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Open AccessArticle

Distributed Learning Based Joint Communication and Computation Strategy of IoT Devices in Smart Cities

1
International School, Beijing University of Posts and Telecommunications, Beijing 100876, China
2
School of Information and Telecommunication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
3
Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(4), 973; https://doi.org/10.3390/s20040973
Received: 30 December 2019 / Revised: 23 January 2020 / Accepted: 10 February 2020 / Published: 12 February 2020
(This article belongs to the Special Issue Big Data Driven IoT for Smart Cities)
With the development of global urbanization, the Internet of Things (IoT) and smart cities are becoming hot research topics. As an emerging model, edge computing can play an important role in smart cities because of its low latency and good performance. IoT devices can reduce time consumption with the help of a mobile edge computing (MEC) server. However, if too many IoT devices simultaneously choose to offload the computation tasks to the MEC server via the limited wireless channel, it may lead to the channel congestion, thus increasing time overhead. Facing a large number of IoT devices in smart cities, the centralized resource allocation algorithm needs a lot of signaling exchange, resulting in low efficiency. To solve the problem, this paper studies the joint policy of communication and computing of IoT devices in edge computing through game theory, and proposes distributed Q-learning algorithms with two learning policies. Simulation results show that the algorithm can converge quickly with a balanced solution. View Full-Text
Keywords: smart city; Internet of Things; mobile edge computing; potential game; Q-learning smart city; Internet of Things; mobile edge computing; potential game; Q-learning
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Liu, T.; Luo, R.; Xu, F.; Fan, C.; Zhao, C. Distributed Learning Based Joint Communication and Computation Strategy of IoT Devices in Smart Cities. Sensors 2020, 20, 973.

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