In Proceedings of the 22nd International Computer Science and Engineering Conference (ICSEC’18). Distinguishing drone types based on acoustic wave by IoT device. In Proceedings of the IEEE 29th International Conference on Advanced Information Networking and Applications (AINA’15). Fog computing micro datacenter based dynamic resource estimation and pricing model for IoT. In Proceedings of the IEEE 19th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD’14). Key ingredients in an IoT recipe: Fog computing, cloud computing, and more fog computing. In Proceedings of the Workshop on Mobile Big Data. A survey of fog computing: Concepts, applications and issues. In Proceedings of the 1st MCC Workshop on Mobile Cloud Computing. Fog computing and its role in the internet of things. Mobile edge computing potential in making cities smarter. In Proceedings of the 4th World Congress on InfOrmation and Communication Technologies (WICT’14). Cloudlet-based cyber foraging framework for distributed video surveillance provisioning. Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks. The role of edge computing in internet of things. Efficient next generation emergency communications over multi-access edge computing. Integration of cloud computing and internet of things: A survey.
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A numerical analysis is carried out in a use case to show how to use the model introduced in the article to decide the computation power of the computing element in terms of number of available CPUs and CPU clock speed, and evaluate the achieved performance gain of the proposed framework. Reinforcement Learning (RL) is used to support SC in its decisions. A System Controller (SC) is in charge of deciding the number of active CPUs at runtime by maximizing an objective function weighing power consumption, job loss probability, and processing latency. Although this idea is not new, this is the first work that considers power consumption of the computing element installed on board UAVs, which is crucial, since it may influence flight mission duration. To this purpose, in this article, we propose to use unmanned aerial vehicles (UAVs) as fog nodes. Nevertheless, in some IoT scenarios there are remote or challenging areas where it is difficult to connect an IoT network to a fog platform with appropriate links, especially if IoT devices produce a lot of data that require processing in real-time. The most common solution is offloading these tasks to external devices with higher computational and storage capabilities, usually provided by centralized servers in remote clouds or on the edge by using the fog computing paradigm. Data produced by IoT devices can generate a number of computational tasks that cannot be executed locally on the IoT devices.
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