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Task allocation based multi-agent reinforcement learning for LoRa nodes in gas wellhead monitoring service

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Title Task allocation based multi-agent reinforcement learning for LoRa nodes in gas wellhead monitoring service
 
Creator Ismail, Z H
Hong, B L L
Elfakharany, A
 
Subject Internet of things
Monitoring
Reinforcement learning
Task allocation
Wellhead
 
Description 897-903
This paper investigates a new alternative approach to handle the tasks allocation problem that associate with numerous
Long Range (LoRa) nodes in the High-Pressure High-Temperature (HPHT) gas wellhead monitoring service. A Multi-
Agent Reinforcement Learning approach is proposed in this paper to overcome this problem with the Proximal Policy
Optimization (PPO) is chosen as the policy gradient method. An action space is the spreading factor and other parameters
such as frequency and transmission power has been kept constant. The reward function for the training process will be
determined by two parameters which are the acknowledge flag (ACK) and collision between packets. Each node will be
distributed across a defined disc radius. Each node will be represented as an agent. Each agent will undergo packet
transmission and the packet will be evaluated according to the reward function. The results show that PPO with Multi Agent
Reinforcement Learning was able to determine the optimal configuration for each LoRa node. The total reward value
corresponds to the total number of nodes. Furthermore, since this study also implements the use of CUDA, the training was
able to done in 200 steps and 45 minutes.
 
Date 2022-03-14T10:25:26Z
2022-03-14T10:25:26Z
2021-11
 
Type Article
 
Identifier 2582-6727 (Online); 2582-6506 (Print)
http://nopr.niscair.res.in/handle/123456789/59329
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source IJMS Vol.50(11) [November 2021]