关键词： 5G network   call flow   load balancing   mobile edge computing   task allocation scheme   task verification  

摘要： Mobile edge computing is an important element in ensuring the efficiency of the 5G network as a whole, as it enables data storage and computing at the network edge. Existing solutions do not fully address the issues of load distribution between computing nodes, and most solutions do not offer methods for verifying computations and controlling errors. Accordingly, this paper aims to develop an approach to the organization of mobile edge computing in a 5G mobile network that would authenticate distribution servers and computing nodes, manage the process of distributing computing nodes, have a procedure for verifying the correctness of calculations, and take into account the parameters of computing nodes during distribution. To achieve this goal, we propose to use the developed method. The method of load balancing and selection of computing nodes for edge computing via 5G allows for identifying available nodes and distributing computing blocks among them. It also provides mutual authentication of elements and includes a method of data verification and error detection for the MEC system. The provided solution allows for controlling errors during calculations and protecting the server from incorrect data. These methods are optimized according to minimum network resources and computing time criteria. These improvements increase the efficiency of mobile edge computing in a 5G network. © А. Аstrakhantsev, L. Globa, О. Fedorov, D. Degtiarov, Y. Romanko, K. Romanii, 2024ã.

关键词： Wireless communication   Optimization   Servers   Array signal processing   Task analysis   Surgery   Low latency communication   Reconfigurable intelligent surface (RIS)   mobile edge computing   latency minimization   alternating optimization  

摘要： Mobile Edge Computing (MEC) has a widely established merit of bringing powerful computing servers to geographically closer locations to computationally limited devices;hence, reducing the task offloading latency from these devices to the servers. However, the frequent communication between devices and edge servers increases the network-wide traffic, therefore, stands in the way of enjoying notable improvements in network-wide latency. The optimization of this problem becomes of utmost importance to boldly underline the advantages of MEC-based task offloading. Reconfigurable Intelligent Surfaces (RISs) offer the potential to improve wireless transmission environment, thereby contributing to achieving this objective. This paper addresses a network-wide latency optimization problem in the context of multi-RIS-assisted MEC offloading scenarios. This problem is, herein, subdivided into two sub-problems, namely: ${a}$ ) path selection and ${b}$ ) joint optimization of RIS phases and offloading volume. The cascaded channel gain is derived for ${a}$ ). A balanced trade-off is sought between passive beamforming gain and reflection loss by reformulating ${a}$ ) as a shortest path problem using Graph Theory (GT). ${b}$ ) requires a different approach than the typical Successive Convex Approximation (SCA) technique used for single RIS. Instead, a Semi-Definite Relaxation (SDR) method is adopted to transform the non-convex sub-problem into a convex one, solved efficiently using an Alternating Optimization (AO) approach. The results of extensive simulations and numerical analyses reported herein reveal the notable latency reduction potency of multi-RIS-assisted MEC systems by, respectively, 24% as compared to their RIS-agnostic counterparts, and, by 15.34% when compared to systems incorporating a single RIS.

关键词： Time factors   Resource management   Cloud computing   Autonomous vehicles   Vehicle dynamics   Surveys   Servers   Connected vehicles   Multi-access edge computing   Connected vehicular   TSSCV applications   communication and computation resources   reservation request   reservation update strategy   mobile edge computing   resource trading and exchange  

摘要： Edge computing resources are crucial for time-sensitive and safety-critical vehicular (TSSCV) applications, such as autonomous and remote driving. These applications require guaranteed resource availability for real-time communication and computation to ensure safety and efficiency. With the emergence of integrated sensing and communication (ISAC) in 6G communication systems, extensive sensing data will be added to the communication data. To meet these challenges, resource reservation becomes a necessity. Resource reservation approaches can be either network-side or vehicle-side. Most existing approaches are network-side. However, these approaches are insufficient for these applications because they only guarantee resources to individual vehicles with a certain probability. Vehicle-side reservation is challenging because vehicles usually do not have sufficient information to reason about future available resources and their costs. This comprehensive survey aims to define the characteristics of TSSCV applications and to analyze existing reservation approaches and cost-effective schemes, considering different reservation scenarios in single and multiple mobile network operator (MNO) environments. In addition, it explores strategies to revise or update reservations when faced with uncertain reservation times and costs. To this end, we propose to enable resource trading and reservation exchange facilitated by blockchain smart contracts. This approach promises a secure and efficient method for resource management in future smart vehicles.

关键词： Mobile edge computing  

摘要： With the rapid development of the Mobile Edge Computing (MEC) and the Internet of Things (IoT), intelligent interconnectivity and data exchange between devices have become feasible, bringing about the need for efficient energy management and complex network communication strategies. This paper proposes an Energy-Aware Low-Latency Routing Data Driven Model in Mobile Edge Computing (EALLR). This model introduces edge nodes that can perform computation and data processing close to the data source, thereby reducing latency and bandwidth consumption, optimizing data transmission paths, and ensuring that messages reach the target nodes quickly and efficiently. It overcomes the challenges posed by the social attributes of nodes, the diversity of network topology, and energy constraints. EALLR utilizes constraints on social strength between nodes, energy status, packet size, and message Time-To-Live (TTL) to optimize routing decisions. By optimizing the selection of routes with minimal energy consumption and adjusting the transmission strategy based on the size of the data packet, the algorithm enhances network energy efficiency and overall transmission efficiency. Simulation tests have proven that EALLR significantly reduces transmission delay while improving message delivery rates, especially in resource-limited and topologically complex network environments, demonstrating superior performance. © 1975-2011 IEEE.

关键词： Mobile edge computing  

摘要： The rapid growth of resource-hungry and time-critical applications reflects the rise of resources needed for communication, processing, and energy consumption. Mobile edge computing (MEC) that offers cloud-computing services proximate to users at the edge of mobile network is considered to be the key technology to facilitate task scheduling closer to data-sources. The objective of this paper is to propose an improved task scheduling strategy that selects the best MEC server to process each task, which reduces the system energy consumption and delay. Therefore, we proposed an improved task scheduling strategy on the basis of non-dominated sorting genetic algorithm II (NSGA-II). To improve the performance of NSGA-II, we proposed a hierarchical search policy (NSGA-H) that eliminates the number of redundant comparisons and thus, enhances time complexity. The simulation results illustrate that the proposed strategy improves average service delay, service time, energy consumption, and system utility compared to baseline approaches. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Mobile edge computing  

摘要： Mobile Edge Computing (MEC) has become a critical paradigm. It extends traditional cloud com- puting capabilities by moving computation and storage closer to the network edge. A significant challenge in MEC is the inefficiency and high energy consumption in data offloading for mobile edge computing (MEC) systems due to traditional offloading techniques. The problems with these techniques are mainly limited cov- erage, high energy consumption and privacy concerns. The study explores the integration of unmanned aerial vehicles (UAVs) with artificial intelligence (AI) techniques to enhance data offloading in MEC environments. The primary objective is to utilize AI algorithms that enable UAVs to make intelligent decisions regarding data transmission. These AI-powered UAVs can dynamically adapt to changing network conditions and optimize data transmission processes by considering factors such as network conditions, device capabilities, and energy constraints. The study examines AI-driven strategies for UAV-based data offloading while focusing on how these techniques can improve network efficiency, reduce latency and enhance scalability in MEC systems. This demonstrates the potential of AI-powered unmanned aerial vehicles (UAV) as a viable solution for efficient data offloading in MEC. This research shows that AI-powered UAVs can enhance data offloading in MEC sys- tems by optimizing flight paths, boosting connectivity and improving overall network performance. While AI integration reduces latency and increases scalability, these UAVs offer high connectivity in demanding areas. However, further research is needed to tackle those challenges. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： cloud computing   delay   energy consumption   mobile devices   mobile edge computing   offloading types  

摘要： Mobile edge computing (MEC) is a very promising paradigm that facilitates efficient processing and analysis of Internet of Things (IoT) data at the network edge. MEC is a cloud-based platform that offers a range of online resources and sophisticated mobile apps to users of mobile devices. The continuing trend among mobile users is the growing need for accessing current apps and cloud-based services on their mobile computing devices (MCDs) with high data transfer rates and low latency. MCD’s frequently experience situations where they are either overwhelmed with excessive resource demands or insufficiently used due to imbalanced requests for resources. Offloading strategies play a crucial role in optimizing the efficiency of real-time data processing and analysis. This study proposes an ETOSP: Energy-efficient Task Offloading Strategy based on Partial Offloading in Mobile Edge Computing framework for efficient resource management as a solution to address the aforementioned difficulty. The application of the genetic algorithm is employed to produce strategies that provide balanced resource allocation for the purpose of identifying the most optimum offloading approach. The performance evaluation of ETOSP is shown through simulated experiments. © 2023 by author(s).

关键词： named data networking   NDN   producer mobility   mobile edge computing   MEC  

摘要： In recent years, with the rapid development of the internet and mobile communication technologies, the mobility issues in traditional IP network architecture have significantly affected the efficiency of task offloading. Therefore, named data networking (NDN) has been proposed, which provides native support for consumer mobility through its receiver-driven content retrieval model. However, producer mobility is still a challenging problem, and the current solutions all have the disadvantage of introducing additional issues. To this end, this paper proposes and comprehensively analyses a scalable mobility management mechanism MECProMo, which combines the characteristics of mobile edge computing (MEC) and NDN network, and utilises the stateful forwarding plane of NDN to promote producer mobility. Combined with MEC, a temporary forwarding path is established on the name-based NDN data plane. Simulation results of ndnSIM show that compared with other schemes, MECProMo reduces the average packet loss rate by 89.04% and 94.54% respectively.

关键词： Servers   Training   Optimization   Computational modeling   Data privacy   Privacy   Data models   Federated learning   Multi-access edge computing   centralized learning   mobile edge computing  

摘要： To alleviate the substantial local training burden on clients in the federated learning (FL) process, this paper proposes a more efficient approach based on hybrid federated and centralized learning (HFCL), leveraging the Mobile Edge Computing (MEC) environment within wireless communication networks. Considering the existence of heterogeneous data types with different privacy levels -such as 1) sensitive data, which can not be exposed, and 2) less-sensitive data, which can be exposed for centralized learning (CL)-we formulate an optimization problem aimed at achieving a balance between 1) total latency, including computation and communication, and 2) the training burden on the MEC server. This balance is achieved by adjusting the set of participants in FL, taking into account client selection under different privacy levels. A multi-objective optimization problem is designed using mixed-integer nonlinear programming, which is generally recognized as NP-hard. We employ relaxation techniques in combination with the Mutas & Simulated Annealing Heuristic algorithm to develop a near-optimal yet practical algorithm. Our numerical and simulation results reveal that the proposed scheme effectively achieves a global model by striking a balance between the total time required for model convergence and the computational load on the MEC server. Furthermore, experimental results on three well-known real-world datasets demonstrate that the proposed scheme maintains an acceptable level of accuracy and loss.

关键词： privacy-preserving   double auction   resource allocation   garbled circuits   secure two-party computation   mobile edge computing   MEC  

摘要： Mobile edge computing (MEC) can deploy applications at the edge of the network in real-time and adapt to diverse service scenarios. In recent years, resource allocation auction in MEC has received widespread attention. Most studies only focus on improving computational efficiency and social welfare, but they neglect the potential security and privacy breaches in resource allocation auctions, which may have a great impact on the authenticity of auction results. In this paper, by combining garbled circuit and homomorphic encryption, we propose a privacy-preserving double auction for resource allocation (PDARA) in satellite MEC, which uses dynamic programming and monotone FPTAS (MFPTAS) algorithm for resource allocation. We propose secure subroutines secure division and oblivious selection, which can serve as building blocks for other applications. Then, we theoretically analysed the complexity and effectiveness of the system and demonstrated that our system is sufficiently secure in a semi-honest adversary model. Finally, evaluate the performance of our system through a quantity of simulation experiments.