关键词： Streaming media   Stochastic processes   Video recording   Quality assessment   Performance evaluation   Delays   Visual analytics   Mobile edge computing   video offloading   mobility   video quality   stochastic program   sample average approximation   clustering   multi-armed bandit   online learning   ENERGY-EFFICIENT   RESOURCE-ALLOCATION   MULTIARMED BANDIT  

摘要： Videos are projected to account for roughly 80% of global mobile data traffic by 2028. Many camera-equipped mobile devices, such as surveillance drones, require realtime video analytics, encompassing tasks like object detection and action recognition. These devices, restricted by limited resource constraints, need to offload videos to Mobile Edge Computing (MEC) to simultaneously optimize video analytics performance and minimize delay. However, MEC is facing significant challenges in providing efficient video offloading solutions, especially due to uncertainties caused by dynamic device mobility and the associated trade offs in selecting video quality for offloading. Offloading high-quality videos enhances video analytics performance, such as object detection accuracy. Yet, as a mobile device relocates, lower video quality or serving by a different MEC cloudlet may be required (triggering a service migration) to maintain a satisfactory service performance. In this paper, we study the Video Offloading Problem (VOP) in MEC to address these challenges. We propose two uncertainty-aware approaches that model the uncertainties in the environment to solve VOP. Our first approach, focusing on system side, is based on Two-stage Stochastic Program and proposing a unique clustering-based Sample Average Approximation to effectively solve TSP-VOP. The second approach, focusing on device side, employs an online learning algorithm based on a multi-armed bandit to learn and select the optimal offloading solution online. Through extensive experiments, we show that our proposed approaches significantly enhance video offloading decisions, with high video quality and reduced service migration costs under uncertain device mobility, compared to other benchmarks.

关键词： Mobile edge computing   Truthfulness   Resource allocation   Energy consumption   Mechanism design   AUCTION MECHANISM  

摘要： Mobile Edge Computing (MEC) aims at decreasing the response time and energy consumption of running mobile applications by offloading the tasks of mobile devices (MDs) to the MEC servers located at the edge of the network. The demands are multi-attribute, where the distances between MDs and access points lead to differences in required resources and transmission energy consumption. Unfortunately, the existing works have not considered both task allocation and energy consumption problems. Motivated by this, this paper considers the problem of task allocation with multi-attributes, where the problem consists of the winner determination and offloading decision problems. First, the problem is formulated as the auction-based model to provide flexible service. Then, a randomized mechanism is designed and is truthful in expectation. This drives the system into an equilibrium where no MD has incentives to increase the utility by declaring an untrue value. In addition, an approximation algorithm is proposed to minimize remote energy consumption and is a polynomial-time approximation scheme. Therefore, it achieves a tradeoff between optimality loss and time complexity. Simulation results reveal that the proposed mechanism gets the near-optimal allocation. Furthermore, compared with the baseline methods, the proposed mechanism can effectively increase social welfare and bring higher revenue to edge server providers.

关键词： Mobile edge computing   Truthfulness   Resource allocation   Algorithm design   TASK ALLOCATION   EFFICIENT  

摘要： We address the problem of resource allocation with multi-attribute demand considering heterogeneous servers in mobile edge computing (MEC). A mobile device (MD) may be within the coverage area of multiple access points and can get resources from any server that can establish direct communication. However, the configurations of heterogeneous servers are different, so the demand for each MEC server is different. We tackle this problem and consider multi-attribute demand, where each MD is assigned exactly one of multiple demands. We formulate the allocation problem in an auction-based setting to provide server scalability. We propose a family of greedy mechanisms to solve the resource allocation problem. However, each MD is self-interested and may try to manipulate the system to maximize its utility. We show our mechanisms are truthful, and they drive the system into an equilibrium where no MD can declare an untrue value to obtain higher utility. We also analyze the approximation ratios of our greedy mechanisms. Experimental results demonstrate that the greedy mechanisms obtain the near-optimal allocation in a short period while benefiting MDs and edge cloud providers.

关键词： Mobile Edge Computing   Smart Manufacturing System   Offloading Decision   E-CARGO   Dynamic Weights   Resource Contention Mitigation   TASK  

摘要： With the widespread use of Mobile Edge Computing (MEC) in smart manufacturing systems in Industrial Internet of Things (IIoT) and 5G networks, determining how to efficiently offload computing tasks has become a hot research area. The Role-Based Collaboration (RBC) Environments-Classes, Agents, Roles, Groups, and Objects (E- CARGO) model is introduced to comprehensively manage MEC servers and user computation tasks in edge network environments, thereby improving the effectiveness and performance of task offloading in smart manufacturing systems. To begin with, latency and energy consumption are important indicators for evaluating the offloading effect. A pre-allocation algorithm based on user latency tolerance is proposed to dynamically adjust the latency-energy consumption weighting factor to optimize system resource allocation for real-time adjustment of offloading decisions. Second, the Group Role Assignment of Agent Role Conflicts (GRACAR) model based on E-CARGO is extended, along with a dynamic weighting of the GRACAR (GRACAR-DW) model and formal modeling. By introducing resource contention constraints, the resource contention caused by excessive task data offloading to the same MEC server is proactively mitigated. Finally, a Gurobi solution based on Mixed-Integer Linear Programming (MILP) is developed to help validate and synthesize the proposed model. Simulation results show that the strategy considerably enhances the MEC system's overall performance in terms of latency and energy consumption while also providing new ideas and technological support for offloading decisions in edge networks.

关键词： Task analysis   Resource management   Optimization   Costs   Load management   Collaboration   Cloud computing   Computation offloading   deep reinforcement learning   mobile edge computing   resource allocation   task migration   EDGE   OPTIMIZATION   MIGRATION  

摘要： Internet-of-Things (IoT) has played a critical role in developing sustainable smart cities and emerging numerous latency-sensitive IoT applications. Mobile edge computing (MEC) federation has the capability to incorporate a transparent resource management approach, which enables the sharing and utilization of MEC services from edge infrastructure providers (EIPs) and provides agile access services to mobile devices (MDs). In this paper, we investigate the joint optimization problem of computation offloading, task migration, and resource allocation in the MEC federation. The objective is to minimize the weighted sum of latency and energy consumption while maintaining load balancing under the constraint of the long-term migration cost budget of EIPs. To address the problem, we decompose it into two sub-problems: 1) the MDs clustering sub-problem and 2) the sub-problem of joint computation offloading, task migration, and resource allocation. First, an MDs clustering matching (MDCM) algorithm is proposed to cluster the MDs in edge servers (ESs) according to the differences in channel gains. Afterward, the second sub-problem is simplified by the Lyapunov optimization technique, and then we propose a Transformer-based mobility prediction model and a decentralized deep deterministic policy gradient (DDPG)-based framework to solve it. Extensive simulation results demonstrate the cost-efficiency of the proposed algorithm.

关键词： Mobile edge computing   Smart driving   Task offloading   Mobility management   Load balancing   SECURITY  

摘要： With the continuous popularization and evolution of 5G and 6G, mobile edge computing has achieved rapid development. This study explores the New Generation Mobile Edge Computing (NGMEC) architecture, which leverages numerous mobile nodes to provide users with enhanced computing services. Despite its advantages, NGMEC faces challenges such as high node mobility, load balancing difficulties, and incomplete environmental perception by agents, particularly in intelligent driving task offloading scenarios. We address these challenges by introducing novel applications of NGMEC and proposing specialized algorithms for node selection and load balancing. Furthermore, to tackle the issue of environmental perception incompleteness in NGMEC task offloading, we develop the Gated Recurrent Self -Encoding Deep Reinforcement Learning (GRSE-DRL) algorithm. Our research also includes the development of two platforms: the End -Edge -Cloud Simulation Experiment Platform and the Edge Computing Offloading Algorithm Energy Efficiency Test Platform. Experimental results demonstrate that our proposed scheme effectively maintains load balance among nodes, enhances task completion and connection success rates, and optimizes the trade-off between transmission delay and intelligent driving algorithms' effectiveness, enabling more efficient decision -making.

关键词： Mobile edge computing  

摘要： Home appliance manufacturers are increasingly focusing on building smarter home systems by incorporating user feedback to enhance products and services. To support this, we designed a federated learning (FL) system that includes a reputation mechanism to help manufacturers leverage customer data to train machine learning models. First, it downloads the initial model provided by the manufacturer and trains it with local data. Then, it asks customers to sign their models and upload them to the blockchain. To protect customer privacy, we implemented differential privacy and introduced a new normalization technique. In addition, we also attract more customers to participate in crowdsourced FL tasks by rewarding their contributions, thereby ensuring that the datasets for model training are robust and diverse. This system not only promotes collaboration between customers and manufacturers, but also facilitates the development of more responsive and smarter home appliance systems through advanced FL and blockchain technologies. © 2024 Faculty of Engineering, Alexandria University

关键词： unmanned aerial vehicle   mobile edge computing   computation offloading   deep deterministic policy gradient   K-nearest neighbor  

摘要： Unmanned aerial vehicles (UAVs) are widely used to improve the coverage and communication quality of wireless networks and assist mobile edge computing (MEC) due to their flexible deployments. However, the UAV-assisted MEC systems also face challenges in terms of computation offloading and trajectory planning in the dynamic environment. This paper employs deep reinforcement learning to jointly optimize the computation offloading and trajectory planning for UAV-assisted MEC system. Specifically, this paper investigates a general scenario where multiple pieces of user equipment (UE) offload tasks to a UAV equipped with a MEC server to collaborate on a complex job. By fully considering UAV and UE movement, computation offloading ratio, and blocked relations, a joint computation offloading and trajectory optimization problem is formulated to minimize the maximum computational delay. Due to the non-convex nature of the problem, it is converted into a Markov decision process, and solved by the deep deterministic policy gradient (DDPG) algorithm. To enhance the exploration capability and stability of DDPG, the K-nearest neighbor (KNN) algorithm is employed, namely KNN-DDPG. Moreover, the prioritized experience replay algorithm, where the constant learning rate is replaced by the decaying learning rate, is utilized to enhance the converge. To validate the effectiveness and superiority of the proposed algorithm, KNN-DDPG is compared with the benchmark DDPG algorithm. Simulation results demonstrate that KNN-DDPG can converge and achieve 3.23% delay reduction compared to DDPG.

关键词： Mobile edge computing   Workflow scheduling   Security   Remora optimization algorithm   Feedback artificial tree   ERROR-DETECTION   HARDWARE CONSTRUCTIONS   EFFICIENT  

摘要： The rapid advancement of mobile communication technology and devices has greatly improved our way of life. It also presents a new possibility that data sources can be used to accomplish computing tasks at nearby locations. Mobile Edge Computing (MEC) is a computing model that provides computer resources specifically designed to handle mobile tasks. Nevertheless, there are certain obstacles that must be carefully tackled, specifically regarding the security and quality of services in the workflow scheduling over MEC. This research proposes a new method called Feedback Artificial Remora Optimization (FARO)-based workflow scheduling method to address the issues of scheduling processes with improved security in MEC. In this context, the fitness functions that are taken into account include multi-objective, such as CPU utilization, memory utilization, encryption cost, and execution time. These functions are used to enhance the scheduling of workflow tasks based on security considerations. The FARO algorithm is a combination of the Feedback Artificial Tree (FAT) and the Remora Optimization Algorithm (ROA). The experimental findings have demonstrated that the developed approach surpassed current methods by a large margin in terms of CPU use, memory consumption, encryption cost, and execution time, with values of 0.012, 0.010, 0.017, and 0.036, respectively.

关键词： Low earth orbit satellites   Satellites   Task analysis   Computational modeling   Edge computing   Heuristic algorithms   Energy consumption   Mobile edge computing   low earth orbit satellite   D3QN   computation offloading   queue  

摘要： Efficient computation offloading is crucial for resource-constrained users in low earth orbit (LEO) satellite edge computing system. The proposed Dueling Double Deep Q Network (D3QN)-based computation offloading algorithm considers LEO satellite mobility, dynamic load levels, queuing theory, and jointly optimizes system delay and energy consumption. Simulation results show that the proposed algorithm has better system cost than other comparison algorithms.