关键词： Lyapunov theory   mobile edge computing   task offloading   evolutionary algorithm  

摘要： Mobile edge computing (MEC) is a novel technique that can reduce computational burden of local terminal devices by tasks offloading, which emerges as a promising paradigm to provide computing capabilities near mobile users. Furthermore, the computing power consumption and battery capacity in the terminal devices is generally limited in MEC, which directly affect the quality of service (QoS), which brings new challenge in achieving the stability of the system. The trade-off between delay and energy consumption of task offloading process under multi-user MEC scenario is in general a complicated problem that highly correlates to the computation offloading decisions of computation tasks, i.e., whether or not to offload a task for edge execution. To address the above issues, the task offloading decision to solve the queuing problem of tasks to be processed in the local terminals was being considered, with an application of Lyapunov theory to ensure the queue stability in this paper. Then, a trade-off model was formulated to minimize the delay and energy consumption to achieve a minimum execution cost. Moreover, an improved Dynamic Niche-based Self-organizing Learning Algorithm was presented to accelerate the speed of the search process to gain an optimal task offloading scheme. The simulation results provided supporting evidence that the proposed optimal scheme IDO could achieve a lower average energy consumption than LFO and a lower average execution delay than EFO under the scenarios of the waiting queue length varies within [10, 50], the number of users varies within [4, 20], and the number of tasks varies within [20, 100].

关键词： Reliability   NOMA   Servers   Delays   Resource management   Edge computing   Error probability   finite blocklength   NOMA   mobile edge computing   reliability  

摘要： In this paper, we consider mission-critical task offloading in the non-orthogonal multiple access (NOMA)-assisted mobile edge computing (MEC) networks, where local information collected from multiple local devices are processed at the MEC node. The process of the MEC service contains a NOMA-assisted communication phase and a computation phase, which is required to be reliable and low-latency. For such network, we derive the overall reliability of the service. On the one hand, we characterize the communication behavior in the finite blocklength (FBL) regime, where the impact of imperfect successive interference cancellation in the NOMA scheme is considered. On the other hand, we exploit the extreme value theory (EVT) to study the delay violation error of the computation phase. Following the characterizations, we provide an optimal design framework minimizing the overall error probability via a joint time and power allocation. To address the formulated non-convex problem, a modified block coordinate descent method is proposed, with which the original problem is decomposed into two sub-problems that can be solved optimally after conducting a set of analytical results. We validate our analytical model via simulations and demonstrate the proposed design's improved performance in comparison to benchmarks.

关键词： Resource management   Ultra reliable low latency communication   Task analysis   Downlink   Delays   Uplink   Optimization   Branch-and-bound   finite blocklength transmission   mobile edge computing   successive convex approximation   ultra-reliable low latency communication  

摘要： In this paper, we study resource allocation algorithm design for multi-user orthogonal frequency division multiple access (OFDMA) ultra-reliable low latency communication (URLLC) in mobile edge computing (MEC) systems. To meet the stringent end-to-end delay and reliability requirements of URLLC MEC systems, we employ joint uplink-downlink resource allocation and finite blocklength transmission. Furthermore, we propose a partial time overlap between the uplink and downlink frames to minimize the end-to-end delay, which introduces a new time causality constraint. The proposed resource allocation algorithm is formulated as an optimization problem for minimization of the total weighted power consumption of the network under a constraint on the number of URLLC user bits computed within the maximum allowable computation time, i.e., the end-to-end delay of a computation task of each user. Despite the non-convexity and the complicated structure of the formulated optimization problem, we develop a globally optimal solution using a branch-and-bound approach based on discrete monotonic optimization theory. The branch-and-bound algorithm minimizes an upper bound on the total power consumption until convergence to the globally optimal value. Furthermore, to strike a balance between computational complexity and performance, we propose two efficient suboptimal algorithms. For the first suboptimal scheme, the optimization problem is reformulated in the canonical form of difference of convex programming. Then, successive convex approximation (SCA) is used to determine a locally optimal solution. For the second suboptimal scheme, we use a high signal-to-noise ratio approximation for the channel dispersion. Then, via novel transformations, we convert the non-convex quality-of-service constraints of the original problem into equivalent second-order-cone constraints. Our simulation results reveal that the proposed resource allocation algorithm design facilitates URLLC in MEC systems, an

关键词： Roads   Vehicles   Cloud computing   Computer architecture   Sensors   Wireless sensor networks   Safety   Vehicular networks   VANETs   intelligent vehicles   vehicular cloud computing   cloudlets   cloud infrastructure   mobile edge computing   fog computing   road traffic management   intelligent transportation systems   ITS   machine-to-machine communications  

摘要： In this survey, we analyze the proposals of vehicular communication systems in the context of road traffic management. Starting with the definition of communications between vehicles (V2V), vehicles-to-infrastructure (V2I) and vehicles-to-everything (V2X), we first focus on the requirements and current standards for the Intelligent Transport Systems (ITS), including the maximum communication delay, the communication range and the size of messages (in the case of V2I transmission). After that, we analyze the use cases in line with the implementation of intelligent traffic management and review the respective methods that support or directly manage traffic on roads. One of the primary objectives of this paper is to highlight the architectures of four classes of systems able to support vehicular traffic management and communication between vehicles and roadside infrastructure, namely: vehicular cloud computing (VCC), cloudlets, Mobile Edge Computing (MEC) and fog computing. In this context, we also present our classification of the methods for these four classes of architectures. In the end, we provide our opinion on problems and limitations concerning the deployment of mechanisms belonging to each considered architecture class.

关键词： Containers   Cloud computing   Costs   Quality of service   Licenses   Virtualization   Real-time systems   Quality of experience (QoE)   container migration   mobile edge computing   virtualization   real-time applications   edge cloud   migration cost   inter-edge cloud bandwidth  

摘要： Mobile Edge Cloud (MEC) technology is envisioned to play a key role in next generation mobile networks by supporting low-latency applications using geographically distributed local cloud clusters. However, MEC faces challenges of resource assignment and load balancing to support user mobility and latency-sensitive applications. Virtualized resource reallocation techniques including dynamic service migration are evolving to achieve load balance, fault tolerance and system maintenance objectives for resource constrained edge nodes. In this work, a compute and network-aware lightweight resource sharing framework with dynamic container migration, ShareOn, is proposed. The migration framework is validated using a set of heterogeneous edge cloud nodes distributed in San Francisco city, serving mobile taxicab users across that region. The end-to-end system is implemented using a container hypervisor called LXD (Linux Container Hypervisor) executing a real-time application to detect license number plates in automobiles. The system is evaluated based on key metrics associated with application quality-of-service (QoS) and network efficiency such as the average system response time and the migration cost for different combinations of load, compute resources, inter-edge cloud bandwidth, network and user latency. A detailed migration cost analysis enables evaluation of migration strategies to improve ShareOn's performance in comparison to alternative migration techniques, achieving a gain of 15-22% in system response time for highly loaded edge cloud nodes.

关键词： Edge computing   Internet of Things   5G mobile communication   Deep learning   Servers   Computational modeling   Reinforcement learning   Deep learning   deep reinforcement learning   Internet of Things   mobile edge computing   task offloading  

摘要： Computation offloading has proven to be an effective method for facilitating resource-intensive tasks on IoT mobile edge nodes with limited processing capabilities. Additionally, in the context of Mobile Edge Computing (MEC) systems, edge nodes can offload its computation-intensive tasks to a suitable edge server. Hence, they can reduce energy cost and speed up processing. Despite the numerous accomplished efforts in task offloading problems on the Internet of Things (IoT), this problem remains a research gap mainly because of its NP-hardness in addition to the unrealistic assumptions in many proposed solutions. In order to accurately extract information from raw sensor data from IoT devices deployed in complicated contexts, Deep Learning (DL) is a potential method. Therefore, in this paper, an approach based on Deep Reinforcement Learning (DRL) will be presented to optimize the offloading process for IoT in MEC environments. This approach can achieve the optimal offloading decision. A Markov Decision Problem (MDP) is used to formulate the offloading problem. Delay time and consumed energy are the main optimization targets in this work. The proposed approach has been verified using extensive simulations. Simulation results demonstrate that the proposed model can effectively improve the MEC system latency, energy consumption, and significantly outperforms the Deep Q Networks (DQNs) and Actor Critic (AC) approaches.

关键词： mobile edge computing   MEC platform   human resource system   human resource management  

摘要： The purpose is to process large data, classify unclear information in the human resource management system, and improve the human resource management efficiency for enterprises. Here, a new system is proposed for human resource analysis and management based on Mobile Edge Computing (MEC) technology. First, the MEC technology is analysed. Then, the MEC technology is integrated into the human resource management system through network protocol according to the distributed computing function of MEC technology and human resources management requirements. Subsequently, the B/S and C/S data models are combined. Finally, the new human resource analysis and management model is proposed through the combination of B/S and C/S data models based on MEC. The proposed system can manage human resources and business operations locally to save transportation costs and increase system calculation speed through a series of analyses and research, and thus improving system performance.

关键词： Maritime video surveillance   Maritime network   Mobile edge computing   Binary search algorithm   Multi-user offloading  

摘要： Video surveillance has broad application prospects in maritime rescue, ship transportation and other fields. Mobile edge computing can effectively guarantee low-latency and highly reliable data transmission in maritime video surveillance. This paper comprehensively formulates the edge computing offloading schemes in the middle-sea scenario. The middle-sea scenario has sufficient edge computing nodes as in the offshore scenario and delay constraints due to limited network connectivity as in the far-sea scenario. Taking into account these characteristics, a single-user single-hop unicast offloading model is established and extends to a multi-user model. In addition, for the sufficient and limited edge computing nodes, the multi-user model is further divided into the multi-user single-hop unicast situation 1 and 2 models. We split the mixed-integer nonlinear programming problem and approximate the optimal transmission power by the binary search method. We use the offloading decision allocation algorithm based on alternating selection, offloading decision allocation algorithm based on multi-objective alternating selection, and offloading decision allocation algorithm based on node redistribution to optimize the offloading decisions of the above models. Subsequently, we analyze the simulation results from algorithm comparisons, changes in the number of subtasks, data, and oceanic user equipments. We verify the effectiveness of the proposed schemes and algorithms in saving delay.

关键词： Task analysis   Servers   Reinforcement learning   Energy consumption   Autonomous aerial vehicles   Multi-access edge computing   Trajectory   Mobile edge computing   unmanned aerial vehicles   user equipments   deep reinforcement learning   path planning   task offloading  

摘要： With the development of Unmanned Aerial Vehicles (UAVs) in recent years, UAV-mounted Mobile Edge Computing (MEC) systems are widely used for broadband connectivity and emergency communications in areas without internet. However, the onboard energy of UAVs is limited, and how to provide long-term computing services for ground User Equipments (UEs) still faces significant challenges. In this paper, UAVs are used as mobile base stations to offer MEC services for UEs. Unlike existing solutions that mainly solve UAV path planning from convex optimization methods, we propose a Multi-Agent Path planning (MAP) scheme based on deep reinforcement learning. We aim to minimize the total energy consumption of UAVs while completing the offloading tasks of UEs. At the same time, fairness is taken into account to ensure UE offloading balance and UAV load balance. To this end, we establish the optimization problem and design the state space, action space, and reward function for modeling each UAV through Deep Neural Networks (DNNs). Extensive simulation experiments show that the proposed scheme outperforms other benchmark schemes.

关键词： Autonomous aerial vehicles   Blockchains   Security   Computer architecture   Privacy   Multi-access edge computing   Collaborative work   Unmanned aerial vehicles   mobile edge computing   federated learning   blockchain   privacy  

摘要： Unmanned aerial vehicles (UAVs) extend the traditional ground-based Internet of Things (IoT) into the air. UAV mobile edge computing (MEC) architectures have been proposed by integrating UAVs into MEC networks during the current novel coronavirus disease (COVID-19) era. UAV mobile edge computing (MEC) shares personal data with external parties (such as edge servers) during intelligent medical analytics. However, this technique raises privacy concerns about patients' health data. More recently, the concept of federal learning (FL) has been set up to protect mobile user data privacy. Compared to traditional machine learning, federated learning requires a decentralized distribution system to enhance trust for UAVs. Blockchain technology provides a secure and reliable solution for FL settings between multiple untrusted parties with anonymous, immutable, and distributed features. Therefore, blockchain-enabled FL provides both theories and techniques to improve the performance of intelligent UAV edge computing networks from various perspectives. This survey begins by discussing the current state of research on blockchain and FL. Then, compare the leading technologies and limitations. Second, we will discuss how to integrate blockchain and FL into UAV edge computing networks and the associated challenges and solutions. Finally, we discuss the fundamental research challenges and future directions.