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As users have higher and higher requirements for the quality of experience, traditional cloud computing is gradually unable to meet the needs of user equipments. Hence mobile edge computing networks mounted by unmanned aerial vehicles are introduced to improve user experience and reduce energy consumption. However, most current work is based on neural networks, which require large amounts of labeled data or long training times. Given these challenges, this paper proposes an energy-efficient multi-stage alternating optimization scheme to reduce the weighted energy consumption of the entire network. We analyze the energy consumption of each device and formulate a non-convex optimization problem. Considering the impact of task offloading, resource allocation, and path planning on network energy consumption, we transform the energy consumption problem into three subproblems. And use the coordinate descent algorithm, interior point method, and successive convex approximation method to optimize them alternately. The simulation results show that the proposed optimization scheme can significantly reduce the network's total energy consumption. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) applications have invaded several domains (supply chain, healthcare, etc.). To enhance the quality of the provided service in terms of latency, response time, etc., service providers such as Amazon, Google, and Microsoft turned to running IoT tasks near the end user by invoking the fog computing concept. Fog computing extends cloud services to the edge of the network. It provides a variety of computing resources in the form of fog nodes, which offer multiple services known as fog services. These latter are used to store and process the data generated by IoT devices. Fog services are characterized by their high reusability. It enables the construction of a composite service to provide complicated IoT tasks. In this paper, we introduce an adaptive requirements-aware approach for configuring IoT applications in fog computing. The configuration is based on a Composite Fog Service (CFS) model and is restricted by a set of constraints. The proposed approach is implemented in a Smart Car Parking (SCP) scenario. Simulation results reveal the effectiveness of our approach. [ABSTRACT FROM AUTHOR]