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Multi-access edge computing (MEC), as a computing model that provides services on the user side, can effectively solve the problems of high delay and resource shortage in traditional cloud computing when processing massive data. However, existing edge computing resources are still limited, and difficult to provide services to users in inaccessible remote areas. Considering that unmanned aerial vehicle (UAV) has the advantages of easy deployment, high flexibility and low cost, a UAV-assisted MEC hierarchical computation offloading framework is proposed. Firstly, contract theory is used to solve the information asymmetry problem between the platform and the UAV, and the UAV is encouraged to provide computing services. By analyzing the attributes and conditions of feasible contracts, the optimal contract is designed using the Lagrange multiplier method. Secondly, by constructing the preference set between UAV and mobile user (MU), a mobile user and unmanned aerial vehicle bilateral matching (MUBM) algorithm is proposed to establish the connection between user tasks and UAV computing resources. Finally, the feasibility and effectiveness of the contract were verified through experiments. The experimental results also prove the stability of the MUBM. [ABSTRACT FROM AUTHOR]

UAV-assisted MEC networks provide extensive communication coverage and massive computation services for mobile terminals (MTs), which are considered a promising edge paradigm to support future air–ground integrated communications. In this paper, an energy-efficient scheme in NOMA-based UAV-assisted MEC systems is proposed to address the system's energy constraints and its inability to support massive MT access. Our goal is to minimize system-weighted energy consumption by jointly optimizing the allocation of transmission power, computation resources, and UAV trajectory scheduling. As the formulated problem is non-convex and difficult to solve directly, we decompose it into two manageable sub-problems and propose an iterative algorithm based on successive convex approximations (SCA) to solve each sub-problem alternatively. Simulation results show that the proposed joint optimization algorithm achieves a significant performance improvement compared to other benchmark approaches. [ABSTRACT FROM AUTHOR]

This paper presents an Optimal Power Management System (OPMS) for smart homes in 6G environments, which are designed to enhance the sustainability of Green Internet of Everything (GIoT) applications. The system employs a brute-force search using an exact solution to identify the optimal decision for adapting power consumption to renewable power availability. Key techniques, including priority-based allocation, time-shifting, quality degradation, battery utilization and service rejection, will be adopted. Given the NP-hard nature of this problem, the brute-force approach is feasible for smaller scenarios but sets the stage for future heuristic methods in large-scale applications like smart cities. The OPMS, deployed on Multi-Access Edge Computing (MEC) nodes, integrates a novel demand response (DR) strategy to manage real-time power use effectively. Synthetic data tests achieved a 100% acceptance rate with zero reliance on non-renewable power, while real-world tests reduced non-renewable power consumption by over 90%, demonstrating the system's flexibility. These results provide a foundation for further AI-based heuristics optimization techniques to improve scalability and power efficiency in broader smart city deployments. [ABSTRACT FROM AUTHOR]

Serverless computing has shifted cloud server management responsibilities away from end users and towards service providers. Serverless computing offers greater scalability, flexibility, ease of deployment, and cost-effective resource utilization. Previous serverless computing research excluded semantic communication, software-defined networking (SDN) and network function virtualization (NFV)-enabled service function chaining (SFC) orchestration, and minimum latencyaware hybrid worker selection based on user and service provider-retained resource usage, federated learning (FL), and blockchain (BC) operations. To overcome these issues, this article proposes cognitive intelligence, personalized offloading, FL and BC-based semantic and nonsemantic serverless applications with user satisfaction, multiple benefits, minimum hybrid latency cost-aware resource slicing, physical, digital, and NFV worker selection, and an SFC orchestration policy for SDN and NFV-enabled 6G networks that leverage user and service provider retained resource usage. Simulation results indicate that the proposed scheme improves E2E delay by over 40%, user financial gain by 4%, user satisfaction by 8%, and throughput by 6% when compared to existing schemes. [ABSTRACT FROM AUTHOR]

This research is the first application of Unmanned Aerial Vehicles (UAVs) equipped with Multi-access Edge Computing (MEC) servers to offshore wind farms, providing a new task offloading solution to address the challenge of scarce edge servers in offshore wind farms. The proposed strategy is to offload the computational tasks in this scenario to other MEC servers and compute them proportionally, which effectively reduces the computational pressure on local MEC servers when wind turbine data are abnormal. Finally, the task offloading problem is modeled as a multi-intelligent deep reinforcement learning problem, and a task offloading model based on Multi-Agent Deep Reinforcement Learning (MADRL) is established. The Adaptive Genetic Algorithm (AGA) is used to explore the action space of the Deep Deterministic Policy Gradient (DDPG), which effectively solves the problem of slow convergence of the DDPG algorithm in the high-dimensional action space. The simulation results show that the proposed algorithm, AGA-DDPG, saves approximately 61.8%, 55%, 21%, and 33% of the overall overhead compared to local MEC, random offloading, TD3, and DDPG, respectively. The proposed strategy is potentially important for improving real-time monitoring, big data analysis, and predictive maintenance of offshore wind farm operation and maintenance systems. [ABSTRACT FROM AUTHOR]

With the rapid development of the internet of things (IoT) era, IoT devices may face limitations in battery capacity and computational capability. Simultaneous wireless information and power transfer (SWIPT) and mobile edge computing (MEC) have emerged as promising technologies to address these challenges. Due to wireless channel fading and susceptibility to obstacles, this paper introduces intelligent reflecting surfaces (IRS) to enhance the spectral and energy efficiency of wireless networks. We propose a system model for IRS-assisted uplink offloading computation, downlink offloading computation results, and simultaneous energy transfer. Considering constraints such as IRS phase shifts, latency, energy harvesting, and offloading transmit power, we jointly optimize the CPU frequency of IoT devices, offloading transmit power, local computation workload, power splitting (PS) ratio, and IRS phase shifts. This establishes a multi-variate coupled nonlinear problem aimed at minimizing IoT devices energy consumption. We design an effective alternating optimization (AO) iterative algorithm based on block coordinate descent, and utilize closed-form solutions, Dinkelbach-based Lagrange dual method, and semidefinite relaxation (SDR) method to minimize IoT devices energy consumption. Simulation results demonstrate that the proposed scheme achieves lower energy consumption compared to other resource allocation strategies. [ABSTRACT FROM AUTHOR]

Edge devices generate a lot of data, and the data is received and utilized according to each service cycle of applications. It is difficult to handle the amount of data in existing cloud environments. MEC environments can reduce network latency and eliminate performance bottlenecks so that attempts have been made to run DL services on various heterogeneous devices. However, due to limited computing resources, inference may fail to work or may be time-consuming. This paper proposes a MEC-based AI computing partitioning model that enables distributed execution of AI applications on heterogeneous edge devices. The suggested model allows users to determine the number of divisions of AI network models, and has partitioned models with uniform parameters. According to the experimental results, as the compute partitioning increases, the edge device's overhead decreases on average by 25.8%, 14.3%, and 3.27% in terms of execution time, CPU usage, and memory usage, respectively, making it possible to provide seamless AI application services through distributed execution. [ABSTRACT FROM AUTHOR]

Decentralized autonomous organization (DAO)-based applications can revolutionize traditional centralized decision-making procedures and services by enabling scalable, decentralized, autonomous, and democratized decision-making processes. Unlike traditional applications, DAO-based decentralized applications use Ethereum blockchain-based smart contract programs to execute policies or make automated decisions. To that end, 6 G technologies can improve the latency and reliability of many existing blockchain-based DAOs. Previous research did not investigate the execution of DAO and non-DAO-based applications, as well as an adaptive resource choreography scheme, while accounting for various 6 G technologies, blockchain, and mobile-edge cloud (MEC). To prevail over the previous shortcomings, this article supplies a street smart multi-platform coordination, application scheduling, and low-latency-aware resource choreography scheme for both DAO and non-DAO-based application execution over blockchain and MEC-enabled 6 G networks by taking heterogenous DAO and non-DAO application count, application requirements, physical worker, virtual worker, and communication resource status into account. The results verified that the proposed kaizen scheme delivers at least 11.26% app work completion delay gain, 7.2% user energy overhead gain, 6.55% user economic charge gain, and 21% service provider profit than the compared schemes. [ABSTRACT FROM AUTHOR]

Every institution in China has undergone reform and piloting based on its unique situation to enhance the quality of instruction as a result of the ongoing advancement of the times and the educational idea in the information age. The effectiveness of education is increased by hybrid teaching, which combines offline and online approaches. It empowered educators and motivates learners to take responsibility for their learning. Classes in physical education (PE) that place a strong emphasis on training and technical performance are especially well-suited for hybrid instruction. This mode improves teaching abilities and supports the long-term growth of education. As a result, we offer an online and offline technique for assessing the quality of PE instruction that is relied on mobile edge computing (MEC). With the use of the target, index, weight, and standard assessments, this study builds a quality evaluation index (QEI) approach for PE that combines online and offline techniques. Assessing the corresponding significance of every index, factor, and cluster analysis compresses index elements. The weights of the QEIs for integrated instruction are determined using MEC. The efficacy of combined PE is assessed using the fuzzy logic-based comprehensive assessment methodology. According to the analytical model, this strategy increases teaching quality while reducing costs and mistakes and improves assessment effectiveness. The experimental results proved that the proposed model has provided an accuracy of 95.98%.

Multi-Access Edge Computing (MEC) represents the central concept that enables the creation of new applications and services that bring the benefits of edge computing to networks and users. By implementing applications and services at the edge, close to users and their devices, it becomes possible to take advantage of extremely low latency, substantial bandwidth, and optimized resource usage. However, enabling this approach requires careful integration between the MEC framework and the open 5G core. This work is dedicated to designing a new service that extends the functionality of the Multi-Access Traffic Steering (MTS) API, acting as a strategic bridge between the realms of MEC and the 5G core. To accomplish this objective, we utilize free5GC (open-source project for 5G core) as our 5G core, deployed on the Kubernetes cluster. The proposed service is validated using this framework, involving scenarios of high user density. To conclude whether the discussed solution is valid, KPIs of 5G MEC applications described in the scientific community were sought to use as a comparison parameter. The results indicate that the service effectively addresses the described issues while demonstrating its feasibility in various use cases such as e-Health, Paramedic Support, Smart Home, and Smart Farms.

With the development of the Internet of Things (IoT), IoT nodes with limited energy and computing capability are no longer able to address increasingly complex computational tasks. To address this issue, an Unmanned Aerial Vehicle (UAV)-assisted Wireless Power Transfer (WPT) Mobile Edge Computing (MEC) system is proposed in this study. By jointly optimizing variables such as energy harvesting time, user transmission power, user offloading time, CPU frequency, and UAV deployment location, the system aims to maximize the number of computation bits by the users. This optimization yields a challenging non-convex optimization problem. To address these issues, a two-stage alternating method based on the Lagrangian dual method and the Successive Convex Approximation (SCA) method is proposed to decompose the initial problem into two sub-problems. Firstly, the UAV position is fixed to obtain the optimal values of other variables, and then the UAV position is optimized based on the solved variables. Finally, this iterative process continues until the algorithm convergences, and the optimal solution for the given problem is obtained. The simulation results indicate that the proposed algorithm exhibits good convergence. Compared to other benchmark solutions, the proposed approach performs optimally in maximizing computation bits. [ABSTRACT FROM AUTHOR]

In recent years, mobile edge computing (MEC), as a powerful computing paradigm, provides sufficient computing resources for Internet of Things (IoT). Generally, the deployment of MEC servers closer to mobile users has effectively reduced access delays and the cost of using cloud services. However, the multi-objective resource allocation for IoT applications to meet service requirements (i.e., the shortest completion time of IoT applications, the load balance and lower energy consumption of MEC servers, etc.) still faces severe challenges. To address this challenge, a multi-objective resource allocation method, named MRAM, is proposed in this paper for IoT. Technically, the pareto archived evolution strategy is leveraged to optimize the time cost of IoT applications, load balance and energy consumption of MEC servers. Furthermore, the multiple criteria decision making and the technique for order preference by similarity to ideal solution are utilized to obtain the optimal multi-objective resource allocation strategy. Ultimately, the comprehensive analysis of MRAM is introduced in detail. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) presents complex challenges in task offloading, especially within Mobile Edge Computing (MEC) environments and under conditions of data insecurity. Addressing these challenges, particularly the balance between energy consumption, cost, and latency, necessitates intelligent decision-making strategies. This study introduces a blockchain-based offloading framework that leverages a Double Deep Q-Network (DDQN), a cutting-edge algorithm of Deep Reinforcement Learning (DRL) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a Multi-Criteria Decision-Making (MCDM) technique. This approach strategically frames the MEC-based offloading problem using a Markov Decision Process (MDP), allowing the DDQN to learn the optimal policy. Subsequently, TOPSIS is applied to finalize offloading decisions based on predetermined criteria. The proposed framework significantly outperforms other strategies, demonstrating improvements in energy consumption, cost, and latency in the most complex scenarios by at least 35.83%, 4.65%, and 14.17%, respectively. These results underscore the efficiency and robustness of the presented multi-faceted approach in addressing the inherent complexities of task offloading within IoT environments. [ABSTRACT FROM AUTHOR]

The recent advancements of mobile edge computing (MEC) technologies and unmanned aerial vehicles (UAVs) have provided resilient and flexible computation services for ground users beyond the coverage of terrestrial service. In this paper, we focus on a UAV-assisted MEC system in which the UAV equipped with MEC servers is used to assist user devices in computing their tasks. To minimize the weighted average energy consumption and delay in the UAV-assisted MEC system, a LQR-Lagrange-based DDPG (LLDDPG) algorithm, which jointly optimizes the user task offloading and the UAV trajectory design, is proposed. To be specific, the LLDDPG algorithm consists of three subproblems. The DDPG algorithm is used to address the issue of UAV desired trajectory planning, and subsequently, the LQR-based algorithm is employed to achieve the real-time tracking control of UAV desired trajectory. Finally, the Lagrange duality method is proposed to solve the optimization problem of computational resource allocation. Simulation results indicate that the proposed LLDDPG algorithm can effectively improve the system resource management and realize the real-time UAV trajectory design. [ABSTRACT FROM AUTHOR]

In the mobile edge computing (MEC) architecture, base stations with computational capabilities are subject to service coverage limitations, and the mobility of devices leads to dynamic changes in their connections, directly impacting the offloading decisions of agents. The connections between base stations and mobile devices, as well as the connections between base stations themselves, are abstracted into an MEC structural diagram due to the difficulty of deep reinforcement learning (DRL) in capturing the complex relationships between nodes and their multi-order neighboring nodes in the graph; decisions solely generated by DRL have limitations. To address this issue, this study proposes a hierarchical mechanism strategy based on Graph Neural Reinforcement Learning (M-GNRL) under multiple constraints. Specifically, the MEC structural graph constructed with the current device as an observation point aggregates to learn node features, thus comprehensively considering the contextual information of nodes, and the learned graph information serves as the environment for deep reinforcement learning, effectively integrating a graph neural network (GNN) with DRL. In the M-GNRL strategy, edge features from GNN are introduced into the architecture of the DRL network to enhance the accuracy of agents' decision-making. Additionally, this study proposes an updated algorithm to obtain graph data that change with observation points. Comparative experiments demonstrate that the M-GNRL algorithm outperforms other baseline algorithms in terms of system cost and convergence performance. [ABSTRACT FROM AUTHOR]

Multi-Access Edge Computing (MEC) reduces latency, provides high-bandwidth applications with real-time performance and reliability, supporting new applications and services for the present and future Beyond the Fifth Generation (B5G). Radio Network Information Service (RNIS) plays a crucial role in obtaining information from the Radio Access Network (RAN). With the advent of 5G, RNIS requires improvements to handle information from the new generations of RAN. In this scenario, improving the RNIS is essential to boost new applications according to the strict requirements imposed. Hence, this work proposes a new RNIS as a service to the MEC framework in B5G networks to improve MEC applications. The service is validated and evaluated, and demonstrates the ability to adequately serve a large number of MEC apps (two, four, six and eight) and from 100 to 2000 types of User Equipment (UE). [ABSTRACT FROM AUTHOR]

Mobile Edge Computing (MEC) is a distributed computing paradigm that delivers processing and data storage capabilities closer to the network edge, which is adjacent to mobile consumers and devices. MEC lowers latency, reduces data transmission times, and improves overall performance for mobile apps by relocating computing resources to the network's edge. But, due to higher average load and longer elapsed time, modern end devices such as smartphones and tablets cause major load challenges in mobile computing networks. Furthermore, if smartphones cause unpredictable traffic patterns, it becomes impossible to model and forecast the nature of communication. Such confusing traffic figures are caused not just by bursty Internet traffic, but also by multitasking operating systems that allow users to swiftly switch between active apps. Mobility of users and end devices impose a difficult challenge to provide continuous services in mobile computing. In this paper, this issue is addressed using the Contextual Information Based Scheduling (CIBS) technique to optimally allocate resources and provide seamless service to the users. The proposed method is implemented with NS-3, an open-source network simulator that provides a comprehensive set of modules for Mobile Edge Computing (MEC) simulations, including mobility modelling support. The experimental results show that CIBS offers migration time of 97512ms, delay time of 372115ms, execution time of 1061328ms and downtime of 98715ms. The results are compared with the existing Mobility-Aware Joint Task Scheduling (MATS) approach. The obtained results show that CIBS outperforms MATS with regard to migration time, latency, execution time and downtime. [ABSTRACT FROM AUTHOR]

Multi-Access Edge Computing (MEC) represents the central concept that enables the creation of new applications and services that bring the benefits of edge computing to networks and users. By implementing applications and services at the edge, close to users and their devices, it becomes possible to take advantage of extremely low latency, substantial bandwidth, and optimized resource usage. However, enabling this approach requires careful integration between the MEC framework and the open 5G core. This work is dedicated to designing a new service that extends the functionality of the Multi-Access Traffic Steering (MTS) API, acting as a strategic bridge between the realms of MEC and the 5G core. To accomplish this objective, we utilize free5GC (open-source project for 5G core) as our 5G core, deployed on the Kubernetes cluster. The proposed service is validated using this framework, involving scenarios of high user density. To conclude whether the discussed solution is valid, KPIs of 5G MEC applications described in the scientific community were sought to use as a comparison parameter. The results indicate that the service effectively addresses the described issues while demonstrating its feasibility in various use cases such as e-Health, Paramedic Support, Smart Home, and Smart Farms. [ABSTRACT FROM AUTHOR]

The transmission of point cloud video streaming requires the scheduling of various computing and transmission resources. Existing research rarely considers the energy consumption issues caused by the computing tasks of terminal display devices. To solve this problem, a point cloud video streaming transmission scheme assisted by mobile edge computing (MEC) was proposed, which offloaded part of computing tasks to the MEC server based on the access bandwidth and point cloud video content. A joint optimization model was established in this scheme to maximize the quality of user’s viewing experience and minimize the energy consumption of terminal device under the constraints of network resources, terminal and edge computing resources. Experimental results show that the proposed scheme can improve the viewing quality of users and reduce the energy consumption of terminal equipment compared with the contrast scheme under different conditions.

Abstract Nowadays, the paradigm of mobile computing is evolving from a centralized cloud model towards Mobile Edge Computing (MEC). In regions without ground communication infrastructure, incorporating aerial edge computing nodes into network emerges as an efficient approach to deliver Artificial Intelligence (AI) services to Ground Devices (GDs). The computation offloading and resource allocation problem within a HAP-assisted MEC system is investigated in this paper. Our goal is to minimize the energy consumption. Considering the randomness and dynamism of the task arrival of GDs and the quality of wireless communication, stochastic optimization techniques are utilized to transform the long-term dynamic optimization problem into a deterministic optimization problem. Subsequently, the problem is further decomposed into three sub-problems which can be solved in parallel. An online Energy Efficient Dynamic Offloading (EEDO) algorithm is proposed to address these problems. Then, we conduct the theoretical performance analysis for EEDO. Finally, we carry out parameter analysis and comparative experiments, demonstrating that the EEDO algorithm can effectively reduce system energy consumption while maintaining the stability of the system.

Background/objective Salivary Mucoepidermoid Carcinoma (MEC) is a heterogeneous malignancy, whose molecular characteristics extend beyond CRTC1/3:MAML2 fusion. This study describes the immunohistochemical expression of CD133, CD44, OCT4, and Mammalian ENA (MENA) in Salivary MEC and their potential relevance to molecular profiling.Methods This study tested forty cases of MAML2-rearranged MEC using Immunohistochemistry (IHC) for the expression of CD133, CD44, OCT4, and MENA. Specific antibodies for each marker were utilized (CD133, CD44, OCT4, and MENA). The POU5F1 FISH probe from Empire Genomics (USA) was also employed to detect any alteration corresponding to OCT4. The selection of these adult stem markers as targets in our study is based on their established associations with cancer stem cells and the possible roles in tumorigenesis, metastasis, and treatment resistance in some carcinomas and adenocarcinomas.Results The investigation demonstrated a negative expression pattern for CD133, CD44, and OCT4 immunostains in all cases, questioning the involvement in the pathogenesis of salivary MEC. In contrast, MENA showed a diffuse positive expression in all cases. Furthermore, the POU5F1 FISH probe revealed no alterations in the forty samples analyzed.Conclusion These findings suggested the noninvolvement of cancer stem cells in the pathogenesis of salivary MEC. The overexpression of MENA suggests its co-expression that may extend beyond stemness or pluripotency.

Delay-sensitive task offloading in a device-to-device assisted mobile edge computing (D2D-MEC) system with energy harvesting devices is a critical challenge due to the dynamic load level at edge nodes and the variability in harvested energy. In this paper, we propose a joint dynamic task offloading and CPU frequency control scheme for delay-sensitive tasks in a D2D-MEC system, taking into account the intricacies of multi-slot tasks, characterized by diverse processing speeds and data transmission rates. Our methodology involves meticulous modeling of task arrival and service processes using queuing systems, coupled with the strategic utilization of D2D communication to alleviate edge server load and prevent network congestion effectively. Central to our solution is the formulation of average task delay optimization as a challenging nonlinear integer programming problem, requiring intelligent decision making regarding task offloading for each generated task at active mobile devices and CPU frequency adjustments at discrete time slots. To navigate the intricate landscape of the extensive discrete action space, we design an efficient multi-agent DRL learning algorithm named MAOC, which is based on MAPPO, to minimize the average task delay by dynamically determining task-offloading decisions and CPU frequencies. MAOC operates within a centralized training with decentralized execution (CTDE) framework, empowering individual mobile devices to make decisions autonomously based on their unique system states. Experimental results demonstrate its swift convergence and operational efficiency, and it outperforms other baseline algorithms. [ABSTRACT FROM AUTHOR]

As wireless networks continue to advance, virtual reality (VR) transmission over wireless connections is progressively transitioning from concept to practical application. Although this technology can significantly enhance the VR user experience, its development bottleneck lies in the computing capacity of devices and transmission latency. Considering the limited computational resources of VR devices for rendering tasks, multi-access edge computing (MEC) servers are introduced to provide powerful computing capabilities. To cope with transmission latency, reconfigurable intelligent surface (RIS) enhances links between base stations (BSs) and users. Based on these two technologies, we propose a RIS-assisted VR streaming model, where BSs are equipped with MEC servers to assist data rendering. Firstly, the user association, power control, and RIS phase shift optimization problems in the VR transmission system are jointly modeled and analyzed, establishing a long-term minimization of the interaction delay model. Secondly, by modeling the optimization problem as a Markov decision process (MDP), a joint optimization framework based on multi-agent deep reinforcement learning (MADRL) is proposed. In this framework, we have separately designed two dedicated algorithms for discrete and continuous variables. Furthermore, multiple agents can provide feedback based on user experience and cooperate with each other to improve the joint strategy. Finally, the performance and superiority of the proposed solution and algorithm are validated through simulation experiments in different application scenarios. [ABSTRACT FROM AUTHOR]

Summary: By providing distributed and ultra‐low‐latency communication between industrial devices and resource components, the Industrial Internet of Things (IIoT) is at the forefront of a new trend. Such a distributed paradigm is viewed as a collection of autonomous computing resources utilized by multiple heterogeneous devices to achieve higher‐quality interconnection and data exchange. However, stringent requirements of exceptional service and fairness guarantees pose many formidable challenges. To this end, this study investigates the aforementioned concerns in an integrated manner and further proposes a fairness‐aware task offloading method, called FOIMAM. Specifically, the p$$ p $$‐norm is introduced to accommodate the Pareto plane under the non‐Euclidean geometry framework while the evaluation and elimination of low‐quality solutions are completed based on survival scores. Particularly, the fairness requirements are formulated as a multi‐constraint problem and resolved using weighted max‐min fairness. Eventually, numerical results indicate that the proposed method brings substantial improvement in both service efficiency and fairness guarantees. [ABSTRACT FROM AUTHOR]

Nowadays, the paradigm of mobile computing is evolving from a centralized cloud model towards Mobile Edge Computing (MEC). In regions without ground communication infrastructure, incorporating aerial edge computing nodes into network emerges as an efficient approach to deliver Artificial Intelligence (AI) services to Ground Devices (GDs). The computation offloading and resource allocation problem within a HAP-assisted MEC system is investigated in this paper. Our goal is to minimize the energy consumption. Considering the randomness and dynamism of the task arrival of GDs and the quality of wireless communication, stochastic optimization techniques are utilized to transform the long-term dynamic optimization problem into a deterministic optimization problem. Subsequently, the problem is further decomposed into three sub-problems which can be solved in parallel. An online Energy Efficient Dynamic Offloading (EEDO) algorithm is proposed to address these problems. Then, we conduct the theoretical performance analysis for EEDO. Finally, we carry out parameter analysis and comparative experiments, demonstrating that the EEDO algorithm can effectively reduce system energy consumption while maintaining the stability of the system. [ABSTRACT FROM AUTHOR]

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Recently, multi-access edge computing (MEC) has emerged as a promising technology to alleviate the computing burden of vehicular terminals and efficiently facilitate vehicular applications. The vehicle can improve the quality of experience of applications by offloading their tasks to MEC servers. However, channel conditions are time-varying due to channel interference among vehicles, and path loss is time-varying due to the mobility of vehicles. The task arrival of vehicles is also stochastic. Therefore, it is difficult to determine an optimal offloading with resource allocation decision in the dynamic MEC system because offloading is affected by wireless data transmission. In this paper, we study computation offloading with resource allocation in the dynamic MEC system. The objective is to minimize power consumption and maximize throughput while meeting the delay constraints of tasks. Therefore, it allocates resources for local execution and transmission power for offloading. We define the problem as a Markov decision process, and propose an offloading method using deep reinforcement learning named deep deterministic policy gradient. Simulation shows that, compared with existing methods, the proposed method outperforms in terms of throughput and satisfaction of delay constraints. [ABSTRACT FROM AUTHOR]

Infiltrating epitheliosis (IE) is an uncommon type of complex sclerosing lesion in the breast. This condition is characterized by the infiltration of ducts into a scleroelastotic stroma, along with the presence of cells that display architectural and cytological patterns similar to those observed in usual ductal hyperplasia. We herein report a case of a 24-year-old woman who presented with bilateral breast nodules, which were initially identified as multiple fibroadenomas based on ultrasound findings. The patient underwent Mammotome system and regional mastectomy procedures, and subsequent pathological analysis confirmed the presence of multiple fibroadenomas with atypical ductal hyperplasia and infiltrating epitheliosis. This case discusses the challenges faced in diagnosing malignancy in a patient with multiple fibroadenomas accompanied by atypical ductal hyperplasia and infiltrating epitheliosis. [ABSTRACT FROM AUTHOR]

Methicillin-resistant (MR) Staphylococcus aureus (SA) and others, except for Staphylococcus aureus (SOSA), are common in healthcare-associated infections. SOSA encompass largely coagulase-negative staphylococci, including coagulase-positive staphylococcal species. Biofilm formation is encoded by the icaADBC operon and is involved in virulence. mecA encodes an additional penicillin-binding protein (PBP), PBP2a, that avoids the arrival of β-lactams at the target, found in the staphylococcal cassette chromosome mec (SCCmec). This work aims to detect mecA, the bap gene, the icaADBC operon, and types of SCCmec associated to biofilm in MRSA and SOSA strains. A total of 46% (37/80) of the strains were S. aureus, 44% (35/80) S. epidermidis, 5% (4/80) S. haemolyticus, 2.5% (2/80) S. hominis, 1.25% (1/80) S. intermedius, and 1.25% (1/80) S. saprophyticus. A total of 85% were MR, of which 95.5% showed mecA and 86.7% β-lactamase producers; thus, Staphylococcus may have more than one resistance mechanism. Healthcare-associated infection strains codified type I-III genes of SCCmec; types IV and V were associated to community-acquired strains (CA). Type II prevailed in MRSA mecA strains and type II and III in MRSOSA (methicillin-resistant staphylococci other than Staphylococcus aureus). The operon icaADBC was found in 24% of SA and 14% of SOSA; probably the arrangement of the operon, fork formation, and mutations influenced the variation. Methicillin resistance was mainly mediated by the mecA gene; however, there may be other mechanisms that also participate, since biofilm production is related to genes of the icaADBC operon and methicillin resistance was not associated with biofilm production. Therefore, it is necessary to strengthen surveillance to prevent the spread of these outbreaks both in the nosocomial environment and in the community. [ABSTRACT FROM AUTHOR]

In uplink non-orthogonal multiple access (NOMA)-based unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system, the successive interference cancellation (SIC) order of NOMA became a bottleneck limiting the transmission performance of task offload in uplink link.To reduce the energy consumption of the system, the SIC order was discussed and the optimal SIC order based on channel gain and task delay constraint was proposed.The optimization problem of minimizing the system energy consumption was proposed based on the optimal SIC order while satisfying the constraints of the given task delay of the device, the maximum transmit power constraint of the device, and the UAV trajectory.Since the problem was a complex non-convex problem, an alternating optimization method was adopted to solve the optimization problem to achieve power allocation and UAV trajectory optimization.A low-complexity algorithm based on matching theory was proposed to obtain the optimal device grouping in different time slots.Simulation results show that the optimal SIC order can realize smaller system energy consumption under the same task delay constraint compared with other SIC order, the proposed low-complexity device grouping algorithm can obtain the optimal device grouping.

Multi-access Edge Computing (MEC) is an emerging and promising computing paradigm that distributes computational resources closer to users at the network edge, effectively reducing both computation and communication latency. In practical Internet of Things (IoT) systems, many applications consist of interdependent subtasks. Therefore, determining how to offload these tasks while maintaining their dependencies to minimize latency becomes a challenging problem. Particularly in a dynamic environment with multiple users and MEC servers. Most existing studies rely on heuristic approaches, which lack adaptability in dynamic MEC environments, while machine learning-based methods often overlook task dependencies. Unlike previous work, our research focuses on the problem of offloading dependent tasks in multi-user, multi-MEC server scenarios. In this article, we first model the dependent task offloading problem as a Markov Decision Process (MDP). Then we propose a deep reinforcement learning (DRL)-based framework called GDDTO, with the aim of reducing task completion time. Specifically, this framework employs a Graph Convolutional Network (GCN) to extract task dependencies and dynamic MEC environment features, combined with a Double Deep Q-learning Network (DDQN) model and an optimized experience replay mechanism to select and evaluate task offloading strategies. Finally, comparative experiments demonstrate that this method significantly reduces task completion latency across various scenarios, proving its effectiveness.

Mobile Edge Computing (MEC) is pivotal for supporting compute-intensive and latency-critical applications in forthcoming mobile networks. It plays an essential role in providing network services through Low Earth Orbit (LEO) satellites, especially in demanding environments. This study proposes an Orthogonal Frequency Division Multiple Access (OFDMA)-based joint optimization framework for Offloading Decision and Dynamic Resource Allocation (ODDRA) Strategy. This framework dynamically allocates computing and bandwidth resources based on the current task load managed by LEO satellites. Moreover, it introduces a Time Division Multiple Access (TDMA)-based joint optimization for Offloading Decision and Task Offloading Sequence (ODTOS) Strategy. This strategy models the task offloading sequence as a permutation flow shop problem, targeting the minimization of total flowtime, and employs the heuristic Liu and Reeves algorithm (LR). The offloading decision challenge is addressed using matching theory and coalition game theory. Simulation outcomes demonstrate that the proposed strategies substantially decrease system delay and energy consumption relative to existing methods.

Mobile edge computing (MEC) represents a promising computing paradigm within Artificial Intelligence Generated Content (AIGC), offering users instant, customized, and personalized services. However, with the continued growth of the AIGC user base and the expansion of service demands, edge computing nodes are required to handle an increasing number and complexity of offloading tasks, leading to significant energy consumption issues in edge systems. This paper introduces a distributed task offloading framework (EE-A2C) that utilizes the Advantage Actor-Critic algorithm to enhance energy efficiency in edge cloud environments. This framework facilitates distributed interactions among multiple agents and edge environments with communication queues, aiming to minimize average energy consumption and latency for AIGC users. Secondly, to achieve adaptive and efficient task offloading decisions, we have developed a complex reward-sharing model based on latency and energy consumption. Finally, we have also incorporated LSTM to enhance the model’s ability to capture critical information about energy consumption, thereby promoting more robust decision-making. Compared to eight state-of-the-art energy-saving algorithms, the proposed EE-A2C framework more effectively utilizes the computing power of edge nodes, significantly reduces average energy consumption and latency, and enhances the energy efficiency of the edge cloud system.

5G/6G networks offer high-capacity, low-latency and high-speed communications to new services associated with vertical industries like smart cities, automotive and energy sectors. To achieve this objective, new networking and computing paradigms such as Network Function Virtualization (NFV), Multi-access Edge Computing (MEC) and Software Defined Networking (SDN) are required to bring flexibility and adaptability to networks, while optical networks are envisioned to build the backhaul of 5G/6G networks thanks to their high capacity. Current studies on NFV-5G network planning with MEC resources follow either the online trend or the offline trend. In the online trend, resource allocation to service connection requests is determined upon request arrival based on the available resources. Conversely, in the offline trend, network planning is conducted assuming that the expected traffic (or the full set of service requests) is known in advance. However, to the best of our knowledge, previous works have not assessed the performance of these static offline network plannings when operating under dynamic traffic. In this paper, we evaluate the performance of a previously proposed static planning algorithm for NFV-5G networks under dynamic traffic conditions and analyze the impact of dynamic traffic on the request blocking ratio. Moreover, we also examine the performance of the static planning for each individual service type in a dynamic network scenario with three different available services. The simulation results show that overdimensioning is necessary when planning NFV-5G networks statically, and that the overdimensioning should consider different criteria based on the network expected performance objectives.

The application of UAV-aided MEC well-suited for the execution of the data-intensive and latency-sensitive tasks in the infrastructure-deprived regions. However, the growing number of UAVs and smart devices causing a major difficulty in the devising an effective scheme for the task offloading and resource allocation in multi-UAV-aided MEC networks. Furthermore, the resource deficient environments unable to sustain prolonged resource-intensive activities, additional complexities are posed on the optimum utilization of the resources. In this paper, we introduced a multi-agent deep reinforcement learning scheme for the task offloading in the multi-UAV-assisted networks (MUAVDRL). In this configuration, the mobile users fetch computational resources from the UAVs with the goal of minimizing the computation cost which incorporates both the energy consumption and the computation delay. Initially, we start with the optimization problem which is defined as the minimizing the computational costs. Through modelling it as MDP, we aim to reduce the computational costs for mobile users. Leveraging the dynamic and high-dimensional nature of the challenge, the MUAVDRL algorithm solves this problem efficiently. Comprehensive simulation results exhibit the efficacy and superiority of our projected framework when compared to existing state-of-the-art methods, illustrating its potential in the practice.

Unmanned aerial vehicles (UAVs) play an important role within mobile edge computing (MEC) networks in improving communications for ground users during emergency situations. However, sustaining high-quality service for extended periods is challenging because of constraints on battery capacity and computing capabilities of UAVs. To address this issue, we leverage zero-energy reconfigurable intelligent surfaces (ze-RIS) within UAV-MEC networks and introduce a comprehensive strategy that combines task offloading and resource sharing. A deep reinforcement learning (DRL) driven energy efficient task offloading (DEETO) scheme is presented. The primary objective is to minimize UAV energy ingestion. DEETO aims to enhance task offloading decision mechanism, computing and communication resource allocation, while adopting hybrid task offloading mechanism with intelligent RIS phase-shift control. We begin by modeling it as a DRL problem, structuring it as a Markov decision process (MDP), and subsequently resolving it effectively through the use of the advantage actor-critic (A2C) algorithm. Our simulation results highlight the superiority of the DEETO scheme compared to alternative approaches. DEETO excelled by achieving a notable energy savings of 16.98% from the allocated energy resources, coupled with the highest task turnover rate of 94.12%, all achieved within a shorter learning time frames per second (TFPS) and yielding higher rewards.

Advances in connectivity and computing infrastructure facilitate the introduction of innovative Cooperative Intelligent Transport Systems (C-ITS) services. However, meeting the requirements of these highly demanding services calls for novel computing architectures that handle extensive device connections, minimize latency, and support multiple resource-intensive services concurrently. To overcome these challenges, this work presents an architecture that comprises three layers: 1) on-board unit (OBU) mainly as a data producer; 2) intermediate edge layer where low-latency backend services can be deployed; and 3) cloud layer for non-real-time backend services. The OBU software stack implements the ETSI C-ITS standard and supports multicast over the cellular network. The edge layer includes an in-memory database, and the cloud layer a persistent database. Each layer has its own Application Programming Interface (API) for data consumption. We conducted several experiments to demonstrate the feasibility of our proposed system that ensures scalability and interconnection between vehicles, edge and cloud servers. We also assess the delay caused by each of the elements of the architecture, and we discuss the potential solutions for the identified issues.