关键词： Sensors   Task analysis   Resource management   Hardware   Delays   Servers   Wireless sensor networks   Integrated sensing and communication   mobile edge computing   resource allocation   action recognition  

摘要： Integrated sensing, computation, and communication (ISCC) has been recently considered as a promising technique for beyond 5G systems. In ISCC systems, the competition for communication and computation resources between sensing tasks for ambient intelligence and computation tasks from mobile devices becomes an increasingly challenging issue. To address it, we first propose an efficient sensing framework with a novel action detection module. In this module, a threshold is used for detecting whether the sensing target is static and thus the overhead can be reduced. Subsequently, we mathematically analyze the sensing performance of the proposed framework and theoretically prove its effectiveness with the help of the sampling theorem. Based on sensing performance models, we formulate a sensing performance maximization problem while guaranteeing the quality-of-service (QoS) requirements of tasks. To solve it, we propose an optimal resource allocation strategy, in which the minimum resource is allocated to computation tasks, and the rest is devoted to the sensing task. Besides, a threshold selection policy is derived and the results further demonstrate the necessity of the proposed sensing framework. Finally, a real-world test of action recognition tasks based on USRP B210 is conducted to verify the sensing performance analysis. Extensive experiments demonstrate the performance improvement of our proposal by comparing it with some benchmark schemes.

关键词： vehicle-to-everything   mobile edge computing   offloading   transmit power   deep reinforcement learning  

摘要： Offloading computation-intensive tasks to mobile edge computing (MEC) servers, such as road-side units (RSUs) and a base station (BS), can enhance the computation capacities of the vehicle-to-everything (V2X) communication system. In this work, we study an MEC-assisted multi-vehicle V2X communication system in which multi-antenna RSUs with liner receivers and a multi-antenna BS with a zero-forcing (ZF) receiver work as MEC servers jointly to offload the tasks of the vehicles. To control the energy consumption and ensure the delay requirement of the V2X communication system, an energy consumption minimization problem under a delay constraint is formulated. The multi-agent deep reinforcement learning (MADRL) algorithm is proposed to solve the non-convex energy optimization problem, which can train vehicles to select the beneficial server association, transmit power and offloading ratio intelligently according to the reward function related to the delay and energy consumption. The improved K-nearest neighbors (KNN) algorithm is proposed to assign vehicles to the specific RSU, which can reduce the action space dimensions and the complexity of the MADRL algorithm. Numerical simulation results show that the proposed scheme can decrease energy consumption while satisfying the delay constraint. When the RSUs adopt the indirect transmission mode and are equipped with matched-filter (MF) receivers, the proposed joint optimization scheme can decrease the energy consumption by 56.90% and 65.52% compared to the maximum transmit power and full offloading schemes, respectively. When the RSUs are equipped with ZF receivers, the proposed scheme can decrease the energy consumption by 36.8% compared to the MF receivers.

关键词： Mobile edge computing   computation offloading   code analysis   object-oriented application   software adaptation  

摘要： Mobile edge computing (MEC) offers a promising technology that deploys computing resources closer to mobile devices for improving performance. Most of the existing studies support on-demand remote execution of the computing tasks in applications through program transformation, but they commonly assume that mobile devices merely resort to a single server for computation offloading, which cannot make full use of the scattered and changeable computing resources. Thus, for object-oriented applications, we propose a novel approach, called FUNOff, to support the dynamic offloading of applications in MEC at the function granularity. First, we extract a call tree via code analysis and locate the function invocations that are suitable for offloading. Next, we refactor the code of related object functions according to a specific program structure. Finally, we make offloading decisions referring to the context at runtime and send function invocations to multiple remote servers for execution. We evaluate the proposed FUNOff on two real-world applications. The results show that, compared with other approaches, FUNOff better supports the computation offloading of object-oriented applications in MEC, which reduces the response time by 10.7%-58.2%.

关键词： Mobile edge computing   Unmanned aerial vehicles   UAV-enabled MEC   Energy efficient   Resource allocation   Trajectory control   Latency   Privacy  

摘要： The huge prospects of the internet of things (IoT) have led to an ever-growing demand for computing power by IoT users to enable various applications. Multi-access edge computing (MEC) research and development has rapidly gained attention during the last decade. The ability to deploy edge servers at different points across a content delivery network that can offer communication and computing services close to mobile user devices is one of the main factors driving the evolution of MEC. Furthermore, MEC has been considered a potentially transformational approach for fifth -generation (5 G) and beyond 5 G (B5G) networks, as well as a potential improvement to conventional cloud computing. Unmanned aerial vehicles (UAVs) can be used as effective aerial platforms to offer reliable and ubiquitous connections in wireless communication networks due to their distinctive qualities, such as high cruising altitude, on -demand deployment, and three-dimensional (3D) maneuverability. The number of research studies published in this area has dramatically increased due to the growing interest in UAV-enabled MEC. Although UAV-enabled MEC systems have been well studied, the existing models are becoming increasingly heterogeneous and scattered without harmony. This paper provides a comprehensive analysis of the literature on UAV-enabled MEC systems with a special focus on the system modeling, and optimization techniques for five identified domains, such as energy efficiency, resource allocation, trajectory control, latency, and security. For each domain, we have highlighted the recent advances, critical findings, and the advantages and disadvantages. Additionally, the identified proposed techniques were analyzed and discussed, with emphasize on the constraints and performance metrics. We also discuss a general system model for each highlighted domain. Moreover, the lessons are also derived from the study on system optimization and system modeling techniques identified in this paper. The

关键词： Mobile edge computing   Unmanned aerial vehicle   Deep reinforcement learning   Offload pairing  

摘要： Dynamically moving Unmanned Aerial Vehicles (UAVs) have emerged as an effective means to significantly enhance the flexibility and transmission performance of mobile edge computing (MEC). However, in practical scenarios, UAVs often face limitations in terms of data storage capacity and computational power. In this paper, a UAV-enabled MEC network with multiple users and multiple edge computing servers is proposed, where the UAV is equipped with limited-size buffers. An optimization problem is formulated to jointly optimize UAV flight trajectories, offload server pairings, task offload ratios, and UAV transmit power to minimize transmission delay, computation delay, and system energy consumption. To tackle the intractable non-convex optimization issue, an intelligent optimization algorithm based on Deep Dueling Double Q-Network (D3QN)-Twin Delayed Deep Deterministic Policy Gradient (TD3) is proposed, which is able to efficiently determine the optimal solution. Simulation results demonstrate that our proposed intelligent algorithm exhibits good convergence and achieves a favorable balance between delay and energy consumption.

关键词： Training   Recommender systems   Computational modeling   Servers   Data models   Data privacy   Federated learning   recommendation system   mobile edge computing   incentive mechanism   game theory  

摘要： With the rapid development of consumer electronics and communication technology, a large amount of data is generated from end users at the edge of the networks. Modern recommendation systems take full advantage of such data for training their various artificial intelligence (AI) models. However, traditional centralized model training has to transmit all the data to the cloud-based servers, which suffers from privacy leakage and resource shortage. Therefore, mobile edge computing (MEC) combined with federated learning (FL) is considered as a promising paradigm to address these issues. The smart devices can provide data and computing resources for the FL and transmit the local model parameters to the base station (BS) equipped with edge servers to aggregate into a global model. Nevertheless, due to the limited physical resources and the risk of privacy leakage, the users (the owners of the devices) would not like to participate in FL voluntarily. To address this issue, we take advantage of game theory to propose an incentive mechanism based on the two-stage Stackelberg game to inspire users to contribute computing resources for FL. We define two utility functions for the users and the BS, and formulate the utility maximization problem. Through theoretical analysis, we obtain the Nash equilibrium strategy of the users and the Stackelberg equilibrium of the utility maximization problem. Furthermore, we propose a game-based incentive mechanism algorithm (GIMA) to achieve the Stackelberg equilibrium. Finally, simulation results are provided to verify the performance of our GIMA algorithm. The experimental results show that our GIMA algorithm converges quickly, and can achieve higher utility value compared to other incentive methods.

关键词： Deep learning   point cloud video   dual-scale pruning   mobile edge computing   transformer  

摘要： 3D point cloud videos are widely used in edge computing scenarios to achieve seamless connectivity and extremely low latency in applications such as autonomous driving. It faces difficulties primarily because point cloud data is often voluminous and contains redundancies. These issues can lead to significant drawbacks in practical applications, causing inference delays and reducing accuracy. To tackle these challenges, we propose a novel approach: a transformer-based spatio-temporal pruning method designed specifically for point cloud videos. Our goal is to expedite network inference. Initially, we utilize the attention mechanism of the transformer to calculate importance scores for different frames and tokens within 3D point cloud videos. To capture the temporal context of each point cloud token, we introduce a frame-wise relation matrix for importance score computation. Subsequently, we use these importance scores as criteria for pruning at both frame and token levels. The experimental results indicate that our approach outperforms other methods. For example, on NTU RGB+D 60 dataset, it shows improvement in accuracy by 0.5% and the average inference time decrease by 28%, while memory consumption is reduced by 28%.

关键词： Task analysis   Massive MIMO   Servers   Optimization   Wireless communication   Energy consumption   Resource management   Cell-free massive MIMO   mobile edge computing   task offloading   optimization  

摘要： A mobile edge computing (MEC) aided cell-free massive MIMO network is investigated in this paper that aims at optimizing the task offloading process from the wireless devices to the MEC server. Partial computation offloading strategies are designed that aim at optimizing either the aggregated latency or the energy consumption by considering the finite computational capability of the MEC server and assuming that the tasks are subject to a maximum latency constraint. The proposed optimization approach considers the joint optimization of both the per-task offloading ratio and the corresponding computational resources allocated to the tasks at the MEC server. This optimization is performed by taking into account the computing capabilities of both the wireless devices and the MEC server and both the UL and DL spectral efficiencies provided by the cell-free massive MIMO network. The proposed optimization problems are shown to be convex and, inspired by the well-known waterfilling algorithm, optimal solutions based on low-complexity iterative algorithms are devised. Extensive numerical results reveal the potential of the proposed MEC-enabled cell-free massive MIMO network.

关键词： Mobile edge computing   unmanned aerial vehicle   resource allocation   task offloading   federated reinforcement learning   digital twin  

摘要： Federated learning (FL) is proposed as a different approach for distributed learning on the edges while maintaining privacy. Existing FL methods, are mainly focused on learning deep classifying and clustering models, with little consideration offered to the federated reinforcement learning (FRL) task on the edge, a difficult task in which several trained agents track local state and take local actions to train a global learning model without disclosing their local dataset. Several neural network models based on FRL have been presented recently to determine the best method for computation offloading (CO) and resource allocation (RA), particularly in Unmanned Aerial Vehicle (UAV) assisted Mobile Edge Computing (MEC). However, because of the complexity and variety of computational tasks involved in 6G and beyond networks, the FRL algorithms are challenging to apply directly to complicated UAV-assisted MEC scenarios. In this study, we present a generalized FRL approach based on a meta learning technique that incorporates RL models explained by numerous smart devices into a generic model. This research uses a normalized characteristic matrix to divide a complex network into small-scale units and provides a normalized network model for complex network situations based on the FRL meta critic method to determine the CO and RA strategy in a Digital Twin (DT)-enabled UAV-assisted MEC system. Numerical results show that the proposed scheme achieves higher and more reliable overall rewards. Proposed method achieves a 73.15% reduction in reward variance and a 14.23% increase in average rewards over 570 continuous operations.

关键词： Task analysis   Servers   Jamming   Delays   Security   Eavesdropping   Wireless communication   Mobile edge computing   service caching   secure computation offloading   cooperative jamming  

摘要： Mobile edge computing (MEC) enables the caching of various services close to users, thereby reducing service delay for emerging applications. However, realizing efficient and secure computation offloading is challenging due to the limited storage capacity of MEC servers and the offloading security issue arising from the open nature of wireless channels. In this letter, we investigate a MEC network with an eavesdropper, where the MEC server caches service programs required for task execution. Moreover, we propose a cooperative jamming-assisted scheme to enhance the security of computation offloading, in which a friendly jammer actively transmits signals to disrupt eavesdropping. Considering the limited caching capacity of the MEC server and the security concerns of computation offloading, we formulate a delay minimization problem by jointly optimizing service caching placement, transmit power, and devices' computation offloading decisions. To tackle this problem, we develop an efficient layered algorithm using successive convex approximation and the cross entropy-based technique. Numerical results demonstrate the effectiveness of our proposed scheme in reducing the overall delay in completing devices' computation tasks compared to baseline schemes.