Smart Packet Transmission Scheduling in Cognitive IoT Systems: DDQN Based Approach

The convergence of Artificial Intelligence (AI) can overcome the complexity of network defects and support a sustainable and green system. AI has been used in the Cognitive Internet of Things (CIoT), improving a large volume of data, minimizing energy consumption, managing traffic, and storing data....

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Bibliographic Details
Published in:IEEE access 2022, Vol.10, p.50023-50036
Main Authors: Salh, Adeeb, Audah, Lukman, Alhartomi, Mohammed A., Kim, Kwang Soon, Alsamhi, Saeed Hamood, Almalki, Faris A., Abdullah, Qazwan, Saif, Abdu, Algethami, Haneen
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Cognitive Internet of Things
Delays
Energy consumption
Generative adversarial networks
Internet of Things
packet error rate
Packet transmission
Power consumption
Power demand
Radial basis function
Real-time systems
Scheduling
Signal to noise ratio
Throughput
Traffic management
transmission delay
Ultra reliable low latency communication
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Summary:The convergence of Artificial Intelligence (AI) can overcome the complexity of network defects and support a sustainable and green system. AI has been used in the Cognitive Internet of Things (CIoT), improving a large volume of data, minimizing energy consumption, managing traffic, and storing data. However, improving smart packet transmission scheduling (TS) in CIoT is dependent on choosing an optimum channel with a minimum estimated Packet Error Rate (PER), packet delays caused by channel errors, and the subsequent retransmissions. Therefore, we propose a Generative Adversarial Network and Deep Distribution Q Network (GAN-DDQN) to enhance smart packet TS by reducing the distance between the estimated and target action-value particles. Furthermore, GAN-DDQN training based on reward clipping is used to evaluate the value of each action for certain states to avoid large variations in the target action value. The simulation results show that the proposed GAN-DDQN increases throughput and transmission packet while reducing power consumption and Transmission Delay (TD) when compared to fuzzy Radial Basis Function (fuzzy-RBF) and Distributional Q-Network (DQN). Furthermore, GAN-DDQN provides a high rate of 38 Mbps, compared to actor-critic fuzzy-RBF's rate of 30 Mbps and the DQN algorithm's rate of 19 Mbps.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3168549