关键词： unmanned aerial vehicle   mobile edge computing   stackelberg game  

摘要： When an unmanned aerial vehicle (UAV) performs tasks such as power patrol inspection, water quality detection, field scientific observation, etc., due to the limitations of the computing capacity and battery power, it cannot complete the tasks efficiently. Therefore, an effective method is to deploy edge servers near the UAV. The UAV can offload some of the computationally intensive and real-time tasks to edge servers. In this paper, a mobile edge computing offloading strategy based on reinforcement learning is proposed. Firstly, the Stackelberg game model is introduced to model the UAV and edge nodes in the network, and the utility function is used to calculate the maximization of offloading revenue. Secondly, as the problem is a mixed-integer non-linear programming (MINLP) problem, we introduce the multi-agent deep deterministic policy gradient (MADDPG) to solve it. Finally, the effects of the number of UAVs and the summation of computing resources on the total revenue of the UAVs were simulated through simulation experiments. The experimental results show that compared with other algorithms, the algorithm proposed in this paper can more effectively improve the total benefit of UAVs.

关键词： Task analysis   Servers   Games   Delays   Edge computing   Cloud computing   Energy consumption   Mobile edge computing   computation offloading   aggregative game   instantaneous load billing  

摘要： Mobile edge computing (MEC) is a promising approach that can reduce the latency of task processing by offloading tasks from user equipments (UEs) to MEC servers. Existing works always assume that the MEC server is capable of executing the offloaded tasks, without considering the impact of improper load on task processing efficiency. In this article, we present a two-stage computing offloading scheme to minimize the task processing delay while managing the server load properly. To minimize the task processing delay, each UE optimizes how much workload to be offloaded to the MEC server. To improve the task processing efficiency of the server, we arrange the processing order of offloading tasks by introducing an aggregative game with an instantaneous load billing mechanism. The proposed game can obtain the optimal task offloading and processing strategy with limited information and a small number of iterations. Simulation results show that our scheme approaches the optimal offloading strategy in terms of minimizing task processing delay for each UE and improving processing efficiency for the server.

关键词： blockchain   mobile edge computing   computation offloading   deep deterministic policy gradient (DDPG)  

摘要： A mobile edge computing (MEC)-enabled blockchain system is proposed in this study for secure data storage and sharing in internet of things (IoT) networks, with the MEC acting as an overlay system to provide dynamic computation offloading services. Considering latency-critical, resource-limited, and dynamic IoT scenarios, an adaptive system resource allocation and computation offloading scheme is designed to optimize the scalability performance for MEC-enabled blockchain systems, wherein the scalability is quantified as MEC computational efficiency and blockchain system throughput. Specifically, we jointly optimize the computation offloading policy and block generation strategy to maximize the scalability of MEC-enabled blockchain systems and meanwhile guarantee data security and system efficiency. In contrast to existing works that ignore frequent user movement and dynamic task requirements in IoT networks, the joint performance optimization scheme is formulated as a Markov decision process (MDP). Furthermore, we design a deep deterministic policy gradient (DDPG)-based algorithm to solve the MDP problem and define the multiple and variable number of consecutive time slots as a decision epoch to conduct model training. Specifically, DDPG can solve an MDP problem with a continuous action space and it only requires a straightforward actor-critic architecture, making it suitable for tackling the dynamics and complexity of the MEC-enabled blockchain system. As demonstrated by simulations, the proposed scheme can achieve performance improvements over the deep Q network (DQN)-based scheme and some other greedy schemes in terms of long-term transactional throughput.

关键词： Mobile edge computing   Computation offloading   Task scheduling   Object-oriented applications   UAV  

摘要： The high mobility and maneuverability of unmanned aerial vehicles (UAVs) enable them to act as temporary base stations (BSs) in extreme environments, expanding the computing capacities of terminals for intelligent applications, most of which are object-oriented ones. Computation offloading in a UAV-based edge-cloud environment is an excellent way to improve the performance of these object-oriented intelligent applications. In contrast, the computation-intensive tasks are offloaded to the cloud, and the data-intensive ones are offloaded to the edge. Though computation offloading over the cloud, edge, and terminals has been broadly studied, existing researches primarily establish scheduling algorithms on program high-level abstraction without consideration of challenges from program structures. We focus on task scheduling for offloading object-oriented applications while considering the 'encapsulation' characteristic. We proposed a time-driven offloading strategy based on a particle swarm optimization algorithm employing the genetic algorithm operators with floating encoding (PGFE). This strategy introduces a genetic algorithm's randomly two-point crossover and mutation operator to avoid converging on local optima effectively. The simulation results show that our strategy can reduce the average execution time of object-oriented applications by 11.78-48.02%, compared with other classic algorithms in a UAV-based edge-cloud environment.

关键词： Autonomous aerial vehicles   Task analysis   Servers   Security   Resource management   Optimization   Energy consumption   Intelligent omni-surface   mobile edge computing   physical layer security   secrecy energy efficiency   unmanned aerial vehicle  

摘要： Different from conventional reflecting-only metasurfaces, intelligent omni-surfaces (IOSs) are capable of reflecting and transmitting the received signals simultaneously. As such, users located at the both sides of IOSs can be served efficiently. In this paper, a novel IOS-enhanced aerial secure offloading system is proposed in the presence of multiple ground eavesdroppers. Specifically, the IOS is adopted to prevent information leakage, improve legitimate reception quality and expand the security deployment range of unmanned aerial vehicles (UAVs). To maximize the secrecy energy efficiency (SEE) of the considered system, a non-convex resource allocation problem is formulated by allocating computing frequency, determining offloading strategy, controlling transmit power, designing phase shifts, and deploying UAV locations. The formulated problem is non-trivial and challenging to be solved directly due to the highly coupled variables. To tackle this issue, the alternating optimization technique is invoked to decouple the original one into five subproblems, and then the computing and communication settings are alternatively optimized using a low complexity iterative algorithm. Finally, simulation results demonstrate that: i) In terms of the SEE performance, our IOS-enhanced aerial secure offloading system significantly outperforms the benchmark schemes;ii) Compared with reflecting-only surfaces, IOSs can take full advantage of the flexible deployment ability of UAVs, such that their secure offloading space can be considerably expanded.

关键词： Computation offloading   Convex optimization   Deep reinforcement learning   Mobile edge computing   Unmanned aerial vehicle  

摘要： With the increasing growth of internet-of-things (IoT) devices, effective computation performance has become a critical issue. Many services provided by IoT devices (e.g., augmented reality, location-tracking, traffic systems, and autonomous driving) require intensive real-time data processing, which demands powerful computational resources. Mobile edge computing (MEC) has been introduced to effectively handle this problem reliably over the internet. The inclusion of a MEC server allows computationally intensive tasks to be offloaded from IoT devices. However, communication overhead and delays are major drawbacks. With the advantages of high mobility and low cost, unmanned aerial vehicles (UAVs) can mitigate this issue by acting as MEC servers. The offloading decisions for such scenarios involve service latency, energy/power consumption, and execution delays. For this reason, this study reviews UAV-enabled MEC solutions in which offloading was the focus of research. We compare the algorithms qualitatively to assess features and performance. Finally, we discuss open issues and research challenges in terms of design and implementation.

关键词： Computational modeling   Task analysis   Optimization   Energy consumption   Costs   Connected vehicles   Resource management   Mobile edge computing   multitier roadside computing   system stability   vehicular communication  

摘要： Owing to the heterogeneity and massiveness of data generated by connected vehicles, multitier roadside computing (MRC) plays a key role in an intelligent transportation system (ITS). MRC provides a localized cloudization capability in close proximity to the connected vehicles. Because the massive data correspondingly necessitate a high computing energy consumption, stable workload processing with respect to energy efficiency is a crucial problem of MRC. To address this problem, we propose an energy-efficient workload (E2W) scheduling algorithm for flexibly handling the random offloading traffic from connected vehicles. In our E2W algorithm, an MRC is transformed into a multitier queuing system, where the workload arrived from the vehicles and the computing capability of the roadside units are considered to be arrival and departure processes, respectively. The departure rate that closely impinges on the computing energy consumption is supervised using the Lyapunov drift-plus-penalty policy to achieve efficient energy reduction while maintaining service satisfaction. In addition, the deterministic upper bound of the Lyapunov optimization provides the MRC system with stable operation. Simulation results demonstrate that the E2W algorithm outperforms existing optimization strategies in terms of energy efficiency and system stability.

关键词： Streaming media   Quality of experience   Resource management   Wireless communication   Bit rate   Transcoding   Bandwidth   Adaptive video streaming   mobile edge computing   resource allocation   wireless networks   online optimization  

摘要： The emerging mobile edge computing (MEC) technology has been recently applied to improve adaptive bitrate (ABR) streaming service quality under time-varying wireless channels. In this paper, we consider a heterogeneous multi-user MEC-enabled video streaming network with time-varying wireless channels in sequential time frames. In particular, we aim to design an online joint transcoding and transmission resource allocation algorithm to maximize the ABR streaming user's quality of experience (QoE) subject to the bandwidth and CPU constraints. The algorithm is "online" in the sense that the bitrate and resource allocation decisions made at each frame depend only on the observation of past events. We formulate the problem as a mixed integer non-linear programming (MINLP) that jointly determines bitrate adaptation, bandwidth allocation, and CPU cycle assignment. To cope with the challenge arising from the coupling decisions of adjacent frames, we propose a low-complexity online algorithm, named OCCA. Specifically, by introducing queueing model constraints, we transform the offline non-conex MINLP problem into a multi-frame problem. Then, we analytically decouple the multi-stage(frame) problem to multiple per-frame convex subproblems that can be solved with high robustness and low computational complexity. We perform simulations with realistic scenarios to evaluate the performance of the proposed algorithm. Results manifest that compared with state-of-the-art approaches, our proposed algorithm can provide 97.84% (on average) extra QoE.

关键词： lightweight network   fast semantic segmentation   mobile edge computing   fundus image  

摘要： Fundus is the only structure that can be observed without trauma to the human body. By analyzing color fundus images, the diagnosis basis for various diseases can be obtained. Recently, fundus image segmentation has witnessed vast progress with the development of deep learning. However, the improvement of segmentation accuracy comes with the complexity of deep models. As a result, these models show low inference speeds and high memory usages when deploying to mobile edges. To promote the deployment of deep fundus segmentation models to mobile devices, we aim to design a lightweight fundus segmentation network. Our observation comes from the fact that high-resolution representations could boost the segmentation of tiny fundus structures, and the classification of small fundus structures depends more on local features. To this end, we propose a lightweight segmentation model called LightEyes. We first design a high-resolution backbone network to learn high-resolution representations, so that the spatial relationship between feature maps can be always retained. Meanwhile, considering high-resolution features means high memory usage;for each layer, we use at most 16 convolutional filters to reduce memory usage and decrease training difficulty. LightEyes has been verified on three kinds of fundus segmentation tasks, including the hard exudate, the microaneurysm, and the vessel, on five publicly available datasets. Experimental results show that LightEyes achieves highly competitive segmentation accuracy and segmentation speed compared with state-of-the-art fundus segmentation models, while running at 1.6 images/s Cambricon-1A speed and 51.3 images/s GPU speed with only 36k parameters.

关键词： mobile edge computing   online resource allocation   task arrival prediction   long short-term memory   Lyapunov optimization  

摘要： MEC servers (MESs) support multiple queues to accommodate the delay requirements of tasks offloaded from end devices or transferred from other MESs. The service time assigned to each queue trades off the queue backlog and energy consumption. Because multiple queues share the computational resources of a MES, optimally scheduling the service time among them is important, reducing the energy consumption of a MES and ensuring the delay requirement of each queue. To achieve a balance between these metrics, we propose an online service-time allocation method that minimizes the average energy consumption and satisfies the average queue backlog constraint. We employ the Lyapunov optimization framework to transform the time-averaged optimization problem into a per-time-slot optimization problem and devise an online service-time allocation method whose time complexity is linear to the number of queues. This method determines the service time for each queue at the beginning of each time slot using the observed queue length and expected workload. We adopt a long short-term memory (LSTM) deep learning model to predict the workload that will be imposed on each queue during a time slot. Using simulation studies, we verify that the proposed method strikes a better balance between energy consumption and queuing delay than conventional methods.