Towards Enabling IoT Systems with Edge Intelligence

In this paper, we design, prototype and evaluate the performance of a novel IoT system with an intelligent edge. Our proposed design spans a modular three-tier IoT system architecture including the edge, gateway and cloud tiers. The proposed system leverages the local data acquired at the edge to build a distributed machine learning framework for local predictions and real-time responses at the edge. In addition, it proposes a more comprehensive model at the gateway for more centralized decision making, leveraging the data collected from multiple edges devices. One of the novel features of our system is minimizing the data transfer from the edge to the gateway tier. This is attained through intelligent data filtering at the edge to focus on the key events that provide the highest information gain to the gateway machine learning model. This, in turn, reduces the amount of noise that can impact the gateway model as well as minimizes the inter-tier communications overhead. To test our proposed system, we implement a prototype spanning the first two-tiers, and show through experiments how our system is capable of achieving a comparable performance to traditional centralized machine learning frameworks, while reducing the inter-tier communications overhead by up to 50%.