Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100101, China.
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China.
Comput Intell Neurosci. 2022 May 29;2022:1153208. doi: 10.1155/2022/1153208. eCollection 2022.
The current development of blockchain, technically speaking, still faces many key problems such as efficiency and scalability issues, and any distributed system faces the problem of how to balance consistency, availability, and fault tolerance need to be solved urgently. The advantage of blockchain is decentralization, and the most important thing in a decentralized system is how to make nodes reach a consensus quickly. This research mainly discusses the blockchain and K-means algorithm for edge AI computing. The natural pan-central distributed trustworthiness of blockchain provides new ideas for designing the framework and paradigm of edge AI computing. In edge AI computing, multiple devices running AI algorithms are scattered across the edge network. When it comes to decentralized management, blockchain is the underlying technology of the Bitcoin system. Due to its characteristics of immutability, traceability, and consensus mechanism of transaction data storage, it has recently received extensive attention. Blockchain technology is essentially a public ledger. This is done by recording data related to trust management to this ledger. To collaboratively complete artificial intelligence computing tasks or jointly make intelligent group decisions, frequent communication is required between these devices. By integrating idle computing resources in an area, a distributed edge computing platform is formed. Users obtain benefits by sharing their computing resources, and nodes in need complete computing tasks through the shared platform. In view of the identity security problems faced in the sharing process, this article introduces blockchain technology to realize the trust between users. All participants must register a secure identity in the blockchain network and conduct transactions in this security system. A K-means algorithm suitable for edge environments is proposed to identify different degradation stages of equipment operation reflected by multiple types of data. Based on the prediction of the fault state for a single type of data, the algorithm uses the historical data of multiple types of data together with the prediction data to predict the fault stage. During the research process, the average optimization energy consumption of K-means algorithm is 14.6% lower than that of GA. On the basis of designing a resource allocation scheme based on blockchain, the problem of how the participants can realize reliable resource use according to the recorded data on the chain is studied. The article implements the verification of the legality of the use of blockchain resources. In addition, a control node is introduced to master the global real-time information of the network to provide data support for the user's choice.
目前区块链的发展，从技术上讲，仍然面临着许多关键问题，如效率和可扩展性问题，任何分布式系统都面临着如何平衡一致性、可用性和容错性的问题，这些问题亟待解决。区块链的优势在于去中心化，去中心化系统最重要的是如何让节点快速达成共识。本研究主要讨论区块链和 K-means 算法在边缘 AI 计算中的应用。区块链的自然去中心化分布式可信性为设计边缘 AI 计算的框架和范例提供了新的思路。在边缘 AI 计算中，运行 AI 算法的多个设备分散在边缘网络中。在去中心化管理方面，区块链是比特币系统的底层技术。由于其交易数据存储的不可变、可追溯和共识机制的特点，最近受到了广泛关注。区块链技术本质上是一个公共账本。通过将与信任管理相关的数据记录到这个账本上来实现。为了共同完成人工智能计算任务或共同做出智能群体决策，这些设备之间需要频繁进行通信。通过整合一个区域内的空闲计算资源，形成一个分布式边缘计算平台。用户通过共享计算资源获得收益，有需求的节点通过共享平台完成计算任务。针对共享过程中面临的身份安全问题，本文引入区块链技术实现用户之间的信任。所有参与者必须在区块链网络中注册安全身份，并在该安全系统中进行交易。提出了一种适用于边缘环境的 K-means 算法，用于识别多种数据反映的设备运行不同的退化阶段。该算法基于对单一类型数据的故障状态进行预测，使用多种类型数据的历史数据和预测数据来预测故障阶段。在研究过程中，K-means 算法的平均优化能耗比 GA 低 14.6%。在基于区块链设计资源分配方案的基础上，研究了参与者如何根据链上记录的数据实现可靠的资源利用的问题。文章实现了区块链资源使用合法性的验证。此外，引入了一个控制节点来掌握网络的全局实时信息，为用户的选择提供数据支持。
