With the emergence of computation-intensive and delay-sensitive applications, such as face recognition, virtual reality, augmented reality, and Internet of Things (IoT) devices ; Mobile Edge Computing (MEC) allows the IoT devices to offload their heavy computation tasks to nearby edge cloud network rather than to compute the tasks locally. Therefore, it helps to reduce the energy consumption and execution delay in the ground mobile users. Flying Unmanned Aerial Vehicles (UAVs) integrated with the MEC server play a key role in 5G and future wireless communication networks to provide spatial coverage and further computational services to the small, battery-powered and energy-constrained devices. The UAVenabled MEC (U-MEC) system has flexible mobility and more computational capability compared to the terrestrial MEC network. They support line-of-sight (LoS) links with the users offloading their tasks to the UAVs. Hence, users can transmit more data without interference by mitigating small-scale fading and shadowing effects. UAVs resources and flight time are very limited due to size, weight, and power (SWaP) constraints. Therefore, energy-aware communication and computation resources are allocated in order to minimize energy consumption.In this paper, a brief survey on U-MEC networks is presented. It includes the brief introduction regarding UAVs and MEC technology. The basic terminologies and architectures used in U-MEC networks are also defined. Moreover, mobile edge computation offloading working, different access schemes used during computation offloading technique are explained. Resources that are needed to be optimized in U-MEC systems are depicted with different optimization problem, and solution types. Furthermore, to guide future work in this area of research, future research directions are outlined. At the end, challenges and open issues in this domain are also summarized.

Summary With the development in the technology, a rapid increase in the use of unmanned aerial vehicle (UAV) is observed. UAVs are executing tasks that were previously performed by humans or manned aircraft, thus reducing the man workload and saving time. Path planning of UAVs is one of the most researched topics these days. Researchers are investigating more about UAVs and path planning to make it more feasible and economical. The major issue faced while planning a feasible path is to detect and avoid the obstacles encountered during a mission. This paper presents a detailed analysis of path planning in UAVs. Important aspects of path planning, that is, environment, dimensions, obstacles, and type and number of UAVs used in path planning, are also being discussed. Obstacle scenarios which UAV may come across during a mission and constraints which UAV has to follow for successful mission are briefly mentioned. Optimization techniques used to optimize the UAV path are also presented. In addition, future challenges and issues are also discussed.

Mobile edge cloud networks can be used to offload computationally intensive tasks from Internet of Things (IoT) devices to nearby mobile edge servers, thereby lowering energy consumption and response time for ground mobile users or IoT devices. Integration of Unmanned Aerial Vehicles (UAVs) and the mobile edge computing (MEC) server will significantly benefit small, battery-powered, and energy-constrained devices in 5G and future wireless networks. We address the problem of maximising computation efficiency in U-MEC networks by optimising the user association and offloading indicator (OI), the computational capacity (CC), the power consumption, the time duration, and the optimal location planning simultaneously. It is possible to assign some heavy tasks to the UAV for faster processing and small ones to the mobile users (MUs) locally. This paper utilizes the k-means clustering algorithm, the interior point method, and the conjugate gradient method to iteratively solve the non-convex multi-objective resource allocation problem. According to simulation results, both local and offloading schemes give optimal solution.

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