关键词： Mobile edge computing   task offloading   high speed railway   fluctuation of transmission and processing   potential game   stochastic network calculus   THROUGHPUT MAXIMIZATION   VEHICULAR NETWORKS   DELAY ANALYSIS   EDGE   DECISION  

摘要： The advanced 5G and mobile edge computing promote the development of the intelligent high-speed railway and enable computing-intensive and latency-sensitive tasks. Mobile edge computing can effectively release the pressure of tasks on on-board computing resources by migrating communication, computing, and storage to edge servers. However, the fluctuation of data transmission and task processing duration exists in practical projects. The current work assumes that the traditional communication model is ideal and that the latency can be derived as a fixed value, which is the most conservative bound. Obviously, there will be some system performance loss because the transmission latency is usually much shorter than this conservative bound. Therefore, an offloading strategy is proposed to maximize the task completion rate, considering the fluctuation of transmission latency and processing time in this paper. First, the stochastic network calculus is adopted to evaluate the fluctuation and probability of the transmission latency. Additionally, the task processing duration is regarded as an exponential distribution that is affected by the computing resource. Then, the task offloading model is generated based on the potential game to maximize the task completion rate. Moreover, the Nash Equilibrium and best response are derived. A reinforcement learning algorithm combined with a game is proposed to reach the Nash Equilibrium and obtain the task offloading strategy. Finally, extensive theoretical analysis and simulations are illustrated to prove the effectiveness of the model.

关键词： Autonomous aerial vehicles   Task analysis   Trajectory   Energy consumption   Wireless communication   Uncertainty   Relays   Mobile edge computing   unmanned aerial vehicle   channel uncertainty   information security   RESOURCE-ALLOCATION   JOINT RESOURCE   TRAJECTORY OPTIMIZATION   DESIGN   TASK   NOMA   TRANSMISSION   EFFICIENCY  

摘要： This paper investigates the robust and secure task transmission and computation scheme in multi-antenna unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks, where the UAV is dual-function, i.e., aerial MEC and aerial relay. The channel uncertainty is considered during information offloading and downloading. An energy consumption minimization problem is formulated under some constraints including users' quality of service and information security requirements and the UAV's trajectory's causality, by jointly optimizing the CPU frequency, the offloading time, the beamforming vectors, the artificial noise and the trajectory of the UAV, as well as the CPU frequency, the offloading time and the transmit power of each user. To solve the non-convex problem, a reformulated problem is first derived by a series of convex reformation methods, i.e., semi-definite relaxation, S-Procedure and first-order approximation, and then, solved by a proposed successive convex approximation (SCA)-based algorithm. The convergence performance and computational complexity of the proposed algorithm are analyzed. Numerical results demonstrate that the proposed scheme outperforms existing benchmark schemes. Besides, the proposed SCA-based algorithm is superior to traditional alternative optimization-based algorithm.

关键词： Deep reinforcement learning   industrial Internet of Things   mobile edge computing   online offloading   time-energy tradeoff   COMPUTATION RATE MAXIMIZATION   EDGE   OPTIMIZATION  

摘要： Mobile edge computing (MEC) has recently emerged as a promising technology to boost the integration ability of sensing, transmission and computation in industrial Internet of Things (IIoT). This paper investigates an MEC-enabled IIoT system, where multiple industrial devices may offload computation-intensive tasks to an edge server through wireless communication. We focus on the online offloading problem to optimize the tradeoff of the task accomplishing time and energy consumption. Time-varying wireless channels, random targeted task data sizes and dynamically changing residual energy as well as adaptively adjusted tradeoff weights make this problem highly challenging. Conventional optimization methods may lead to inefficient or even infeasible solutions. To efficiently tackle this problem, we leverage the deep reinforcement learning (DRL) technology to propose a time-energy tradeoff online offloading algorithm called TETO. In TETO, the online offloading decision policies are empirically learned via a well-designed DRL framework. TETO algorithm incorporates a stochastic strategy, the crossover and mutation technology and a novel feasible suboptimal offloading method to expand the offloading action search space with the provable feasibility guarantee. Extensive experimental results based on a real-world dataset show that, our TETO algorithm performs better than existing baseline algorithms, and obtains near-optimal performance with low CPU execution latency.

关键词： Internet of things   Mobile edge computing   Multi-agent deep reinforcement learning   Computation offloading   RESOURCE-ALLOCATION  

摘要： As a promising computing paradigm, mobile edge computing (MEC) can assist Internet of Things (IoT) devices in processing computation-intensive tasks. However, because of the different locations, limited resources, and dynamic loads of IoT devices and edge servers, designing a distributed computation offloading approach for the IoT system is challenging. In this paper, considering the delay-sensitive tasks and the binary offloading mechanism, we focus on designing a collaborative offloading scheme between different IoT devices. In order to reduce the overall consumed energy of IoT devices based on guaranteeing the delay constraints of tasks, we propose a distributed offloading algorithm by utilizing the value-based multi-agent deep reinforcement learning (MADRL) method. Specifically, with the individual networks deployed locally and the collaborative network deployed at the edge, IoT devices can learn their offloading policies from expericence data, and then determine their offloading decisions independently. Compared with several baseline algorithms, the proposed algorithm can better achieve our optimiaztion goal, which is demonstrated by the experiment results.

关键词： Mobile edge computing   reinforcement learning   satellite-terrestrial network   task offloading   velocity control   SERVICE MIGRATION   EDGE   OPTIMIZATION   AWARE  

摘要： Benefiting from the fusion of communication and intelligent technologies, network-enabled robots have become important to support future machine-assisted and unmanned applications. To provide high-quality services for robots in wide areas, hybrid satellite-terrestrial networks are a key technology. Through hybrid networks, computation-intensive and latency-sensitive tasks can be offloaded to mobile edge computing (MEC) servers. However, due to the mobility of mobile robots and unreliable wireless network environments, excessive local computations and frequent service migrations may significantly increase the service delay. To address this issue, this paper aims to minimize the average task completion time for MEC-based offloading initiated by satellite-terrestrial-network-enabled robots. Different from conventional mobility-aware schemes, the proposed scheme makes the offloading decision by jointly considering the mobility control of robots. A joint optimization problem of task offloading and velocity control is formulated. Using Lyapunov optimization, the original optimization is decomposed into a velocity control subproblem and a task offloading subproblem. Then, based on the Markov decision process (MDP), a dual-agent reinforcement learning (RL) algorithm is proposed. The convergence and complexity of the improved RL algorithm are theoretically analyzed, and the simulation results show that the proposed scheme can effectively reduce the offloading delay.

关键词： Sensors   Internet of Things   Servers   Task analysis   Optimization   Monitoring   Process control   Age of information   mobile edge computing   computation offloading   status update   RESOURCE-ALLOCATION   AGE   INFORMATION   INTERNET   FRESHNESS   EFFICIENT   NETWORKS   DESIGN   THINGS  

摘要： IoT devices have been widely utilized to detect state transition in the surrounding environment and transmit status updates to the base station for system operations. To guarantee the accuracy of system control, age of information (AoI) is introduced to quantify the freshness of the sensory data and meet the stringent timeliness requirement. Due to the limited computing resources, the status update can be offloaded to the mobile edge computing (MEC) server for execution. Since status updates generated by insufficient sensing operations may be invalid and lead to additional processing time, a joint data sensing and processing optimization problem needs to be considered. Therefore, this work formulates an NP-hard problem that considers the freshness of the status updates and energy consumption of the IoT devices. Subsequently, the problem is decomposed into sampling, sensing, and computation offloading optimization problems. To optimize the system overhead, a multi-variable iterative system cost minimization algorithm is proposed. Simulation results illustrate the efficacy of our method in decreasing the system cost, and indicate the influence of sensing and processing under different scenarios.

关键词： Mobile edge computing   Distributed computing   DNN offloading   Interleaved offloading   Edge computing   Computational offloading  

摘要： Deep Neural Networks (DNNs) are increasingly used on the edge in mobile and other resource-constrained devices for inference tasks ranging from object detection and image recognition to video processing. Many of these tasks have low latency requirements that cannot be satisfied if they are processed locally due to their high computational complexity. Offloading computation to the edge and cloud offers a way to alleviate this computational latency. Doing so introduces communication delays, which makes offloading a balancing act between the benefits of reduced processing time and the communication delays incurred. Existing algorithms for DNN offloading based on DNN partitioning are optimised for handling successive tasks on a single remote server, and perform sub-optimally when tasks are interleaved, or with multiple servers, or when privacy concerns require local processing of certain DNN layers. A viable alternative are generic computational offloading algorithms (GOAs), which can break down DNN tasks into their components and perform fine-grained offloading. We perform a simulation-based comparison of traditional GOAs with various levels of proactivity and offloading constraints and a naive DNN partitioning approach. We identify key requirements for offloading algorithms in the ability to create processing chains between several remote servers in order to reduce communication overheads, and the ability to prioritise already running tasks. The results confirm the expectations about the shortcomings of DNN partitioning, and show that GOA algorithms can provide a significant performance improvement.

关键词： Aerial data aggregator   Data aggregation   Data gathering   Internet of things   Mobile edge computing   Unmanned aerial vehicle   SENSOR DATA AGGREGATION   PERFORMANCE ANALYSIS   OBSTACLE AVOIDANCE   IOT NETWORKS   ENERGY   COMMUNICATION   CHALLENGES   DESIGN   MINIMIZATION   COMPUTATION  

摘要： In recent years, unmanned aerial vehicles (UAVs) have been used to extend the Internet of things (IoT) frame-work owing to their vast applications, monitoring and surveillance capability, ubiquity, and mobility. To support IoT requirements, UAVs must be capable of aggregating, processing, and transmitting data in real-time basis. As not only the number of IoT devices but also the amount of data to be collected is increased, data aggregation is of great importance. Recently, the UAV can also function as a mobile edge computing server in association with aerial data aggregation. This paper is the first to survey the various aspects and techniques of UAV-based aerial data aggregation for IoT networks. After addressing key design issues, we review the existing data aggregation techniques along with possible future direction. They are then compared with each other in terms of major operational features, performance characteristics, advantages, and limitations. Open issues and research chal-lenges are also discussed with possible solution approaches.

关键词： Mobile edge computing   HTTP-based video streaming   Adaptive streaming   Quality of experience   Streaming media   RATE ADAPTATION   QUALITY  

摘要： The traditional client-based HTTP adaptation strategies do not explicitly coordinate between the clients, servers, and cellular networks. A lack of coordination leads to suboptimal user experience. In addition to optimizing Quality of Experience (QoE), other challenges in adapting HTTP adaptive streaming (HAS) to the cellular environment are overcoming unfair allocation of the video rate and inefficient utilization of the bandwidth under the high-dynamics cellular links. Furthermore, the majority of the adaptive strategies ignore important video content characteristics and HAS client information, such as segment duration, buffer size, and video duration, in the video quality selection process. In this paper, we present a content-aware hybrid multi-access edge computing (MEC)-assisted quality adaptation algorithm by taking advantage of the capabilities of edge cloud computing. The proposed algorithm exploits video content characteristics, HAS client settings, and application-layer information to jointly adapt the bitrates of multiple clients. We design separate strategies to optimize the performance of short and long duration videos. We then demonstrate the efficiency of our algorithm against client-based solutions as well as MEC-assisted algorithms. The proposed algorithm guarantees high QoE, equitably selects video rates for clients, and efficiently utilizes the bandwidth for both short and long duration videos. The results from our extensive experiments reveal that the proposed long video adaptation algorithm outperforms state-of-the-art algorithms, with improvements in average video rate, QoE, fairness, and bandwidth utilization of 0.4%-12.3%, 8%-65%, 3.3%-5.7%, and 60%-130%, respectively. Furthermore, when high bandwidth is available to competing clients, the proposed short video adaptation algorithm improves QoE by 11.1% compared to the long video adaptation algorithm.

关键词： Mobile edge computing   Task offloading   Genetic algorithm   VANETs   ALLOCATION  

摘要： With the advancement of information technology, Mobile Edge Computing (MEC) has been integrated into Vehicular Ad Hoc Networks (VANETs) to efficiently address the resource constraints of vehicle terminals. Within the realm of MEC, the effective utilization of wireless channels and edge server resources for task offloading to meet users' Quality of Service (QoS) requirements has become a significant research area. This paper presents a queuing model and proposes a Predictive Vehicle-to-Vehicle Task Offloading (PV2VO) strategy based on predictable MEC state. The PV2VO strategy employs Vehicle-to-Vehicle (V2V) communication for task data upload and computation result download, thus reducing result return delay. Selective offloading is performed based on the edge server's load status and waiting time, resulting in reduced waiting time for the edge server's computing tasks and meeting users' QoS requirements under high load conditions. By considering both the delay model and energy model, two preference factors are defined, and an objective function for local execution and offloading to the edge server is formulated. The genetic algorithm is then employed to determine the optimal local computing frequency, optimal transmission power, and the most suitable computation offloading approach. The proposed PV2VO strategy is simulated and compared with conventional strategies through experiments involving five different types of typical computing tasks. The comparison of the results validates the superiority of the PV2VO strategy.