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.

关键词： Cognitive network   Security analysis   Mobile edge computing   Machine learning model   B5G  

摘要： The proliferation of wireless applications at an exponential rate has made spectrum problems worse. Saturation in the unlicensed frequency spectrum is rapidly increasing as a result of the increasing data rates required by new wireless devices. A proposed solution to this problem is cognitive radio, which allows for the opportunistic use of licenced spectrum in less crowded areas. Cognitive network-based security evaluations using mobile edge computing and a Beyond 5G' (B5G) machine learning (ML) model are the focus of this research. In this case, the security study was carried out using cognitive network data transfer and multi-agent reinforcement encoder neural network and mobile edge computing (MRENN-MEC), a multi-agent reinforcement encoder neural network with mobile edge computing. Scalability, quality of service, throughput, and forecast accuracy are some of the network properties that undergo experimental analysis.

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： 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： 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： 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： 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： 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： Trust assessment   Security communication   Green UAV network   QoS   Mobile edge computing  

摘要： Green edge-assisted unmanned aerial vehicle (eUAV) network is an emerging network architecture enhanced with the energy efficiency technology that provides an ubiquitous communication for air and ground. It is considered a promising technology that can maintain a balance between a clean environment and a rich human life to build a sustainable world. However, the security issues exposed by UAVs have raised concerns about the trust and security for adopting eUAV network for communication. To this end, a trust assessment as a service (TaaS) scheme for secure communication of green eUAV network is proposed. The TaaS can effectively collect the valid data of UAVs about diverse and dynamic quality of service (QoS) attributes related to energy efficiency. To predict the actual data of UAVs about QoS attributes in the real green eUAV environment, an service level object calibration method is presented in TaaS. In addition, to accurately obtain the trust level of UAVs in the eUAV network, TaaS presents a trust level assessment method integrated the interval multi-attribute decision-making method and the objective weight assignment method based on deviation maximization. A case study with open source dataset and a performance analysis experiment are conducted to show that the proposed TaaS scheme can accurately and effectively assess the trust level of UAVs while outperforming other traditional trust assessment methods.

关键词： Mobile edge computing   connected autonomous vehicle   joint computation and communication cooperation   computation offloading   resource allocation  

摘要： With the development of information technology, a variety of mobile devices with large computing requirements and delay constraints are growing rapidly, such as Connected Autonomous Vehicles (CAVs). In addition to the energy consumption required for vehicle driving, the CAV also needs to support the normal operation of sensors and computing platforms, which brings huge energy consumption and affects the endurance of vehicles. In this paper, a Mobile Edge Computing (MEC) system is considered that includes three types of network nodes: connected Autonomous Vehicles, Roadside Units (RSUs), and a Base Station (BS). Considering the task duration of the CAV, tasks on the CAVs are partitioned into three sections, which are executed at CAVs, RSUs, and BS, respectively. It is challenging to correlate resource-limited CAVs with other types of network nodes with high performance and implement partial computation offloading between them to minimize the total energy consumption of CAVs and RSUs under the delay constraint of CAVs. Based on the classical orthogonal-frequency-division multiple-access (OFDMA), a joint computing and communication cooperation offloading protocol is proposed to minimize the total energy consumption of all CAVs and RSUs under delay constraints and is described as a constrained Mixed-Integer Nonlinear Program (MINLP) problem. To address this problem, a Reference Vector-Guided Evolutionary Algorithm for Multi-Objective Optimization based on Prior Experience (RVEAPE) is designed in this paper. RVEAPE jointly optimizes the task division ratio variables and resource allocation variables (computation resources, communication resources). Simulations demonstrate that RVEAPE significantly outperforms the GA, NSGA-II, and NSGA-II algorithms in terms of the total energy consumption of CAVs and RSUs in the whole system consisting of CAVs, RSUs, and a BS. Note to Practitioners-This paper is motivated by the use of multi-objective optimization algorithms to improve t