An important issue for service providers to consider before building a mobile edge services network is the limited budget for edge server deployment. In addition, the geographic position of unmanned aerial vehicle (UAV) edge server will affect its energy consumption. Therefore, we have established a UAV-assisted mobile edge computing (MEC) system. UAV acts as a mobile edge server to provide computing services for user equipment (UE). This system aim to minimize the total energy consumption and deployment cost required for the UAVs to complete offload tasks and hover. To optimize the total energy consumption and deployment cost of UAVs, we proposed an improved mean shift (IMS) algorithm, which jointly optimizing the location and number of UAV edge servers. Various simulation results show the effectiveness of our proposed scheme in reducing energy consumption compared to other deployment methods. Furthermore, our research is of great significance for service providers in control the trade-off between investment cost and energy consumption.

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In smart city IoT applications, the deployment of edge servers has problems such as unbalanced servers load and low servers utilization. Therefore, we study the edge servers deployment problem in mobile edge computing environments for smart cities through an improved Top‐K algorithm in this paper. This algorithm comprehensively considering the distance between base stations and edge servers, the weight ratio of base stations in the base station cluster, the coverage of edge servers, and the upper limit of computing tasks, which aims to reduce the access delay of tasks and deployment cost of edge servers, balance the load among edge servers, improve quality of user experience (QoE), and quality of service (QoS) of the smart city. Firstly, deploy an edge server at the base station with the most tasks and divide base station clusters according to the minimum distance strategy. Then, the location of edge servers adjusted according to the cumulative sum of the weight ratio of base station tasks and distance product in each base station cluster. Finally, the simulation results show that the deployment strategy in this paper is better than other methods in terms of server utilization, load balancing and cost, and is slightly better than other algorithms in terms of delay.

In industrial scenarios, it is very common to replace manual detection with new intelligent applications such as image recognition based on deep learning. However, these applications often have the characteristics of high computational energy consumption and high computational density. It is impossible to deal with these tasks only by relying on intelligent detection equipment. Mobile edge computing (MEC) is an effective method to solve the problems of high energy consumption and insufficient computing power of detection equipment.A reasonable offloading strategy can reduce equipment energy consumption and reduce system maintenance costs. This article studies the intelligent inspection scenarios of multi-user and multi-MEC servers, and considers the resource competition and mobility among users. We divided the entire inspection process into several computing cycles, and designed a mobile migration model based on the traditional computing offloading model. Finally, a distributed algorithm based on game theory is proposed to solve the problem of minimizing energy consumption. The proposed intelligent inspection and offloading migration algorithm is evaluated through simulation experiments. Theoretical and experimental results show that our method can better adapt to the scenario proposed in this article, with low algorithm complexity and can effectively reduce the energy consumption of the whole inspection system. Compared with random and greedy algorithms, the energy consumption is reduced by 60% and 50% respectively.