关键词： Extended reality   immersive experience   5G-advanced   mobile edge computing   quality of experience  

摘要： Extended reality (XR) would become the main enabler of future immersive experience services. The challenge today is how to make this new set of immersive experience services running through a mobile network such as beyond 5G efficiently in terms of latency and Quality of experience (QoE). Academics and industry have already started to study and provide solutions towards enabling XR services in beyond 5G networks. These solutions affect both the network and the service elements by either introducing new ones or modifying existing ones. In the present paper, we proffer an extensive survey in this topic with details on the solutions proposed by academia per part of the network, i.e. radio access, core, edge etc. Moreover, we provide a thorough overview of 3rd Generation Partnership Project (3GPP) standards in Releases 17 and 18 focusing on the proposed solutions, Key Performance Indicators (KPIs), services and network elements. Finally, we summarize the lessons gained from this survey and draw the attention regarding open affairs for further research exploration for enabling XR services in beyond 5G networks and towards 6G.

关键词： Mobile edge computing   resource allocation   task offloading   user-centric network  

摘要： In the traditional cellular-based mobile edge computing (MEC), users at the edge of the cell are prone to suffer severe inter-cell interference and signal attenuation, leading to low throughput even transmission interruptions. Such edge effect severely obstructs offloading of tasks to MEC servers. To address this issue, we propose user-centric mobile edge computing (UCMEC), a novel MEC architecture integrating user-centric transmission, which can ensure high throughput and reliable communication for task offloading. Then, we formulate an long-term delay minimization problem by jointly optimizing task offloading, power allocation, and computing resource allocation in UCMEC. To solve the intractable problem, we propose two decentralized joint optimization schemes based on multi-agent deep reinforcement learning (MADRL) and convex optimization, which consider both cooperation and non-cooperation among network nodes. Simulation results demonstrate that the proposed schemes in UCMEC can significantly improve the uplink transmission rate by at least 176.99% and reduce the long-term average total delay by at least 16.36% compared to traditional cellular-based MEC. © 2002-2012 IEEE.

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： cyber-physical-social systems   CPSS   Society 50   Industry 40   resource scheduling   mobile edge computing   MEC   metaverse   digital twin   6G   blockchain   effective throughput   energy expense   financial expense   service delivery delay  

摘要： To ensure better living for humans through different collaborative systems/services, a human-centred Society 5.0 has emerged. Society 5.0 can balance economic and social sustainability via the high integration of cyber-physical-social systems (CPSS). The literary works on CPSS-based task execution did not examine a suitable mission-driven/super-smart service orchestration strategy by taking both Society 5.0 and Industry 4.0 tasks, multi-task to resource mapping, task impact, appropriate worker and communication resource selection with minimum delivery delay, energy, financial expense, and service requirements, into account. To master the challenges, this paper presents a task impact-based service orchestration scheme that selects appropriate resources for Society 5.0 task execution over a CPSS-enhanced network by examining distinguished tasks, resources, requirements, and minimum latencies. The simulation outcome ensures that up to 44.32% service delivery delay and 39.79% social welfare gain are obtained in the proposed impact-based scheme over existing schemes.

关键词： Servers   Costs   Resource management   Fault tolerant systems   Fault tolerance   Energy consumption   Encoding   Mobile edge computing   5G   edge server placement   load balancing   hexagonal coverage  

摘要： Edge Servers (ESs) enhance the efficiency and reliability of modern Beyond Fifth-Generation (B5G) network systems by reducing latency, boosting responsiveness, and optimizing network usage. From an industry standpoint, the optimal positioning of ESs is critical to enhancing coverage reach while reducing potential conflicts with neighboring servers, guaranteeing consistent and reliable user access. Traditional circular coverage for ESs is suboptimal due to inherent constraints. The hexagonal model, known for symmetrical and consistent distribution, emerges as a superior choice. Implementing Fault Tolerance Edge Server Placement (FT-ESP) demands workload distribution across servers to prevent overloading and mitigate service failures. The complexity of assigning multiple Base Stations (BSs) to ESs categorizes FT-ESP as a Nondeterministic Polynomial Time (NP-hard) problem. To address this, the Subset Sum Encoding Scheme (S2ES) has been introduced, linking BSs to ESs. This research Fault Tolerance Subset Sum Encoding Scheme (FT-S2ES) examines various Edge Server Placement Problem (ESPP) strategies, highlighting the proposed method's efficacy compared to traditional techniques. The empirical findings indicate that, when tested on real-world datasets, the suggested strategy successfully achieves a balanced workload and utilization rate of 8.82% and 7.67%, respectively. Compared to the Grey Wolf Optimizer (GWO) algorithm, energy usage is significantly reduced by 32.23%. In addition, the proposed approach ensures a reduction in latency by 22.80% with an increasing number of BSs and 9.47% with an increasing number of ESs.

关键词： Disasters   Sensors   Satellites   Processor scheduling   Resource management   Autonomous aerial vehicles   Internet of Things   Edge information hub   latency   mobile edge computing   non-terrestrial network   resource orchestration  

摘要： Quick response to disasters is crucial for saving lives and reducing loss. This requires low-latency uploading of situation information to the remote command center. Since terrestrial infrastructures are often damaged in disaster areas, non-terrestrial networks (NTNs) are preferable to provide network coverage, and mobile edge computing (MEC) could be integrated to improve the latency performance. Nevertheless, the communications and computing in MEC-enabled NTNs are strongly coupled, which complicates the system design. In this paper, an edge information hub (EIH) that incorporates communication, computing and storage capabilities is proposed to synergize communication and computing and enable systematic design. We first address the joint data scheduling and resource orchestration problem to minimize the latency for uploading sensing data. The problem is solved using an optimal resource orchestration algorithm. On that basis, we propose the principles for resource configuration of the EIH considering payload constraints on size, weight and energy supply. Simulation results demonstrate the superiority of our proposed scheme in reducing the overall upload latency, thus enabling quick emergency rescue.

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： 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.

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： Task offloading   vehicular networks   matching algorithm   matching algorithm   genetic algorithm   genetic algorithm   mobile edge computing   mobile edge computing   mobile edge computing  

摘要： With the developments of the intelligent vehicles, the most significant contradiction is coming to the limited computing resource and the unlimited requirements. Task offloading has been proposed to overcome this. Most of the existing task offloading methods aim to optimize the global delay. However, from the user's point of view, different in-vehicle applications (such as entertainment and autonomous driving) have different importance and delay requirements. Therefore, simply minimizing the latency of all tasks does not meet the QoS (Quality of Service) of each user. Unfortunately, very few people discuss this practical issue. In this paper, the problem of vehicular task offloading in resource-constrained scenarios is studied, and a two-stage task offloading scheme MAT-IGA based on multi-node collaboration is proposed. In the first stage, the complex tasks are pre-segmented adaptively, and the optimal matching set is solved by combining the improved multi-round deferred-acceptance algorithm, which prioritizes the resource requirements of emergency tasks and improves the reliability of the strategy. In the second stage, a chaotic genetic algorithm based on opposition-based learning is used to optimize resource allocation, and the global delay and cost are optimized on the basis of ensuring the success rate. The simulation results show that the proposed method is superior to the common baseline algorithm in different vehicle numbers and task characteristics.