关键词： Mobile edge computing   backscatter communications   partial offloading   computation energy efficiency  

摘要： In the Internet of Things (IoT) environment, a wide variety of mobile devices (MDs) have become part of it, leading to a dramatic increase in the amount of task data. However, due to the limited battery capacity and computing resources of MDs, a lot of effort is required to be taken on how to process more data with less energy. In this paper, we take into account the low utilization of spectrum resources and the short battery life of the equipment, and a backscatter communication-mobile edge computing (BC-MEC) network system based on Non-orthogonal multiple access (NOMA) communication mode is proposed. In order to maximize the computation energy efficiency (CEE) of the system, we jointly optimize the backscatter coefficient of each MD, the backscatter communication duration, the direct offloading duration, the MEC server processing time, the local processing time, the direct offloading power of each MD, the calculation frequency of the MEC server, and the local calculation frequency of each MD. We then formulate it as a joint fractional optimization problem, which is a non-convex optimization problem that is difficult to solve by heuristic algorithms with high computational complexity. To this end, we transform such a problem into a convex problem and apply the Lagrangian dual method to solve it efficiently. Furthermore, in order to meet different user requirements, two effective iterative Dinkelbach algorithms based on Backscatter Coefficient Updates (DBCU) are proposed to solve this problem. Extensive simulation results demonstrate the superiority of our proposed approach, which improves the system CEE by at least 10% compared to state-of-the-art methods.

关键词： Mobile edge computing   Computation offloading   Multi-agent deep reinforcement learning   Energy harvesting   Resource allocation   Non-orthogonal multiple access  

摘要： Supporting latency -sensitive and computation -intensive applications is hardly possible for mobile devices (MDs) with limited battery capacity and low computing resources. Therefore, mobile edge computing (MEC) and wireless power transfer (WPT) have emerged as promising technologies that allow MDs to offload part or all of their workloads to MEC servers and harvest energy for prolonging their battery lifetime. However, the MEC server's limited computing resources, available communication channel quality, and time -limited energy harvesting (EH) challenge the computation offloading. In this paper, we study the joint problem of decentralized computation offloading and resource allocation (JDCORA) in the environment of non -orthogonal multiple access (NOMA)-assisted MEC with multiple EH -enabled MDs. To learn decentralized offloading policies, we propose a multi -agent deep reinforcement learning (MADRL)-based scheme towards minimizing energy consumption and task completion time, which considers the cooperation between MDs to adjust their strategies. In particular, we improve the multi -agent deep deterministic policy gradient (MADDPG) by applying the features of double actors, double centralized critics, soft value estimation, critic regularization and proportional -based prioritized experience replay (pPER) and propose an algorithm called multi -agent twin actors regularized critics (MATARC). Simulation results demonstrate that the proposed MATARC has a better convergence performance compared to other baseline methods and also reduces the average energy consumption, task completion time and task drop rate.

关键词： Mobile edge computing   unmanned aerial vehicle   reinforcement learning   Markov decision process (MDP)   Mobile edge computing   unmanned aerial vehicle   reinforcement learning   Markov decision process (MDP)  

摘要： Mobile edge computing (MEC), located at the network's edge, enhances distributed computing. However, its fixed position presents limitations during emergencies. Integrating unmanned aerial vehicles (UAVs) into MEC systems offers a solution but introduces challenges in managing UAV collaboration. This paper proposes a Reinforcement Deep Q-Learning based multi-UAV MEC framework to optimize quality of service (QoS) and route planning. The proposed framework addresses these challenges by modeling user demand and using multi-factor optimization considering user demand, risk, and distance. A Markov Decision Process (MDP) models user demand for higher QoS. The reinforcement learning reward matrix incorporates terminal user demand, risk, and distance for efficient energy use and resource allocation. Simulations demonstrate the effectiveness of our proposed method, offering valuable insights for future research in this domain.

关键词： Vehicular edge computing   latency   communication   storage   mobile edge computing  

摘要： The latency associated with accessing data stored on edge computing servers for vehicles encompasses both the communication between a vehicle and a server as well as a latency of a data storage system. To enable low-latency vehicular services, an efficient resource management should consider the communication as well as the storage I/O cache resource allocation along with a data access pattern and a priority of individual vehicular services. Therefore, we focus on a joint optimization of communication and storage I/O cache resource allocation for access to data of vehicular services hosted by the edge computing servers. The proposed framework determines the data placement for the services and allocates communication and storage I/O cache resources to each service. The objective is to minimize the overall latency experienced by vehicular services for access to data. The edge computing platforms share storage and communication resources among various vehicular services, each having distinct priorities and data access rates or patterns. Hence, to reflect different priorities of services in resource allocation, our objective metric takes into account the service priority, data access frequency, and latency. We propose a feasible solution using dual relaxation considering both communication and storage latencies. The proposed solution reduces the average latency of vehicular services by up to 1.8x compared to the state-of-the-art resource allocation method for vehicular edge computing. Even more notable improvement is observed for high priority vehicular services, where the proposal leads to 2.5x lower latency compared to the state-of-the-art storage I/O cache architecture for virtualized cloud services.

关键词： Computation offloading   Task dependency   Mobile edge computing   Multi-edge cloud computing   Multi-objective optimization Enhanced Whale   Optimization dynamic allocation  

摘要： In this paper, we introduce the Enhanced Whale Optimization Algorithm (EWA) to optimize dependent task offloading in a multi-edge cloud computing environment. Our proposed algorithm aims to identify the most suitable offloading scenario for dependent tasks, focusing on minimizing total processing latency, energy consumption, and associated costs. We operate within a system comprising many decentralized Mobile Edge Computing servers (MECs) and a centralized cloud server. Two novel improvement operations, namely Frame Shifting (FS) and Load Redistribution Strategy (LRS), are introduced to enhance the performance of the whale algorithm. Through simulation, our results demonstrate the superior performance of EWA. Specifically, compared to the Whale Optimization Algorithm (WOA), EWA achieves a remarkable reduction in latency by 22.84%, a substantial decrease in energy consumption by 78.28%, and a notable reduction in cost usage by 61.47%. These outcomes underscore the efficacy and practical significance of the proposed EWA in addressing the challenges posed by dependent task offloading in the multi-edge cloud computing landscape.

关键词： Mobile edge computing   Status update system   Age of information   Priority queueing model   Stochastic hybrid system  

摘要： Timely status updating in mobile edge computing (MEC) systems has recently gained the utmost interest in internet of things (IoT) networks, where status updates may need higher computations to be interpreted. Moreover, in real-life situations, the status update streams may also be of different priority classes according to their importance and timeliness constraints. The classical disciplines used for priority service differentiation, preemptive and non-preemptive disciplines, pose a dilemma of information freshness dissatisfaction for the whole priority network. This work proposes a hybrid preemptive/non-preemptive discipline under an M/M/1/2 priority queueing model to regulate the priority-based contention of the status update streams in MEC systems. For this hybrid discipline, a probabilistic discretionary rule for preemption is deployed to govern the server and buffer access independently, introducing distinct probability parameters to control the system performance. The stochastic hybrid system approach is utilized to analyze the average age of information (AoI) along with its higher moments for any number of classes. Then, a numerical study on a three-class network is conducted by evaluating the average AoI performance and the corresponding dispersion. The numerical observations underpin the significance of the hybrid-discipline parameters in ensuring the reliability of the whole priority network. Hence, four different approaches are introduced to demonstrate the setting of these parameters. Under these approaches, some outstanding features are manifested: exploiting the buffering resources efficiently, conserving the aggregate sensing power, and optimizing the whole network satisfaction. For this last feature, a near-optimal low-complex heuristic method is proposed.

关键词： Congestion control   AGCM   Mobile edge computing   Fog computing   K-means   5G  

摘要： The congestion management mechanism is essential to manage the explosive evolution of data traffic associated with advanced applications and services in the 5G system. As a result, we suggest a novel methodology to manage congestion for mobile edge computing in the 5G system. Furthermore, the proposed model enhances delay, energy consumption, and throughput. The enhanced random early detection strategy and the K-means approach are used in the suggested model to execute this. Also, a virtual list is realized to maintain packet information and suit more packets. The proposed model is realized in NS2 green cloud simulator. In comparison with the traditional cloud model and the fog computing model, the simulation results confirm that the proposed model reduces delay, boosts throughput, and decreases energy consumption.

关键词： Internet of things   edge computing environment   deep learning   safety warning   intelligent router   long short-term memory network   anomaly detection  

摘要： Aiming at the problems of poor real-time processing and low security performance caused by the massive information of the Internet of things, a security early warning model of the Internet of things based on deep learning in edge computing environment is proposed. Firstly, the system architecture of the Internet of things is designed by using the edge computing technology, in which the intelligent router is used to obtain the network stub and send it to the nearest edge computing node for anomaly detection. Then, the attention mechanism is used to improve the long short-term memory network (LSTM), and the multidimensional LSTM model is constructed. At the same time, it is used to analyze the combined network data. Finally, according to the set threshold value, judge whether there is abnormal behavior in the network, and give early warning in time to take security defense measures. The experimental analysis of the proposed model based on NS2 simulation platform shows that its early warning success rate and time are 95.2% and 20.6ms, respectively, and it can detect and defend various network attacks well, with high security performance.

关键词： Delays   Task analysis   Artificial neural networks   Resource management   Training   Inference algorithms   Heuristic algorithms   Mobile edge computing   inference delay   dynamic neural network   training cost  

摘要： Mobile edge computing (MEC) can be used to reduce the task delay for users with limited computing resources. However, in 6G networks, the diversity of tasks is greatly increased. For those extremely delay-sensitive small-size computing tasks, the inference delay of neural network (NN)-based algorithms such as resource allocation and task offloading cannot be ignored. As a hyperparameter, the inference cost of NN is usually difficult to adjust. Dynamic neural network (DyNN) is an emerging technique that improves the model efficiency by adjusting the network architecture on-demand according to the sample characteristics during inference. In this paper, we propose a DyNN-based resource management method for MEC that dynamically adjusts the depth and width of the NN according to the features of the task, improving computational efficiency and achieving a balance between inference delay and the management performance of computational and communication resources. Furthermore, to reduce the training cost of DyNN, a new training method is proposed in this paper, where all the blocks in DyNN are gradually trained in the order of size. Simulation results demonstrate that the proposed DyNN-based resource management method outperforms the traditional optimization algorithm and the static-NN-based method.

关键词： Mobile edge computing   Cyber-physical system   Workflow application   Task scheduling   Metaheuristic  

摘要： This paper proposes a cost-minimization scheduling algorithm for the joint optimization of task offloading and resource allocation for workflow applications in a mobile edge computing (MEC)-supported cyber-physical system. We model this scheduling problem as an integer program that minimizes the total communication and computation cost under resources and deadline constraints upon workflow applications. To cope with this complicated optimization problem, we develop an efficient heuristic method to dispatch the workflow tasks onto MEC servers with sufficient resources. By using the task dispatching heuristic to evaluate the quality of each candidate solution, we construct a novel gene-inspired metaheuristic algorithm (GIMA) that incorporates an offspring-production operator and a conditional insertion scheme into the improvement strategy to explore high-quality solutions. Specifically, the offspring-production operator can enhance the scheduling quality by generating improved offspring solutions based on existing solutions. The conditional insertion scheme is further incorporated to reduce the computational overhead involved in solution exploration. We perform extensive simulations to justify the performance of the proposed GIMA in solving the scheduling problem studied in this work. Experimental results on real-world and synthetic workflow applications show that GIMA outperforms other metaheuristics in terms of cost reduction and computational efficiency.