Blockchain and K-Means Algorithm for Edge AI Computing

Abstract

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.

Retracted: Smart optimal path with blockchain modelling in internet of things using enhanced K‐means clustering and advance encryption standard

“…The distance is typically used to compare and contrast the characteristics of different data objects. The typical K-Means algorithm uses K = 2, meaning that it clusters data into pairs; when K equals 1, the K-Means algorithm in this study is enhanced, K's coordinates were found and then the nodes that form the cluster with K and those that are not in it were identified [23,24]. Given that we wish to search for a particular group of stores, the enhanced K-Means (EK-M) algorithm fits the concept of a market; the necessary product is available, and these stores are conveniently located; the location of customer represents the location of K. Based on the Euclidean Distance, the EK-M algorithm calculates the distance between two points.…”

Aviation and Airspace Management under Rough Set Theory

“…represents the degree to which x i belongs to the kth class [26][27][28][29][30][31][32][33][34][35][36][37]. e algorithm framework of FCM is shown in Figure 2.…”

“…FCM is an extension of the traditional K-means algorithm, which continuously updates the membership degree by minimizing the intra-class spacing. When the membership degree of all objects to all classes is determined, the class with the largest membership degree is selected as the class to which the object belongs [26][27][28][29][30][31][32][33][34][35][36][37].…”

Research on Enterprise Digital Precision Marketing Strategy Based on Big Data

“…e customer segmentation technology based on a clustering algorithm has been applied in various areas as early as the 19th century, such as retail, financial stocks, banking, e-commerce, telecommunications, tourism, aviation, and other industries. e similarity function of the K-means algorithm is mainly determined based on the distance between the data, and the distance measurement method constitutes the primary method of clustering [25][26][27]. e algorithm in this paper adopts the Euclidean distance because the Euclidean distance is mostly used to analyze individual differences in values, such as using user behavior indicators to analyze the similarity of user values.…”