关键词： Semantics   Jamming   Wireless communication   Heuristic algorithms   Resource management   Computational modeling   Autonomous aerial vehicles   Semantic communication   unmanned aerial vehicle   mobile edge computing   deep reinforcement learning   resource management   anti-jamming  

摘要： The integration of semantic communication with mobile edge computing (MEC) has emerged as a prominent research area. In this paper, we explore a novel scenario where semantic communication is integrated with unmanned aerial vehicles (UAVs) to enhance MEC, particularly in the face of jamming attacks. Our research focuses on addressing the resource management challenge to minimize task completion time and maximize semantic spectral efficiency (SSE) while adhering to quality of service requirements and resource constraints. Given the non-convexity of this problem and the dynamic behavior of jamming attacks, this paper proposes a deep reinforcement learning (DRL) algorithm by jointly optimizing UAV trajectories, user associations, and channel selections against jamming. In detail, the proposed anti-jamming DRL-based resource management approach can effectively capture the jammer's behavior, and learn to adjust semantic task and resource scheduling strategies with the objective to minimize the negative effect of jamming attacks on task offloading and semantic communication. Simulation results demonstrate that the proposed approach outperforms baseline algorithms in terms of task completion time and total SSE under different real-world settings.

关键词： Mobile handsets   Computational modeling   Servers   Artificial intelligence   Quality of experience   Artificial neural networks   Accuracy   DNN model partitioning   deep learning   mobile edge computing   mobile vision application   quality of experience  

摘要： Recently, applications using artificial intelligence (AI) technique in mobile devices such as augmented reality have been extensively pervasive. The hardware specifications of mobile devices, dynamic service demands, stochastic network states, and characteristics of DNN (Deep Neural Network) models affect the quality of experience (QoE) of such applications. In this paper, we propose CutEdge , that leverages a virtual queue-based Lyapunov optimization framework to jointly optimize DNN model partitioning between a mobile device and a mobile edge computing (MEC) server and processing/networking resources in a mobile device with respect to internal/external system dynamics. Specifically, CutEdge makes decisions of (i) the partition point of DNN model between the mobile device and MEC server, (ii) GPU clock frequency, and (iii) transmission rates in a mobile device, simultaneously. Then, we theoretically show the optimal trade-off curves among energy consumption, throughput, and end-to-end latency yielded by CutEdge where such QoE metrics have not been jointly addressed in the previous studies. Moreover, we show the impact of joint optimization of three control parameters on the performances via real trace-driven simulations. Finally, we show the superiority of CutEdge over the existing algorithms by experiment on top of implemented testbed using an embedded AI device and an MEC server.

关键词： UAV-enabled wireless communications   A2A   RSSI   path loss   ground reflection   mobile edge computing  

摘要： Unmanned aerial vehicle (UAV)-enabled wireless communications are becoming increasingly important in applications such as maritime and forest rescue operations. UAV systems often depend on wireless networking and mobile edge computing (MEC) devices for effective deployment, particularly in swarm UAV-enabled MEC configurations focusing on channel modeling and path loss characteristics for air-to-air (A2A) communications. This paper examines path loss characteristics in far-field (FF) ground reflection scenarios, specifically comparing two environments: FF1 (forest floor) and FF2 (seawater floor). LoRa modules operating at 868 MHz were deployed for communication between a transmitting UAV (Tx-UAV) and a receiving UAV (Rx-UAV) to conduct this study. We investigated the received signal strength indicator (RSSI) and path loss characteristics across channel bandwidths of 125 kHz and 250 kHz and spread factors (SF) of 7, 9, and 12. Experimental results show that ground reflection has minimal impact in the FF1 scenario, whereas, in the FF2 scenario, ground reflection significantly influences communication. Therefore, in the seawater environment, a UAV-enabled LoRa MEC configuration using a 250 kHz bandwidth and an SF of 7 is recommended to minimize the effects of ground reflection.

关键词： Servers   Costs   Heuristic algorithms   Base stations   Optimization   Clustering algorithms   Mobile handsets   Telecommunications   Symbols   Programming   Mobile edge computing   profit-driven optimization   reinforcement learning   integer nonlinear programming  

摘要： Edge computing has emerged as a promising paradigm to meet the increasing demands of latency-sensitive and computationally intensive applications. In this context, efficient server deployment and service placement are crucial for optimizing performance and increasing platform profit. This paper investigates the problem of server deployment and service placement in a multi-user scenario, aiming to enhance the profit of Mobile Network Operators while considering constraints related to distance thresholds, resource limitations, and connectivity requirements. We demonstrate that this problem is NP-hard. To address it, we propose a two-stage method to decouple the problem. In stage I, server deployment is formulated as a combinatorial optimization problem within the framework of a Markov Decision Process (MDP). We introduce the Server Deployment with Q-learning (SDQ) algorithm to establish a relatively stable server deployment strategy. In stage II, service placement is formulated as a constrained Integer Nonlinear Programming (INLP) problem. We present the Service Placement with Interior Barrier Method (SPIB) and Tree-based Branch-and-Bound (TDB) algorithms and theoretically prove their feasibility. For scenarios where the number of users changes dynamically, we propose the Distance-and-Utilization Balance Algorithm (DUBA). Extensive experiments validate the exceptional performance of our proposed algorithms in enhancing the profit.

关键词： Unmanned aerial vehicle   Mobile edge computing   Evolutionary algorithm   Autonomous system  

摘要： In the domain of 5G/6G wireless communications, mobile edge computing (MEC) technology is widely utilized for efficient data transmission. Unmanned aerial vehicles (UAVs) have emerged as the most recent transmission carriers in this landscape. However, the challenges associated with UAV deployment and path planning are often regarded as NP-hard, nonconvex, and nonlinear problems. Traditional optimization techniques struggle to address these complexities effectively. To address this issue, this study proposes "Edge-UAV", a novel mobile edge computing system specifically designed for UAVs. The primary objective of Edge-UAV is to minimize energy consumption and optimize the operational efficiency of UAVs. To achieve this goal, this study introduces a two-stage optimization strategy and a nonhovering transmission strategy. First, the coordinate updating operation of the UAVs' hovering points is decoupled from the path planning task. Additionally, the perturbation-inheritance algorithm is employed to enhance the coordinate updating process. In the second stage, the nonhovering transmission strategy enables UAVs to perform data transmission tasks while in flight, effectively optimizing their working time. This paper provides a detailed elucidation of the roles played by these innovative strategies within the Edge-UAV system. To assess the superiority of the proposed strategies, eight sets of comparative experiments involving 60-200 individual devices awaiting data transmission are conducted. The experimental results demonstrate the significant advantages of the Edge-UAV system in terms of reducing total energy consumption and optimizing the operational hours of UAVs. Through comprehensive experimentation and analysis, this study contributes to the advancement of UAV-assisted mobile edge computing in 5G/6G wireless communication networks. The proposed strategies exhibit promising potential for enhancing the efficiency and performance of UAVs.

关键词： Mobile edge computing   Smart driving   Task offloading   Mobility management   Load balancing  

摘要： With the continuous popularization and evolution of 5G and 6G, mobile edge computing has achieved rapid development. This study explores the New Generation Mobile Edge Computing (NGMEC) architecture, which leverages numerous mobile nodes to provide users with enhanced computing services. Despite its advantages, NGMEC faces challenges such as high node mobility, load balancing difficulties, and incomplete environmental perception by agents, particularly in intelligent driving task offloading scenarios. We address these challenges by introducing novel applications of NGMEC and proposing specialized algorithms for node selection and load balancing. Furthermore, to tackle the issue of environmental perception incompleteness in NGMEC task offloading, we develop the Gated Recurrent Self -Encoding Deep Reinforcement Learning (GRSE-DRL) algorithm. Our research also includes the development of two platforms: the End -Edge -Cloud Simulation Experiment Platform and the Edge Computing Offloading Algorithm Energy Efficiency Test Platform. Experimental results demonstrate that our proposed scheme effectively maintains load balance among nodes, enhances task completion and connection success rates, and optimizes the trade-off between transmission delay and intelligent driving algorithms' effectiveness, enabling more efficient decision -making.

关键词： Computational modeling   Internet of Things   Heuristic algorithms   Optimization   Servers   Delays   Cloud computing   Training   Processor scheduling   Privacy   FDAHO   mobile edge computing   multi-objective optimization   task offloading  

摘要： The widespread adoption of the Internet of Things (IoT) has increased demand for task processing via mobile edge computing (MEC). In this study, we designed a directed acyclic graph (DAG) task offloading workflow in MEC. Traditional task offloading often does not simultaneously take into account task upload delay and task communication delay, failing to accurately reflect real-world issues. The constraints between task execution delay, upload delay and communication delay were introduced to model system response time and energy consumption for optimization. To satisfy task dependencies, the edge rank_u sorting (ERS) algorithm is used to generate specific offloading queues. A federated deep q-network (FDQN) algorithm addresses the offloading issue. It is different from the traditional approach of uploading task information data to the edge and facing data privacy risks. FDQN deploies the model locally and only collects model parameters for aggregation to update the local model. The algorithm improves the performance and stability of the model while protecting user privacy. To automatically tune hyperparameters for multiple devices, we used the tree of parzen estimators (TPE) algorithm, and named the whole process federated DQN with automated hyperparameter optimization (FDAHO). Experimental results show that FDAHO outperforms other algorithms in scenarios of different task number, task types, and user numbers, with consideration of benchmarks.

关键词： IIoT   mobile edge computing   multi-agent deep reinforcement learning   attention mechanism   IIoT   mobile edge computing   multi-agent deep reinforcement learning   attention mechanism  

摘要： The widespread adoption of Industrial Internet of Things (IIoT) has significantly transformed various aspects of industrial manufacturing. However, the massive volume and complexity of IIoT data highlight the need for innovative solutions to enhance the overall performance of IIoT systems. In this regard, mobile edge computing, assisted by deep reinforcement learning, can alleviate the burden on IIoT systems through computation offloading. Nevertheless, in increasingly digitized industrial environments, how to make real-time, efficient task offloading decisions remains a subject of deep exploration. To address this issue, we propose a two-stage resource allocation and task offloading method, named MCORM. In the first stage, we use the Combinatorial Upper Confidence Bound algorithm, based on the combinatorial multi-armed bandit problem, to solve the resource allocation problem. In the second stage, the Multi-Agent Proximal Policy Optimization algorithm is employed to determine the approximate optimal offloading strategy. Specifically, the Convolutional Block Attention Module is utilized to process observation information, focusing on important features. Finally, extensive experiments are conducted using both simulated and real datasets. The results demonstrate that our proposed algorithm MCORM can reduce latency and energy consumption effectively, promoting efficient industrial production.

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

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： Protocols   Autonomous aerial vehicles   Security   Authentication   Task analysis   Servers   Real-time systems   Mobile edge computing   mutual authentication   self-certified public key cryptosystem   security analysis   UAV  

摘要： Unmanned Aerial Vehicle(UAV)-assisted mobile edge computing(MEC) ensures continuous MEC services by promptly restoring overloaded or disabled MEC infrastructure. Equipped with sufficient computing resources, UAVs deploy to areas needing MEC, such as task offloading and entertainment services. However, in areas with paralyzed edge nodes, vehicle users are unable to verify the legitimacy of UAVs as they cannot access to the trusted authority(TA). Furthermore, the integrity of UAV-assisted MEC environments can be compromised by malicious attackers, as all network participants rely on wireless communication for their interactions. Many authentication schemes have been proposed for UAV environments. However, these schemes encounter a problem of having to communicate through TA for authentication with vehicle users, which makes them difficult to apply to UAV-assisted MEC environments. Therefore, we propose a new broadcast authentication protocol, which can recover MEC services using MEC-equipped UAVs. The proposed protocol can provide UAVs and vehicle users with high reliability via a self-certified public-key cryptosystem, which can verify the legitimacy of the communication partner without the TA. Moreover, we guarantee the user privacy and preserve sensitive information using biohash technology. We verify the security robustness of the proposed protocol using various simulation tool, informal, and formal analyses. We also estimate the practical deployment of the proposed protocol using "Network Simulator-3". Moreover, we estimate the computation and communication overheads and compare with other existing protocols. The results show that the proposed protocol is feasible and provides users with convenient and seamless intelligent transportation services in UAV-assisted MEC environments.