关键词： Mobile edge computing   Task offloading   Cloud computing   Mobility prediction   Fault tolerance   Deep reinforcement learning   Energy efficiency  

摘要： Mobile edge computing (MEC) improves resource-limited mobile devices by transferring demanding computational activities to edge servers and cloud infrastructure, thereby mitigating issues related to user mobility and network fluctuations. This paper introduces Flexible Mobility-Based Task Offloading (FlexiMRO), a mobility-oriented, fault-tolerant framework for adaptive task offloading in MEC and cloud settings. The suggested methodology incorporates a long short-term memory model to forecast user trajectories and network states with high transmission precision, alongside a deep reinforcement learning strategy utilizing Q-Deep networks for optimal job distribution at the edge and cloud layers. A random forest classifier guarantees fault tolerance by accurately predicting server failures and facilitating the reallocation of ongoing jobs. Comprehensive simulations illustrate the superiority of FlexiMRO compared to current methodologies, achieving a latency reduction of up to 70%, an enhancement in energy efficiency of 60%, and a failure rate of 80% in contrast to 70%. FlexiMRO offers a scalable and adaptable solution for 5G and IoT applications by minimizing latency, energy consumption, and quality of experience, efficiently utilizing edge and cloud computing to guarantee uninterrupted service delivery in dynamic MEC environments.

关键词： Mobile edge computing   Blockchain   Computation offloading   Pricing strategy   Admission control  

摘要： The solution of proof-of-work in blockchain requires a large amount of resources;the lack of computing power on mobile devices limits the development of blockchain in mobile applications. In order to cope with the problem of insufficient resources on mobile devices, the combination of blockchain and Mobile Edge Computing has attracted much attention. In this paper, we consider an edge-enabled blockchain system containing two types of miners and a single Edge Service Provider (ESP). Different types of miners have different resource requirements and corresponding prices. Each miner makes a computation offloading decision based on the resource price set by ESP. Different miners possess varying resource requirements and correspond to distinct prices. Miners formulate task offloading decisions based on the prices set by ESP for different resource types. The ESP dynamically adjusts resource price based on miners' offloading requests and makes admission control according to resource usage in each time slot. The problem is a joint optimization problem of computation offloading, price strategy, and admission control, which is challenging to solve due to the interaction among three issues. In this paper, we model the problem as a sequential decision-making problem and use a dynamic programming algorithm to obtain the optimal solution. Through simulation experiments, our results demonstrate that the proposed algorithm can effectively improve the overall social utility.

关键词： Bilinear pairing   Extension field   Authentication   Key agreement   Mobile edge computing  

摘要： We show the Rakeei-Moazami key agreement scheme [Wireless Networks, 2022, 28, 2983-2999] is flawed. It neglects the basic structure of bilinear pairing over elliptic curve, and presents some false equalities. Some point multiplications over the underlying elliptic curve are falsely specified, which result in the failure of later integrity authentication for the user and server. These false computations can be revised by introducing another hash function which maps a value of bilinear pairing to an element in the base finite field.

关键词： Autonomous vehicles   Mobile edge computing   Deep neural networks   Refined pelican optimizer   LiDAR   Vehicle control   MobileNet   Microcontroller  

摘要： Autonomous vehicles and mobile edge computing's confluence have raised an innovative model for immediate decision-making and improved computational abilities. But, enhancing vehicle management systems to guarantee effective enactment remains an important challenge. Present approaches regularly depend on intricate algorithms and multiple sensors, that result in improved computational overhead and potential latency. The current study resolves the present gap by offering a new hybrid framework, which synergistically mixes optimization algorithms and deep neural networks through the advantages of mobile edge computing. Precisely, this research presents a hybrid model for autonomous vehicle management by integrating a refined version of the RPO or Pelican Optimizer with deep neural networks attuned to mobile edge computing environments. The chief contributions of the present study have been threefold: (1) the improvement of a particular autonomous driving method optimized for mobile edge computing platforms;(2) the arrangement of an optimized MobileNet method employing the RPO algorithm that uses LiDAR sensor data for effective object recognition and path design;and (3) the construction of an indoor vehicle prototype by mean of a microcontroller and LiDAR sensors, after a comprehensive performance evaluation of inference models, and analyzing the trade-offs between input size and computational effectiveness. Experimental outcomes show the efficiency and reliability of the suggested hybrid model, through improving autonomous vehicle management and decision-making abilities within the mobile edge computing paradigm. The current study contributes to the enhancement of autonomous vehicle research and provides an innovative solution for effective and precise vehicle control within edge computing environments.

关键词： Connected car   Mobile edge computing   Deep reinforcement learning  

摘要： With the rapid development of intelligent transportation systems,vehicular networks(VANETs)have become an essential part of the intelligent transportation *** to the high dynamics of efficient vehicular network nodes and the low latency requirement of data interaction,the traditional cloud computing model makes it difficult to meet the real-time and performance criteria,and the storage optimization strategy based on edge computing can effectively improve the data access efficiency and system *** paper aims to explore how to optimize the data caching mechanism of intelligent telematics using edge computing to reduce network latency,improve data availability,and enhance overall system performance.

关键词： 5G networks   Mobile edge computing   Non-orthogonal multiple access   Deep learning   Bird swarm optimization   Resource allocation   Multi-objective optimization   Energy efficiency   Latency reduction  

摘要： The increase in mobile communication has escalated network load, necessitating more intelligent resource management in 5G and forthcoming networks. This research introduces a novel methodology to enhance network performance by integrating bird swarm optimization (BSO) with deep learning (DL). We investigate a configuration in which several users inside a single cell solicit services from proximate edge servers and remote cloud servers, employing Non-Orthogonal Multiple Access (NOMA) to optimize radio spectrum utilization. BSO, influenced by avian flocking patterns, aggregates users and distributes workloads among servers to reduce energy consumption, service latency, and operational expenses. Simultaneously, DL examines historical network data to forecast traffic and user requirements, enabling BSO to make immediate, educated decisions. We utilize queuing theory to model this system, addressing a complicated optimization problem that concurrently reduces energy, latency, and costs. In contrast to conventional solutions, our technology dynamically adjusts to fluctuating network conditions, providing an effective remedy for the requirements of 5G. Simulations demonstrate that the integrated BSO-DL approach diminishes energy usage, delays, and expenses by around 54 %, substantiating its efficacy in improving broadband performance. This research facilitates the development of more efficient and sustainable 5G networks, addressing the increasing demands of contemporary mobile communication through a scalable and intelligent approach.

关键词： Internet of vehicles   Multi-intelligence deep reinforcement learning   Mobile edge computing   Queuing theory   Non-orthogonal multiple access   Task offloading  

摘要： With the rapid advancement of autonomous driving technology, the volume of data generated by vehicle sensors is growing exponentially. Due to spatial limitations within vehicles, high-performance computing equipment cannot be accommodated, resulting in limited vehicle resources being unable to meet the real-time and low-energy consumption demands of vehicular network data processing. To address this challenge, Vehicular Edge Computing (VEC) has emerged as a new development trend in intelligent transportation. VEC effectively relieves the pressure of data transmission and processing by integrating edge computing into the in-vehicle network. Based on this, an efficient offloading strategy for VEC system under resource-constrained conditions is proposed. Firstly, a vehicular edge computing system framework incorporating Non-Orthogonal Multiple Access (NOMA) and queuing theory is constructed, involving multiple servers and multi-priority tasks. The transmission model and computation model from vehicles to edge nodes are derived. This framework significantly reduces inter-vehicle signal interference using the characteristics of NOMA, and optimizes the average delay and energy consumption performance during multi-task processing. Secondly, considering the resource constraints in practical operations, this paper establishes a stochastic game with delay, energy consumption, and cost as the optimization objectives. Given the superiority of Multi-Agent Deep Reinforcement Learning (MADRL) in complex environments, a MADRL-based task offloading algorithm, named Multi-Agent Distributed Deep Action Semantic Deterministic Policy Gradient (MADSASPG), is proposed for vehicular task computation offloading. Finally, to further enhance the action prediction accuracy and performance of the algorithm, the Actor network of the algorithm is improved into an Action Semantic Network (ASN). Through information sharing among Agents, the cooperation between Agents is strengthened. The experiment

关键词： Visual analytics   Accuracy   Resource management   Analytical models   Computational modeling   Image edge detection   Feature extraction   Bandwidth   Numerical models   Servers   Video analytics   mobile edge computing   knob configuration   and resource allocation  

摘要： Characterized by its ease of low-latency response, edge computing is capable of supporting real-time video analytics applications, constituting an edge video analytics paradigm, where the joint knob configuration and network scheduling design has drawn ever-escalating research attention. However, the potential of edge video analytics has not been fully exploited, owing to the limitations of the state-of-the-art as follows. i) The eminent impact of video content on accuracy performance has been ignored. ii) The variables that can be tuned are not fully considered in scheduling. iii) The heuristic algorithm-based solutions are far from the optimal. To fill in this gap, in this paper, we conceive a content-aware joint knob configuration and resource allocation scheme for edge video analytics. Concretely, fed with the features extracted from the video content, a deep neural network (DNN)-based predictor is proposed to predict the configuration-accuracy performance in a real-time manner. With an aid of the predictive results, we formulate an accuracy-maximization problem as an integer programming problem, by optimizing the variables, including resolution, frame rate, video analytic model, network bandwidth, and computational resource subject to the latency constraints. To solve this problem in an efficient manner, we devise a novel low-complexity dynamic programming method. Simulation results verify the efficiency of our content-aware joint knob configuration and resource allocation scheme. Quantitatively, a 3.3% gap is attained towards the upper bound in terms of the accuracy in an object detection scenario, relying on the scheme proposed.

关键词： Servers   Data retrieval   Uncertainty   Costs   Optimization   Data centers   Multi-access edge computing   Fluctuations   Data models   Cloud computing   Mobile edge computing   edge data caching   data popularity   optimization approach  

摘要： In the mobile edge computing environment, caching data in edge storage systems can significantly reduce data retrieval latency for users while saving the costs incurred by cloud-edge data transmissions for app vendors. Existing edge data caching (EDC) methods prioritize popular data and aim to minimize users' data retrieval latency and system storage costs jointly. However, these EDC methods often rely on the assumption that data popularity always follows certain distributions. As a result, they cannot properly adapt to the fluctuations in data popularity due to user mobility or unexpected increases in user demands. Meanwhile, unlike cloud data centers, complex and fragile edge servers are more likely to experience physical failures or network outages, presenting new challenges for EDC strategies. Specifically, when an edge server fails or experiences an outage, cached data may become temporarily unavailable, leading to increased latency as requests are redirected to alternative servers or the cloud. In this paper, to enable uncertainty-aware edge data caching (uEDC), we first model the problem as a robust optimization problem and propose an optimal algorithm named uEDC-B to find the optimal uEDC solution. To address the high computational complexity of uEDC-B, we introduce an approximate algorithm named uEDC-L based on linear decision rules. Theoretical analysis and extensive experiments on a real-world dataset demonstrate that the proposed methods outperform two state-of-the-art approaches in handling the uncertainties in data popularity and edge server failure with a significant performance improvement of 59.27% in data retrieval latency and 55.07% in data caching cost.

关键词： Mobile Edge Computing   Server selection   Reinforcement learning   Non-stationary systems   Serverauswahl   Reinforcement Learning   Nicht-station & auml   re Systeme  

摘要： Mobile Edge Computing (MEC) is an architecture that brings computational capabilities to the edge of mobile networks. It enables low-latency and efficient task execution by allowing the Mobile Units (MU) to offload computation tasks to nearby computing servers. However, server selection in MEC remains an open and complex challenge due to the non-stationary nature of the system dynamics, where channel conditions, server loads, and resource availability change over time. Traditional reinforcement learning approaches, while effective for adaptive decision-making, often assume stationary system dynamics. In this perspective, we discuss the role of learning and forgetting mechanisms in MEC server selection, emphasizing the need for adaptive mechanisms that can retain and exploit relevant experience while discarding outdated information. Mobile Edge Computing (MEC) ist eine Architektur, die Rechenkapazit & auml;ten an den Rand mobiler Netzwerke bringt. Sie erm & ouml;glicht eine latenzarme und effiziente Ausf & uuml;hrung von Aufgaben, indem sie mobilen Einheiten (MUs) erlaubt, Rechenaufgaben an nahegelegene Server auszulagern. Die Auswahl des geeigneten Servers im MEC stellt jedoch nach wie vor eine offene und komplexe Herausforderung dar, da die Systemdynamik nicht station & auml;r ist - Kanalbedingungen, Serverauslastung und Ressourcenverf & uuml;gbarkeit & auml;ndern sich im Zeitverlauf. Traditionelle Ans & auml;tze des Reinforcement Learning sind zwar wirksam f & uuml;r adaptive Entscheidungsfindung, gehen jedoch h & auml;ufig von station & auml;ren Systemdynamiken aus. In dieser Perspektive er & ouml;rtern wir die Rolle von Lern- und Vergessensmechanismen bei der Serverauswahl im MEC und betonen die Notwendigkeit adaptiver Mechanismen, die relevante Erfahrungen bewahren und nutzen k & ouml;nnen, w & auml;hrend veraltete Informationen verworfen werden.