Integration of Edge Computing and Blockchain for Provision of Data Fusion and Secure Big Data Analysis for Internet of Things

Abstract

Intelligent computing provides efficient, real-time, and secure data analysis services for the Internet of Things (IoT). As the number of IoT devices increases, IoT generates massive, diverse, and multisourcing datasets that can be used to improve IoT services further. Models trained by intelligent computing from a single system or sensor are often not global, and sending all data directly to the computing platform wastes network bandwidth and may cause network congestion and even privacy leakage. To ensure IoT applications’ quality of service and privacy, we propose a framework that integrates edge computing and blockchain to provide lightweight data fusion and secure data analysis for IoT. We propose a lightweight data fusion method that can reduce the amount of data at the node level and prevent network congestion and bandwidth waste. Furthermore, we propose a hierarchical fuzzy hashing method to check and locate anomalies of IoT machine learning models to ensure the validity of IoT intelligent computing and the security of sensitive data. Finally, we demonstrate the effectiveness of the method proposed in this paper through experiments.

An efficient routing protocol to reduce traffic and congestion control in cloud edge networks of wireless sensor networks

“…In the point‐to‐point (P2P) communication traffic method, packet loss happens at node and communication link levels in the network, and limitations of the hub and the correspondence channel of a remote medium interact with one another. The restrictions of network nodes are the size of the queue, buffer, reception rate, storage, and processing, as traffic volume increases, the likelihood of a collision in the node rises 22 . In order to minimize impact, this barrier makes an effort to concentrate on the hub that is just getting started and has lower traffic loads.…”

“…The restrictions of network nodes are the size of the queue, buffer, reception rate, storage, and processing, as traffic volume increases, the likelihood of a collision in the node rises. 22 In order to minimize impact, this barrier makes an effort to concentrate on the hub that is just getting started and has lower traffic loads. A combination of the neighbors' local load, as well as packet traffic load by Equation (22), is used in this work to define the candidate node's traffic load.…”

“…22 In order to minimize impact, this barrier makes an effort to concentrate on the hub that is just getting started and has lower traffic loads. A combination of the neighbors' local load, as well as packet traffic load by Equation (22), is used in this work to define the candidate node's traffic load. 23…”

The Development of Privacy Protection Standards for Smart Home

“…Device application standards for smart home device consider the differences in privacy data in different devices. According to different types of devices and various characteristics of business scenarios, the norms restrain the implementation of edge computing, blockchain, and other technologies [33] and guide the realization of personal protection requirements. For example, cloud computing may include cloud privacy security, terminal privacy collection, and guarantee capacity certification [34].…”

A novel cloud-edge collaboration based short-term load forecasting method for smart grid

“…In China, the edge computing alliance ECC is working hard to promote the integration of three technologies, that is, the integration of OICT (Operational, Information, and Communication Technology). The computing objects of edge computing are mainly in four fields, namely the device field, the network field, the data field and the application field (Dong et al, 2022), (Song et al, 2022).…”