【作者】 张浩；

【导师】 陈智；

【作者基本信息】 电子科技大学 ， 电子信息（专业学位）， 2023， 硕士

【摘要】 在即将到来的工业物联网(Industrial Internet of Things,IIo T)时代,无线控制系统有望成为海量面向任务应用的重要支撑。例如,建筑自动化、工业自动化、智慧城市和智能化生产等。然而,由于无线控制网络中缺乏对攻击的防御以及难以平衡整体系统性能和隐私保护,网络内分布式传感器节点的安全可靠的数据收集极具挑战性。最近,区块链技术引起了学术界和工业界的关注。区块链是一种基于密码学和网络共享系统的分布式系统,可以为海量分布式传感器收集的数据提供高度安全性保障,而无需集中管理机构。然而,区块链支持的数据聚合在无线控制系统中仍然面临一些极端的挑战。首先尽管区块链上的数据不可篡改,但如果数据在上传前被篡改,则无法保证所收集数据的可靠性和质量。其次,传统的共识机制,例如工作量证明(Proof of Work,Po W),将导致沉重的计算开销。为了解决上述问题,本论文中提出了一种新的基于区块链的数据收集方法,该方法以贝叶斯推理为基础,赋予了网络内无线传感器节点可信度的属性,并基于节点可信度进行观测数据融合,结合区块链技术本身固有的密码学技术,保证网络内采集到的传感数据的可靠性和安全性。具体来说,为了防御攻击以保证数据安全,本论文将区块链引入无线控制系统,通过分布式传感器进行数据采集。为了应对无线控制中的实时性要求,本论文提出了一种新的用于区块生成的共识机制,即可信度证明(Proof of Trustness,Po T)共识机制,在有了节点信任属性的网络内,该机制可以显著降低共识算法的计算复杂度以获得轻量级的区块链。更重要的是,为了进一步克服大规模区块链中的可扩展性和效率问题,本论文从通信质量和信任的角度提出了一种新的基于传感器和无线控制设备之间映射的分片方法。然后,可以生成一个轻量级的区块链来防御攻击并保证所收集传感数据的安全性,同时保持无线控制系统中的低延迟要求。最后,在分布式无线控制网络中,通过基于卡尔曼滤波的控制状态更新验证了所提出的方法,并通过仿真结果证明了所提出的数据收集方法的性能优势。更多还原

【Abstract】 In the coming era of Industrial Internet of Things(IIo T),wireless control systems are expected to enable a large number of task-oriented applications,such as building automation,industrial automation and Internet-based autonomous driving,smart healthcare,smart cities,etc.However,in such wireless control networks,safe and reliable data collection from distributed sensors is extremely challenging due to lack of defense against attacks and difficulty in balancing overall system performance and privacy protection.Recently,blockchain technology has attracted attention from academia and industry.Blockchain is a distributed ledger based on cryptography and a network sharing system that can provide high security for data collected by massive distributed sensors without the need for a centralized management agency.However,blockchain-supported data aggregation still faces some extreme challenges in wireless control systems.First of all,although the data on the blockchain cannot be tampered with,if the data is tampered with before uploading,the reliability and quality of the collected data cannot be guaranteed.In particular,when the malicious sensor nodes exceed 51%,the reliability of the collected data Security and quality will be determined by malicious sensor nodes.Second,traditional consensus mechanisms,such as proof of work(Po W),will incur heavy computational overhead,which cannot be used for data collection in distributed sensor networks due to limited sensor capacity.to solve the above problems,a new blockchain-based data collection method is proposed in this thesis.This method is based on Bayesian reasoning,endows the wireless sensor nodes with the attribute of credibility in the network,and based on the node trustworthiness Combined with the inherent cryptography technology of blockchain technology,it can ensure the reliability and security of the sensory data collected in the network.Specifically,to defend against attacks and ensure data security,this thesis introduces the blockchain into the wireless control system,and collects data through distributed sensors.Then,to meet the real-time requirements in wireless control,this thesis proposes a new consensus mechanism for block generation,namely the proof of trust(Po T)consensus mechanism,in a network with node trust attributes,this mechanism can significantly reduce the computational complexity of the consensus algorithm to obtain a lightweight blockchain.More importantly,to further overcome the scalability and efficiency issues in large-scale blockchains,this thesis proposes a new sharding method based on the mapping between sensors and wireless control devices from the perspective of communication quality and trust.Then,a lightweight blockchain can be generated to defend against attacks and guarantee the security of the collected sensory data while maintaining low latency requirements in wireless control systems.Finally,the proposed method is verified by Kalman filter-based control state update in a distributed wireless control network,and the performance advantages of the proposed data collection method are demonstrated through simulation results.更多还原