关键词： Bits allocation   Computation   Mobile computing   Mobile edge computing   Offloading   Trajectory design   Unmanned aerial vehicles   Wireless communication  

摘要： The unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) system is attracting a lot of attentions for the potential of low latency and low transmission energy consumption, due to the advantages of high mobility and easy deployment. It has been widely applied to provide communication and computing services, especially in Internet of Things (IoT). However, there are still some challenges in the UAV-enabled MEC system. Firstly, the endurance of the UAV is limited and further impacts the performance of the system. Secondly, mobile devices are battery-powered and the batteries of some devices are hard to change. Therefore, in this paper, a UAV-enabled MEC system in which the UAV is empowered to have computing capability and provides tasks offloading service is studied. The total energy consumption of the UAV-enabled system, which includes the energy consumption of the UAV and the energy consumption of the ground users, is minimized under the constraints of the UAV’s energy budget, the number of each task’s bits, the causality of the data and the velocity of the UAV. The bits allocation of uploading data, computing data, downloading data and the trajectory of the UAV are jointly optimized with the goal of minimizing the total energy consumption. Moreover, a two-stage alternating algorithm is proposed to solve the non-convex formulated problem. Finally, the simulation results show the superiority of the proposed scheme compared with other benchmark schemes. Finally, the performance of the proposed scheme is demonstrated under different settings. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： mobile edge computing   cognitive computing   deep learning   computation offloading   mobility   optimization  

摘要： Mobile edge computing (MEC) can augment the computation capabilities of a vehicle terminal (VT) through offloading the computational tasks from the VT to the mobile edge computing-enabled base station (MEC-BS) covering them. However, due to the limited mobility of the vehicle and the capacity of the MEC-BS, the connection between the vehicle and the MEC-BS may be intermittent. If we can expect the availability of MEC-BS through cognitive computing, we can significantly improve the performance in a mobile environment. Based on this idea, we propose a offloading optimization algorithm based on availability prediction. We examine the admission control decision of MEC-BS and the mobility problem, in which we improve the accuracy of availability prediction based on Empirical Mode Decomposition(EMD) and LSTM in deep learning. Firstly, we calculate the availability of MEC, completion time, and energy consumption together to minimize the overall cost. Then, we use a game method to obtain the optimal offloading decision. Finally, the experimental results show that the algorithm can save energy and shorten the completion time more effectively than other existing algorithms in the mobile environment.

关键词： Cognitive computing   Computation offloading   Deep learning   Mobile edge computing   Mobility   Optimization  

摘要： Mobile edge computing (MEC) can augment the computation capabilities of a vehicle terminal (VT) through offloading the computational tasks from the VT to the mobile edge computing-enabled base station (MEC-BS) covering them. However, due to the limited mobility of the vehicle and the capacity of the MEC-BS, the connection between the vehicle and the MEC-BS may be intermittent. If we can expect the availability of MEC-BS through cognitive computing, we can significantly improve the performance in a mobile environment. Based on this idea, we propose a offloading optimization algorithm based on availability prediction. We examine the admission control decision of MEC-BS and the mobility problem, in which we improve the accuracy of availability prediction based on Empirical Mode Decomposition(EMD) and LSTM in deep learning. Firstly, we calculate the availability of MEC, completion time, and energy consumption together to minimize the overall cost. Then, we use a game method to obtain the optimal offloading decision. Finally, the experimental results show that the algorithm can save energy and shorten the completion time more effectively than other existing algorithms in the mobile environment. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： mobile edge computing   tasks offloading   optimal stopping theory   sequential decision making  

摘要： In recent years, there has been a significant increase in the use of mobile devices and their applications. Meanwhile, cloud computing has been considered as the latest generation of computing infrastructure. There has also been a transformation in cloud computing ideas and their implementation so as to meet the demand for the latest applications. mobile edge computing (MEC) is a computing paradigm that provides cloud services near to the users at the edge of the network. Given the movement of mobile nodes between different MEC servers, the main aim would be the connection to the best server and at the right time in terms of the load of the server in order to optimize the quality of service (QoS) of the mobile nodes. We tackle the offloading decision making problem by adopting the principles of optimal stopping theory (OST) to minimize the execution delay in a sequential decision manner. A performance evaluation is provided using real world data sets with baseline deterministic and stochastic offloading models. The results show that our approach significantly minimizes the execution delay for task execution and the results are closer to the optimal solution than other offloading methods.

关键词： Mobile edge computing   UAV   user association   uploading power   trajectory optimization  

摘要： Mobile edge computing (MEC) provides computational services at the edge of networks by offloading tasks from user equipments (UEs). This letter employs an unmanned aerial vehicle (UAV) as the edge computing server to execute offloaded tasks from the ground UEs. We jointly optimize user association, UAV trajectory, and uploading power of each UE to maximize sum bits offloaded from all UEs to the UAV, subject to energy constraint of the UAV and quality of service (QoS) of each UE. To address the non-convex optimization problem, we first decompose it into three subproblems that are solved with integer programming and successive convex optimization methods respectively. Then, we tackle the overall problem by the multi-variable iterative optimization algorithm. Simulations show that the proposed algorithm can achieve a better performance than other baseline schemes.

关键词： UAV   mobile edge computing   resource scheduling   bandwidth allocation   trajectory optimization  

摘要： In this paper, we study an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) architecture, in which a UAV roaming around the area may serve as a computing server to help user equipment (UEs) compute their tasks or act as a relay for further offloading their computation tasks to the access point (AP). We aim to minimize the weighted sum energy consumption of the UAV and UEs subject to the task constraints, the information-causality constraints, the bandwidth allocation constraints and the UAV's trajectory constraints. The required optimization is nonconvex, and an alternating optimization algorithm is proposed to jointly optimize the computation resource scheduling, bandwidth allocation, and the UAV's trajectory in an iterative fashion. The numerical results demonstrate that significant performance gain is obtained over conventional methods. Also, the advantages of the proposed algorithm are more prominent when handling computation-intensive latency-critical tasks.

关键词： Mobile data offloading   Deep learning   Mobile edge computing  

摘要： Mobile Edge Computing (MEC) has been regarded as a key technology of the future communication systems in the industry due to its capability to satisfy a wide range of requirements of the emerging wireless terminals (virtual reality devices, augmented reality, and Intelligent Vehicles), such as high data rate, low latency, and huge computation. Besides, difficulties in the lack of resources in the licensed band have prompted researches on mobile data offloading. Owing to the cheap and effective characteristics of WiFi AP, it is utilized to offload some devices from small base stations (SBS) in this paper. Furthermore, a multi-Long Short Term Memory (LSTM) based deep-learning model is constructed to predict the real-time traffic of SBS, which may help us perform the offloading process accurately. According to the prediction results, an mobile data offloading strategy based on cross entropy (CE) method has been proposed. The presented results based on actual dataset provide strong proofs of the applicability of the prediction and offloading scheme we proposed. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Small cell networks   Mobile edge computing   Spectrum allocation   Computation offloading  

摘要： As a promising way to offset the low computation capacity of user equipments (UEs), mobile edge computing (MEC) has attracted great attention of academy and industrial recently. By deploying MEC servers in small cell networks, the communication rate and computation speed could be enhanced efficiently. However, due to the different computation capability of various small cell base stations, micro base stations and UEs, the computation offloading balance becomes a challenging problem. Furthermore, the wireless channel condition and transmission delay also take important roles in the computation offload. In this paper, we jointly consider the spectrum allocation and computation offload selection to achieve desired balance in small cell networks with MEC. In this process, we formulate the spectrum allocation factor, computation offloading decision and UEs' transmission power as an optimization problem with the objective of minimizing the energy consumption and the constraints of each UE's latency. Then, we propose a two-step solution algorithm to solve the formulated problem. Finally, simulation results are presented to show the effectiveness of the proposed scheme in the performance of energy consumption, latency, computation offloading ratio and convergence.

关键词： Mobile edge computing   Wireless energy transfer   Computation offloading   Resource allocation  

摘要： Limited battery capacity and poor computing capability of wireless devices have been longstanding performance limitations in the Internet of Things (IoT) era. Employing Wireless Energy Transfer (WET) technology, wireless devices of the Mobile Edge Computing (MEC) systems can be released from these limitations and achieve a better quality of experience (QoE). This paper considered a wireless powered mobile edge computing (WP-MEC) system with one mobile device, where a double antenna hybrid access point (HAP) (integrated with a MEC server) transmits wireless energy to the device and communicates with the wireless terminal to assist in its data processing. We investigated the average computation rate maximization problem in this system and proposed an online service rate maximization (OSRM) algorithm to tackle this problem. In each time slot, the proposed algorithm optimally decides the time allocation policy and the CPU-frequency for the mobile device. Simulation results show that the proposed algorithm can balance the time average computation rate and the task buffer queue length, and outperforms the benchmark schemes. (C) 2019 Elsevier B.V. All rights reserved.

关键词： 5G   dynamic system environment   mobile edge computing   task offloading   computation reusing  

摘要： The task offloading problem, which aims to balance the energy consumption and latency for Mobile Edge Computing (MEC), is still a challenging problem due to the dynamic changing system environment. To reduce energy while guaranteeing delay constraint for mobile applications, we propose an access control management architecture for 5G heterogeneous network by making full use of Base Station's storage capability and reusing repetitive computational resource for tasks. For applications that rely on real-time information, we propose two algorithms to offload tasks with consideration of both energy efficiency and computation time constraint. For the first scenario, i.e. the rarely changing system environment, an optimal static algorithm is proposed based on dynamic programming technique to get the exact solution. For the second scenario, i.e. the frequently changing system environment, a two-stage online algorithm is proposed to adaptively obtain the current optimal solution in real time. Simulation results demonstrate that the exact algorithm in the first scenario runs 4 times faster than the enumeration method. In the second scenario, the proposed online algorithm can reduce the energy consumption and computation time violation rate by 16.3% and 25% in comparison with existing methods.