关键词： Energy efficiency   Deep reinforcement learning   Computation offloading   Mobile edge computing   Unmanned aerial vehicles  

摘要： Industrial Internet of Things (IIoT) has been envisioned as a killer application of 5G and beyond. However, due to the shortness of computation ablility and batery capacity, it is challenging for IIoT devices to process latency-sensitive and resource-sensitive tasks. Moblie Edge Computing (MEC), as a promising paradigm for handling tasks with high quality of service (QoS) requirement and for energy-constrained IIoT devices, allows IIoT devices to offload their tasks to MEC servers, which can significantly reduce the task process delay and energy consumptions. However, the deployment of the MEC servers rely heavily on communication infrastructure, which greatly reduce the flexibility. Toward this end, in this paper, we consider multiple Unmanned Aerial Vehicles (UAV) eqqipped with transceivers as aerial MEC servers to provide IIoT devices computation offloading opportunities due to their high controbility. IIoT devices can choose to offload the tasks to UAVs through air-ground links, or to offload the tasks to the remote cloud center through ground cellular network, or to process the tasks locally. We formulate the multi-UAV-Enabled computation offloading problem as a mixed integer non-linear programming (MINLP) problem and prove its NP-hardness. To obtain the energy-efficient and low complexity solution, we propose an intelligent computation offloading algorithm called multi-agent deep Q-learning with stochastic prioritized replay (MDSPR). Numerical results show that the proposed MDSPR converges fast and outperforms the benchmark algorithms, including random method, deep Q-learning method and double deep Q-learning method in terms of energy efficiency and task successful rate.

关键词： Autonomous driving   Task offloading   Mobile edge computing  

摘要： With the increasing complexity of autonomous driving tasks, the computational demands on single vehicular computing units have escalated, more and more tasks need to be offloaded to the edge. These tasks vary in latency sensitivity: real-time tasks, critical for passenger safety, require strict deadline adherence, whereas the latency of standard tasks mainly affects the user experience and has more flexible constraints. Addressing the challenge of selecting suitable edge computing nodes to enhance the offloading success rate of real-time tasks amidst a vast and heterogeneous cluster becomes crucial. This paper introduces the adaptive priority-based hierarchical task offloading (APHTO) algorithm, which optimizes task offloading strategies by accounting for the diverse latency constraints of different task types. Experiments demonstrate that under optimal performance conditions, APHTO significantly outperforms existing algorithms such as Min-Min, Max-Min, CUS, and FMS in reducing task latency by 20.31%, increasing offloading success rates by 35.83%, and improving resource utilization by 30.21%, marking a substantial advancement in task offloading strategies for autonomous driving integrated with MEC.

关键词： Sensors   Radar   Reliability   Interference   Performance evaluation   Wireless communication   Resource management   Mobile edge computing   integrated sensing and communication   short-packet transmissions  

摘要： Integrated sensing and communication (ISAC) provides an emerging paradigm for enabling a variety of next-generation wireless services and applications. Due to the limited computation resources on ISAC devices and the latency as well as the reliability requirements, we propose a paradigm of mobile edge computing (MEC) aided ISAC with short-packet transmissions, where multiple ISAC devices adopt short-packet transmissions to offload their sensed radar data to an edge-server for analysis. We adopt the mutual information to measure the performance of radar sensing and quantify the reliability and latency performances for analyzing the radar-data via edge computing. We formulate an energy minimization problem that jointly optimizes the size of each short packet, the duration of each short packet, the computing-capacity allocations of edge-server, the beamforming of the radar sensing and the offloading transmission, while providing guaranteed performances for the radar sensing, the latency for radar-data analysis, and the reliability of offloading transmission. We identify the hierarchical structure of the formulated problem and divide the problem into three subproblems. For both the bottom-layer problem optimizing the computing-capacity allocations of the edge-server and the middle-layer problem optimizing the size of each short packet and the duration of each short packet, we derive their solutions analytically. Finally, for the top-layer problem optimizing the beamforming of the radar sensing and the offloading transmission, we transform it into a difference of convex (DC) problem which can be efficiently solved. We show the performance advantages of our proposed scheme. The simulation results show that our proposed algorithm can outperform the benchmark algorithms.

关键词： Edge computing   Delays   Serverless computing   Task analysis   Computational modeling   Costs   Processor scheduling   Mobile edge computing   deviceless edge compu- ting   function-as-a-services  

摘要： Serverless Computing and Function-as-a-Service (FaaS) offer convenient and transparent services to developers and users. The deployment and resource allocation of services are managed by the cloud service providers. Meanwhile, the development of smart mobile devices and network technology enables the collection and transmission of a huge amount of data, which shifts tasks to the network edge for mobile users. In this paper, we propose a deviceless edge computing system targeting the mobility of end users using the data migration of virtual functions. We focus on the adjustment of migration among virtual functions to provide uninterrupted services to mobile users. We introduce the deviceless edge computing model and propose a seamless data migration scheme of virtual functions with limited involvement of function developers. We formulate the migration decision problem into integer linear programming and use receding horizon control (RHC) for online solutions. We implement the migration system to support delay-sensitive scenarios over real edge devices and develop a streaming game as the virtual function to test the performance. Extensive experiments in real scenarios exhibit the system has the ability to support high-mobility and delay-sensitive application scenarios. Extensive simulation results show the applicability of the proposed system over large-scale networks.

关键词： Task analysis   Autonomous aerial vehicles   Optimization   Resource management   Heuristic algorithms   Delays   Trajectory   Deep reinforcement learning   mobile edge computing   multi-UAV cooperative computation   resource allocation   trajectory planning  

摘要： In the multiple unmanned aerial vehicle (UAV) mobile edge computing (MEC) systems, the cooperative computation among multiple UAVs can improve the overall computation service capability. Multi-UAV MEC systems can meet the quality of service requirements for computation intensive applications of ground terminals (GTs) in complex field environments, emergency disaster relief and other special scenarios. In this paper, a multi-UAV cooperative computation framework is proposed while taking the GT movement and random arrival of computation tasks into consideration. A long-term optimization problem is formulated for the joint optimization of UAV trajectory and resource allocation, subject to minimizing the total GT computation task completion time and the total system energy consumption. To solve this problem, a joint multiple time-scale optimization algorithm is proposed. In particular, the optimization problem is decomposed into a long time-scale multi-UAV trajectory planning subproblem and a short time-scale resource allocation subproblem. The proximal policy optimization algorithm is invoked to solve the long time-scale subproblem. The greedy algorithm and the successive convex approximation (SCA) method are employed to solve the short time-scale subproblem. Finally, a joint multiple time-scale optimization algorithm with a two-layer loop structure is proposed. Simulation results show that: 1) the proposed multi-UAV cooperative computation MEC system outperforms the conventional MEC system without collaboration among UAVs;and 2) the proposed algorithm can quickly adapt to different degrees of environmental dynamics and outperforms the benchmark algorithm for different network sizes, task requirements, and available resources.

关键词： Fluid antenna   mobile edge computing   antenna positioning   computation offloading   particle swarm   Fluid antenna   mobile edge computing   antenna positioning   computation offloading   particle swarm  

摘要： In the evolving environment of mobile edge computing (MEC), optimizing system performance to meet the growing demand for low-latency computing services is a top priority. Integrating fluid antenna (FA) technology into MEC networks provides a new approach to address this challenge. This letter proposes an FA-enabled MEC scheme that aims to minimize the total system delay by leveraging the mobility of FA to enhance channel conditions and improve computational offloading efficiency. By establishing an optimization problem focusing on the joint optimization of computation offloading and antenna positioning, we introduce an alternating iterative algorithm based on the interior point method and particle swarm optimization (IPPSO). Numerical results demonstrate the advantages of our proposed scheme compared to traditional fixed antenna positions, showing significant improvements in transmission rates and reductions in delays. The proposed IPPSO algorithm exhibits robust convergence properties, further validating the effectiveness of our method.

关键词： Mobile edge computing   Multi-ES   Multi-MU   Energy consumption   Time consumption   Resource utilization  

摘要： With the drastic development of Internet of things, the number of connected mobile users (MUs) is increasing at an unprecedented speed. The increasing popularity of MUs has triggered more and more new mobile applications. However, these applications are sensitive to latency, which inevitably increases pressure on MUs. Fortunately, computation offloading of mobile edge computing is becoming a promising technology that can improve quality of service for MUs. However, it becomes much difficult when there are multiple edge servers (ESs) near to the MU, even to the multiple MUs. On the other hand, as the resources of ESs are heterogeneous and finite, and thus it is challenge to design effective offloading strategies for multiple MUs. To tackle the above challenges, we firstly establish a multi-objective optimization model concerning time consumption and energy consumption of MUs, and resource utilization of ESs. Moreover, we devise an end-edge-cloud collaborative computing offloading method based on improved Strength Pareto Evolutionary Algorithm 2 for addressing this mode. Finally, compared to benchmark methods, numerous experiments have proved that our proposed method is effective and efficient and can be widely used for the scenario of multiple MUs and multiple heterogeneous ESs.

关键词： Mobile edge computing   Wireless power transfer   UAV   Cooperation  

摘要： Internet of Things (IoT) devices will generate massive fragmented data that requires real-time computing and intelligent analysis, which makes it facing the challenge of insufficient computing resources and limited battery energy. With the assistance of mobile edge computing (MEC), IoT devices can offload computing tasks to the MEC server to reduce their computing pressure. Cooperation between devices and wireless power transmission technology (WPT) can be used to solve the problem of limited battery energy. Moreover, the use of unmanned aerial vehicles (UAVs) in the WPT-MEC system can improve signal coverage and energy collection efficiency. In this paper, we propose an UAV-based wireless power transmission and collaborative MEC scheme for IoT networks, in which IoT devices utilize different subcarriers to transmit the information. We aim to minimize UAV's transmitting power via optimizing the transmitting power of IoT devices with the constraints of delay and offloading task size. The performance of the proposed scheme is verified by simulation results.

关键词： Servers   Autonomous aerial vehicles   Energy consumption   Dispatching   Base stations   Heuristic algorithms   Optimization   Mobile edge computing   server placement   Unmanned Aerial Vehicle (UAV)   path planning   Internet of Vehicles (IoV)  

摘要： In future Internet of Vehicles (IoV), edge servers will be deployed to the proximity of vehicles on road to offer realtime computation offloading services. However, servers deployed at fixed sites are static, while the traffic load on road is highly dynamic, incurring the mismatch between the demand and supply from time to time. In this paper, we study using multiple Unmanned Aerial Vehicles (UAVs) to act as edge servers for the IoV in a city-wide area. We first build a model on how to dispatch multiple UAVs to multiple hovering locations where they can cover a maximum number of user requests on discretized time slots. As the problem is NP-hard, we design a heuristic algorithm to solve it in polynomial time, namely the UAV pie-eating dispatching algorithm. Next, we propose a matching-based strategy to reduce the total travel distance of the UAVs when they move between the hovering locations, with the purpose of saving energy. We run extensive trace-driven experiments on the trajectory data of the taxicabs in Shanghai. The results verify that our UAV pie-eating algorithm effectively dispatches the UAVs to different locations at different time to adapt to the spatio-temporal demand variation, and our matching-based path planning strategy also reduces the total travel distance significantly compared with other popular strategies.

关键词： Service migration   Internet of Vehicles   Mobile edge computing   Decision theory  

摘要： In the Internet of Vehicles driven by mobile edge computing, the service requests are offloaded to the roadside units (RSUs) via wireless network, reducing the service latency and enhancing the utilization of resources of RSUs. However, the high mobility of vehicles leads to the frequent switching of services, decreasing the quality of service, which fails to meet the requirements of latency-sensitive vehicular services. In this paper, we proposed a latency-aware service migration method with decision theory, named LSMD. Specifically, we first model the network architecture and introduce the transmission and computation of the service requests in detail. Then, considering the high mobility of vehicles, we analyze the dynamic change of vehicle locations and transform the service migration problem into an uncertain decision optimization problem. Afterward, we find the optimal service migration strategy with the objectives of minimizing the service latency and balancing the workload on RSUs. Finally, numerical experiment results on real-world datasets demonstrate that our method outperforms the other two baselines.