关键词： Task analysis   Device-to-device communication   Autonomous aerial vehicles   Optimization   Resource management   Servers   Heterogeneous networks   Heterogeneous network   mobile edge computing   potential game   resource management   trajectory planning  

摘要： As user equipments (UEs) and infrastructure evolve, the network architecture becomes increasingly heterogeneous, presenting challenges in managing diverse communication technologies and varied network demands. Recognizing these challenges, the integration of unmanned aerial vehicles (UAVs) and device-to-device (D2D) communication emerges as an effective strategy to enhance network capacity and address the complexities of a heterogeneous network. In this context, mobile edge computing (MEC), facilitated by servers at the network edge, significantly reduces data processing latency and alleviates backhaul congestion. This paper proposes a UAV and D2D-enabled heterogeneous MEC framework that supports two offloading modes for UEs: the first is to offload tasks to UAVs equipped with computing servers, and the second is to offload tasks to nearby idle UEs via D2D links. Unlike existing schemes, the selection of offloading modes is determined by the network load, which improves resource utilization. To further reduce computing latency, we formulate an optimization problem that jointly considers UAV trajectory planning, resource management, and task offloading policy. Since the problem is non-convex and challenging to solve in polynomial time, we decompose it into a joint resource optimization and an offload scheme optimization. Based on this, an optimization algorithm based on block descent and potential game is proposed to obtain the optimal solution. Simulation results indicate that compared to typical UAV-based MEC without D2D, our solution can reduce latency by about 20%. In comparison to equivalent resource allocation, the proposed algorithm can reduce latency by about 25%.

关键词： incentive mechanism   mobile edge computing   privacy protection   sharding blockchain   smart contract  

摘要： Mobile edge computing (MEC) technology is widely used for real-time and bandwidth-intensive services, but its underlying heterogeneous architecture may lead to a variety of security and privacy issues. Blockchain provides novel solutions for data security and privacy protection in MEC. However, the scalability of traditional blockchain is difficult to meet the requirements of real-time data processing, and the consensus mechanism is not suitable for resource-constrained devices. Moreover, the access control of MEC data needs to be further improved. Given the above problems, a data privacy protection model based on sharding blockchain and access control is designed in this paper. First, a privacy-preserving platform based on a sharding blockchain is designed. Reputation calculation and improved Proof-of-Work (PoW) consensus mechanism are proposed to accommodate resource-constrained edge devices. The incentive mechanism with rewards and punishments is designed to constrain node behavior. A reward allocation algorithm is proposed to encourage nodes to actively contribute to obtaining more rewards. Second, an access control strategy using ciphertext policy attribute-based encryption (CP-ABE) and RSA is designed. A smart contract is deployed to implement the automatic access control function. The InterPlanetary File System is introduced to alleviate the blockchain storage burden. Finally, we analyze the security of the proposed privacy protection model and statistics of the GAS consumed by the access control policy. The experimental results show that the proposed data privacy protection model achieves fine-grained control of access rights, and has higher throughput and security than traditional blockchain.

关键词： Task analysis   Energy consumption   Switches   Cloud computing   Array signal processing   Minimization   Optimization   Reconfigurable intelligent surface   mobile edge computing   active-passive mode switching   number configuration  

摘要： Mobile edge computing (MEC) has been regarded as a promising technique to support latency-sensitivity and computation-intensive serves. However, the low offloading rate caused by the random channel fading characteristic becomes a major bottleneck in restricting the performance of the MEC. Fortunately, reconfigurable intelligent surface (RIS) can alleviate this problem since it can boost both the spectrum- and energy- efficiency. Different from the existing works adopting either fully active or fully passive RIS, we propose a novel hybrid RIS in which reflecting units can flexibly switch between active and passive modes. To achieve a tradeoff between the latency and energy consumption, an optimization problem is formulated by minimizing the total cost via jointly optimizing the transmission time, the transmit power, the receive beamforming vector, the phase-shift matrix, the mode-switching factor, the amplification factor, the offloading ratio factor, and the computation ability of the user, where the constraints of the maximum energy of users, the maximum power of the RIS, the minimum computation tasks, the transmission time, the offloading ratio factor, the mode-switching factor, the unit moduli of passive units, and the computation ability are taken into account. Considering the complexity of the aforementioned problem, we develop an alternating optimization-based iterative algorithm by combining the successive convex approximation method, the variable substitution, and the singular value decomposition to obtain sub-optimal solutions. Furthermore, in order to gain more insight into the problem, we consider two special cases involving a latency minimization problem and an energy consumption minimization problem, and respectively analyze the tradeoff between the number of active and passive units. Simulation results verify that the proposed algorithm can achieve flexible mode switching and significantly outperforms existing algorithms.

关键词： Mobile edge computing   Wireless power technology   Deep reinforcement learning   Markov decision process   Non-orthogonal multiple access  

摘要： Wireless powered mobile edge computing (MEC) has become a vital component of future 6G networks, offering efficient computational capabilities to internet of things (IoT) devices and ensuring a stable energy supply. In this paper, we propose an innovative design for a wireless powered MEC-assisted energy-sensitive IoT systems. The system integrates wireless power technology (WPT), allowing IoT devices to efficiently perform tasks under wireless power supply without consuming their battery energy. Therefore, we formulate a dynamic computational task execution latency minimization (DCTELM) problem to tackle the impacts of unpredictable energy requirements and potential communication latency on resource allocation. We also analyze the differences between time division multiple access and non-orthogonal multiple access communication protocols. Given the complexity and dynamic nature of this problem, we define it as a mixed integer nonlinear programming problem. To address this problem, we introduced an enhanced deep reinforcement learning (DRL) algorithm. First, the DCTELM problem is transformed into a Markov decision process (MDP), which simplifies the structure of the problem. Then, after determining the offloading decision, we reveal the convex relationship between resource allocation and task latency. Furthermore, the action space of MDP is further reduced, and a multihead proximal policy optimization (PPO) algorithm is proposed to deal with the simplified MDP problem. This process reduces the complexity of problem solving, decreases the consumption of computational resources. Simulation results demonstrate that our method outperforms other baselines in terms of computation latency, particularly with the introduction of an exploration strategy during the training phase, which significantly reduces task latency.

关键词： Task analysis   Containers   Image edge detection   Internet of Things   Clustering algorithms   Heuristic algorithms   Dynamic scheduling   Container scheduling   mobile edge computing   reinforcement learning  

摘要： In Mobile Edge Computing (MEC), Internet of Things (IoT) devices offload computationally-intensive tasks to edge nodes, where they are executed within containers, reducing the reliance on centralized cloud infrastructure. Cluster software upgrades are essential to maintain the efficient and secure operation of edge clusters. However, traditional cloud cluster upgrade strategies are ill-suited for edge clusters due to their geographically distributed nature and resource limitations. Therefore, it is crucial to properly schedule containers during edge cluster upgrades to minimize the impact on running tasks. This article proposes a latency-aware container scheduling algorithm for efficient edge cluster upgrading. Specifically: 1) We formulate the online container scheduling problem for edge cluster upgrade to minimize the total task latency. 2) We propose a policy gradient-based reinforcement learning algorithm that addresses this problem by considering the characteristics of MEC, including heterogeneous resources, image distribution, and low-latency requirements. Subsequently, a location feature extraction method based on self-attention is designed to fully extract and utilize edge node distribution. 3) Experiments based on simulated and real-world data traces demonstrate that our algorithm reduces total task latency by approximately 30% compared to baseline algorithms.

关键词： capsule network   deepfake detection   forgery classification   mobile edge computing   multi-scale feature  

摘要： Face recognition is one of the most important applications of MEC. However, there have been many fake face data that can deceive MEC devices, causing serious problems such as information leakage. Face forgery detection can effectively solve this problem. Current face forgery detection methods have achieved high accuracy. However, most of the methods are researched on the classification of face authenticity. We find that studying multi-classification of forgery methods can not only improve the accuracy of the model to identify fake faces, but also help improve the generalization ability of fake face classification. We argue that multi-scale features and high-frequency features can expose more detailed forgery artifacts. So, we design four modules, which take advantage of the complementarity of RGB features and frequency features, global features and local features. The first module is a residual-guided multi-scale spatial attention module, which uses residuals to guide the RGB feature extractor to extract fake features from a multi-scale perspective. The second module is a multi-scale retinal feature extraction module. The third module is a multi-scale channel attention-guided local frequency statistics module, which extracts local frequency responses using sliding-window DCT. The last module is a capsule network classification module with overall correlation to classify the fused features. The information transfer between the subject capsule and the classification capsule can increase the integrity of the model, making the model converge faster. We conduct experiments on the databases FaceForensics++, DeepfakeDetection, and FakeAVCeleb. Experimental result shows that our method performs well on forgery classification.

关键词： mobile edge computing   differential privacy   location privacy protection   deep reinforcement learning  

摘要： Mobile Edge Computing (MEC) has been widely applied in various Internet of Things (IoT) scenarios due to its advantages of low latency and low energy consumption. However, the offloading of tasks generated by terminal devices to edge servers inevitably raises privacy leakage concerns. Given the limited resources in MEC networks, this paper proposes a task scheduling strategy, named DQN-DP, to minimize location privacy leakage under the constraint of offloading costs. The strategy is based on a differential privacy location obfuscation probability density function. Theoretical analysis demonstrates that the probability density function employed in this study is valid and satisfies & varepsilon;-differential privacy in terms of security. Numerical results indicate that, compared to existing baseline approaches, the proposed DQN-DP algorithm effectively balances privacy leakage and offloading cost. Specifically, DQN-DP reduces privacy leakage by approximately 20% relative to baseline approaches.

关键词： Task analysis   Resource management   Autonomous aerial vehicles   Servers   Computer architecture   Sun   Optimization   Mobile edge computing   post-disaster rescue   task offloading   UAV communication   vehicle fog computing  

摘要： Unmanned aerial vehicles (UAVs) are playing an increasingly important role in assisting fast-response post-disaster rescue due to their fast deployment, flexible mobility, and low cost. However, UAVs face the challenges of limited battery capacity and computing resources, which could shorten the expected flight endurance of UAVs and increase the rescue response delay during performing mission-critical tasks. To address these challenges, we first present a three-layer post-disaster rescue computing architecture by leveraging the aerial-terrestrial edge capabilities of mobile edge computing (MEC) and vehicle fog computing (VFC), which consists of a vehicle fog layer, a UAV client layer, and a UAV edge layer. Moreover, we formulate a joint task offloading and resource allocation optimization problem (JTRAOP) with the aim of maximizing the time-average system utility. Since the formulated JTRAOP is proved to be NP-hard, we propose an MEC-VFC-aided task offloading and resource allocation (MVTORA) approach, which consists of a game theoretic algorithm for task offloading decision, a convex optimization-based algorithm for MEC resource allocation, and an evolutionary computation-based hybrid algorithm for VFC resource allocation. Simulation results validate that the proposed approach can achieve superior system performance compared to alternative approaches, especially under heavy system workloads.

关键词： Mobile edge computing   Mobility awareness   Mobility management   Service migration   6G network  

摘要： The evolution of science has given rise to many technologies that have changed the world. The upcoming SixGeneration (6G) mobile network indicates a fundamental transformation in wireless technologies, enhancing connectivity and data transmission rates. In this circumstance, Mobile Edge Computing (MEC) is a paradigm technology that emerges as a key major supporter of enhancing mobility awareness. Edge computing offers improved efficiency for service migration from the edge node to the user. However, mobility management in MEC is a complex challenge as seamless handovers between edge nodes must be efficiently executed to ensure uninterrupted service for mobile devices, demanding intricate coordination and low-latency decision-making. To the best of the author's knowledge, there has been no comprehensive work on the most recent developments in mobility awareness using mobile edge computing in 6G. However, this paper aims to present a general overview of the intersection between mobility awareness and MEC over 6G networks. The general concept of MEC in 6G mobile networks is comprehensively introduced. This will highlight the integration between MEC and 6G for bringing more efficient network and service migration to the edge, reducing latency, and enhancing the user experience. Meanwhile, this survey discusses augmented reality with MEC applications. This survey discusses the integration of mobility awareness and mobile edge computing in upcoming mobile applications and emphasizes the need for 6G networks. This integration results in providing seamless communication during handovers between the serving base station and the target base station. This study contributes to understanding the upcoming trends that will enable the operation of mobility awareness and MEC operation in the 6G mobile communication. Furthermore, we delve into a comprehensive overview of the challenges and future research directions for mobility management with MEC in 6G mobile networks, underlining

关键词： deep reinforcement learning   local differential privacy   machine learning   mobile edge computing   task offloading  

摘要： As machine learning (ML) technologies continue to evolve, there is an increasing demand for data. Mobile crowd sensing (MCS) can motivate more users in the data collection process through reasonable compensation, which can enrich the data scale and coverage. However, nowadays, users are increasingly concerned about their privacy and are unwilling to easily share their personal data. Therefore, protecting privacy has become a crucial issue. In ML, federated learning (FL) is a widely known privacy-preserving technique where the model training process is performed locally by the data owner, which can protect privacy to a large extent. However, as the model size grows, the weak computing power and battery life of user devices are not sufficient to support training a large number of models locally. With mobile edge computing (MEC), user can offload some of the model training tasks to the edge server for collaborative computation, allowing the edge server to participate in the model training process to improve training efficiency. However, edge servers are not fully trusted, and there is still a risk of privacy leakage if data is directly uploaded to the edge server. To address this issue, we design a local differential privacy (LDP) based data privacy-preserving algorithm and a deep reinforcement learning (DRL) based task offloading algorithm. We also propose a privacy-preserving distributed ML framework for MEC and model the cloud-edge-mobile collaborative training process. These algorithms not only enable effective utilization of edge computing to accelerate machine learning model training but also significantly enhance user privacy and save device battery power. We have conducted experiments to verify the effectiveness of the framework and algorithms.