IoT systems with multi-tier, distributed intelligence: From architecture to prototype

Abstract

In this paper, we propose an architecture, design and build a prototype of a novel IoT system with intelligence, distributed at multiple tiers including the network edge. Our proposed architecture hosts a modular, three-tier IoT system including the edge, gateway (fog) and cloud tiers. The proposed system relies on data acquired by edge devices to realize a distributed machine learning model and achieve timely response at the edge using a lightweight machine learning model. In addition, it employs more sophisticated machine learning models at the higher fog and cloud tiers for wider-scope, long-term decision making. One of the prime objectives of the proposed system is reducing the volume of data transferred across tiers. This is attained through intelligent data filtering at the edge/gateway tiers to distill key events that avail the most relevant data points to higher-tier machine learning models at the gateway and cloud. This, in turn, reduces the outliers and the redundant data that may impact the gateway and cloud models and reduces the inter-tier communications overhead. To demonstrate the merits of our proposed system, we build a proof-of-concept prototype hosting the three tiers, using COTS components and supporting networking technologies. We demonstrate through extensive experiments the merits of the proposed system. A major finding is that our system is capable of achieving prediction performance comparable to the centralized machine learning baseline model, while reducing the inter-tier communications overhead by up to 80%.