Optimal and Privacy-Aware Resource Management in Artificial Intelligence of Things Using Osmotic Computing

Abstract

Critical infrastructure comprising on-demand devices, including secondary servers, comes into play when a situation like an overload is involved. The on-demand servers and devices require smart management solutions that form an integral part of Artificial Intelligence of Things (AIoT). This work considers AIoT as a combination of Mobile-Internet of Things (M-IoT) and AI requiring immediate response, secondary support system, and computational resources. Privacy in AIoT is always a concern when sharing information as intruders can eavesdrop on the settings of the system. This article uses an osmotic computing paradigm, which enables the derivation of strategies to decide on the methods of sharing services via optimal and privacy-aware resource management in AIoT. A safety competition is built on top of configuration rewards that help to attain privacy-by-design. The contributions of this article are expressed using theoretical analysis and numerical simulations.