Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.

/pdf/a-survey-of-multi-access-edge-computing-in-5g-and-beyond-25j4pqqhet.pdf

A survey of multi-access edge computing in 5G and beyond : fundamentals, technology integration, and state-of-the-art.

Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

By Mobile Edge Computing (MEC), computation-intensive tasks are offloaded from mobile devices to cloud servers, and thus the energy consumption of mobile devices can be notably reduced. In this paper, we study task offloading in multi-user MEC systems with heterogeneous clouds, including edge clouds and remote clouds. Tasks are forwarded from mobile devices to edge clouds via wireless channels, and they can be further forwarded to remote clouds via the Internet. Our objective is to minimize the total energy consumption of multiple mobile devices, subject to bounded-delay requirements of tasks. Based on dynamic programming, we propose an algorithm that minimizes the energy consumption, by jointly allocating bandwidth and computational resources to mobile devices. The algorithm is of pseudo-polynomial complexity. To further reduce the complexity, we propose an approximation algorithm with energy discretization, and its total energy consumption is proved to be within a bounded gap from the optimum. Simulation results show that, nearly 82.7% energy of mobile devices can be saved by task offloading compared with mobile device execution.

Energy-optimal and delay-bounded computation offloading in mobile edge computing with heterogeneous clouds

/pdf/a-comprehensive-survey-on-aerial-mobile-edge-computing-1zhh7hb0.pdf

A comprehensive survey on aerial mobile edge computing: Challenges, state-of-the-art, and future directions

Recently, due to the deployment flexibility of unmanned aerial vehicles (UAVs), UAV-assisted mobile relay communication system has been widely used in the maritime communication. However, the performance of UAV-assisted mobile relay communication system is limited by the capacity of wireless backhaul link between base station and UAV In this paper, we consider a caching UAV-assisted decode-and-forward relay communication system in a downlink maritime communication. For the general case with multiple users, the optimal placement of UAV is obtained by solving the average achievable rate maximization problem through the one-dimensional linear search. For a special case with single user, we derive a semi closed-form expression of the optimal placement of UAV. Simulation results confirm the accuracy of analytical results and show that the optimal placement of UAV and the average achievable rate significantly depend on the cache capacity at UAV We also show the difference between the performances of the air-to-ground model and the air-to-sea model.

Placement optimization of caching UAV-assisted mobile relay maritime communication

This paper considers a UAV communication system with mobile edge computing (MEC). We minimize the energy consumption of the whole system via jointly optimizing the UAV's trajectory and task assignment as well as CPU's computational speed under the set of resource constrains. To this end, we first derive the energy consumption model of data processing, and then obtain the energy consumption model of fixed-wing UAV's flight. The optimization problem is mathematically formulated. To address the problem, we first obtain the approximate optimization problem by applying the technique of discrete linear state-space approximation, and then transform the non-convex constraints into convex by using linearization. Furthermore, a concave-convex procedure (CCCP) based algorithm is proposed in order to solve the optimization problem approximately. Numerical results show the efficacy of the proposed algorithm.

An energy efficient design for UAV communication with mobile edge computing

The invention discloses an unmanned aerial vehicle data transmission method with low transmission energy consumption. The method comprises the following steps of step S1, establishing a mathematical model according to parameters and tasks of an unmanned aerial vehicle and an actual condition of an environment; S2, utilizing a discretization and linearization technology to process the model; and S3, obtaining a final result by an iterative method based on a CCCP (concave-convex procedure) concept. Compared with the prior art, firstly, the mathematical optimization model is established by utilizing the requirements for the parameters and the tasks of the unmanned aerial vehicle and the actual condition of the environment; secondly, discretization and linearization approximate treatment is carried out on the mathematical model; lastly, the optimization problem is solved by an iterative algorithm based on the CCCP concept; and finally, a flight trajectory of the unmanned aerial vehicle during a task completing period and transmission power distribution and task allocation coefficients of a plurality of ground terminals are given, so that the transmission energy consumption of the unmanned aerial vehicle to the ground terminals is reduced.

Unmanned aerial vehicle data transmission method with low transmission energy consumption

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An energy efficient design for UAV communication with mobile edge computing

Unmanned aerial vehicle data transmission method with low transmission energy consumption