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In this paper, we investigate a key problem of Internet of Things (IoT)applications in practice. Our research objective is to optimize thetransmission frequencies for a group of IoT edge devices under practicalconstraints. Our key assumption is that different IoT devices may havedifferent priority levels when transmitting data in a resource-constrainedenvironment and that those priority levels may only be locally defined andaccessible by edge devices for privacy concerns. To address this problem, weleverage the well-known Alternating Direction Method of Multipliers (ADMM)optimization method and demonstrate its applicability for effectively managingvarious IoT data streams in a decentralized framework. Our experimental resultsshow that the transmission frequency of each edge device can converge tooptimality with little delay using ADMM, and the proposed system can beadjusted dynamically when a new device connects to the system. In addition, wealso introduce an anomaly detection mechanism to the system when a device'stransmission frequency may be compromised by external manipulation, showingthat the proposed system is robust and secure for various IoT applications.

An ADMM-based Optimal Transmission Frequency Management System for IoT Edge Intelligence