Intelligent IoT Connectivity: Deep Reinforcement Learning Approach
- 주제(키워드) Intelligent IoT connectivity , network formation , network topology design , deep reinforcement learning , wireless ad hoc networks , mobile relay networks
- 주제(기타) Engineering, Electrical & Electronic
- 주제(기타) Instruments & Instrumentation
- 주제(기타) Physics, Applied
- 설명문(일반) [Kwon, Minhae; Lee, Juhyeon; Park, Hyunggon] Ewha Womans Univ, Dept Elect & Elect Engn, Seoul 03760, South Korea; [Kwon, Minhae] Rice Univ, Dept Elect & Comp Engn, Houston, TX 77005 USA; [Kwon, Minhae] Baylor Coll Med, Dept Neurosci, Ctr Neurosci & Artificial Intelligence, Houston, TX 77030 USA; [Lee, Juhyeon] Univ Massachusetts, Coll Informat & Comp Sci, Amherst, MA 01003 USA
- 등재 SCIE, SCOPUS
- OA유형 hybrid
- 발행기관 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- 발행년도 2020
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000169433
- 본문언어 영어
- Published As https://dx.doi.org/10.1109/JSEN.2019.2949997
초록/요약
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.
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