Edge Servers Research Articles

Optimal deployment of private 5G multi-access edge computing systems at smart factories: Using hybrid crow search algorithm

In smart factories, an increasing number of mobile intelligent devices are deployed to meet the growing demands for flexible manufacturing. These devices, equipped with various sensors, synchronize a substantial amount of data with cloud servers for real-time monitoring and control. Fifth generation mobile networks (5G) combined with multi-access edge computing (MEC) provide the capabilities for multiple connections, large-scale data transmission, and efficient response times, becoming the mainstream network architectures for the needs. Additionally, with the emerging trend of energy-saving, recent works have addressed energy consumption as a significant cost factor. However, most previous studies tended to focus solely on cloud servers, neglecting energy consumption at edge computing and underestimating the impact of heterogeneous mobile device. As the scope of smart factory networks continues to expand, the challenges become increasingly critical. This study presents a private 5G MEC system architecture tailored for smart panel factories. The concerned problem is formulated as an integer programming model, which determines deployment locations and quantities for MEC servers and 5G small cells, including picocells and femtocells, so as to minimize the overall deployment costs while meeting the constraints of connectivity, capacity, latency, coverage, serviceability, and energy consumption. Since the edge server deployment problem is NP-hard, this study further proposes a hybrid metaheuristic algorithm, CSAVNS, which combines the strengths of crow search algorithm (CSA) and variable neighborhood search (VNS). In the global search phase of CSA, the VNS local search is introduced to enhance the algorithm's capabilities. Experimental analysis demonstrates that the proposed CSAVNS outperforms other algorithms in terms of solution solving capabilities.

A secure and efficient authentication key agreement scheme for industrial internet of things based on edge computing

Industrial Internet of Things (IIoT) is a pivotal driving force behind the intelligent transformation of the global industrial system, bringing about a gradual shift in production methods. However, due to users completing remote access and data transmission through open channels, ensuring user legitimacy and data security has become an important factor in IIoT. In addition, resource limited users or devices need to collect and publish corresponding messages while also timely processing instant messages obtained from the network. Leveraging edge computing technology effectively mitigates the transmission time of messages, addressing the challenges of high computing pressure and prolonged response time associated with cloud server computing. Therefore, investigating authentication key agreement based on edge computing in the IIoT environment holds substantial theoretical significance and practical importance. This article introduces an IIoT authentication that ensures both security and efficiency by integrating elliptic curve cryptography and three-factor authentication. Within this scheme, the user and the edge server accomplish security authentication. Through security analysis, the proposed scheme is proven to meet the essential security requirements. Performance analysis shows that it achieves higher security, supports more functions, and achieves lower computational and communication costs compared to related schemes.

Pedestrian Abnormal Behavior Detection System Using Edge–Server Architecture for Large–Scale CCTVEnvironments

As the deployment of CCTV cameras for safety continues to increase, the monitoring workload has significantly exceeded the capacity of the current workforce. To overcome this problem, intelligent CCTV technologies and server-efficient deep learning analysis models are being developed. However, real-world applications exhibit performance degradation due to environmental changes and limited server processing capacity for multiple CCTVs. This study proposes a real-time pedestrian anomaly detection system with an edge–server structure that ensures efficiency and scalability. In the proposed system, the pedestrian abnormal behavior detection model analyzed by the edge uses a rule-based mechanism that can detect anomalies frequently, albeit less accurately, with high recall. The server uses a deep learning-based model with high precision because it analyzes only the sections detected by the edge. The proposed system was applied to an experimental environment using 20 video streams, 18 edge devices, and 3 servers equipped with 2 GPUs as a substitute for real CCTV. Pedestrian abnormal behavior was included in each video stream to conduct experiments in real-time processing and compare the abnormal behavior detection performance between the case with the edge and server alone and that with the edge and server in combination. Through these experiments, we verified that 20 video streams can be processed with 18 edges and 3 GPU servers, which confirms the scalability of the proposed system according to the number of events per hour and the event duration. We also demonstrate that the pedestrian anomaly detection model with the edge and server is more efficient and scalable than the models with these components alone. The linkage of the edge and server can reduce the false detection rate and provide a more accurate analysis. This research contributes to the development of control systems in urban safety and public security by proposing an efficient and scalable analysis system for large-scale CCTV environments.

Mobility-aware proactive video caching based on asynchronous federated learning in mobile edge computing systems

With the rapid advance of the Internet of Things (IoT) and the surging user-centric smart devices, video services such as short videos, real-time video streaming, and Virtual Reality (VR) gaming are emerging. Traditional cloud-centric caching is no longer able to satisfy the low-latency needs of mobile user groups. Predicting the most popular content in advance and caching it on edge devices such as small base stations (SBS) is a promising solution to alleviate network congestion and supply better Quality of Service (QoS) for mobile users. However, frequently changing popular content, limited edge caching resources, privacy of user data, and user mobility pose significant challenges to edge video caching. To tackle these challenges, we propose an efficient asynchronous federated learning (AFL)-based mobility-aware proactive video caching scheme (AF-MPVC), which significantly improves the overall network performance while preventing the risk of user data leakage. First, we model the proactive video caching problem in mobile edge computing systems and consider the impact of user mobility on AFL, representing the problem as an autoencoder (AE) model loss minimization problem. Then, a proactive video caching algorithm is proposed based on a filtering model, which uses the hidden features obtained from the trained AE model to compute the user similarity to find the popular videos that are most likely to be accessed by users and combine them with the most frequently requested videos. Meanwhile, we take into account the short duration of high mobility users staying on the current SBS and cache some videos to neighboring edge servers to have more redundant space to store more less popular videos. Finally, experiments on three real-world datasets demonstrate that the presented caching scheme outperforms other baseline caching schemes with respect to cache hit rate and latency reduction.

Dynamic task offloading and service caching based on game theory in vehicular edge computing networks

Vehicular edge computing (VEC) enables task offloading from vehicle to edge servers deployed on Road Side Units (RSUs) and to vehicle with idle resources, which is considered as a promising vehicle network architecture. However, efficient task offloading and task resource cache management in the VEC network is challenging. In this work, we formulate a joint task offloading and service caching problem (JTOSCP) for VEC with edge-vehicle cooperation, aiming to minimize the total task delay-energy value and cost value of all vehicles while guaranteeing task processing delay tolerance. The formulated JTOSCP is proven as NP-hard problem. Due to the complexity of JTOSCP, we propose a cooperative task service caching and task offloading algorithm named CO-MATCH. In this algorithm, we design the dynamic programming-based service caching (DPSC) Algorithm to incentivize edge services and volunteer vehicles to cache, which takes into account both service caching preferences and social similarity. The Many-to-One Matching Game (MOMG) Algorithm is proposed to stimulate edge device games and achieve the optimal task offloading. Then, the CO-MATCH method is proven to be stable. Simulation results demonstrate that our proposed approach can significantly improve the quality of service of user vehicles compared to the other methods.

Reliable incentive mechanism in hierarchical federated learning based on two-way reputation and contract theory

Hierarchical federated learning (HFL) can effectively alleviate the communication bottleneck of traditional federated learning. However, the long-term healthy development of federated learning needs to continue to attract reliable participants with high-quality data. Therefore, we propose a novel reliable incentive mechanism based on two-way reputation and contract theory. Firstly, a two-way reputation mechanism named TWRM is introduced to evaluate the reliability of cluster heads and mobile devices simultaneously. Secondly, a reputation blockchain is designed to prevent malicious nodes from tampering with reputation scores, and effectively deal with malicious edge servers colluding with mobile devices. Finally, contract theory is used to reward customers based on data quality and encourage them to provide high-quality training data. Experimental results can verify that the scheme proposed in this paper effectively ensures the reliability and data quality of the participants, which can help to attract more reliable participants with high-quality data to join in federated learning.

Optimal service caching, pricing and task partitioning in mobile edge computing federation

Mobile Edge Computing (MEC) federations aim to establish a joint edge service model between Edge Infrastructure Providers (EIPs) and clouds, facilitating the sharing and utilization of MEC services and resources. However, in such a hierarchical multi-EIP MEC federation environment, optimizing service caching, task partitioning, and pricing strategies to maximize the profits gained by EIPs and minimize the cost of mobile devices (MDs) is challenging. To address this challenge, This paper first formulates a two-stage multi-leader, multi-follower Stackelberg game between EIPs and MDs. Then, a new method combining a Stackelberg-based Multi-Agent Deep Deterministic Policy Gradient (STMADDPG) algorithm and a Transformer-based Popularity Prediction (TPP) model is devised to learn the optimal strategies. Extensive experiments with real-world datasets are conducted to demonstrate the excellent performance of the proposed STMADDPG-TPP method.

Edge server enhanced secure and privacy preserving federated learning

Federated learning (FL) has been smoothly embedded into current IoT edge computing architecture, and hatching advanced IoT-FL applications. While so, sensitive messages generated on terminal sides brings privacy issues; and vulnerable terminal devices are easy targets for Byzantine adversaries, they inject malicious data to manipulate the IoT-FL system. Now an inherent dilemma is, the privacy preserving requires anonymous indistinguishable individual characteristics, while the malicious adversary detection requires transparent distinguishable results. Existing fragmented schemes are not able to handle both problems coherently. This time, inherit our previous study, based on the IoT edge computing architecture, we utilize Homomorphic Encryption (HE) and Threshold Secret Sharing (SS) methods, propose a joint scheme (namely, Sec-IoTFL) for anonymous adversary detection. Specifically, we batch encode the clients’ training result vectors (message) as polynomials, and split them as secret pieces with a unified SS key set; then edge server and cloud server joint deal with these secret pieces in a specialized flow, where Byzantine adversaries will be filtered out. The theoretical analysis and solid experimental results suggest, in our scheme, local sensitive data is well preserved, and malicious behavior is precisely screened. Comparing with traditional methods, our Sec-IoTFL scheme shows the superiority in accuracy and efficiency.

Dynamic edge clustering and task scheduling for edge assisted metaverse system in the field of remote work and collaboration

SummaryThe metaverse, a future digital world for living, working, learning, and interacting, is rapidly gaining significance, particularly in the domain of remote work and collaboration. This emerging digital landscape demands high‐performance, low‐latency, and scalable services to provide an immersive user experience. The current technology used in metaverse systems has some limitations, which emphasize the importance of adopting emerging technologies like Edge Computing (EC). However, as the number of users and data volume increases, it can impact both system performance and scalability of the edge‐assisted metaverse system. Additionally, the uneven distribution of edge servers can cause inconsistencies and result in high latency. To overcome these challenges, this paper proposes a dynamic edge clustering and task scheduling approach for edge‐assisted metaverse systems (DTAM) in the field of remote work and collaboration. The proposed approach addresses the challenges of high user volume and uneven resource distribution by incorporating dynamic clustering and edge server assistance to improve clustering performance. Furthermore, a Prioritized Experience Replay‐based Deep Q‐learning algorithm with state augmentation (PERDQSA) for task scheduling is introduced to improve sample efficiency and performance. The performance of the proposed DTAM is evaluated against existing techniques, and experimental results demonstrate its significant superiority in terms of specific metrics such as bandwidth, task response time, energy efficiency, and latency. The experiments demonstrated that DTAM outperforms Transformation‐based Edge Computing Deep Q‐Learning (TransEC‐DQL) in several key metrics. Specifically, DTAM achieves 28.5% reduction in latency, 13.7% reduction in response time, and 6.4% improvement in bandwidth compared to TransEC‐DQL. These results signify that DTAM can deliver a significantly enhanced user experience in the metaverse, particularly in the context of remote work and collaboration.

Delay-guaranteed Mobile Augmented Reality Task Offloading in Edge-assisted Environment

With the introduction of Augmented Reality (AR) into mobile devices, it becomes a trend to develop mobile AR applications in various fields. However, the execution of AR task demands the extensive resources of computation, memory and storage, which makes it difficult for mobile terminals with constrained hardware resources to carry out AR applications within the limited delay. In response to this challenge, we propose a mobile AR offloading method under the edge-assisted environment. Firstly, we divide an AR task into consecutive subtasks, and then collect the features of hardware, software, configuration, and runtime environments from the edge servers to be offloaded. With the features, we construct an AR subtask Execution delay Prediction Bayesian Network (EPBN) to predict the execution delay of different subtasks on each edge platform. Based on the prediction, we model the task offloading as the NP-hard Traveling Salesman Problem (TSP), and then propose a PSO-GA based solution by adopting the heuristic algorithm of Particle Swarm Optimization (PSO) to encode the offloading strategy and using Genetic Algorithm (GA) for particle update. The extensive experiments prove that the average performances of EPBN outperform the others with 17.23%, 23.97%, and 20.67% on micro-P, micro-R, and micro-F1 respectively, and the PSO-GA ensures that the offloading latency is reduced by nearly 5% compared to the suboptimal algorithm.

A decentralized federated learning based interoperable and heterogeneity aware predictive optimization method for energy and comfort in smart homes environment

It is a known fact that a comfortable residential space or workspace is crucial to productivity and well-being. In smart home environments, sensors can be used to sense environment and occupant data and actuators can be controlled accordingly for maximizing thermal comfort. However, keeping in view the growing demand of energy and scarcity of energy generation sources, it is mandatory to optimize the energy usage complementary to optimize thermal comfort. In order for the developed method to be interoperable and heterogeneity aware across different edge device vendors and for ease of hardware connectivity in smart homes, a REST API based framework is developed. Sensors mounted with IoT devices collect data from its respective smart home environment and store the data in its local database. An edge server is used to control the connectivity among these IoT devices and to initiate the decentralized federated learning mechanism. Each IoT device train local deep learning models for predicting energy usage, indoor temperature, indoor humidity for maintaining a comfortable environment state using minimal energy. Models from each IoT devices are aggregated at each IoT device iteratively to make federated learning independent of central server. Global deep learning model is iteratively trained for each prediction requirement. Later, these models are used to predict indoor temperature, humidity and energy consumption for time stamp (t+1), keeping in view environmental and occupant related sensed data at time stamp (t). Optimization of energy consumption and thermal comfort is mathematically modeled and based on the predictions of DL models, environment is controlled by handling actuators using a particle swarm optimization (PSO) mechanism. Relevant constraints are incorporated in PSO for limiting the positional variations in particles for efficient learning. The method continually learns and adapts to changes in the surroundings, providing real-time monitoring and control of thermal comfort to the end users. To validate our approach, data is collected and experiments are conducted in a real-time smart home’s environment. The results are compared with those obtained without using the proposed predictive optimization mechanism. The study demonstrates the effectiveness of proposed predictive optimization mechanism in providing a comfortable and productive smart home or workspace environment utilizing 38% less energy as compared to competitor.

Traffic Violation Detection System

Travel convenience is impacted by a number of factors, including the condition of the road, traffic, length of trip, accidents, speed, etc. The daily increase in accident rates poses the biggest concern in Sri Lanka. These events not only result in fatalities but also increase the nation's financial losses. Traffic congestion is brought on by road users' lack of discipline and sentiments, which may result in traffic offenses. The goals of this research are to curb any traffic infractions as well as reduce corruption and nepotism on the road. This system was developed as separate three models namely edge server, fine management system and centralized server. Edge server developed in python is to detect traffic violations such as parking, single line cutting, double line cutting, one way and u turn in real time according to the changing rules and constraints configured by the traffic police admin via the fine management system which supports a configurable platform designed using canvas designing tools. Upon edger server identifying violations, the functions in the centralized server responsible to recognize the number plate of the violated vehicle will be triggered and send a sms to driver for further fine management and payment supported by the fine management system. The mobile application controlling server is responsible to track and inform the violations to nearest police officer in any case the system could not rectify the vehicle number plate.

Edge HPC Architectures for AI-Based Video Surveillance Applications

The introduction of artificial intelligence (AI) in video surveillance systems has significantly transformed security practices, allowing for autonomous monitoring and real-time detection of threats. However, the effectiveness and efficiency of AI-powered surveillance rely heavily on the hardware infrastructure, specifically high-performance computing (HPC) architectures. This article examines the impact of different platforms for HPC edge servers, including x86 and ARM CPU-based systems and Graphics Processing Units (GPUs), on the speed and accuracy of video processing tasks. By using advanced deep learning frameworks, a video surveillance system based on YOLO object detection and DeepSort tracking algorithms is developed and evaluated. This study thoroughly assesses the strengths, limitations, and suitability of different hardware architectures for various AI-based surveillance scenarios.

Edge Caching Data Distribution Strategy with Minimum Energy Consumption.

In the context of the rapid development of the Internet of Vehicles, virtual reality, automatic driving and the industrial Internet, the terminal devices in the network show explosive growth. As a result, more and more information is generated from the edge of the network, which makes the data throughput increase dramatically in the mobile communication network. As the key technology of the fifth-generation mobile communication network, mobile edge caching technology which caches popular data to the edge server deployed at the edge of the network avoids the data transmission delay of the backhaul link and the occurrence of network congestion. With the growing scale of the network, distributing hot data from cloud servers to edge servers will generate huge energy consumption. To realize the green and sustainable development of the communication industry and reduce the energy consumption of distribution of data that needs to be cached in edge servers, we make the first attempt to propose and solve the problem of edge caching data distribution with minimum energy consumption (ECDDMEC) in this paper. First, we model and formulate the problem as a constrained optimization problem and then prove its NP-hardness. Subsequently, we design a greedy algorithm with computational complexity of O(n2) to solve the problem approximately. Experimental results show that compared with the distribution strategy of each edge server directly requesting data from the cloud server, the strategy obtained by the algorithm can significantly reduce the energy consumption of data distribution.

Federated Learning With Dynamic Epoch Adjustment and Collaborative Training in Mobile Edge Computing

As a distributed learning paradigm, federated learning (FL) can be applied in mobile edge computing (MEC) to support real-time artificial intelligence by leveraging edge computation resources while preserving data privacy in the end devices. However, the unpredictable wireless connections between end devices and edge servers in MEC (e.g., frequent handovers and unstable wireless channels) may result in the loss of important model parameters, which slows down the FL training process and degrades the quality of the global model. In this paper, we propose an adaptive collaborative federated learning (ACFL) scheme to accelerate the convergence and improve model reliability by mitigating communication-based parameter loss under a three-layer MEC architecture. Firstly, a dynamic epoch adjustment method is proposed to reduce communication rounds by dynamically adjusting the training epochs in end devices. In addition, to accelerate the FL convergence, we present an edge server collaborative training scheme by leveraging a multi-layer computing architecture, where edge servers utilize their maintained data to collaboratively train models with end devices. Finally, extensive simulations are conducted and show that ACFL can efficiently improve model reliability and accelerate the convergence of the FL process in MEC.

NOMA Assisted Two-Tier VR Content Transmission: A Tile-Based Approach for QoE Optimization

Virtual reality (VR) provides users with an immersive and interactive experience through head-mounted devices, which has attracted increasing attention in recent years. Specifically, tile-based VR content transmission provides a promising approach to alleviate the conflict between limited bandwidth and high-performance requirements (e.g., high-resolution and low-delay). However, the tiling pattern affects the encoding efficiency and visual distortion of the VR content. Accounting for this issue, in this paper, a quality of experience (QoE)-aware cost minimization problem is investigated for a tile-based VR content transmission scenario. In particular, an edge server (ES) co-located at a cellular base station (BS) separates its generated VR content into several tiles according to the tiling pattern selection, and a weighted-to-spherically-uniform quality model is used to evaluate the effect of different tiling patterns on QoE. Moreover, to improve the transmission performance between the edge server and VR users (VRUs), unmanned aerial vehicles (UAVs) are leveraged as relay points to provide line of sight channels. Then, we formulate an optimization problem to minimize the sum of weighted total energy consumption and VR content distortion (i.e., QoE-aware cost) by jointly optimizing the tiling pattern selections, the VRUs-UAV grouping, partial computing decisions, and resource allocation. The formulated problem is a mixed integer non-linear programming problem, which is challenging to solve. To address this difficulty, we equivalently decompose the formulated problem into three subproblems and propose corresponding algorithms to solve them, respectively. Numerical results demonstrate that our proposed solution can effectively reduce the QoE-aware cost for VR content transmission in comparison with other baseline algorithms.

ParallEdge: Exploiting Computing-Mobility Parallelism for Efficient 5G/6G Edge Computing

With the emergence of the 5G/6G communications, edge computing has attracted increasing research interests in recent years. To provide pervasive 5G/6G edge computing services, numerous edge servers are required for service coverage, and the deployment cost can be <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\gt$</tex-math></inline-formula> 10000 times larger than the deployment cost of the 4G infrastructure. To address this fundamental limit, we propose ParallEdge, a deployment scheme that employs mobile edge servers for cost-effective service coverage. ParallEdge is designed based on the observation that the processing delay and server moving delay become comparable in many computing-intensive applications in the 5G/6G edge computing. Unlike the traditional “move-then-process” frameworks, ParallEdge follows a “move-while-processing” paradigm, which exploits the parallelism between task processing and server movement to enable resource sharing among more users. Moreover, with the joint optimization of path planning and task scheduling for multiple mobile servers, the deployment cost for service coverage can be further reduced. We analyze the approximation gap of the proposed algorithm, and conduct extensive simulation experiments based on the real-world application data, and the results show that ParallEdge can significantly reduce deployment cost and improve resource utilization compared to the state-of-the-art schemes.

Research on Edge Cloud Data Storage Method of Power Operation Site in Internet of Things Environment Based on Paxos Algorithm

Abstract In order to realize the efficient storage of electric power job site data and ensure the utilization effect of the stored data, a method of electric power job site edge cloud data storage based on the Paxos algorithm is proposed. In this method, power field operation data are collected comprehensively through the edge server and edge controller in the edge computing module, the collected data are processed, and the data storage tasks are allocated reasonably. The allocated data are transferred to the data storage module, which identifies the bad data in the data through a gap statistics algorithm and cluster analysis and retains the valid and normal data. After the primary and secondary nodes are determined based on the Paxos algorithm, the data storage model of power operation site is constructed. After the consistency detection of the reserved data, the data storage of power operation site is completed. The platform management module can analyze the stored data and present the analysis results to the application decision-making module for presentation. The test results show that the maximum time delay and energy consumption are 2.15 s and 42.6 W, respectively, when the method is used for data transmission, which can accurately identify the bad data in the field data of electric power operation, and the reliability index results of data consistency detection are all above 0.95, which ensures the good consistency of the stored power operation site data and effectively completes the power operation site data storage.

A Novel Federated Learning Scheme for Generative Adversarial Networks

Generative adversarial networks (GANs) have been advancing and gaining tremendous interests from both academia and industry. With the development of wireless technologies, a huge amount of data generated at the network edge provides an unprecedented opportunity to develop GANs applications. However, due to the constraints such as bandwidth, privacy, and legal issues, it is inappropriate to collect and send all data to the cloud or servers for analysis, training, and mining. Thus, deploying and training GANs at the edge becomes a promising alternative solution. The instability of GANs introduced by non-independent and identical data (Non-IID) poses significant challenges to training GANs. To address these challenges, this paper presents a novel federated learning framework for GANs, namely, <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> ollaborated g <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> me <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> arallel Learning (CAP). CAP supports parallel training of data and models for GANs, breaking the isolated training among generators that exists in the previous distributed algorithms, and achieving collaborative learning among cloud, edge servers, and devices. Then, to further enhance the ability of CAP-GAN for addressing Non-IID issues, we propose a Mix-Generator module (Mix-G) which divides a generator into the sharing layer and personalizing layer. The Mix-G module extracts the generic and personalization features and improves the performance of CAP-GAN on extremely personalizing datasets. Experimental results and analysis substantiate the usefulness and superiority of our proposed CAP-GAN scheme which can achieve better results in the Non-IID scenarios compared with the state-of-the-art algorithms.

Graph Partition and Multiple Choice-UCB Based Algorithms for Edge Server Placement in MEC Environment

Graph Partition and Multiple Choice-UCB Based Algorithms for Edge Server Placement in MEC Environment