Real-time Services Research Articles

Development of Traffic Scheduling Based on TSN in Smart Substation Devices

Smart substations are an important trend in substation construction. With increasing data traffic, it is difficult for the traditional Ethernet network to meet the real-time requirements of control information in smart substations. Hence, in this paper, a deterministic network architecture for substations based on time-sensitive networks (TSN) has been developed in order to realize the domain-wide time synchronization and efficient real-time communication of the “three-layer and two-network” model in smart substations. Furthermore, a design scheme for substation automation equipment based on TSN is proposed. The proposed device realizes the timely transmission of real-time control information packets by utilizing the Earliest TxTime First (ETF) Qdisc technology of Linux and the timing sending capability of Intel 210 NIC. Furthermore, it collaborates with the time-aware shaper (TAS) traffic scheduling mechanism of TSN switches to ensure the end-to-end deterministic delay of time-sensitive traffic. As a result, it provides efficient real-time communication services with low latency and jitter for smart substation automation systems.

ACORN+: Adaptive Compression-Reconstruction for Device-Cloud Collaboration Video Services

With the improvement of edge-based autonomous systems such as mobile Industrial IoT(IIoT) networks, edge devices can capture and upload videos with increasing bitrates. Massive edge-computing end nodes are eager for adequate multimedia data to satisfy the requirements of real-time video services. However, existing encoding standards for video services in Web 2.0 are specifically designed for something other than IoT video streaming. We have improved our Adaptive Compression-Reconstruction framework ACORN to obtain ACORN+, based on compressed sensing and recent advances in deep learning. At end nodes, we compress multiple sequential video frames into a single frame to reduce video volume. Given that multiple kinds of intelligent tasks are expected to be finished on the device side, we also designed a device-cloud collaboration scheme where deep learning-based algorithms can be executed on both the device and server sides. Experiments reveal that video analytics can be conducted on compressed frames. Taking action recognition as a device-cloud collaboration use case, we find ACORN+ obtains more than 3x speedup on compressed frames. The reconstruction algorithm in ACORN+ is with 1- 4 dB improvements. Moreover, the encoding time cost and the encoded video volume are reduced by more than 4x under the ACORN+ framework. 1

To ride-hail or not to ride-hail? Complementarity and competition between public transit and transportation network companies through the lens of app data

AbstractTransportation Network Company (TNC) services have become a prominent factor in urban transportation in recent years, and there is an ongoing debate regarding their relationship with public transit. While many argue that TNCs draw passengers away from public transportation, others believe the two modes complement each other. However, due to the inadequate sample size of rider surveys as primary data sources, our understanding of how riders choose between these two modalities remains limited. This study uses nine months of trip planning data generated by the Transit App, which captures how travelers engage with multiple options in real time, including TNC and public transit services. We extract measures from Transit describing the travel options and the habits of each individual user for sessions in which the user “tapped” on one of these two modes, indicating consideration of it as an option. Machine learning models predict the likelihood of a rider tapping TNC based on features of the available public transit options and other contextual factors (e.g., time of day, weather conditions). The models find that these taps are driven by factors that highlight the convenience of TNC, such as the waiting time, walking distance, and the number of transfers for public transportation trips. We also find that the majority of TNC trips tapped by app users combine the two modes when using the Transit App, with TNC acting as a connection to or from public transit. These results provide detailed additional evidence for current arguments for both competition and complementarity between TNC and public transit from a population that uses an app to navigate public transit.

Enhancing Urban Surveillance with Fog Computing, Mobile Cloud, and Big Data Analytics in 5G Networks

The new emerging applications in 5G network, in the context of the Internet of Everything (IoE), will introduce high mobility, high scalability, real-time, and low latency requirements that raise new challenges on the services being provided to the users. Fortunately, Fog Computing and Cloud Computing, with their service orchestration mechanisms offer virtually unlimited dynamic resources for computation, storage and service provision, that will effectively cope with the requirements of the forthcoming services. 5G will use the benefits of centralized high performance computing cloud centers, cloud and fog RANs and distributed peer-to-peer mobile cloud that will create opportunities for companies to deploy many new real-time services that cannot be delivered over current mobile and wireless networks. This paper evaluates a model for fog and cloud hybrid environment service orchestration mechanisms for 5G network in terms of energy efficiency per user for different payloads.

Automated Real-Time Tool for Promoting Crisis Resource Use for Suicide Risk (ResourceBot): Development and Usability Study.

Real-time monitoring captures information about suicidal thoughts and behaviors (STBs) as they occur and offers great promise to learn about STBs. However, this approach also introduces questions about how to monitor and respond to real-time information about STBs. Given the increasing use of real-time monitoring, there is a need for novel, effective, and scalable tools for responding to suicide risk in real time. The goal of this study was to develop and test an automated tool (ResourceBot) that promotes the use of crisis services (eg, 988) in real time through a rule-based (ie, if-then) brief barrier reduction intervention. ResourceBot was tested in a 2-week real-time monitoring study of 74 adults with recent suicidal thoughts. ResourceBot was deployed 221 times to 36 participants. There was high engagement with ResourceBot (ie, 87% of the time ResourceBot was deployed, a participant opened the tool and submitted a response to it), but zero participants reported using crisis services after engaging with ResourceBot. The most reported reasons for not using crisis services were beliefs that the resources would not help, wanting to handle things on one's own, and the resources requiring too much time or effort. At the end of the study, participants rated ResourceBot with good usability (mean of 75.6 out of 100) and satisfaction (mean of 20.8 out of 32). This study highlights both the possibilities and challenges of developing effective real-time interventions for suicide risk and areas for refinement in future work.

Wearable Biosensor Smart Glasses Based on Augmented Reality and Eye Tracking.

With the rapid development of wearable biosensor technology, the combination of head-mounted displays and augmented reality (AR) technology has shown great potential for health monitoring and biomedical diagnosis applications. However, further optimizing its performance and improving data interaction accuracy remain crucial issues that must be addressed. In this study, we develop smart glasses based on augmented reality and eye tracking technology. Through real-time information interaction with the server, the smart glasses realize accurate scene perception and analysis of the user's intention and combine with mixed-reality display technology to provide dynamic and real-time intelligent interaction services. A multi-level hardware architecture and optimized data processing process are adopted during the research process to enhance the system's real-time accuracy. Meanwhile, combining the deep learning method with the geometric model significantly improves the system's ability to perceive user behavior and environmental information in complex environments. The experimental results show that when the distance between the subject and the display is 1 m, the eye tracking accuracy of the smart glasses can reach 1.0° with an error of no more than ±0.1°. This study demonstrates that the effective integration of AR and eye tracking technology dramatically improves the functional performance of smart glasses in multiple scenarios. Future research will further optimize smart glasses' algorithms and hardware performance, enhance their application potential in daily health monitoring and medical diagnosis, and provide more possibilities for the innovative development of wearable devices in medical and health management.

Interpretable Mixture of Experts for Decomposition Network on Server Performance Metrics Forecasting

The accurate forecasting of server performance metrics, such as CPU utilization, memory usage, and network bandwidth, is critical for optimizing resource allocation and ensuring system reliability in large-scale computing environments. In this paper, we introduce the Mixture of Experts for Decomposition Kolmogorov–Arnold Network (MOE-KAN), a novel approach designed to improve both the accuracy and interpretability of server performance prediction. The MOE-KAN framework employs a decomposition strategy that breaks down complex, nonlinear server performance patterns into simpler, more interpretable components, facilitating a clearer understanding of how predictions are made. By leveraging a Mixture of Experts (MOE) model, trend and residual components are learned by specialized experts, whose outputs are transparently combined to form the final prediction. The Kolmogorov–Arnold Network further enhances the model’s ability to capture intricate input–output relationships while maintaining transparency in its decision-making process. Experimental results on real-world server performance datasets demonstrate that MOE-KAN not only outperforms traditional models in terms of accuracy but also provides a more trustworthy and interpretable forecasting framework. This makes it particularly suitable for real-time server management and capacity planning, offering both reliability and interpretability in predictive models.

Estimating the GPS time-varying differential code bias between C1 and P1 observations to provide a model product

Abstract Based on the time-varying characteristics of GPS satellite differential code biases (DCB) between C1 and P1 observations, a method for directly estimating its model coefficients is proposed to streamline processing and enhance product accuracy. Evaluation over three days compares estimated DCB (C1–P1) values and assesses real-time service (RTS) capabilities in Single Point Positioning (SPP). Three-day DCB (C1-P1) estimates match single-epoch time-varying DCB (C1-P1) series fitting accuracy. SPP results show significant improvement over Orbit Determination in Europe (CODE), reducing average root mean square error in three dimensions by 12 cm and by 4 cm compared to Chinese Academy of Sciences (CAS). Real-time DCB (C1-P1) service maintains stable and accurate performance, comparable to fitted values of single-epoch time-varying series. Real-time SPP application shows the method enhances positioning accuracy compared to CODE by up to 35%, confirming the feasibility and effectiveness of the proposed model estimation method.

Application of Real-Time Data Service Technology in Smart oilfield

Abstract Real time data services play an important role in the construction of Smart oilfields, which can help oilfield production managers better understand the production situation and make more accurate decisions. Real time data services based on the Data Lake platform will bring more efficient and safer production methods to oil and gas fields, promoting the upgrading and sustainable development of the Smart oilfield industry. The technical architecture of Smart oilfield real-time data service mainly includes data lake platform, big data processing, edge computing and other core technologies. The data lake platform provides infrastructure, platform services and application services for Smart oilfield. Big data processing technology is responsible for rapid and accurate analysis of massive data. edge computing refers to computing at the data source to reduce data transmission and improve computing efficiency. The key technologies include data collection, transmission, storage, analysis, etc. Among them, data collection mainly uses sensors, PLCs and other equipment to collect production data. Data transmission uses technologies such as the Internet of Things and industrial Ethernet to transmit data to the data lake platform. Data storage adopts distributed storage technology to ensure the safety and reliability of data. Data analysis uses machine learning, deep learning and other technologies to analyze data, Provide decision support.

Hybrid vehicular access protocol and message prioritization for real-time safety messaging

Real-time safety services rely on the exchange of messages to enhance the operations of connected and automated vehicles (CAVs). These safety messages convey vital information about traffic conditions, enabling drivers to take necessary measures to prevent accidents. The timely and reliable delivery of these messages is essential, necessitating efficient channel access. Vehicular Deterministic Access (VDA) is employed as a channel access scheme with distinct priorities and stringent timing guidelines, particularly for urgent safety warnings. In this paper, we propose a hybrid approach that combines VDA and Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocols, along with a message prioritization algorithm, to ensure efficient and reliable communication of safety messages in vehicular networks. Our approach leverages the strengths of both VDA and CSMA/CA to avoid message collisions; VDA is more efficient under high traffic loads, while CSMA/CA is better suited for low traffic loads. Additionally, the incorporation of the message prioritization algorithm ensures strict deadline guarantees for high-priority messages, such as Decentralized Environmental Notification Messages (DENMs). We evaluate our proposed solution using the Artery simulation framework. Our results show over a 93% delivery rate for DENM exchanges while maintaining low collision probability across various traffic loads. This research provides practical guidance for the development of efficient and reliable communication systems for CAVs. It also offers a detailed analysis of the trade-offs among different access protocols and message prioritization strategies in vehicular networks.

텍스트 분할을 적용한 KoBART 기반의 실시간 장문 뉴스 요약 시스템 구현

In this study, a real-timelong-news summarization system is implemented based on the model. Owing to its characteristics, the KoBART model cannot summarize news with a token length of 1024 or more. Hence, we implemented a method of dividing long news into paragraphs, summarizing the divided paragraphs, and then resummarizing the summarized sentences. First, we evaluated the performance using an AI Hub dataset to validate the implemented two-stage summarization method. However, because the token length of most of the news provided in the AI Hub dataset is 1024 or less, we analyzed the performance for long news by applying the dataset provided by Hugging Face with a token length of 1024 or more. When summarizing long news with a token length of 1024 or more by dividing it into 512 paragraphs, the average Luge score is 33.99% and the runtime required for summarization is 0.8492 s. Therefore, we confirmed that the implemented long-news summarization system can provide real-time services, even for long news with a token length of 1024 or more.

Constructed encoded data based coded distributed DNN training for edge computing scenario

Deep learning in unmanned aerial vehicle (UAV) deployment encounters two problems: 1, One UAV may fail to store and execute large Deep Neural Network (DNN) model. 2, One UAV fails to accomplish real time services. One possible solution is the group of UAVs (nodes) collectively generate swarm intelligence in the form of edge computing scenario, namely in the distributed computing (DC) mode with clever arrangement, say Coded Distributed Computing (CDC). In CDC systems, the redundant computation introduced by linear coding can compensate stragglers. However, since linear property cannot pass the nonlinear activation function in Deep Neural Network (DNN) training, coding/decoding for CDC need to be applied layer by layer, which slows down the training. To avoid layer-by-layer coding/decoding, we propose a novel DNN training scheme based on constructing encoded data. This construction process lies before the training process (can be done before training without any impact on training efficiency). Based on both the original data and the newly constructed encoded data, the training phase can take advantage of the (n,k) property (Wait for the first k returned data) and hence improve the training speed. The training process does not require encoding/decoding operations, and hence significantly improves the training speed. Experimental results show that the training scheme based on constructed encoded data can achieve prediction accuracy approximating that of the centralized one and significantly reduce the latency compared to the layer-by-layer linear encoding and decoding scheme.

Mustang: Improving QoE for Real-Time Video in Cellular Networks by Masking Jitter

The advent of 5G and interactive live broadcasting has led to a growing trend of people preferring real-time interactive video services on mobile devices, particularly mobile phones. In this work, we measure the performance of Google congestion control (GCC) in cellular networks, which is the default congestion control algorithm for Web Real-Time Communication (WebRTC). Our measurements show that GCC sometimes makes bitrate decisions which are harmful to quality of experience (QoE) in cellular networks with high jitter. We further find that the frame delivery time (FDT) in the player can mitigate network jitter and maintain QoE. Moreover, the receiving rate is better to reflect the network congestion than RTT in cellular networks. Based on these measurements and findings, we propose Mustang, an algorithm designed to overcome the jitter in cellular networks. Mustang makes use of the FDT and receiving rate as feedback information to the sender. Then the sender adjusts its sending rate based on the information to guarantee QoE. We have implemented Mustang in WebRTC and evaluated it in both emulated and real cellular networks. The experimental results show that Mustang can improve WebRTC’s QoS and QoE performance. For QoS, Mustang increases the sending rate by 72.1% and has similar RTT and packet loss when compared with GCC, while it is about 30% better for QoE.

A Multi-Lingual Conversational AI Chatbot for Effective Educational Consultations: A Study of ACE-DS, University of Rwanda

The demand for real-time consultation services from organizations is increasing, leading to prolonged waiting times, primarily due to limited opportunities for face-to-face interactions and language barriers. This study addresses this challenge by leveraging Artificial Intelligence (AI), Natural Language Processing (NLP), and linguistic technologies to develop a multilingual conversational AI Chabot for managing educational consultation services, using the African Center of Excellence in Data Science (ACE-DS), University of Rwanda, as a case study. Information and frequently asked questions (FAQs) about ACE-DS were used to train a Deep Learning Gated Recurrent Units (GRUs) algorithm to power the Chabot. Language detection and translation APIs were integrated to facilitate seamless multilingual conversations. The result of user survey conducted revealed that over 60% of respondents expressed high satisfaction with the Chabot’s performance including grammar, efficiency, language preferences, and response quality. This study showcases the potential of AI particularly NLP in enhancing educational consultation services, providing a framework for efficient information acquisition.

Smarter but more unequal transport? How socioeconomic and digital inequalities hinder adoption of mobility apps in the Global South

All over the world, cities are becoming “smarter.” The improved use of data generated by the transport system (e.g. smart cards data), combined with GPS trackers and citizens’ mobile data enabled the development of a new generation of smarter mobile apps (e.g., Waze) and several local real-time public transport services and trip planners. These technologies have the potential to reduce transport inequalities via two mechanisms. First, governments can develop more efficient and evidence-based public transport plans. Second, citizens have access to better, real-time information about public transport for trip planning and, thus, can make better—i.e., more efficient or cheaper—decisions regarding their everyday mobility. To assess whether citizens’ appropriation of smart mobility—as with their appropriation of any other digital service—depends strongly on having a certain set of socioeconomic and digital attributes, we analyzed both a mobility survey and a digital inequalities survey based on representative samples of adult Uruguayans living in the capital Montevideo. We find that while the use of mobility apps increased in the second half of the 2010s, the adoption of these tools was stratified by traditional digital inequalities, such as type of internet access and digital literacy, and by variables that predict inequalities in both digital and transport fields, such as age, gender and education. As a result, mobility continues to be stratified in favor of those who are more knowledgeable (i.e., tech-savvy) and have greater access to digital technology. Thus, “smart mobility” has failed to ameliorate—and may even exacerbate—transport inequality. Policy implications of these findings are discussed.

From Fax to Secure File Transfer Protocol: The 25-Year Evolution of Real-Time Syndromic Surveillance in England.

The purpose of syndromic surveillance is to provide early warning of public health incidents, real-time situational awareness during incidents and emergencies, and reassurance of the lack of impact on the population, particularly during mass gatherings. The United Kingdom Health Security Agency (UKHSA) currently coordinates a real-time syndromic surveillance service that encompasses 6 national syndromic surveillance systems reporting on daily health care usage across England. Each working day, UKHSA analyzes syndromic data from over 200,000 daily patient encounters with the National Health Service, monitoring over 140 unique syndromic indicators, risk assessing over 50 daily statistical exceedances, and taking and recommending public health action on these daily. This English syndromic surveillance service had its origins as a small exploratory pilot in a single region of England in 1999 involving a new pilot telehealth service, initially reporting only on "cold or flu" calls. This pilot showed the value of syndromic surveillance in England, providing advanced warning of the start of seasonal influenza activity over existing laboratory-based surveillance systems. Since this initial pilot, a program of real-time syndromic surveillance has evolved from the single-system, -region, -indicator pilot (using manual data transfer methods) to an all-hazard, multisystem, automated national service. The suite of systems now monitors a wide range of syndromes, from acute respiratory illness to diarrhea to cardiac conditions, and is widely used in routine public health surveillance and for monitoring seasonal respiratory disease and incidents such as the COVID-19 pandemic. Here, we describe the 25-year evolution of the English syndromic surveillance system, focusing on the expansion and improvements in data sources and data management, the technological and digital enablers, and novel methods of data analytics and visualization.

Assessing the Tropospheric Impacts on Positioning Accuracy Using IGS02 Real-Time Service Data Versus Long-Convergence Static PPP in Gwagwalada, Abuja, Nigeria

Abstract: The International Association of Geodesy (IAG) has established the International GNSS Service-Real Time Service (IGS-RTS) as a service provider, offering real-time access to precise products like orbits, clock corrections, and code biases regarding satellite navigation and positioning system. These products serve as an alternative to ultra-rapid products in real-time applications. The performance of these products is assessed through daily statistics from Analysis Centres, which compare them to IGS rapid products. However, the accuracy of GPS real-time corrections for satellites during eclipsing periods was slightly reduced, attributed to the impact of environmental factors on the services. The speed of GNSS signals can be impacted by various atmospheric factors, including troposphere, temperature, pressure, and humidity, resulting in positioning inaccuracies and even giving rooms for signal jamming and hijacking. However, the unique weather conditions prevalent in the African continent are often overlooked during the development of error mitigation parameters and algorithms, which can lead to reduced accuracy in GNSS positioning in a region like Nigeria. The purpose of this study is to estimate the tropospheric impact on positioning with IGS02 Real Time Service data compared to long convergence Static-PPP in Gwagwalada Area Council, Abuja, Nigeria. The study adopts the determination of the GNSS Static observations (minimum of two hours per session) on the chosen stations as standard, determination of the IGS-RTS data observations using RTKLIB software; observations were done with IGS-RTS data stream of IGS02 and statistical tests were performed. The GNSS Static coordinates and IGS-RTS coordinates were validated from error due to troposphere, temperature, pressure, etc., with the computation of their mean horizontal and vertical uncertainties which have a similar level of accuracy but slightly differ at centimeter levels. The result shows the Root Mean Square (RMS) Error discrepancy of IGS02 at the Wet and Dry season, as compared with the Static-PPP was within 0.065(m) and 0.046(m) respectively.

A joint optimization of resource allocation management and multi-task offloading in high-mobility vehicular multi-access edge computing networks

Vehicular communications have advanced data exchange and real-time services in intelligent transportation systems by exploiting advanced communication between vehicles and infrastructure. The emergence of Multi-access Edge Computing (MEC) has further elevated this field by utilizing distributed edge resources near vehicles for low-latency data processing and high-reliability communication. In this dynamic environment, adequate resource allocation and task offloading are pivotal to ensure superior performance, lower latency, and efficient network resource utilization, enhancing Quality of Service (QoS) and overall driving experience and safety. This paper presents a developed vehicular network and offloading mechanism, introducing a resource management model with real-time allocation and load balancing. The proposed method integrates task prioritization, multi-agent collaboration, context-aware decision-making, and distributed learning to optimize network performance. The introduced optimized algorithm initializes Q-networks and target networks, sets up an experience replay buffer, and configures agents with local state representations. Agents use an ε-greedy policy for action selection, update Q-values through experience replay, and prioritize tasks based on urgency while sharing state information for collaborative decision-making. Evaluations through simulation demonstrate optimized performance, enhancing efficiency in vehicular MEC networks compared to baseline and the other well-known algorithms.

Network Optimization using Real Time Polling Service With and Without Relay Station in WiMAX Networks

IEEE 802.16 can be seen as a compelling replacement for conventional broadband technologies because its primary goal is to provide Broadband Wireless Access (BWA). The variable and uncertain nature of wireless networks makes it much more challenging to ensure QoS in this network. WiMAX Technology is used to support various quality of services which includes UGS, rtps, nrtps, ertps and Best Effort. This study employs an IEEE 802.16 network simulator, which offers adaptable and reliable features for assessing a particular QoS parameters for rtps. Achieving better internet performance in real time services is a challenge now a days, and it is also need of present scenario. This work emphasized on better internet service with good quality of service using rtps with Relay Station and Without Relay Station. In this work the CBR packet size, CBR data rate and data rate with rtps service are fine-tuned for achieving the better performance with good quality of service. When comparing uplink connections in rtps with and without relay station, it is found that the throughput in the uplink is 200% greater when using a relay station. The throughput and goodput are evaluated in uploading and downloading with single and multiple subscriber stations and observed that the multiple subscriber stations in downloading give better performance as compare to single subscriber stations. The throughput and goodput in single subscriber stations is better than multiple subscriber station in uploading. the Academic researchers and commercial developers can use this analysis to validate different WiMAX Network implementation mechanisms and parameters.

Real-time service task scheduling with fine-grained resource utilization to benefit important industrial business

Scheduling of service-oriented industrial businesses to meet the real-time requirements and improve the computing resources utilization is important. To this end, a Fine-grained Online Service Task Scheduling (FOSTS) method is proposed. First, a service task model that separates the invoked service from the task is established. Then, a service invocation-oriented allocation mechanism that allocates the invoked tasks based on priority is proposed to achieve fine-grained resource utilization. In addition, a task suspension mechanism to release resources for important tasks by suspending non-important tasks is proposed. The proposed FOSTS is tested in a typical industrial scenario and its performance is compared with many common and state-of-the-art methods. The simulation results show the effectiveness and superior performance of the proposed FOSTS compared to the other methods.