Hybrid Decentralized Data Analytics in Edge-Computing-Empowered IoT Networks

Edge computing is emerging as a new infrastructure for Internet-of-Things (IoT) networks by placing computation and analytics near to where data are generated. This article presents a novel data analytics framework for edge computing. The framework is based on a new decentralized algorithm, which enables all the nodes to obtain the global optimal model without sharing raw data. The resulting scheme executes in a hybrid mode: local IoT nodes send computed information to edge nodes. The edge nodes cooperate with each other by exchanging analytics with their neighbors only. The presenting approach is analyzed and evaluated on various applications and the experimental results demonstrate the effectiveness of the proposed methodology in providing fast data analytics to edge computing infrastructure.

Automated summary

The article presents a novel data analytics framework for edge computing using a decentralized algorithm, allowing all nodes to obtain global optimal model without sharing raw data. The local IoT nodes send calculated information to edge nodes, which cooperate with each other by exchanging analytics with their neighbors, demonstrating effective fast data analytics in the edge computing infrastructure.