Data Transmission Cost Research Articles

Enhancing Data Collection Time Intervals and Modeling the Structural Behavior of Bridges in Response to Temperature Variations

The impact of temperature on bridges represents one of the main long-term challenges of structural health monitoring (SHM). Temperature is an environmental variable that changes both throughout the day and between different seasons, and its variations can induce thermal loads on bridges, potentially resulting in considerable displacements and deformations. Therefore, it is essential to obtain current data on the impact of daily and seasonal temperature variations on bridge displacements. Unfortunately, the maintenance costs associated with using precise estimates of thermal loads in a bridge design are quite high. The introduction of more accessible structural monitoring services is imperative to increase the number of observed structures. Viable solutions to make SHM more efficient include minimizing the costs of equipment, sensors, data loggers, data transmission systems, or monitoring data processing software. This research aims to improve the time intervals for collecting data on external temperature variations measured on a bridge structure through a sensor-based detection system and the integration of results into a regression analysis model. The paper aims to determine the appropriate interval for capturing and transmitting the structural response influenced by temperature variations over a year and to develop a behavioral mathematical model for the concrete structural components of a monitored bridge. The structural behavior was modeled using the statistical software TableCurve 2D, v.5.01. The results indicate that extending the data collection periods from 15 min to 4 h, in a static regime, maintains the accuracy of the regression model; instead, the effects of this integration are a significant reduction in the costs of data collection, transmission, and processing. The practical implications of this study consist of improving the monitoring of the structural behavior of bridges and the prediction under thermal stress, aiding in the design of more resilient structures, and enabling the implementation of efficient maintenance strategies.

Optimization of Federated Learning Communication Costs through the Implementation of Cheetah Optimization Algorithm

Recently, Federated Learning (FL) has promptly gained aggregate interest owing to its emphasis on the data privacy of the user. As a privacy-preserving distributed learning algorithm, FL enables multiple parties to construct machine learning (ML) algorithms without exposing sensitive information. The distributed computation of FL may lead to drawn-out learning and constrained communication processes, which necessitate client-server communication cost optimization. The two hyperparameters that have a considerable effect on the FL performance are the number of local training passes and the ratio of chosen clients. Owing to training preference across different applications, it is challenging for the FL practitioner to manually choose these hyperparameters. Even though FL has resolved the problem of collaboration without compromising privacy, it has a transmission overhead because of repetitive model updating during training. Various researchers have introduced transmission-effective FL techniques for addressing these issues, but sufficient solutions are still lacking in cases where parties are in charge of data features. Therefore, this study develops an Optimization of Federated Learning Communication Costs through the Implementation of the Cheetah Optimization Algorithm (OFLCC-COA) technique. The OFLCC-COA technique is mainly applied for effectually optimizing the communication process in the FL to minimize the data transmission cost with the guarantee of enhanced model accuracy. The OFLCC-COA technique enhances the robust performance in unsteady network environment via the transmission of score values instead of large weights. Besides, the OFLCC-COA technique improves the communication efficiency of the network by transforming the form of data that clients send to servers. The performance analysis of the OFLCC-COA model occurs utilizing different performance measures. The simulation outcomes indicated that the OFLCC-COA model obtains superior performances over other methods in terms of distinct metrics

Federated Learning: Navigating the Landscape of Collaborative Intelligence

As data become increasingly abundant and diverse, their potential to fuel machine learning models is increasingly vast. However, traditional centralized learning approaches, which require aggregating data into a single location, face significant challenges. Privacy concerns, stringent data protection regulations like GDPR, and the high cost of data transmission hinder the feasibility of centralizing sensitive data from disparate sources such as hospitals, financial institutions, and personal devices. Federated Learning addresses these issues by enabling collaborative model training without requiring raw data to leave its origin. This decentralized approach ensures data privacy, reduces transmission costs, and allows organizations to harness the collective intelligence of distributed data while maintaining compliance with ethical and legal standards. This review delves into FL’s current applications and its potential to reshape IoT systems into more collaborative, privacy-centric, and flexible frameworks, aiming to enlighten and motivate those navigating the confluence of machine learning and IoT advancements.

Predicting the Prognosis of Stroke Patients Based on Personalized Federated Learning

In recent years, the incidence of stroke has been increasing and showing a trend of younger people. Based on the distributed stroke risk assessment modeling scenario, this paper solves the problems of insufficient data and difficult data sharing in medical institutions through federated learning. Considering the features of structured data, the proposed algorithm takes the non-neural network model as the base model, and combines bagging and gradient boosting algorithms to achieve model updating and aggregation. This paper also proposes the model pruning method to realize the personalization of each participant’s model and reduces the data transmission cost of the algorithms by separating the weight matrix of the model and the model parameters. Experiments show that the proposed method greatly outperforms existing baseline approaches according to the predictive results, and the accuracy of the personalized model and the global model in the International Stroke Trial (IST) dataset reaches 78.20% and 76.85%, respectively, which has broader application scenarios.

Response reconstruction based on measurement matrix optimization in compressed sensing for structural health monitoring

Structural health monitoring (SHM) data have a large volume, increasing the cost of data storage and transmission and the difficulties of structural parameter identification. The compressed sensing (CS) theory provides a signal acquisition and analysis strategy. Signal reconstruction using limited measurements and CS has attracted significant interest. However, the dynamic responses obtained from civil engineering structures contain noise, resulting in sparse samples and reducing the signal reconstruction accuracy. Therefore, we propose an optimization algorithm for the measurement matrix integrating the Karhunen-Loeve transform (KLT) and approximate QR decomposition (KLT-QR) to improve the accuracy of dynamic response reconstruction of SHM data. The KLT reduces the correlation between the measurement matrix and the sparse basis. The approximate QR decomposition is used to improve the independence between the column vectors of the measurement matrix, optimizing the measurement matrix. The experimental results for a laboratory steel beam indicate that the proposed KLT-QR algorithm outperforms three other algorithms regarding the accuracy of dynamic response reconstruction (acceleration, displacement, and strain), especially at high compression ratios. The acceleration responses from the Ji’an Bridge are utilized to verify the advantages of the proposed algorithm. The results demonstrate that the KLT-QR algorithm has the highest accuracy of reconstructing the vibration signals and yields better Fourier spectra than the conventional Gaussian measurement matrix.

Methodology for the automated selection of time-frequency representations

Data preprocessing is a key step in extracting useful information from sound and vibration data and often involves selecting a time-frequency representation. No single time-frequency representation is always optimal, and no standard method exists for selecting the appropriate time-frequency representation, so selecting the time-frequency representation requires expert knowledge and is susceptible to human bias. To address this, this work introduces a methodology to automate the selection of a time-frequency representation for a dataset using only a subset of the healthy, or normal, class of data. To select the parameters for each type of time-frequency representation, Bayesian optimization is used. With a candidate from each type of time-frequency representation, the average similarity is used to select the final candidate. Additionally, the use of multiple time-frequency representations within a single model is explored. Because there is currently no objective method to compare the selected time frequency representations against, the proposed methodology is evaluated in two case studies. In the case studies, the time frequency representations are used as inputs to a simple convolutional neural network that achieved 100% accuracy in classifying bearing faults and 94% accuracy in classifying the contact tip to workpiece distance in wire arc additive manufacturing. Additionally, the proposed methodology presents a 75% and 94% reduction in the data size for the two case studies. This offers further benefits for reducing costs of data transmission and storage in modern digital manufacturing architectures.

Low-latency intelligent service combination caching strategy with density peak clustering algorithm in vehicle edge computing

In the dynamic field of Vehicle Edge Computing (VEC), the demand for intelligent vehicular systems to process vast amounts of data is escalating, driven by advancements in autonomous driving and real-time navigation technologies. Optimizing service latency and minimizing transmission costs are crucial for enhancing the performance of vehicular networks. Traditional service caching strategies, which largely rely on the popularity of individual services, often fail to account for the intricate interdependencies between services. The oversight can result in redundant data transfers and inefficient use of storage resources. In response, our paper introduces a novel approach to service combination caching within a heterogeneous computational framework comprising vehicles, edge servers, and the cloud. Our strategy focuses on minimizing user wait times and data transmission costs during task execution, while adhering to the caching budget constraints of service providers. Key contributions include the development of an Improved Density Peak Clustering (IDPC) algorithm to facilitate cooperative clustering among edge servers and the design of a Service Combination Caching Strategy (SCCS). The SCCS approach reduces caching costs by categorizing servers, forming efficient clusters, and strategically allocating storage. Simulation results demonstrate that the method outperforms existing strategies by significantly decreasing task execution delays and transmission costs, thereby greatly enhancing the quality of service in vehicular applications.

Cost Optimisation Tool for Multicommodity Network Flow Problem in Telecommunications

In this paper, we consider the problem of minimising the cost of data transmission as a function of the capacity of telecommunication links. To solve this problem, we first formulated a mathematical model, and then we designed and developed a software that enables the optimisation of the given or randomly generated telecommunications network. Declarative programming is a good choice for optimisation problems because it is enough to specify only the relations that must be satisfied, without giving any effective procedure for finding the values for the decision variables. To test the application, we developed a software that randomly generates a telecommunications network that meets the given requirements. This enables us to test the application on an arbitrary number of different telecommunication networks with different numbers of nodes and links, and analyse the impact of changing network parameters on the flow and results of the optimisation. As telecommunications networks operate in conditions of uncertainty, the subject of special analysis was the potential failure of some of the network links. The paper presents and thoroughly analyses the optimisation results for several selected networks, as well as summary results for a number of telecommunications networks.

Performance research of edge computing nodes based on heterogeneous environment

Abstract Edge computing realizes real-time parallel storage and computing in a distributed way, which can reduce the data transmission cost. The edge nodes, which are composed of different heterogeneous physical devices and hardware resources, assume the computing and storage capacity of the edge network. At present, the resource characteristics and performance of heterogeneous edge nodes are difficult to characterize. Therefore, a resource-aware performance feature detection model for heterogeneous edge compute nodes is proposed in this study. This model is based on a resource utilization matrix and resource relative performance quantization algorithm to extract the performance characteristics of the same type of resources of the edge nodes, to realize multi-dimensional and systematic feature detection and description of heterogeneous edge nodes. On this basis, a weight-based edge node task scheduling algorithm is designed. The final verification results show that the CPU relative performance ratios of Node 1-Node 2, Node 1-Node 3, and Node 2-Node 3 are 6.23, 3.81, and 0.81 respectively. The performance relationship among the three nodes is Node 1> Node 3> Node 2. The test group and the verification group tend to be consistent overall. This method has a good application prospect.

Fixed/Preassigned-time synchronization of quaternion-valued BAM neural networks: An event-based non-separation control method

Quaternions provide expressive power beyond real numbers, allowing neural networks to capture and process correlations and patterns in data with greater complexity. Besides, event-triggering mechanism has significant advantages in reducing redundant data transmission and control costs, since the sampling instant is determined by preset trigger conditions. Based on these fact, this article investigates the fixed-time and preassigned-time synchronization of quaternion-valued Bidirectional Associative Memory (BAM) neural networks via event-triggered control. Firstly, based on a dual-layer network structure with different number of nodes, a type of BAM neural network with generalized time-varying delays is established in the quaternion domain. Secondly, four kinds of Zeno-free dynamic event-triggered control mechanisms are designed, and the trigger condition can be dynamically adjusted according to the changing requirements. In the framework of non-separation analysis, the synchronization the controlled network can be guaranteed within a fixed time, and the upper bound of the setting time is provided explicitly. Furthermore, to better meet practical needs, the above event-triggered mechanism and the criteria are extended to the PAT synchronization. Two illustrate examples are presented at last to validate the developed event-triggered mechanism and synchronization results.

Simplifying Land Cover-Geoprocessing-Model Migration with a PAMC-LC Containerization Strategy in the Open Web Environment

Land cover and its changes over time are significant for better understanding the Earth’s fundamental characteristics and processes, such as global climate change, hydrology, and the carbon cycle. A number of land cover-geoprocessing models have been proposed for land cover-data production with different spatial and temporal resolutions. With the massive growth in land cover data and the increasing demand for efficient model utilization, developing efficient and convenient land cover-geoprocessing models has become a formidable challenge. Although some model-migration methods have been proposed for handling the massive data, the intricacy of land cover-data and -heterogeneity models frequently prevent current strategies from directly meeting demand. In this paper, we propose the PAMC-LC-containerization approach to overcome the difficulties associated with moving existing land cover models in the open web environment. Based on the idea of model migration, we design a standardized model description and hierarchical encapsulation strategy for land cover models, and develop migration and deployment methods. Furthermore, we assess the viability and efficacy of the proposed approach by using coupled workflows for model migration and the introduction of visualization on the Mts-WH dataset and the Google dataset. The experimental results show that the PAMC-LC approach can simplify and streamline the model migration process, with important ramifications for increasing productivity, reusing models, and lowering additional data-transmission costs.

A secure and light‐weight patient survival prediction in Internet of Medical Things framework

SummaryThoracic surgeries in major lung resections for primary lung cancer are fraught with potential risks, emphasising the need to understand factors contributing to postoperative mortality. This study investigates the interplay of objective and subjective data in predicting postoperative outcomes to reduce data transmission costs in the Internet of Medical Things (IoMT). Objective metrics, such as forced vital capacity (FVC), offer consistent, quantifiable insights essential for predictive modelling. Conversely, subjective data derived from patient self‐reports suggest that the patient's personal experiences are crucial for assessing the quality of life postsurgery. Utilising a dataset from the University of California, Irvine's Machine Learning Repository (UCI), 17 distinct attributes were examined. Using ensemble learning classifiers, the extra trees classifier is superior when utilising all features, achieving an accuracy of 0.92. Combining select subjective features, specifically PRE6, PRE8 and AGE (demographic), with objective data, yielded a comparable accuracy of 0.91. Feature importance analysis further highlights the significance of features like PRE5, PRE4 and AGE. This suggests potential redundancies in the full feature set, emphasising the importance of feature selection. Importantly, when compared with existing literature, this study's findings offer insights into the future of predictive modelling in thoracic surgeries, with implications for the rapidly evolving field of the IoMT.

Beyond JSON: Evaluating Serialization Formats for Space-Efficient Communication

Distributed systems rely on efficient inter-service communication, heavily impacted by data transmission costs. This study investigates alternative serialization formats, like Avro and MessagePack, to reduce data size compared to the common JSON format. We utilize a custom model to comprehensively assess the space efficiency of serialization formats across various data types. Our findings demonstrate that adopting alternative formats achieves a median reduction in serialized data exceeding 30 %. Notably, Avro exhibits exceptional efficiency, leading to reductions exceeding 83 % in specific scenarios. These insights empower developers to select optimal formats, potentially leading to significant improvements in data transfer speed, reduced bandwidth consumption, and enhanced scalability for handling larger data volumes within distributed systems.

Application of optimized sparse encoding algorithm in data compression

Data transmission is crucial in the process of equipment monitoring. The compression algorithms are adopted to reduce the amount of data transmission. When sparse encoding algorithms are used for this purpose, despite extensive attempts to improve their performance, some problems still remain. The main issues pertain to: (1) Dictionary construction − As practical dictionary better matches the input signal, such a dictionary must be efficiently established; 2) The setting of sparsity value − It is difficult to determine the size of sparsity values when prior knowledge is insufficient (when the parameter settings are too large, the cost of data transmission increases considerably, while excessively small values can result in poor reconstruction accuracy); and (3) How to implement engineering applications of data compression based on sparse encoding. To address these issues, in this work, an optimized sparse encoding algorithm is proposed by combining the non-negative Online Dictionary Learning (ODL) with the optimized Orthogonal Matching Pursuit (OMP) algorithm. First, a non-negative ODL algorithm is adopted for dictionary learning based on the training dataset to obtain an effective dictionary. Next, the optimized OMP algorithm is used to obtain the sparsity and the sparse coefficient matrix. Further analyses confirm that the reconstructed signal has a small reconstruction error. Finally, A lossy compression framework is proposed for the compressing equipment condition monitoring data using sparse encoding algorithms. Compared with the compression algorithms based on compressive sensing and DCT, the average compression ratio can reach 42.7 when the reconstruction accuracy is similar. In terms of comprehensive compression performance, the quality score is also higher compared to other algorithms.

A Privacy Protection Scheme of Certificateless Aggregate Ring Signcryption Based on SM2 Algorithm in Smart Grid

With the rapid increase in smart grid users and the increasing cost of user data transmission, proposing an encryption method that does not increase the construction cost while increasing the user ceiling has become the focus of many scholars. At the same time, the increase in users will also lead to more security problems, and it is also necessary to solve the privacy protection for users during information transmission. In order to solve the above problems, this paper proposes an aggregated ring encryption scheme based on the SM2 algorithm with special features, referred to as SM2-CLARSC, based on the certificateless ring signcryption mechanism and combining with the aggregate signcryption. SM2-CLARSC is designed to satisfy the basic needs of the smart grid, and it can be resistant to replay attacks, forward security and backward security, etc. It has better security and higher efficiency than existing solutions. Comparing SM2-CLARSC with existing typical solutions through simulation, the result proves that this solution has more comprehensive functions, higher security, and significant computational efficiency improvement.

A cloud-edge cooperative scheduling model and its optimization method for regional multi-energy systems

In the process of multi-energy system optimal scheduling, due to the high data processing requirements of the multi-energy devices and loads and the complexity of the operating states of the multi-energy devices, the scheduling optimization of the system is to some extent more difficult. To address this problem, this paper proposes a regional multi-energy system optimal scheduling model based on the theory of cloud-edge collaboration. First, based on intelligent data sensors, a cloud-edge cooperative scheduling framework of the regional multi-energy system is constructed. Then, the physical model of operating state data of multi-energy system equipment and the allocation mechanism of system scheduling tasks are studied. With the cloud service application layer and the edge computing layer as the upper and lower optimization scheduling layers, the double-layer optimization scheduling model of the regional multi-energy system is established. The objectives of the model are optimal scheduling cost and minimum delay of scheduling data transmission. The multi-objective whale optimization algorithm is used to solve the model. Finally, a simulation model is built for verification. The simulation results show that the scheduling model established in this paper can effectively improve the scheduling data processing capability and improve the economy of regional multi-energy system scheduling.

Intelligent data-driven condition monitoring of power electronics systems using smart edge–cloud framework

The ongoing revolution in industrial production- Industry 4.0, is driven by transformative technologies such as the Industrial Internet of Things (IIoT), Artificial Intelligence (AI), single-board computers, and 5G communication. As the trend towards IIoT continues, an increasing number of industrial drive systems and their fleets are being connected to the cloud. This enables the manufacturers to perform condition monitoring (CM) and streamlined maintenance activities. At the heart of these drive systems are Power Electronics Systems (PESs), which operate at high switching frequencies (10 kHz–1 MHz) to efficiently transfer electrical power and deliver it to a load in a controlled manner. However, due to their functionalities and the presence of semiconductor switches, PESs are susceptible to failure, necessitating effective condition monitoring (CM) for fault detection and improved lifetime. Link to this issue, to enable CM based on high-frequency data, an industrial site with multiple electric drives is required to record data up to 15TB/week. Therefore, there is a demand from industrial partners to establish intelligent communication between a fleet of physical systems and the cloud to reduce transmission, storage, and bandwidth costs, as well as to enable real-time fault detection and learning from fleet operations. This paper proposes an intelligent edge–cloud computing methodology to address the challenge of high-frequency data monitoring for PESs, focusing on novelty detection and selective data transmission to reduce transmission costs. The methodology involves developing a novel edge–cloud framework that incorporates a neural network-based novelty detector for selective data transmission from physical systems to the cloud. The proposed methodology is evaluated through hardware tests, demonstrating a significant reduction in data transmission (94%) and potential cost savings of up to €5.9k/year for a single remote system. 95.6% detection accuracy of the PQ phase is obtained during experimental tests over 590 samples. Thus, this paper contributes to the vision of the smart grid and IIoT by analyzing the Power Quality (PQ) monitoring problem of a three-phase grid and showcasing the capability of the proposed framework in terms of novelty detection and data transmission cost reduction. To conclude, the proposed intelligent edge–cloud computing methodology offers a promising solution for effective condition monitoring of PESs, with potential cost savings and improved fault detection capabilities. By leveraging advanced technologies and intelligent data-driven approaches, this framework advances the goals of Industry 4.0 and paves the way for efficient and reliable industrial operations in the digital age.

Edge Computing Security of Mobile Communication System Based on Computer Algorithms

With the progress of information technology recently, mobile communication system edge computing (EC) has been widely used in all walks of life, but the traditional mobile communication system EC mode has security problems such as privacy disclosure, malicious tampering, and virus attacks. Computer algorithms has brought new vitality to EC in mobile communication systems. This paper analyzed the application of computer algorithm in EC mode of mobile communication system, and selected 20 users as the research object. This paper adopted traditional computing mode (such as cloud computing) and computer algorithm-based mobile communication system EC security research. This text compared the effects of two modes on security performance, data transmission efficiency, energy consumption, cost savings, and user satisfaction. The experimental results in this paper showed that the average security of EC mode of mobile communication system based on computer algorithm was 84%, and the average data transmission time was 4.8[Formula: see text]s. The energy consumption was 40%, and the cost savings and user satisfaction were 432,000 yuan and 13 points, respectively. Both were superior to the traditional edge counting mode. The EC mode of mobile communication system using computer algorithms can significantly improve the security of mobile communication, data transmission speed, cost savings, user satisfaction, and reduce energy consumption. This model has important significance and value for social development.

An Efficient and Differential Privacy-Based Scheme for Aggregating Mobility Datasets

Mobile smart devices, such as mobile phones, wearable devices, and in-vehicle navigation systems, bring us convenience and have become necessities in modern daily life. The built-in global positioning system (GPS) of these mobile devices collects the users’ mobility data to support path planning, navigation and other location-related applications, which also inevitably causes privacy issues. Previous research has shown that employing count-min sketch (CMS) to aggregate mobility datasets is a valid privacy-preserving method for resisting the reconstruction attack on population distributions. However, as the utility/accessibility of the protected datasets is excessively correlated with the size of CMS, decreasing the data transmission cost has become an unsolved issue of that approach. In this paper, we propose an efficient scheme with differential privacy to protect mobility datasets, which releases the privacy-preserving population distributions and achieves better utility as well as a much smaller data transmission cost compared to the CMS-based method. Our proposed scheme is comprised of two collaborative components, global sketch and temporal sketch. The global sketch is responsible for aggregating the raw mobility data and decreasing the data transmission cost, while the temporal sketch is in charge of guaranteeing the utility of the population distributions aggregated by the global sketch. Besides, to enhance the privacy preservation, we employ the Laplace mechanism to make the transmitted data satisfy ϵ-differential privacy. Through our analysis and empirical experiments, compared to the other three state-of-the-art privacy-preserving methods on mobility datasets, our scheme could preserve the privacy of the mobility datasets with much less data transmission cost under the same utility loss.

High Speed Transmission Characteristics on Glass Based Interposers

Interposers for system in package will became more and more important for advanced electronic systems. As the industry moves toward HPC (High Performance Computing) for huge data transmission with low power consumption. The major engineering requirements for HPC application are highdensity, high speed data transmission, low loss, precision manufacturing and low cost. On the 2.5D heterogenous package structure, key technology is fine wiring connected with CPU chip and HBM chips. The fine wires needed high speed transmission. This paper presents the demonstration of Glass-based twin type interposers, Glass interposer with fine pitch metalized through via and RDL interposer with low loss dielectrics on Glass carrier. We compare twin types of Interposes fabrication process capability with panel size format 300x400mm. Finally, we compare the transmission characteristics using eye diagram for heterogenous integration application. Glass Interposer has low loss glass via, it is effective vertical interconnection. RDL interposer has low loss dielectric layers, excellent transmission characteristics obtained on fine pitch trace area.