关键词： Mobile edge computing   Energy harvesting   Cognitive radio   Unmanned aerial vehicles   Energy efficiency  

摘要： The explosive growth in Internet of Things (IoT) in terms of number of applications and deployed devices has created many challenges over the past decade. Among the most critical of which are the increasing demand on spectrum resources, the growing computation and data processing cost, and the limited energy resources. In this paper, we present a model for IoT networks that incorporates the technologies of cognitive radio (CR), mobile edge computing (MEC), unmanned aerial vehicles (UAVs), and radio-frequency energy harvesting to address the aforementioned challenges. In this model, UAVs provide computation and energy recharging services to IoT devices. These services can be requested/delivered through multiple spectrum bands by exploiting the CR technology. Specifically, aim at scheduling the task offloading and energy transmission/harvesting activities over time and frequency so that the maximum energy consumption rate among IoT devices is minimized. A mixed integer linear program was formulated to find such schedule. A greedy sub-optimal algorithm was also proposed, where our results show that it is within approximate to 11% of the optimal solution. We also investigate the maximum energy consumption rate among IoT devices under several settings regarding number of UAV MEC servers, task size, task offloading cost, and task local computation cost.

关键词： Wireless communication   Costs   Optimization   Fading channels   Resource management   Servers   Delays   Wireless sensor networks   Internet of Things   Vehicle-to-infrastructure   Mobile edge computing   path prediction   task offloading   time-varying channels   vehicular network  

摘要： This article studies the design of vehicular edge computing networks (VECNs) with multiple moving vehicles and roadside units (RSUs). Uniquely, our study reflects a pragmatic situation where wireless channels are time-varying in the duration of task offloading and vehicles can travel with inconstant speeds in a real-world scenario. By jointly optimizing transmit power and time allocation for task offloading as well as computation task partition in the VECN, our goal is to minimize the cost at the vehicles for energy consumption on task offloading and computing, and rent on task computing service at RSUs. However, solving the formulated optimization problem directly is impossible due to the requirement of noncausal vehicular position information (VPI) and noncausal channel state information (CSI) between vehicles and RSUs. To address this issue, a path prediction model is adopted to predict the noncausal VPI, based on which the noncausal CSI can be estimated. Then, a novel receding horizon optimization method is proposed to transform the original problem into a sequence of tractable problems. Despite this, the problems remain complex due to the computationally prohibitive task of identifying the optimal task offloading duration at each vehicle in a centralized manner. To overcome this difficulty, the consensus alternating directions method of multipliers is proposed to solve the problem in a distributed manner with low computational complexity. Numerical results show that our proposed scheme can save at most 30% of monetary cost as compared with existing baseline schemes.

关键词： computation offloading   deep deterministic policy gradient   low earth orbit satellite   mobile edge computing   resource allocation  

摘要： Low earth orbit(LEO)satellites with wide coverage can carry the mobile edge computing(MEC)servers with powerful computing capabilities to form the LEO satellite edge computing system,providing computing services for the global ground *** this paper,the computation offloading problem and resource allocation problem are formulated as a mixed integer nonlinear program(MINLP)*** paper proposes a computation offloading algorithm based on deep deterministic policy gradient(DDPG)to obtain the user offloading decisions and user uplink transmission *** paper uses the convex optimization algorithm based on Lagrange multiplier method to obtain the optimal MEC server resource allocation *** addition,the expression of suboptimal user local CPU cycles is derived by relaxation *** results show that the proposed algorithm can achieve excellent convergence effect,and the proposed algorithm significantly reduces the system utility values at considerable time cost compared with other algorithms.

关键词： Device to device   mobile edge computing   task offloading   reinforcement learning   Device to device   mobile edge computing   task offloading   reinforcement learning  

摘要： Mobile edge computing (MEC), along with device-to-device (D2D) assisted MEC (D-MEC), are promising technologies that could improve the quality-of-experience for mobile devices (MDs) by offloading their tasks to edge servers or nearby idle MDs. There is a popular trend to develop distributed task offloading algorithms using multi-agent reinforcement learning (MARL), whose adoption of central critics during training makes the offloading still size-sensitive. Therefore, this paper proposes a Size-Insensitive Task Offloading (SITOff) algorithm for D-MEC based on fully-distributed offloading without maintaining any central venue. Specifically, taking advantage of the inherent graph-like structure of D-MEC, SITOff adopts graphs to represent MDs' states and relationships and form each MD's local knowledge about D-MEC through graph computation. Furthermore, considering the limitation of local knowledge in performing whole performance-oriented offloading, each MD utilizes D2D-transmitting to exchange knowledge with its neighbors and form a comprehensive knowledge about D-MEC to enhance the coordination of distributed offloading. Additionally, regarding the different impacts of neighbors' knowledge, each MD leverages attention mechanisms to selectively learn its neighbors' knowledge during knowledge-exchange. Extensive experimental results show the superiority of SITOff over state-of-the-art MARL-based offloading algorithms in D-MEC with various MDs, and the easy collaboration of SITOff with curriculum-learning for large-scale D-MEC offloading.

关键词： Ocean internet of things   Mobile edge computing   Secure computation offloading   Enhanced genetic algorithm   Privacy protection  

摘要： The network nodes in the Ocean Internet of Things exhibit strong heterogeneous characteristics, which introduce complex and high-dimensional constraints for the optimization of task offloading in ocean mobile edge computing. To prevent data from being intercepted by malicious attackers during computation offloading and to protect client privacy, an offloading framework named the Enhanced Genetic Algorithm for Secure Computation Offloading is proposed. The population initialization and crossover operators are optimized before the initialization phase to accelerate convergence and avoid the risk of excessive resource consumption due to cyclic scheduling. An elitism strategy is also introduced, along with dynamic mutation rate adjustment, to enhance the search efficiency and global optimization capability of the algorithm. The superiority of the proposed framework is validated through simulation experiments and a comprehensive comparison with other benchmark algorithms. Compared to the Simulated Annealing, Particle Swarm Optimization algorithms, the Min-Min algorithm, Basic Genetic Algorithm and Proximal Policy Optimization, the proposed scheduling algorithm demonstrates a significant enhancement in secure capacity during local computation, with improvements of 47.60%, 42.16%, 15%, 25% and 16.67%, respectively. Additionally, it achieves an increase in security during the computation offloading process by 16.54%, 16.22%, 14.29%, 12.56% and 10.02% respectively.

关键词： deep reinforcement learning   load balancing   mobile edge computing   resource allocation   task offloading  

摘要： In mobile edge computing networks, achieving effective load balancing across edge server nodes is essential for minimizing task processing latency. However, the lack of a priori knowledge regarding the current load state of edge nodes for user devices presents a significant challenge in multi-user, multi-edge node scenarios. This challenge is exacerbated by the inherent dynamics and uncertainty of edge node load variations. To tackle these issues, we propose a deep reinforcement learning-based approach for task offloading and resource allocation, aiming to balance the load on edge nodes while reducing the long-term average cost. Specifically, we decompose the optimization problem into two subproblems, task offloading and resource allocation. The Karush-Kuhn-Tucker (KKT) conditions are employed to derive the optimal strategy for communication bandwidth and computational resource allocation for edge nodes. We utilize Long Short-Term Memory (LSTM) networks to forecast the real-time activity of edge nodes. Additionally, we integrate deep compression techniques to expedite model convergence, facilitating faster execution on user devices. Our simulation results demonstrate that our proposed scheme achieves a 47% reduction in terms of the task drop rate, a 14% decrease in the total system cost, and a 7.6% improvement in the runtime compared to the baseline schemes.

关键词： Mobile edge computing   Energy harvesting   Lyapunov optimization   Stackelberg game  

摘要： The rapid advancement of 5G technology has indirectly propelled the growth of connected devices within the Internet of Things (IoT). Within the IoT domain, mobile edge computing (MEC) has demonstrated potential in task processing. However, as computational services expand, the reliable determination of user offloading strategies and the rational establishment of service prices offered by servers to users continue to present challenging research directions. The primary focus of this paper revolves around task offloading in the MEC system, encompassing numerous user terminal devices that support energy harvesting (EH), a MEC server and a central cloud server. The optimization goals are to maximize the utilities for both users and the MEC server by adjusting offloading and pricing strategies. To guarantee the task queue's stability within the system and achieve a reasonable allocation of system resources, we propose a dynamic task offloading approach rooted in Lyapunov optimization theory and Stackelberg game theory. In this algorithm, the MEC server takes on the role of the leader, while each user terminal device acts as the follower. Aiming at the game equilibrium existence of the algorithm, a series of mathematical analysis is carried out. Additionally, we conduct extensive simulation experiments to validate the proposed algorithm's effectiveness. The proposed algorithm achieves improvements in user utility, with a 6.43% increase compared to the average time-constrained task offloading (ATCTO) scheme, a 61.80% improvement over the local-only processing (LOP) scheme, and a 23.97% enhancement over the genetic algorithm (GA) scheme. Meanwhile, it achieves a task queue backlog reduction of 50.00% compared to ATCTO, 70.00% compared to LOP and 15.28% compared to GA.

关键词： Costs   Games   Pricing   Task analysis   Servers   Containers   Biological system modeling   Discount rate   layer dependency   mobile edge computing   multi-round Stackelberg game   user utility  

摘要： Mobile Edge Computing (MEC) tackles the challenges associated with the rapid proliferation of User Equipment (UE) and limited computing resources. Containerization, essential for MEC deployments, encapsulates applications and dependencies, optimizing resource utilization. In containerized MEC networks, UEs offload computational tasks to edge server containers, enabling service providers to profit from offering scalable and portable services, thereby establishing a symbiotic economic ecosystem. However, traditional models, which often separate cost and delay assessments, fail to consider these factors holistically. Furthermore, they underutilize the potential of container images' hierarchical structure, which could optimize storage and reduce costs. Our research introduces a novel multi-round Stackelberg game framework that incorporates the hierarchical structure of container images to enhance resource management in MEC networks. Additionally, we integrate discount rates to model long-term economic interactions accurately, and develop two innovative algorithms: the Distributed Ant Colony Pricing (DACP) and the Multi-Round Simulated Annealing Pricing (MRSAP). These algorithms account for both immediate and long-term impacts, redefining user utility and significantly improving system efficiency. Simulation results validate the effectiveness of our algorithms in optimizing resource allocation and enhancing efficiency in dynamic MEC scenarios.

关键词： Adaptation models   Federated learning   Computational modeling   Computer architecture   Channel allocation   Data models   Servers   Resource management   Maritime communications   Load modeling   One-shot federated learning   mobile edge computing   dataset distillation   communication reduction   bandwidth allocation  

摘要： The goal of 6G is to achieve ubiquitous connectivity in heterogeneous and large-scale networks, and Hierarchical Federated Learning (HiFL) is an effective approach to assist AI technologies at the network edge. However, the "waist-shaped client-channel-cloud" architecture of maritime communication network greatly limits the implementation of hierarchical federated edge learning. Considering the aforementioned limitations, a hierarchical federated data distillation method based on UAV-assisted MEC systems is proposed, called Data Augmentation Distilled Federated Learning (DADFL). Specifically, DADFL reduce the communication process between clients and edge servers to just one round. Each client extracts their private dataset, sends synthesized data to the server, and collectively trains a global model. Building upon this, we propose a Global Optimal Iterative Search Algorithm (GOISA) considering client heterogeneity and resource constraints. GOISA minimizes overall latency by optimizing client heterogeneity, bandwidth allocation, and UAV transmission rates. Simulation experiments demonstrate that our proposed system achieves performance comparable to or even better than traditional federated learning, while reducing communication overhead by three orders of magnitude, greatly improving communication efficiency.

关键词： Simulation   Roads   Minimization   Frequency conversion   Complexity theory   Delays   Resource management   Doppler effect   Autonomous vehicles   Vehicle-to-everything   Autonomous vehicle   frequency reuse   mobile edge computing   resource allocation   task offloading   vehicle-to-everything communication  

摘要： Delay-sensitive applications for autonomous vehicles (AVs) require a substantial amount of computational resources. However, the onboard computation resources may be insufficient, resulting in long processing latencies. To deal with this critical issue, we jointly consider roadside unit (RSU) and assistant vehicle offloading, along with resource allocation, to minimize latency for vehicular tasks. This approach also takes into account frequency reuse among sub-areas for assistant vehicle offloading. The latency minimization problem can be formulated as a mixed-integer non-linear programming (MINLP) problem. Given the inherent complexity of the MINLP problem, we propose a two-step solution. The first step focuses on the combined decision of assistant vehicle offloading and transmit power allocation. To solve this problem, we propose a particle swarm optimization (PSO) algorithm with low complexity and low average transmit power. The second step deals with RSU offloading/local computation decision, bandwidth allocation, and computation resource allocation. An iterative algorithm is proposed to achieve the optimal solution. Without adding additional computation resources, simulation results demonstrate that the proposed vehicular task offloading approach improves overall delay performance than the adaptive MEC offloading scheme and the pure MEC computing scheme.