期刊
IEEE ACCESS
卷 9, 期 -, 页码 103822-103834出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3098933
关键词
Routing; Blockchains; Wireless sensor networks; Energy consumption; Markov processes; Peer-to-peer computing; Licenses; Wireless sensor networks; trusted routing; blockchain; deep learning; Markov decision
Routing is a critical process in Wireless Sensor Networks (WSNs) to ensure data transmission to base stations. Existing routing methods are often impractical in practice and difficult to identify untrusted activities of routing nodes effectively. This article proposes a trusted routing method that combines deep blockchain and Markov Decision Processes (MDPs) to enhance the routing security and efficiency of WSNs.
Routing is a critical process in Wireless Sensor Networks (WSNs) since it is responsible for data transmission to base stations. Routing attacks are capable of completely destroying and degrading the function of WSNs. A trustworthy routing system is critical for ensuring routing security and WSN efficiency. Numerous studies have been conducted to increase trust between routing nodes, including cryptographic techniques, and centralized routing decisions. Nonetheless, the majority of routing methods are impractical in practice, since it is difficult to identify untrusted activities of routing nodes effectively. Meanwhile, there is no efficient method of preventing malicious node attacks. As a result of these issues, this article offers a trusted routing method that combines deep blockchain and Markov Decision Processes (MDPs) in order to enhance the routing security and efficiency of WSNs. To authenticate the process of transmitting the node, the proposed approach utilizes a Proof of Authority (PoA) method inside the blockchain network. The validation group necessary for proofing is selected using a deep learning methodology that focuses on the properties of each node. MDPs are then used to choose the appropriate next hop as a forwarding node capable of transferring messages simply and securely. According to testing data, our routing system still performs well in a 50% malicious node routing environment when compared to existing routing algorithms.
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