Cost-Effective Processing in Fog-Integrated Internet of Things Ecosystems

The emerging Internet of Things (IoT) paradigm creates a growing need to analyze a significant amount of data produced by the interconnected IoT devices. Since IoT devices have limited computation capabilities, Fog Computing is a natural complement, to provide distributed, location-aware, and easy-to-access computation resources. In this work, we address the problem of application processing and data offloading in a Fog-integrated IoT ecosystem. By leveraging the Lyapunov optimization technique, we design an online and distributed system control policy called the Distributed Weighted Backpressure (DWB) policy that asymptotically minimizes the cost of IoT devices. A three-way tradeoff among queue backlogs, communication cost, and computation cost is then investigated. Finally, simulation study has been conducted to validate the correctness and usefulness of the proposed DWB policy.