Provision of a Recommender Model for Blockchain-Based IoT with Deep Reinforcement Learning

• Published in: 2021 5th International Conference on Internet of Things and Applications (IoT) pp. 1 - 8
• Main Authors: Rabieinejad, Elnaz, Mohammadi, Shahriar, Yadegari, Mahdi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 19-05-2021
• Subjects: Analytical models; Blockchain; Computational modeling; deep reinforcement learning; IoT; Performance evaluation; Reinforcement learning; Scalability; Throughput
• DOI: 10.1109/IoT52625.2021.9469708

With developments in human societies and the information and communication technology, the Internet of Things (IoT) has penetrated various aspects of daily life and different industries. The newly emerging blockchain technology has become a viable solution to the IoT security due to its inherent characteristics such as distribution, security, immutability, and traceability. However, integrating the IoT with the blockchain technology faces certain challenges such as latency, throughput, device power limitation, and scalability. Recent studies have focused on the role of artificial intelligence methods in improving the IoT performance in a blockchain. According to their results, there are only a few effects on the improvement of IoT-based performance with limited power. This study proposes a conceptual model to improve the blockchain throughput in IoT-based devices with limited power through deep reinforcement learning. This model benefits from a recommender agent based on deep reinforcement learning in the mobile edge computing layer to improve the throughput and select the right mining method.