Smart packet transmission scheduling in cognitive IoT systems: DDQN based approach

Salh, Adeeb and Audah, Lukman and Alhartomi, Mohammed A. and Kim, Kwang Soon and Alsamhi, Saeed Hamood and Almalki, Faris A. and Abdullah, Qazwan and Saif, Abdu and Algethami, Haneen (2022) Smart packet transmission scheduling in cognitive IoT systems: DDQN based approach. IEEE Access, 10. pp. 50023-50036. ISSN 2169-3536, DOI

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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.

Item Type: Article
Funders: Universiti Tun Hussein Onn Malaysia [Grant No: E15216], University of Tabuk, Saudi Arabia [Grant No: S-0237-1438], Deanship of Scientific Research at Taif University, Saudi Arabia, through Taif University Researchers Supporting Project [Grant No: TURSP-2020/265], European Union (EU) [Grant No: 847577], Science Foundation Ireland, European Union (EU) [Grant No: 16/RC/3918]
Uncontrolled Keywords: Signal to noise ratio; Throughput; Power demand; Delays; Real-time systems; Internet of Things; Ultra reliable low latency communication; Artificial intelligence; Cognitive Internet of Things; Transmission delay; Packet error rate
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering > Department of Electrical Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 25 Aug 2023 07:13
Last Modified: 25 Aug 2023 07:13

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