关键词： Servers   Internet of Things   Resource management   Array signal processing   Optimization   Backscatter   Wireless communication   Backscatter communications   mobile edge computing   multi-agent deep reinforcement learning  

摘要： Mobile edge computing (MEC) provides an economical way for the resource-constrained edge users to offload computational workload to MEC servers co-located with the access point (AP). In this article, we consider a hybrid computation offloading scheme that allows edge users to offload workloads by using active RF communications and backscatter communications. We aim to maximize the overall energy efficiency subject to the completion of all workload by jointly optimizing the AP's beamforming and the users' offloading decisions. Considering a dynamic environment, we propose a hierarchical multi-agent deep reinforcement learning (H-MADRL) framework to solve this problem. The high-level agent resides in the AP and optimizes the beamforming strategy, while the low-level user agents learn and adapt individuals' offloading strategies. To further improve the learning efficiency, we propose a novel optimization-driven learning algorithm that allows the AP to estimate the low-level users' actions by solving approximate optimization problem efficiently. Then, the action estimation can be shared with all users and drive them to update individuals' actions independently. Simulation results reveal that our algorithm can improve the system performance by 50%. The learning efficiency and reliability are also improved significantly comparing to the model-free learning methods.

关键词： unmanned aerial vehicle communication   mobile edge computing   secrecy energy efficiency   convex optimization algorithm  

摘要： A secrecy energy efficiency optimization scheme for a multifunctional unmanned aerial vehicle (UAV) assisted mobile edge computing system is proposed to solve the computing power and security issues in the Internet-of-Things scenario. The UAV can switch roles between a computing UAV and jamming UAV based on the channel conditions. To ensure the security of the content and the system energy efficiency in the process of offloading computing tasks, the UAV trajectory, uplink transmit power, user scheduling, and offload task are jointly optimized, and an updated-rate assisted block coordinate descent (BCD) algorithm is used. Simulation results show that this scheme efficiently improves the secrecy performance and energy efficiency of the system. Compared with the benchmark scheme, the secrecy energy efficiency of the scheme is improved by 38.5%.

关键词： caching   mobile edge computing   content preferences   user mobility   MovieLens  

摘要： High-speed internet has boosted clients' traffic needs. Content caching on mobile edge computing (MEC) servers reduces traffic and latency. Caching with MEC faces difficulties such as user mobility, limited storage, varying user preferences, and rising video streaming needs. The current content caching techniques consider user mobility and content popularity to improve the experience. However, no present solution addresses user preferences and mobility, affecting caching decisions. We propose mobility- and user-preferences-aware caching for MEC. Using time series, the proposed system finds mobility patterns and groups nearby trajectories. Using cosine similarity and CF, we predict and cache user-requested content. CF predicts the popularity of grouped-based content to improve the cache hit ratio and reduce delay compared to baseline techniques.

关键词： Industrial IoT   Internet of Things   microservice offloading   mobile edge computing   resource-agnostic  

摘要： Due to increase in real-time mobile applications and Industrial Internet-of-Things (IIoT) devices, the edge computing paradigm provides a systematic and eccentric platform for real-time Internet-of-Things applications. Though the paradigm provides an effective infrastructure, however the resource requirements of IIoT devices change radically with time, which is described as a resource-agnostic property. Therefore, the estimation of resource requirements of IIoT devices is a critical and resilient assignment. In addition, it requires an extensive amount of resources to process the data traffic flows and microservice offloading. Hence, we present RAISE, a novel resource-agnostic microservice offloading scheme for mobile IIoT devices. RAISE efficiently estimates the resource-agnostic nature of IIoT devices to maximize their resource utilization in the network. Based on the estimated resource requirement, we propose a resource-agnostic microservice offloading scheme to maximize the success rate. Extensive experiments show that RAISE provides better performance in terms of network throughput and Quality-of-Service (QoS) than the other existing methods, SDTO and DTOS, in terms of cost and reliability.

关键词： Unmanned air vehicle   mobile edge computing   task loss rate   multi-agent reinforcement learning  

摘要： In practical systems, a computing task generated by an Internet of Things device (IoTD) is usually given a valid period (vap). The tasks that cannot be executed within the vap will be dropped. The main goal of this letter is to minimize the task loss rate (TLR) in unmanned-air-vehicle (UAV) assisted mobile edge computing (MEC) due to the timeout. Furthermore, to ensure an equal service opportunity for the IoTDs and an equal energy consumption (EC) level for the UAVs, the issues of the TLR-fairness between IoTDs and the EC-fairness between UAVs are also considered. This is formulated as the mixed integer nonlinear programming (MINLP), which is difficult to be addressed by traditional methods, especially when the fast decision-making process is required. To address this problem, we present a new solution based on the multi-agent deep deterministic policy gradient (MA-DDPG) to optimize the flight trajectory, the association between the UAVs and IoTDs and the task scheduling of the IoTDs. Simulation results verify the effectiveness of the proposed MA-DDPG based algorithm.

关键词： UAV   Mobile edge computing   Resource allocation  

摘要： The proliferation of Internet-of-Things (IoTs) devices provides a promising platform for various intelligent applications such as virtual reality. However, because of the limited onboard computation resource of IoT devices, the computation task suffers from long latency. Mobile edge computing (MEC), which is a critical technology, allows offloading the computation tasks to an edge server to alleviate the shortage of computation resources and accelerate the computation tasks of IoT devices. Owing to the flexibility and mobility advantages of unmanned aerial vehicles (UAVs), UAV-assisted MEC has been widely researched. However, existing studies mostly explore a centralized offloading strategy. Therefore, when the number of IoT devices increases, the performance of the centralized strategy is reduced. The present study explores an intelligent strategy to minimize computation latency using a distributed algorithm. We develop a distributed algorithm named DeepMECagent based on deep reinforcement learning to optimize the computation offloading with minimum computation latency. In the considered scenario, a UAV functions as an aerial edge server to collect and process the computation tasks offloaded by ground IoT *** simulation results demonstrate the effective-ness of the proposed approach for minimizing the computation latency, where the computation latency of the proposed algorithm improves 39.71%,87.42%, and 88.55%, respectively, while compared with the centralized-DQN, Q-table, and the random algorithm. Given the expiration time as 1 second, the number of completed tasks within the expiration time of the proposed DeepMECagent is around 2 x and 1.25 x compared with the random algorithm and the Q-table algorithm, respectively.

关键词： association privacy   location privacy   mobile edge computing   privacy protection   task offloading  

摘要： In mobile edge computing (MEC), mobile devices can choose to offload their tasks to edge servers for execution, thereby effectively reducing the completion time of tasks and energy consumption of mobile devices. However, most of the data transfer brought by offloading relies on wireless communication technology, making the private information of mobile devices vulnerable to eavesdropping and monitoring. Privacy leakage, especially the location and association privacies, can pose a significant risk to users of mobile devices. Therefore, protecting the privacy of mobile devices during task offloading is important and cannot be ignored. This paper considers both location privacy and association privacy of mobile devices during task offloading in MEC and targets to reduce the leakage of location and association privacy while minimizing the average completion time of tasks. To achieve these goals, we design a privacy-preserving task offloading scheme to protect location privacy and association privacy. The scheme is mainly divided into two parts. First, we adopt a proxy forwarding mechanism to protect the location privacy of mobile devices from being leaked. Second, we select the proxy server and edge server for each task that needs to be offloaded. In the proxy server selection policy, we make a choice based on the location information of proxy servers, to reduce the leakage risk of location privacy. In the edge server selection strategy, we consider the privacy conflict between tasks, the computing ability, and location of edge servers, to reduce the leakage risk of association privacy plus the average completion time of tasks as much as possible. Simulated experimental results demonstrate that our scheme is effective in protecting the location privacy and association privacy of mobile devices and reducing the average completion time of tasks compared with the-state-of-art techniques.

关键词： Autonomous aerial vehicles   Real-time systems   Sensors   Data acquisition   Servers   Task analysis   Trajectory optimization   data processing   mobile edge computing   trajectory optimization   unmanned aerial vehicles  

摘要： As a promising technology, unmanned aerial vehicles (UAVs)-enabled information gathering has received intensive interest in both academia and industry in recent years. However, the familiar paradigm, namely, data collection can hardly fulfill the active and real-time information gathering demands of many thriving smart applications, such as smart grid, where the UAV needs to serve as an intelligent aerial entity, gathering and analysing the information of surroundings actively and timely. To this aim, this article studies the paradigm data acquisition, where the UAV dynamically gathers information by on-board sensors. We consider a general mission scenario where a UAV acquires data in real time, which needs to be timely processed with the assistance of a mobile edge computing server. To address the real-time characteristics, we construct a novel theoretical data acquisition rate model, based on which the real-time processing quality of requirement (QoR) is defined. We respectively minimize the UAV energy consumption and mission completion time, under the constraints including real-time processing QoR and UAV mobility, by jointly optimizing the UAV trajectory, resources allocation, and time duration. To tackle these highly complex problems, we propose efficient algorithms by means of successive convex approximation and block coordinates descent techniques, where a closed-form solution for the UAV transmission and computation resources allocation is rigorously derived. Moreover, we consider a practical multi-location trajectory optimization where the UAV needs to traverse multiple target locations or areas in addition to the initial and final locations. The simulation results demonstrate the significant system performance gains brought by the proposed design and the effectiveness of the algorithms.

关键词： mobile edge computing   satellite-terrestrial networks   inter-satellite cooperation   offloading decision   resource allocation  

摘要： Mobile edge computing (MEC)-enabled satellite-terrestrial networks (STNs) can provide task computing services for Internet of Things (IoT) devices. However, since some applications' tasks require huge amounts of computing resources, sometimes the computing resources of a local satellite's MEC server are insufficient, but the computing resources of neighboring satellites' MEC servers are redundant. Therefore, we investigated inter-satellite cooperation in MEC-enabled STNs. First, we designed a system model of the MEC-enabled STN architecture, where the local satellite and the neighboring satellites assist IoT devices in computing tasks through inter-satellite cooperation. The local satellite migrates some tasks to the neighboring satellites to utilize their idle resources. Next, the task completion delay minimization problem for all IoT devices is formulated and decomposed. Then, we propose an inter-satellite cooperative joint offloading decision and resource allocation optimization scheme, which consists of a task offloading decision algorithm based on the Grey Wolf Optimizer (GWO) algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method. The optimal solution is obtained by continuous iterations. Finally, simulation results demonstrate that the proposed scheme achieves relatively better performance than other baseline schemes.

关键词： mobile edge computing   partial-di ff erential equations   UAV   polling system   neural   network   machine learning   applied numerical methods  

摘要： As the demand for the internet of things (IoT) continues to grow, there is an increasing need for low-latency networks. Mobile edge computing (MEC) provides a solution to reduce latency by offloading computational tasks to edge servers. However, this study primarily focuses on the integration of back propagation (BP) neural networks into the realm of MEC, aiming to address intricate network challenges. Our innovation lies in the fusion of BP neural networks with MEC, particularly for optimizing task scheduling and processing. Firstly, we introduce a drone-assisted MEC model that categorizes computation offloading into synchronous and asynchronous modes based on task scheduling. Secondly, we employ Markov chains and probability-generation functions to accurately compute parameters such as average queue length, cycle time, throughput, and average delay in the synchronous mode. We also derive the first and second-order derivatives of the probability-generation function to support these computations. Finally, we establish a BP neural network to solve for the average queue length and latency in the asynchronous mode. Our results from the BP neural network closely align with the theoretical values obtained through the probability-generation function, demonstrating the effectiveness of our approach. Additionally, our proposed UAV-assisted MEC model outperforms the synchronous mode. Overall, our MEC scheduling approach significantly reduces latency, enhances speed, and improves throughput, with our model reducing latency by approximately 11.72% and queue length by around 9.45%.