关键词： And reliability   data caching   data dissemination   distributed consensus   efficiency   mobile edge computing   And reliability   data caching   data dissemination   distributed consensus   efficiency   mobile edge computing  

摘要： Mobile edge computing (MEC) enables web data caching in close geographic proximity to end users. Popular data can be cached on edge servers located less than hundreds of meters away from end users. This ensures bounded latency guarantees for various latency-sensitive web applications. However, transmitting a large volume of data out of the cloud onto many geographically-distributed web servers individually can be expensive. In addition, web content dissemination may be interrupted by various intentional and accidental events in the volatile MEC environment, which undermines dissemination efficiency and subsequently incurs extra transmission costs. To tackle the above challenges, we present a novel scheme named EdgeDis that coordinates data dissemination by distributed consensus among those servers. We analyze EdgeDis's validity theoretically and evaluate its performance experimentally. Results demonstrate that compared with baseline and state-of-the-art schemes, EdgeDis: 1) is 5.97x - 7.52x faster;2) reduces dissemination costs by 48.21% to 91.87%;and 3) reduces performance loss caused by dissemination failures by up to 97.30% in time and 96.35% in costs.

关键词： mobile edge computing   unmanned aerial vehicle   deployment optimization   task offloading   evolutionary algorithm  

摘要： With the development of digital economy technologies, mobile edge computing (MEC) has emerged as a promising computing paradigm that provides mobile devices with closer edge computing resources. Because of high mobility, unmanned aerial vehicles (UAVs) have been extensively utilized to augment MEC to improve scalability and adaptability. However, with more UAVs or mobile devices, the search space grows exponentially, leading to the curse of dimensionality. This paper focus on the combined challenges of the deployment of UAVs and the task of offloading mobile devices in a large-scale UAV-assisted MEC. Specifically, the joint UAV deployment and task offloading problem is first modeled as a large-scale multiobjective optimization problem with the purpose of minimizing energy consumption while improving user satisfaction. Then, a large-scale UAV deployment and task offloading multiobjective optimization method based on the evolutionary algorithm, called LDOMO, is designed to address the above formulated problem. In LDOMO, a CSO-based evolutionary strategy and a MLP-based evolutionary strategy are proposed to explore solution spaces with different features for accelerating convergence and maintaining the diversity of the population, and two local search optimizers are designed to improve the quality of the solution. Finally, simulation results show that our proposed LDOMO outperforms several representative multiobjective evolutionary algorithms.

关键词： blockchain   mobile edge computing   task offloading   multi-agent reinforcement learning  

摘要： In recent years, many mobile edge computing network solutions have enhanced data privacy and security and built a trusted network mechanism by introducing blockchain technology. However, this also complicates the task-offloading problem of blockchain-enabled mobile edge computing, and traditional evolutionary learning and single-agent reinforcement learning algorithms are difficult to solve effectively. In this paper, we propose a blockchain-enabled mobile edge computing task-offloading strategy based on multi-agent reinforcement learning. First, we innovatively propose a blockchain-enabled mobile edge computing task-offloading model by comprehensively considering optimization objectives such as task execution energy consumption, processing delay, user privacy metrics, and blockchain incentive rewards. Then, we propose a deep reinforcement learning algorithm based on multiple agents sharing a global memory pool using the actor-critic architecture, which enables each agent to acquire the experience of another agent during the training process to enhance the collaborative capability among agents and overall performance. In addition, we adopt attenuatable Gaussian noise into the action space selection process in the actor network to avoid falling into the local optimum. Finally, experiments show that this scheme's comprehensive cost calculation performance is enhanced by more than 10% compared with other multi-agent reinforcement learning algorithms. In addition, Gaussian random noise-based action space selection and a global memory pool improve the performance by 38.36% and 43.59%, respectively.

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

关键词： online   offline   service caching   inference   mobile edge computing   deep reinforcement learning  

摘要： Edge servers frequently manage their own offline digital twin (DT) services, in addition to caching online digital twin services. However, current research often overlooks the impact of offline caching services on memory and computation resources, which can hinder the efficiency of online service task processing on edge servers. In this study, we concentrated on service caching and task offloading within a collaborative edge computing system by emphasizing the integrated quality of service (QoS) for both online and offline edge services. We considered the resource usage of both online and offline services, along with incoming online requests. To maximize the overall QoS utility, we established an optimization objective that rewards the throughput of online services while penalizing offline services that miss their soft deadlines. We formulated this as a utility maximization problem, which was proven to be NP-hard. To tackle this complexity, we reframed the optimization problem as a Markov decision process (MDP) and introduced a joint optimization algorithm for service caching and task offloading by leveraging the deep Q-network (DQN). Comprehensive experiments revealed that our algorithm enhanced the utility by at least 14.01% compared with the baseline algorithms.

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： 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： Digital twins   Heuristic algorithms   Approximation algorithms   Delays   Edge computing   Containers   Computers   Digital twin   serverless computing   mobile edge computing   user mobility   approximation algorithm   online algorithm   user service satisfaction   delay-sensitive service   utility maximization   resource allocation and optimization  

摘要： Driven by data and models, the digital twin technique presents a new concept of optimizing system design, process monitoring, decision-making and more, through performing comprehensive virtual-reality interaction and continuous mapping. By introducing serverless computing to Mobile Edge Computing (MEC) environments, the emerging serverless edge computing paradigm facilitates the communication-efficient digital twin services, and promises agile, fine-grained and cost-efficient provisioning of limited edge resources, where serverless functions are implemented by containers in cloudlets (edge servers). However, the nonnegligible cold start delay of containers deteriorates the responsiveness of digital twin services dramatically and the perceived user service experience. In this paper, we investigate delay-sensitive query service provisioning in digital twin-empowered serverless edge computing by considering user mobility. With digital twins of users deployed in the remote cloud, referred to as primary digital twins, we deploy their digital twin replicas based on serverless functions in cloudlets to mitigate the query service delay while enhancing user service satisfaction that is expressed as a utility function. We study two optimization problems with the aim of maximizing the accumulative utility gain: the digital twin replica placement problem per time slot, and the dynamic digital twin replica placement problem over a finite time horizon. We first formulate an Integer Linear Program (ILP) solution for the digital twin replica placement problem when the problem size is small;otherwise, we propose an approximation algorithm for the problem with a provable approximation ratio. We then design an online algorithm for the dynamic digital twin replica placement problem, and a performance-guaranteed online algorithm for a special case of the problem by assuming each user issues a query at each time slot. Finally, we evaluate the performance of the proposed algorithms for pl

关键词： Task analysis   Servers   Wireless communication   Resource management   Fault tolerant systems   Fault tolerance   Wireless sensor networks   Mobile edge computing   wireless networks   resource allocation   fault-tolerance  

摘要： This letter studies fault tolerance and resource allocation for offloaded tasks in Mobile Edge Computing (MEC). The limitations of computational resources and wireless transmission power, coupled with server failures in MEC, present challenges for optimizing resource allocation and ensuring uninterrupted task execution. Existing literature primarily addresses resource allocation and fault tolerance separately, resulting in resource wastage and an increased count of faulty tasks. To address these issues, we first propose a novel method for jointly allocating computational resources and wireless transmission power for offload task. We then introduce the Dynamic Fault Tolerance Task Packing (DFTTP) algorithm, an online task packing algorithm. DFTTP, based on a new variant of the primary-standby replica mechanism, can tolerate multiple server failures simultaneously. Theoretical analysis and simulation results demonstrate that DFTTP, together with the proposed resource allocation method, can reduce the number of faulty tasks and enable higher resource utilization compared with existing algorithms.

关键词： Task analysis   Industrial Internet of Things   Smart manufacturing   Servers   Computational modeling   Energy consumption   Data models   Deep reinforcement learning   differential privacy   federated learning   industrial Internet of Things   mobile edge computing  

摘要： Industry 5.0 facilitates the intelligent upgrade of smart factories in Industrial Internet of Things (IIoT), and also introduces a plethora of data processing challenges. Mobile edge computing offloads data to edge servers for processing, easing the data processing pressure and reducing system cost. However, smart factories contain numerous sensitive information, and offloading them to edge servers directly may pose a risk of data leakage. To address these challenges, we investigate a local-edge collaborative smart factory system. Specifically, we first model the tasks as a directed acyclic graph, and formulate the offloading problem as a Markov decision process, considering the optimization of latency, energy consumption and the number of overtime tasks. Then, we propose a security-aware computation offloading method using federated reinforcement learning in IIoT, named SCOF. SCOF employs federated learning, keeping data for local computation and uploading model parameters to edge servers for aggregation. The data transmission passes through an artificial noise channel to protect against eavesdropping. Meanwhile, SCOF utilizes differential privacy to protect data security and employs deep reinforcement learning for selecting near-optimal offloading decisions. Finally, abundant experiments are conducted under a real dataset. The results show that SCOF has better perfomance than the state-of-the-art baseline algorithms.