关键词： Computation offloading   Internet of things   Mobile edge computing   Multi-access edge computing   Task offloading  

摘要： With the rapid development of Internet of Things (IoT) technology, Terminal Devices (TDs) are more inclined to offload computing tasks to higher-performance computing servers, thereby solving the problems of insufficient computing capacity and rapid battery consumption of TD. The emergence of Multi-access Edge Computing (MEC) technology provides new opportunities for IoT task offloading. It allows TDs to access computing networks through multiple communication technologies and supports more mobility of terminal devices. Review studies on IoT task offloading and MEC have been extensive, but none of them focus on IoT task offloading in MEC. To fill this gap, this paper provides a comprehensive and in-depth understanding of the algorithms and mechanisms of multiple IoT task offloading in the MEC network. For each paper, the main problems solved by the mechanism, technical classification, evaluation methods, and supported parameters are extracted and analyzed. Furthermore, shortcomings of current research and future research trends are discussed. This review will help potential and new researchers quickly understand the panorama of IoT task offloading approaches in MEC and find appropriate research paths.

关键词： Radar echo extrapolation   Mobile edge computing   Deep learning   Spatiotemporal  

摘要： With the advent of Mobile Edge Computing (MEC), shifting data processing from cloud centers to the network edge presents an advanced computational paradigm for addressing latency-sensitive applications. Specifically, in radar systems, the real-time processing and prediction of radar echo data pose significant challenges in dynamic and resource-constrained environments. MEC, by processing data near its source, not only significantly reduces communication latency and enhances bandwidth utilization but also diminishes the necessity of transmitting large volumes of data to the cloud, which is crucial for improving the timeliness and efficiency of radar data processing. To meet this demand, this paper proposes a model that integrates a spatiotemporal Attention Module (STAM) with a Long Short-Term Memory Gated Recurrent Unit (ST-ConvLSGRU) to enhance the accuracy of radar echo prediction while leveraging the advantages of MEC. STAM, by extending the spatiotemporal receptive field of the prediction units, effectively captures key inter-frame motion information, while optimizations to the convolutional structure and loss function further boost the model's predictive performance. Experimental results demonstrate that our approach significantly improves the accuracy of short-term weather forecasting in a mobile edge computing environment, showcasing an efficient and practical solution for processing radar echo data under dynamic, resource-limited conditions.

关键词： computation efficiency   mobile edge computing   non-orthogonal multiple access   unmanned aerial vehicles  

摘要： Unmanned aerial vehicles (UAVs) can serve as aerial mobile edge computing (MEC) servers to provide computing services to Internet of Things smart devices (ISDs) with insufficient computing capacity on the ground. However, how to provide energy-efficient computing services by designing appropriate resource optimization strategy is still a challenge issue in UAV-enabled MEC networks. To this end, this paper proposes a computation efficiency (CE)-oriented partial offloading framework for UAV-enabled MEC networks, where the ISDs' computation bits and energy consumption are taken into account simultaneously. Specifically, we firstly divide the ISDs into multiple clusters and determine the deployment of UAVs by k-means method. Meanwhile, ISDs occupying the same subchannel in the same cluster can offload data through non-orthogonal multiple access (NOMA) technology. Then, the problem of maximizing the CE of the system is formulated by optimizing subchannel allocation, transmit power and computation resources of ISDs. To solve it, we propose a staged optimization approach by using matching theory, Dinkelbach and sequential convex programming (SCP) methods. Numerical results demonstrate the proposed scheme can achieve higher CE compared with other baseline schemes.

关键词： Consortium blockchain   Mobile edge computing   Consensus algorithm   E-commerce   Privacy protection  

摘要： The application of blockchain technology to the field of e-commerce has solved many dilemmas, such as low transparency of transactions, hidden risks of data security and high payment costs. Mobile edge computing(MEC) can provide computational power for blockchain, and can meet the demand for high real-time and low latency in e-commerce transaction systems. However, there are still some constraints in the MEC enabled e-commerce consortium blockchain, such as the leakage of user privacy information, low security of consensus algorithm and other security issues. In this paper, we propose a secure transaction model suitable for MEC enabled e-commerce consortium blockchain, aiming to ensure the efficiency of system transaction processing while improving the security of users' privacy information and transaction data. The model adopts the lightweight Paillier encryption algorithm to protect the security of user privacy information and transaction data to prevent the leakage of user privacy information, and optimizes the security of leader election phase of Raft consensus algorithm by introducing the shamir secret sharing protocol to improve the anti-Byzantine failure capabilities of Raft consensus algorithm. The effectiveness of the scheme proposed in this paper is demonstrated by experimental simulations.

关键词： Sensitive data identification   Mobile edge computing   Industrial internet  

摘要： Sensitive data identification represents the initial and crucial step in safeguarding sensitive information. With the ongoing evolution of the industrial internet, including its interconnectivity across various sectors like the electric power industry, the potential for sensitive data to traverse different domains increases, thereby altering the composition of sensitive data. Consequently, traditional approaches reliant on sensitive vocabularies struggle to adequately address the challenges posed by identifying sensitive data in the era of information abundance. Drawing inspiration from advancements in natural language processing within the realm of deep learning, we propose a transferable Sensitive Data Identification method based on Product Quantization, named PQ-SDI. This innovative approach harnesses both the composition and contextual cues within textual data to accurately pinpoint sensitive information within the context of Mobile Edge Computing (MEC). Notably, PQ-SDI exhibits proficiency not only within a singular domain but also demonstrates adaptability to new domains following training on heterogeneous datasets. Moreover, the method autonomously identifies sensitive data throughout the entire process, eliminating the necessity for human upkeep of sensitive vocabularies. Extensive experimentation with the PQ-SDI model across four real-world datasets, resulting in performance improvements ranging from 2% to 5% over the baseline model and achieves an accuracy of up to 94.41%. In cross-domain trials, PQ-SDI achieved comparable accuracy to training and identification within the same domain. Furthermore, our experiments showcased the product quantization technique significantly reduces the parameter size by tens of times for the subsequent sensitive data identification phase, particularly beneficial for resource-constrained environments characteristic of MEC scenarios. This inherent advantage not only bolsters sensitive data protection but also mitigates the ris

关键词： Cooperative caching   Mobile edge computing   Cost effective   Online algorithm  

摘要： The proliferation of technological advancement in the Internet of Things (IoT) and mobile devices accelerates the need for latency-sensitive multimedia applications. The network operator may charge application vendors to cache their content. Vendors risk losing their user base if users don't receive the expected Quality of Service (QoS), which includes meeting specific delay thresholds for services like video on demand. While multiple content copies can enhance QoS by delivering content quickly, maintaining redundant copies is costly. Therefore, deploying edge servers at edge nodes and caching content is a prominent solution for application vendors. This approach guarantees lower latency, fewer content replicas, and reduced strain on backhaul links. However, the limited cache capabilities of individual edge nodes present challenges, such as efficiently allocating scarce resources to meet user demands. This work addresses the earlier challenges by formulating a cooperative content caching and forwarding problem by placing the various latency-sensitive content to minimize the total cost with resource and deadline constraints. The problem is formulated as an integer linear programming problem for cooperative caching in mobile edge networks to mitigate the total cost to the application vendors. In the absence of content popularity information, an online cooperative caching mechanism is presented to solve the dynamic content request and forwarding problem. Extensive simulations have been performed to illustrate that the proposed online algorithm significantly enhances the performance in terms of the total cost, load on the cloud, hit ratio, time utility, and cache utilization.

关键词： air-ground   architecture   collaborative   mobile edge computing  

摘要： By pushing computation,cache,and network control to the edge,mobile edge computing(MEC)is expected to play a leading role in fifth generation(5G)and future sixth generation(6G).Nevertheless,facing ubiquitous fast-growing computational demands,it is impossible for a single MEC paradigm to effectively support high-quality intelligent services at end user equipments(UEs).To address this issue,we propose an air-ground collaborative MEC(AGCMEC)architecture in this *** proposed AGCMEC integrates all potentially available MEC servers within air and ground in the envisioned 6G,by a variety of collaborative ways to provide computation services at their best for ***,we introduce the AGC-MEC architecture and elaborate three typical use ***,we discuss four main challenges in the AGC-MEC as well as their potential ***,we conduct a case study of collaborative service placement for AGC-MEC to validate the effectiveness of the proposed collaborative service placement ***,we highlight several potential research directions of the AGC-MEC.

关键词： Metaverse   Data privacy   Privacy computing   Mobile edge computing   Blockchain   Extended reality  

摘要： Metaverse, the core of the next -generation Internet, is a computer -generated holographic digital environment that simultaneously combines spatio-temporal, immersive, real-time, sustainable, interoperable, and datasensitive characteristics. It cleverly blends the virtual and real worlds, allowing users to create, communicate, and transact in virtual form. With the rapid development of emerging technologies including augmented reality, virtual reality and blockchain, the metaverse system is becoming more and more sophisticated and widely used in various fields such as social, tourism, industry and economy. However, the high level of interaction with the real world also means a huge risk of privacy leakage both for individuals and enterprises, which has hindered the wide deployment of metaverse. Then, it is inevitable to apply privacy computing techniques in the framework of metaverse, which is a current research hotspot. In this paper, we conduct comprehensive research on the necessity, taxonomy and challenges when privacy computing meets metaverse. Specifically, we first introduce the underlying technologies and various applications of metaverse, on which we analyze the challenges of data usage in metaverse, especially data privacy. Next, we review and summarize state-of-theart solutions based on federated learning, differential privacy, homomorphic encryption, and zero -knowledge proofs for different privacy problems in metaverse. Finally, we show the current security and privacy challenges in the development of metaverse and provide open directions for building a well -established privacy -preserving metaverse system. For easy access and reference, we integrate the related publications and their codes into a GitHub repository: https://***/6lyc/***.

关键词： Mobile edge computing  

摘要： In order to increase energy efficiency in Mobile-Edge Computing (MEC) systems, this research suggests a novel approach that combines Non-Orthogonal Multiple Access (NOMA) with energy collecting in a full-duplex (FD) context. The main optimization objective is to increase edge computing capabilities while lowering the system's overall energy usage. A combined resource allocation approach that takes into account CPU frequency, uplink and downlink power control, time scheduling, and data offloading for local computing users is introduced in order to do this. According to the suggested technique, mobile users can delegate their computationally demanding jobs to MEC servers connected by base stations via NOMA, which promotes effective information exchange, to MEC servers. Base stations also use full-duplex technology, which allows them to accept offloading jobs while also transmitting energy to other users, maximizing the efficiency of the available resources. The Block Coordinate Descent (BCD) approach is used to create a joint communication and computing resource allocation algorithm, which addresses the non-convex aspect of the optimization model. The original non-convex problem is effectively solved by this approach, which also produces an ideal answer in closed form. The NOMA-assisted FD-MEC method suggested in this research outperforms conventional Orthogonal Multiple Access (OMA) and Half Duplex (HD) approaches, according to simulation results. Energy efficiency is increased as a result of a large decrease in the system's overall energy usage. This result demonstrates the potential of combining NOMA, energy harvesting, and full-duplex technologies to create MEC systems that are more environmentally friendly and energy-efficient. © 2024, The Authors. All rights reserved.

关键词： Energy efficiency   Mobile edge computing   Mobility-awareness   Resource allocation  

摘要： Mobile edge computing (MEC) is a promising paradigm that helps mobile terminals save energy and increase battery life by offloading tasks to MEC servers. To obtain full benefit from MEC, the joint optimization of offloading decision -making and resource allocation, while considering user mobility, is crucial and challenging. The objective of this paper was to propose mobility -aware and energy -efficient offloading schemes for MEC in cellular networks. The proposed schemes helped to make offloading decisions, allocate transit power, and schedule transmissions among multiple moving users. An optimal scheme was proposed, in terms of minimizing total energy consumed by all users, which was based on Mixed -Integer Programming (MIP). Considering the high computational complexity of solving MIP problems, a practical heuristic scheme was also proposed. Simulation results showed the effectiveness in reducing energy consumption of both the proposed optimal and heuristic schemes and demonstrated the importance of mobility awareness as well.