitors a physical process and should find an optimal sampling frequency to sample the real-time dynamics and send the information to a base station. A novel distributed reinforcement learning approach is proposed to jointly optimize the sampling policy of each device and the device selection scheme of the base station. Simulation results show that the proposed algorithm can effectively reduce the sum of AoI and total energy consumption.
In this paper, the problem of minimizing the weighted sum of age of information (AoI) and total energy consumption of Internet of Things (IoT) devices is studied. In the considered model, each IoT device monitors a physical process that follows nonlinear dynamics. As the dynamics of the physical process vary over time, each device should find an optimal sampling frequency to sample the real-time dynamics of the physical system and send sampled information to a base station (BS). Due to limited wireless resources, the BS can only select a subset of devices to transmit their sampled information. Thus, edge devices can cooperatively sample their monitored dynamics based on the local observations and the BS will collect the sampled information from the devices immediately, hence avoiding the additional time and energy used for sampling and information transmission. To this end, it is necessary to jointly optimize the sampling policy of each device and the device selection scheme of the BS so as to accurately monitor the dynamics of the physical process using minimum energy. This problem is formulated as an optimization problem whose goal is to minimize the weighted sum of AoI cost and energy consumption. To solve this problem, we propose a novel distributed reinforcement learning (RL) approach for the sampling policy optimization. The proposed algorithm enables edge devices to cooperatively find the global optimal sampling policy using their own local observations. Given the sampling policy, the device selection scheme can be optimized thus minimizing the weighted sum of AoI and energy consumption of all devices. Simulations with real PM 2.5 pollution data show that the proposed algorithm can reduce the sum of AoI by up to 17.8% and 33.9%, respectively, and the total energy consumption by up to 13.2% and 35.1%, respectively, compared to a conventional deep Q network method and a uniform sampling policy.
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