PPO-based Autonomous Transmission Period Control System in IoT Edge Computing

With the recent explosive growth of the Internet of Things (IoT), edge computing is emerging as a modern computing paradigm that coexists with the cloud to process massive amounts of data particularly distributed at the edge network. Meanwhile, edge computing directly permits the use of artificial i...

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Bibliographic Details
Published in:IEEE internet of things journal 2023-12, Vol.10 (24), p.1-1
Main Authors: Lee, Gyeong Ho, Park, Hyunseo, Jang, Jae Won, Han, Jaeseob, Choi, Jun Kyun
Format: Article
Language:English
Subjects:
Algorithms
autonomous transmission period control
bollinger bands
Control systems
data quality
data reduction
deep reinforcement learning
Edge computing
Energy consumption
energy efficiency
Environmental monitoring
Internet of Things
Machine learning
Real time
Sensors
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Summary:With the recent explosive growth of the Internet of Things (IoT), edge computing is emerging as a modern computing paradigm that coexists with the cloud to process massive amounts of data particularly distributed at the edge network. Meanwhile, edge computing directly permits the use of artificial intelligent (AI) models at the edge. Currently in numerous IoT applications, a tremendous amount of data is being measured and transmitted from IoT sensors to monitor surrounding information in real-time. Considering that continuous transmission of sensed data substantially requires high energy consumption of IoT sensors, this paper proposes a PPO-based autonomous transmission period control (PPO-ATPC) system in IoT edge computing that can automatically and adaptively control the transmission period of each IoT sensor. Here, the design of state, action, and reward is shaped in an unprecedented way and furthermore, a proximal policy optimization (PPO), which is one of the most effective model-free deep reinforcement learning (DRL) algorithms, is fully leveraged to achieve an optimal or nearly-optimal policy that can significantly shorten the data volume while maintaining high data quality. The merits of the proposed PPO-ATPC are extensively validated through quantitative comparison with other alternative approaches using three different sensor data collected from real-time environmental monitoring, and in-depth insights into the effectiveness of PPO-ATPC are further provided from diverse perspectives. The performance results show that total data volume for each dataset could be reduced by 73.849%, 89.931%, and 81.310%, with the average RMSE of 0.322, 13.896, and 0.048.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2023.3293511