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： Blockchain   Security   key management   group communication   mobile edge computing  

摘要： Mobile edge computing (MEC) is a promising edge technology to provide high bandwidth and low latency shared services and resources to mobile users. However, the MEC infrastructure raises major security concerns when the shared resources involve sensitive and private data of users. This paper proposes a novel blockchain-based key management scheme for MEC that is essential for ensuring secure group communication among the mobile devices as they dynamically move from one subnetwork to another. In the proposed scheme, when a mobile device joins a subnetwork, it first generates lightweight key pairs for digital signature and communication, and broadcasts its public key to neighbouring peer users in the subnetwork blockchain. The blockchain miner in the subnetwork packs all the public key of mobile devices into a block that will be sent to other users in the subnetwork. This enables the mobile device to communicate with its peers in the subnetwork by encrypting the data with the public key stored in the blockchain. When the mobile device moves to another subnetwork in the tree network, all the mobile devices of the new subnetwork can quickly verify its identity by checking its record in the local or higher hierarchy subnetwork blockchain. Furthermore, when the mobile device leaves the subnetwork, it does not need to do anything and its records will remain in the blockchain which is an append-only database. Theoretical security analysis shows that the proposed scheme can defend against the 51 percent attack and malicious entities in the blockchain network utilizing Proof-of-Work consensus mechanism. Moreover, the backward and forward secrecy is also preserved. Experimental results demonstrate that the proposed scheme outperforms two baselines in terms of computation, communication and storage.

关键词： Task analysis   Servers   Cloud computing   Resource management   Optimization   Real-time systems   Delays   Offloading strategy optimization   mobile edge computing   Index Terms   hierarchy   average response time   energy consumption  

摘要： Cloud computing and mobile edge computing techniques supply efficient ways to solve the contradiction between the increasing computing and storage demands of portable terminals and the limited capacity. In this paper, we conduct a three-tier hierarchical service system with multiple mobile users(UEs), multiple mobile edge computing servers(MECs), and a single cloud center(CC). It is worth noting that multiple UEs with personalized options generate a large number of different tasks in real time. To deal with this complex offloading problem, a response ratio offloading strategy (RROS) centered on user preference and real-time nature is designed to make MECs or CC serve as many UEs as possible. Therefore, a MEC-choosing preference list of each UE is created based on its past experiences at first. Then, each MEC iteratively sorts UEs with its ranking in the UEs' preference list. In order to avoid that the first task arriving at MEC occupies too many resources of MEC and cannot achieve global optimization, we also adopt loop iterative sequencing for multiple tasks arriving within a stipulated time. Lastly, by comparing the optimal response ratio on different MECs and CC, multiple MECs and the CC collaborative offload computing tasks of multiple UEs. We demonstrate numerical examples and data of the proposed strategy. Experimental results show that the algorithm significantly outperforms conventional techniques even with the increase of users.

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： 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.

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： 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： NOMA   Task analysis   Uplink   Resource management   Energy consumption   Computational modeling   5G mobile communication   Mobile edge computing   network slicing   uplink energy consumption minimization   energy efficiency   spectral efficiency  

摘要： The 5G network presents a new horizon with tremendous opportunities for future generation wireless networks. Mobile edge computing (MEC), non-orthogonal multiple access (NOMA), and network slicing (NS) are some of the key enablers for 5G. MEC reduces the latency to a great extent for a wireless network, while NOMA gives access to more users with resource constraints. NS provides users with a better quality of service and network operators with more flexibility. In this work, we propose an NS technique enabled with NOMA for a MEC network. The proposed NS technique improves service latency for MEC users and reduces unnecessary allocation of radio resources in NOMA. The saved resources can be leveraged to accommodate more users, thus increasing the spectral efficiency of the network. We consider different types of services based on the task completion time of users in this work. The primary focus is to optimize the total energy consumption for wireless uplink transmission for the NOMA-enabled sliced MEC network. We also propose a heuristic algorithm as an alternative to reduce the time and computational complexities of the optimization algorithm and simulate the results extensively to show the effectiveness of our proposed algorithm.

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.