A Trust-Centric Privacy-Preserving Blockchain for Dynamic Spectrum Management in IoT Networks

【Author】 Ye, Jingwei; Kang, Xin; Liang, Ying-Chang; Sun, Sumei

【Source】IEEE INTERNET OF THINGS JOURNAL

【影响因子】10.238

【Abstract】Blockchain is a promising technology for future dynamic spectrum access (DSA) management due to its decentralization, immutability, and traceability. However, many challenges need to be addressed to integrate the blockchain to DSA, such as the trustworthiness of participating nodes' spectrum sensing results, privacy protection of sensing nodes' identities, and affordable lightweight consensus algorithms for IoT devices. In this article, we propose a trust-centric privacy-preserving blockchain for DSA in IoT networks. To be specific, we propose a trust evaluation mechanism to evaluate the trustworthiness of sensing nodes and design a Proof-of-Trust (PoT) consensus mechanism to build a scalable blockchain with high transaction-per-second (TPS). Moreover, a privacy protection scheme is proposed to protect sensors' real-time geolocation information when they upload sensing data to the blockchain. Two smart contracts are designed to make the whole procedure (spectrum sensing, spectrum auction, and spectrum allocation) run automatically. Simulation results demonstrate the expected computation cost of the PoT consensus algorithm for reliable nodes is low, and the cooperative sensing performance is improved with the help of the trust evaluation mechanism. In addition, incentivization and security are also analyzed, which show that our system can not only encourage nodes' participation, but also resist many kinds of attacks which are frequently arise in the trust management mechanism and blockchain-based IoT systems.

【Keywords】Blockchains; Sensors; Smart contracts; Consensus algorithm; Privacy; Protocols; Radio spectrum management; Blockchain; consensus algorithm; cooperative spectrum sensing; dynamic spectrum access (DSA); trust model

【发表时间】2022 AUG 1

【收录时间】2022-08-28

【文献类型】理论模型

【主题类别】

区块链技术-协同技术-隐私计算