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Intelligent IoT Connectivity: Deep Reinforcement Learning Approach

Title
Intelligent IoT Connectivity: Deep Reinforcement Learning Approach
Authors
Kwon M.Lee J.Park H.
Ewha Authors
박형곤
SCOPUS Author ID
박형곤scopus
Issue Date
2020
Journal Title
IEEE Sensors Journal
ISSN
1530-437XJCR Link
Citation
IEEE Sensors Journal vol. 20, no. 5, pp. 2782 - 2791
Keywords
deep reinforcement learningIntelligent IoT connectivitymobile relay networksnetwork formationnetwork topology designwireless ad hoc networks
Publisher
Institute of Electrical and Electronics Engineers Inc.
Indexed
SCIE; SCOPUS scopus
Document Type
Article
Abstract
In this paper, we propose a distributed solution to design a multi-hop ad hoc Internet of Things (IoT) network where mobile IoT devices strategically determine their wireless transmission ranges based on a deep reinforcement learning approach. We consider scenarios where only a limited networking infrastructure is available but a large number of IoT devices are deployed in building a multi-hop ad hoc network to deliver source data to the destination. An IoT device is considered as a decision-making agent that strategically determines its transmission range in a way that maximizes network throughput while minimizing the corresponding transmission power consumption. Each IoT device collects information from its partial observations and learns its environment through a sequence of experiences. Hence, the proposed solution requires only a minimal amount of information from the system. We show that the actions that the IoT devices take from its policy are determined as to activate or inactivate its transmission, i.e., only necessary relay nodes are activated with the maximum transmit power, and nonessential nodes are deactivated to minimize power consumption. Using extensive experiments, we confirm that the proposed solution builds a network with higher network performance than the current state-of-the-art solutions in terms of system goodput and connectivity ratio. © 2001-2012 IEEE.
DOI
10.1109/JSEN.2019.2949997
Appears in Collections:
엘텍공과대학 > 전자공학과 > Journal papers
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