关键词： Rendering (computer graphics)   Task analysis   Servers   Delays   Costs   Resource management   Cloud computing   Mobile edge computing   task offloading   delay sensitive   multi-dimensional resource sharing   remote rendering   interactive applications  

摘要： Leveraging emerging mobile edge computing and 5G networks, researchers proposed to offload the 3D rendering of interactive applications (e.g. virtual reality and cloud gaming) onto GPU-based edge servers to reduce the user experienced latency. A task offloading problem arises, that is where to offload rendering tasks such that each user will experience tolerable delay and meanwhile the cost of used servers is minimized. The multi-dimensional resource sharing feature of rendering tasks makes the problem challenging. We formulate the task offloading problem into a boolean linear programming. We propose a sharing-aware offloading algorithm which decomposes the problem into two subproblems (user assignment and server packing) and solves them alternately and iteratively. We compare our algorithm with the one without resource sharing in consideration, and the simulations demonstrate that our method can effectively reduce cost as well as satisfy delay requirement.

关键词： Authentication   Security   Servers   Protocols   Internet of Things   Privacy   Impersonation attacks   Anonymous authentication key agreement (AKA)   mobile edge computing   ProVerif   random oracle model   semi-trusted server attacks  

摘要： In mobile edge computing, the computing tasks of IoT terminal devices with limited computing power often need to be offloaded to servers for processing. However, there are malicious attacks by adversaries and malicious behaviors of servers in the network, coupled with the use of insecure network channels for data information transmission. These factors seriously threaten the privacy and data security of terminal devices and users. Therefore, it is urgent to use a safe and efficient anonymous authentication key agreement mechanism to verify the legitimacy of the identities of computing participants and ensure the safe transmission of task data. Recently Jia et al. proposed an identity-based authentication scheme, which combines many advantages of previous work and is resistant to various attacks. However, we found that their scheme has security problems, such as offline key guessing attack, internal attack, and user anonymity problems. We classify them as semi-trusted server attacks. In order to solve these security problems, we propose an improved scheme to better realize the authentication function by using flexible and security-enhanced keys for terminal equipment (TE), while ensuring the anonymity of the TE through implicit ID. Furthermore, we provide formal security proof, formal security verification, and security analysis for the improved protocol. Compared with the previous scheme, the scheme has certain improvements in security and performance.

关键词： Feature extraction   Servers   Data mining   Internet of Things   Cloud computing   Image edge detection   Interference   mobile edge computing   IoT services   discriminative features  

摘要： With the rapid development of the Internet of Things (IoT) and emerging Mobile Edge Computing (MEC) technologies, various IoT image recognition services are revolutionizing our lives by providing diverse cognitive assistance. However, most existing related approaches are difficult to meet the diversified needs of users because they believe that the MEC platform is a single layer. In addition, due to the mutual interference between the data, it is not easy for them to extract the discriminative features (DFs) necessary to analyze the input data. To this end, this article proposes an IoT image recognition services framework for different needs in the MEC environment, which consists of Hierarchical Discriminative Feature Extraction (HDFE) and Sub-extractor Deployment (Sub-ED) algorithms. We first propose HDFE, which can avoid mutual interference between data by separately optimizing the data structure, thereby generating an extractor that extracts effective DFs. Then there is Sub-ED, which divides the extractor into a series of sub-extractors and deploys them on appropriate MEC platforms. By doing so, the IoT device can connect to the corresponding MEC platform according to its service types, and use the sub-extract to extract DFs. Then, the MEC platform uploads the extracted feature data to the cloud server for further processing, e.g., feature matching. Finally, the cloud server sends the processed result back to the IoT device. Experimental results show that compared with the state-of-the-art approaches, the proposed framework improves recognition accuracy by about 6% and reduces network traffic by up to 94%.

关键词： Mobile social cloud   mobile edge computing   multi-objective optimization   resource allocation   computation migration  

摘要： The rapid growth of mobile device (e.g., smart phone and bracelet) has spawned a lot of new applications, during which the requirements of applications are increasing, while the capacities of some mobile devices are still limited. Such contradiction drives the emergency of computation migration among mobile edge devices, which is a lack of research currently. In this article, we focus on addressing the computation migration oriented resource allocation problem among mobile edge devices. Specifically, we first construct a framework for Mobile Social Cloud(MSC), in which the mobile devices with rich resources are abstracted as resource suppliers and those resource-lacking devices are abstracted as resource demanders. Then, a mathematical model is formulated and an evolutionary algorithm is proposed to effectively solve this model based on decomposition, dominance, and genetic operations. Moreover, the parallel computing is introduced to further improve the efficiency of the proposed algorithm. The experimental results indicate that the proposed algorithm outperforms the other state-of-the-art methods and it improves the calculation efficiency by about 178 percent (2 cores) and 262 percent (3 cores) by introducing parallel computing.

关键词： Mobile edge computing   federated learning   aggregator placement and UE assignment   hierarchical federated learning framework   approximation algorithms   online learning algorithms  

摘要： Federated learning (FL) is a distributed machine learning technique that enables model development on user equipments (UEs) locally, without violating their data privacy requirements. Conventional FL adopts a single parameter server to aggregate local models from UEs, and can suffer from efficiency and reliability issues - especially when multiple users issue concurrent FL requests. Hierarchical FL consisting of a master aggregator and multiple worker aggregators to collectively combine trained local models from UEs is emerging as a solution to efficient and reliable FL. The placement of worker aggregators and assignment of UEs to worker aggregators plays a vital role in minimizing the cost of implementing FL requests in a Mobile Edge Computing (MEC) network. Cost minimization associated with joint worker aggregator placement and UE assignment problem in an MEC network is investigated in this work. An optimization framework for FL and an approximation algorithm with an approximation ratio for a single FL request is proposed. Online worker aggregator placements and UE assignments for dynamic FL request admissions with uncertain neural network models, where FL requests arrive one by one without the knowledge of future arrivals, is also investigated by proposing an online learning algorithm with a bounded regret. The performance of the proposed algorithms is evaluated using both simulations and experiments in a real testbed with its hardware consisting of server edge servers and devices and software built upon an open source hierarchical FedML (HierFedML) environment. Simulation results show that the performance of the proposed algorithms outperform their benchmark counterparts, by reducing the implementation cost by at least 15% per FL request. Experimental results in the testbed demonstrate the performance gain using the proposed algorithms using real datasets for image identification and text recognition applications.

关键词： Cloud computing   Servers   Edge computing   Routing   Aerospace electronics   Distributed databases   Control systems   Data placement   data retrieval   cooperative edge clouds   mobile edge computing  

摘要： Mobile edge computing is a new paradigm in which the computing and storage resources are placed at the edge of the Internet. Data placement and retrieval are fundamental services of mobile edge computing when a network of edge clouds collaboratively provide data services. These services require short-latency and low-overhead implementation in network and computing devices and load balance on edge clouds. However existing methods such as distributed hash tables (DHTs) are not enough to achieve efficient data placement and retrieval services for cooperative edge clouds. This article presents GRED, a novel data placement and retrieval service for mobile edge computing, which is efficient in not only the load balance but also routing path lengths and forwarding table sizes. GRED utilizes the programmable switches to support a virtual-space based DHT with only one overlay hop. Data location can be easily implemented on top of the GRED by associating a virtual position with each data by hashing, and storing the data at the edge server connected to the switch whose position is the nearest to the position of the data in the virtual space. We implement GRED in a P4 prototype, which provides a simple and efficient solution. Results from theoretical analysis, simulations, and experiments show that GRED can efficiently balance the load of edge clouds, and can fast answer data queries due to its low routing stretch.

关键词： Security   Quality of service   Forecasting   Real-time systems   Predictive models   Privacy   Servers   Mobile edge computing   joint training   independent learning   privacy-aware forecasting  

摘要： We propose a novel privacy-aware Quality of Service (QoS) forecasting approach in the mobile edge environment - Edge-PMAM (Edge QoS forecasting with Public Model and Attention Mechanism). Edge-PMAM can make real-time, accurate and personalized QoS forecasting on the premise of user privacy preservation. Edge-PMAM comprises a public model for privacy-aware QoS forecasting in an edge region and a private model for personalized QoS forecasting for an individual user. An attention mechanism atop Long Short-Term Memory and an automated edge region division solution are devised to enhance the prediction accuracy of the public and private models. We conduct a series of experiments based on public and self-collected data sets. The results demonstrate that our approach can effectively improve forecasting performance and protect user privacy.

关键词： Mobile edge computing   Reconfigurable intelligent surface   Computation offloading   Resource allocation   Deep Deterministic Policy Gradient   6G  

摘要： Mobile edge computing (MEC) enables mobile devices (MDs) to offload computation-intensive tasks to edge servers to support a variety of latency-sensitive emerging applications (such as the Internet of Vehicles, real-time video analytics, etc.). However, the time-varying communication link environment of signal occlusion and interference between MDs and edge servers often leads to disappointing offloading benefits. Reconfig-urable intelligent surface (RIS) is recognized as a promising technology in sixth-generation communication networks, with great potential to intelligently adjust the phase shift and amplitude of reflective elements to enhance wireless network capabilities. This paper proposes a novel computation offloading algorithm for RIS empowered MEC networks. Specifically, we comprehensively consider the optimization problems of delay, energy consumption, and operator cost in the process of computation offloading, and model it as a Markov decision process. To overcome the continuous action space challenge, we propose a computation offloading algorithm based on Deep Deterministic Policy Gradient (DDPG) to jointly optimize the phase shift and amplitude of RIS, offloading decision, and MEC resource allocation strategy. Finally, compared with various other benchmark algorithms, our proposed algorithm has a significant performance improvement over non-RIS learning algorithms and other classical algorithms, and maintains the optimal performance.

关键词： Resource management   Servers   Quality of service   Task analysis   Costs   Games   Quality of experience   Budget-constrained service allocation   lyapunov optimization method   mobile edge computing   non-cooperative game   nash equilibrium  

摘要： The service resource allocation strategy optimization problem has always been a hot issue in mobile edge computing (MEC). In this article, we formulate the problem as a long-term quality of service (QoS) improvement problem while satisfying the budget of MEC service provider (MSP). Since it is very unrealistic to accurately obtain the request information of user equipments (UEs) over a long time, we first transform the original problem into a series of real-time linear programing sub-problems by using Lyapunov optimization method, and propose a centralized algorithm to determine the resource allocation strategies. However, since the sub-problems are still NP-hard problems, it is a huge challenge to determine the strategies for all UEs with the centralized algorithm in a large scale MEC environment. Thus, we then formulate the sub-problem as an $N$N players non-cooperative game, prove that there exists a Nash equilibrium, and develop two iterative algorithms to find the Nash equilibrium while determining the strategies. Experimental results show that the algorithms can take into account QoS and budget of MSP at the same time, and perform better compared to five other common schemes.

关键词： Cooperative, Cooperative and Automated Mobility (CCAM)   5G Networks   Connected & Automated Vehicles (CAVs)   Cooperative Intelligent Transportation Systems (C-ITS)   Mobile Edge Computing  

摘要： The adoption of 5G cellular communications enables advanced V2X use cases, namely ones involving detailed HD-Maps and real-time holistic sharing of vehicle positions and status between autonomous, connected and even legacy vehicles. The 5G-MOBIX project explores the implications of such use cases. This paper presents the Portuguese 5G Connected Vehicle and Roadside Infrastructure that was developed for supporting the 5G-MOBIX project cross-border trials between Spain and Portugal. The paper details the architecture of the 5G Connected Vehicle and Infrastructure, the technology used, how integrations were achieved and how these CCAM solutions enable the use cases of the 5G-MOBIX project cross-border trials between Spain and Portugal.