关键词： Autonomous aerial vehicles   Optimization   Task analysis   Energy consumption   Bandwidth   Quality of experience   Trajectory planning   Lyapunov optimization   mobile edge computing   unmanned aerial vehicle   successive convex approximation  

摘要： This letter aims to minimize the summation energy consumption of user equipments under controllable task backlog queues with unmanned aerial vehicles (UAV)-assisted mobile edge computing system. Based on Lyapunov optimization, the original stochastic optimization problem can be transformed into a deterministic optimization problem, and system stability is also ensured. To address this nonconvex problem, we employ alternating optimization to achieve the near-optimal transmit power, bandwidth ratio, and trajectory planning, respectively. Furthermore, considering the accuracy of the solution and algorithm complexity, we propose a two-stage operating mode for the UAV based on a recursive successive convex approximation method to optimize trajectory. Finally, the convergence and effectiveness of the proposed algorithm are validated through simulation results.

关键词： unmanned aerial vehicle   mobile edge computing   UAV-aided edge computing   task offloading   offloading decision   trajectory planning  

摘要： Recently, the integration of unmanned aerial vehicles (UAVs) with edge computing has emerged as a promising paradigm for providing computational support for Internet of Things (IoT) applications in remote, disaster-stricken, and maritime areas. In UAV-aided edge computing, the offloading decision plays a central role in optimizing the overall system performance. However, the trajectory directly affects the offloading decision. In general, IoT devices use ground offload computation-intensive tasks on UAV-aided edge servers. The UAVs plan their trajectories based on the task generation rate. Therefore, researchers are attempting to optimize the offloading decision along with the trajectory, and numerous studies are ongoing to determine the impact of the trajectory on offloading decisions. In this survey, we review existing trajectory-aware offloading decision techniques by focusing on design concepts, operational features, and outstanding characteristics. Moreover, they are compared in terms of design principles and operational characteristics. Open issues and research challenges are discussed, along with future directions.

关键词： Internet of vehicle   Mobile edge computing   Offloading strategy   Latency   Energy consumption   Smart IoV  

摘要： The current task offloading technique needs to be performed more effectively. Onboard terminals cannot execute efficient computation due to the explosive expansion of data flow, the quick increase in vehicle population, and the growing scarcity of spectrum resources. As a result, this study suggests a task-offloading technique based on reinforcement learning computing for the Internet of Vehicles edge computing architecture. The system framework for the Internet of Vehicles has been initially developed. Although the control centre gathers all vehicle information, the roadside unit collects vehicle data from the neighborhood and sends it to a mobile edge computing server for processing. Then, to guarantee that job dispatching in the Internet of Vehicles is logical, the computation model, communications approach, interfering approach, and concerns about confidentiality are established. This research examines the best way to analyze and design a computation offloading approach for a multiuser smart Internet of Vehicles (IoV) based on mobile edge computing (MEC). We present an analytical offloading strategy for various MEC networks, covering one-to-one, one-to-two, and two-to-one situations, as it is challenging to determine an analytical offloading proportion for a generic MEC-based IoV network. The suggested analytic offload strategy may match the brute force (BF) approach with the best performance of the Deep Deterministic Policy Gradient (DDPG). For the analytical offloading design for a general MEC-based IoV, the analytical results in this study can be a valuable source of information.

关键词： Federated deep reinforcement learning   mobile edge computing   multiple base stations collaboration   task offloading   user mobility  

摘要： With the proliferation of the Internet of Things (IoT), mobile edge computing (MEC) has great potential to achieve low latency, high reliability, and low energy consumption. However, in collaborative MEC environments, user movement and task migration may cause task transmission and processing delays, resulting in elevated task response times. Therefore, system performance and user experience need to be ensured by rational task offloading and resource management. At the same time, the protection of user data privacy is becoming increasingly important as a challenge to be overcome. To address the problems of intense resource competition and privacy leakage in MEC, the federated learning for the TD3-based task offloading (FedTO) algorithm is proposed. The algorithm has a dual objective of energy consumption and task response time while protecting user privacy. It employs a cryptographic local model update and aggregation mechanism and uses deep reinforcement learning (DRL) to obtain an efficient task offloading decision. Based on the mobile trajectories of real devices, and the pre-deployment of base station locations, experimental results show that the FedTO algorithm ensures task data security. It also effectively reduces the total energy consumption and average task response time of the system, which further improves the system utility.

关键词： Mobile edge computing   Service offloading   Deep reinforcement learning  

摘要： Computing offloading is a potential avenue to reduce transmission delay by moving computing tasks from cloud to edge nodes. Due to the limited computing capacity of edge nodes, performing effective computing offloading strategy is challenging. To simplify the problem, it is often assumed that the timeline is discrete and offloading decisions are only made at the end of each time slot. However, the assumption results in decision waiting time and increases service delay. In this paper, we first formulate a time-continuous computing offloading problem without the assumption of discrete timeline. In order to reduce service delay while guaranteeing user fairness, the optimization objective is constructed as the alpha-fair utility function of average task delay rather than just average task delay. Then, we propose a novel algorithm by adjusting the reward in standard reinforcement learning to solve this problem, and also prove the convergence of our algorithm theoretically. To the best of our knowledge, this paper is the first attempt to study the time-continuous computing offloading problem with fairness. Evaluations show that the proposed algorithm has better performance in terms of service delay and user fairness compared to five baselines.

关键词： Mobile edge computing   Multi-task multi-server   Non-orthogonal multiple access   Processing capability   Resource allocation   Task offloading  

摘要： With the rapid development of technology, the Internet of vehicles (IoV) has become increasingly important. However, as the number of vehicles on highways increases, ensuring reliable communication between them has become a significant challenge. To address this issue, this paper proposes a novel approach that combines Non-Orthogonal Multiple Access (NOMA) with a time-optimized multitask offloading model based on Optimal Stopping Theory (OST) principles. NOMA-OST is a promising technology that can address the high volume of multiple access and the need for reliable communication in IoV. A NOMA-OST-based IoV system is proposed to meet the Vehicle-to-Vehicle (V2V) communication requirements. This approach optimizes joint task offloading and resource allocation for multiple users, tasks, and servers. NOMA enables efficient resource sharing by accommodating multiple devices, whereas OST ensures timely and intelligent task offloading decisions, resulting in improved reliability and efficiency in V2V communication within IoV, making it a highly innovative and technically robust solution. It suggests a low-complexity sub-optimal matching approach for sub-channel allocation to increase the effectiveness of offloading. Simulation results show that NOMA with OST significantly improves the system's energy efficiency (EE) and reduces computation time. The approach also enhances the effectiveness of task offloading and resource allocation, leading to better overall system performance. The performance of NOMA with OST under V2V communication requirements in IoV is significantly improved compared to traditional orthogonal multiaccess methods. Overall, NOMA with OST is a promising technology that can address the high reliability of V2V communication requirements in IoV. It can improve system performance, and energy efficiency and reduce computation time, making it a valuable technology for IoV applications.

关键词： Internet of Things   Mobile edge computing   Lyapunov optimization theory   Deep reinforcement learning   Smart buildings  

摘要： The Internet of Things (IoT) has become an infrastructure that makes smart cities possible. is both accurate and efficient. The intelligent production industry 4.0 period has made mobile edge computing (MEC) essential. Computationally demanding tasks can be delegated from the MEC server to the central cloud servers for processing in a smart city. This paper develops the integrated optimization framework for offloading tasks and dynamic resource allocation to reduce the power usage of all Internet of Things (IoT) gadgets subjected to delay limits and resource limitations. A Federated Learning FL-DDPG algorithm based on the Deep Deterministic Policy Gradient (DDPG) architecture is suggested for dynamic resource management in MEC networks. This research addresses the optimization issues for the CPU frequencies, transmit power, and IoT device offloading decisions for a multi-mobile edge computing (MEC) server and multi-IoT cellular networks. A weighted average of the processing load on the central MEC server (PMS), the system's overall energy use, and the task-dropping expense is calculated as an optimization issue. The Lyapunov optimization theory formulates a random optimization strategy to reduce the energy use of IoT devices in MEC networks and reduce bandwidth assignment and transmitting power distribution. Additionally, the modeling studies demonstrate that, compared to other benchmark approaches, the suggested algorithm efficiently enhances system performance while consuming less energy.

关键词： Data integrity   Data privacy   Servers   Multi-access edge computing   Image edge detection   Electronic mail   Costs   Edge data integrity (EDI)   mobile edge computing   per-edge one-round   minimal verification tree  

摘要： Ensuring edge data integrity is crucial for reliable and secure low-latency services in mobile edge computing. However, existing edge data integrity (EDI) schemes suffer from certain limitations. These include the generation of excessive communication overhead and significant time consumption due to multi-round communications between application vendors and individual edge servers during the integrity verification process. Additionally, there is a notable straggler effect when employing batch verification techniques. To overcome these challenges, this paper presents a pioneering solution scheme for per-edge one-round EDI, referred to as OR-EDI. The proposed scheme effectively mitigates communication overhead, reduces time consumption, and eliminates the straggler effect, while simultaneously maintaining a high level of corruption detection accuracy. To implement OR-EDI, we introduce a novel verification structure known as the Minimal Verification Tree (MVT) and devise algorithms for acquiring Auxiliary Verification Information (AVI) and constructing the MVT. To comprehensively evaluate the performance of OR-EDI, we conduct a rigorous analysis that encompasses its correctness, efficiency, and security aspects. Furthermore, we implement the proposed scheme and perform a comparative assessment against existing EDI schemes, presenting extensive experimental results. These results affirm the effectiveness and efficiency of OR-EDI in effectively addressing the challenges associated with EDI.

关键词： Task analysis   Data models   Cloud computing   Training data   Training   Computational modeling   Costs   Mobile cloud computing   Mobile edge computing   computation offloading   deep learning   incremental learning  

摘要： MobileCloud Computing (MCC) provides computing, storage, and other fruitful services to end users. Offloading such tasks to cloud servers can help to fulfill the demands of extensive computing resources, but may also lead to network congestion and high latency. Mobile Edge Computing (MEC) places the computing nodes near the end users to enable low-latency services, whereas it cannot execute too many computing tasks due to limited computing resources. Therefore, MCC and MEC are highly complementary. For computing offloading problems in a collaborative cloud-edge environment, traditional optimization algorithms require multiple iterations to obtain results, which leads to excessive time spent to obtain offloading strategies. Deep Neural Network (DNN) based offloading algorithms can provide low latency offloading strategies, but training data is difficult to be obtained and the cost of retraining is too high. Therefore, in this article, we adopt an incremental training method to overcome the problem of insufficient training data and high retraining costs in DNN-based offloading algorithms. An incremental DNN-based computation offloading (MEDIA) algorithm is proposed to derive near-optimal offloading strategies for collaborative cloud-edge computing. The task information on the real scenarios is sent to the central cloud to generate training data, and the powerful computing resources of the central cloud improve the efficiency of training model. The continuous incremental training can maintain a high accuracy of the DNN model and reduce the time for training the model. The evaluation results demonstrate that the proposed algorithm substantially reduces the cost for updating the model without loss of performance.

关键词： Task analysis   Privacy   Servers   Delays   Wireless communication   Costs   Perturbation methods   Differential privacy   location privacy   mobile edge computing   task offloading  

摘要： In mobile edge computing (MEC), users can offload tasks to nearby MEC servers to reduce computation cost. Considering that the size of offloaded tasks could disclose user location information, several location privacy-preserving task offloading mechanisms have been proposed under the single-server scenario. However, to the best of our knowledge, none of them could provide a strict privacy protection guarantee or be applicable to the multi-server scenario where the user's location can be inferred more accurately if servers collude with each other. In this paper, we propose a novel location privacy-aware task offloading framework (LPA-Offload) for both single-server and multi-server scenarios, which provides strict and provable location privacy protection while achieving efficient task offloading. Specifically, we propose a location perturbation mechanism that allows each user to perturb its real location within a rational perturbation region and provides a differential privacy guarantee. To make a satisfactory offloading strategy, we propose a perturbation region determination mechanism and an offloading strategy generation mechanism that adaptively select a proper perturbation region according to the customized privacy factor, and then generate an optimal offloading strategy based on the perturbed location within the decided region. The determination of the perturbation region could achieve personalized privacy requirements while reducing computation cost. LPA-Offload is proved to satisfy (epsilon,delta)-differential privacy, and the experiments demonstrate the effectiveness of our framework.