Deep reinforcement learning approach for computation offloading in blockchain-enabled communications systems

【Author】 Alam, Tanweer; Ullah, Arif; Benaida, Mohamed

【Source】JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING

【影响因子】3.662

【Abstract】Blockchain and deep reinforcement learning (DRL) are two separate transaction systems committed to the credibility and usefulness of system functionality. There is rapid growth importance in integrating both technologies into effective and stable information exchange and research solutions. Blockchain is a revolutionary platform for future generational telecommunications networking that will set up the secured and distributed information exchange framework. In combination with DRL, blockchain could significantly improve the efficiency of mobile communications. The rapid growth of networks for the Internet of Things (IoT) necessitates the development of suitable and reliable infrastructure as well as a significant proportion of the information. Blockchain, a distributed and reliable ledger, is often considered a considerably beneficial means of providing scientific confidentiality and security to IoT devices. When interacting with massive IoT records, the Blockchain's decrease becomes a significant issue. Thus, it is necessary to improve transaction performance and deal with massive data transmission situations. As a result, the work presented here explores the DRL fundamental operation of the blockchain-enabled IoT system, where transactions are simultaneously strengthened, and community-based divisibility is ensured. Throughout this paper, the authors first present the decentralised and efficient structure for communication by incorporating DRL and blockchain across wireless services that allow for the scalable and reliable allocation of information. The results show that the proposed method has shorter delays and requires less transmission power.

【Keywords】Blockchain; Internet of Things; Deep reinforcement learning; Wireless networks; Secure communications

【发表时间】

【收录时间】2022-01-30

【文献类型】期刊

【主题类别】

区块链技术--