Real-time Services Research Articles

VidBlock: A Web3.0-Enabled Decentralized Blockchain Architecture for Live Video Streaming

In the digital era, the demand for real-time streaming services highlights the scalability, data sovereignty, and privacy limitations of traditional centralized systems. VidBlock introduces a novel decentralized blockchain architecture that leverages the blockchain’s immutable and transparent characteristics along with direct communication capabilities. This ecosystem revolutionizes content delivery and storage, ensuring high data integrity and user trust. VidBlock’s architecture emphasizes serverless operation, aligning with the principles of decentralization to enhance efficiency and reduce costs. Our contributions include decentralized data management, user-controlled privacy, cost reduction through a serverless architecture, and improved global accessibility. Experiments show that VidBlock is superior in reducing latency and utilizing bandwidth, demonstrating its potential to redefine live video streaming in the Web3.0 era.

The equivalent value (EV)-based workload assessment of primary healthcare workers in Beijing, China

BackgroundQuantitative methods for estimating the workload of primary healthcare (PHC) workers are essential for improving the performance of PHC institutions. However, measuring the workload of PHC workers is challenging due to the diverse and complex range of services covered by PHC. This study aims to use an equivalent value (EV)-based approach to assess the workload of PHC workers and inform policymakers about the current workload burden in Beijing, China.MethodsThe EV-based workload assessment system was designed by three main steps: identifying the list of essential PHC service items provided by PHC workers, quantifying the EV of each service item, and calculating the corresponding workload for PHC workers and community health centers (CHCs). The study included 18 CHCs, which were divided into three groups based on population density and topography: Group I (eight urban CHCs), Group II (six CHCs in semi-mountainous areas), and Group III (four CHCs in mountainous areas). Data were collected from local health information system, which automatically collected real-time service volume data for 500 PHC service items at 18 CHCs in the sample district in Beijing from 2017 to 2021.ResultsThis study identified 503 essential PHC service items and defined their EVs. The theoretical full-capacity workload per PHC worker was 6024 EVs, serving as the base workload. The actual annual workload per PHC worker was 7240.0 EVs during 2017–2021. The base workload per budgeted position for the three types of CHCs was 6468.6 EVs for Group I, 5268.5 EVs for Group II, and 5038.7 EVs for Group III. Compared with the actual workload of 7702.3 EVs, 6568.3 EVs, and 5979.0 EVs in each group, respectively, all PHC workers in the sample district were overburdened during the study period.ConclusionsThe EV-based method provides a feasible solution for comprehensively assessing the workload of publicly funded PHC institutions in other regions. This study offers valuable insights to help local policymakers understand the workload burden of PHC workers, objectively evaluate their performance, and guide future health workforce planning.

Sistem Chabot Layanan Informasi Mahasiswa Menggunakan Algoritma Long Short-Term Memory

In the era of globalization and rapid information flow, the demand for efficient and accurate information, especially within academic institutions, is rising. Students often face challenges in accessing educational resources and real-time information, particularly outside official working hours. Existing online information services have limitations in providing continuous access. This research focuses on developing and evaluating a student information service chatbot system at Universitas Jenderal Achmad Yani Yogyakarta (UNJAYA) using the Long Short-Term Memory (LSTM) algorithm. The primary objective is to create a system that delivers real-time, accurate, and efficient information services to students. The Machine Learning Development Cycle (MLDC) is employed in the model development process, including stages such as data collection, processing, model training, evaluation, and implementation. The system's performance is tested using a questionnaire distributed to students, with responses measured on a Likert scale. The results demonstrate a chatbot with a 97.76% accuracy rate, 98.34% precision, and 97.76% recall. The overall system evaluation yielded an average score of 3.87, categorized as good. This research concludes that the LSTM-based chatbot successfully enhances information services at the Faculty of Engineering and Information Technology, providing an innovative solution to meet student needs in real-time

Cross-Technology Interference-Aware Rate Adaptation in Time-Triggered Wireless Local Area Networks

The proliferation of IoT using heterogeneous wireless technologies within the unlicensed spectrum has intensified cross-technology interference (CTI) in wireless local area networks (WLANs). As WLANs increasingly adopt time-triggered transmission methods to support real-time services, this interference affects throughput, packet loss, and latency. This paper presents a CTI-aware rate adaptation framework designed to mitigate interference in WLANs without direct coordination with heterogeneous wireless devices. The framework includes a CTI identification model and CTI-aware rate selection algorithms. Leveraging short-time Fourier transform, the identification model captures the time–frequency–power characteristics of CTI signals, enabling the estimation of the average power of various heterogeneous wireless technologies employed by interfering devices. The rate selection algorithms predict CTI occurrence times and adjust the transmission rate accordingly, enhancing the performance of existing explicit and implicit interference mitigation methods. Experimental results demonstrated that the lightweight CTI identification model accurately estimated the average power of each type with an error margin of ±1.414 dBm, achieving this in under 1 ms on the target hardware. Additionally, applying the proposed framework to explicit interference mitigation enhanced goodput by 20.67%, reduced packet error rate by 2.38%, and decreased the probability of packets exceeding 1 ms latency by 0.932% compared to conventional methods.

Regional triple-frequency integer clock estimation for augmented real-time positioning services

Regional triple-frequency integer clock estimation for augmented real-time positioning services

Empowering drones in vehicular network through fog computing and blockchain technology.

The performance of drones, especially for time-sensitive tasks, is critical in various applications. Fog nodes strategically placed near IoT devices serve as computational resources for drones, ensuring quick service responses for deadline-driven tasks. However, the limited battery capacity of drones poses a challenge, necessitating energy-efficient Internet of Drones (IoD) systems. Despite the increasing demand for drone flying automation, there is a significant absence of a comprehensive drone network service architecture tailored for secure and efficient operations of drones. This research paper addresses this gap by proposing a safe, reliable, and real-time drone network service architecture, emphasizing collaboration with fog computing. The contribution includes a systematic architecture design and integration of blockchain technology for secure data storage. Fog computing was introduced for the Drone with Blockchain Technology (FCDBT) model, where drones collaborate to process IoT data efficiently. The proposed algorithm dynamically plans drone trajectories and optimizes computation offloading. Results from simulations demonstrate the effectiveness of the proposed architecture, showcasing reduced average response latency and improved throughput, particularly when accessing resources from fog nodes. Furthermore, the model evaluates blockchain consensus algorithms (PoW, PoS, DAG) and recommends DAG for superior performance in handling IoT data. Fog; Drones; Blockchain; PSO; IoT; Vehicular.

A new methodology for the real-time limited-stop bus service design problem

Providing limited-stop bus services can improve the efficiency of bus systems. This paper proposes a new two-stage strategy for providing real-time limited-stop bus services for a corridor and the corresponding model is developed. In the first (tactical planning) stage, given the maximum number of different limited-stop services, an operator determines a set of limited-stop services based on historical bus travel times and passenger arrival rates. In the second (operational) stage, an operator selects one service from the set of limited-stop services obtained in the first stage for each limited-stop vehicle based on (short-term) predictive travel times and passenger arrival rates. Prediction errors are considered in the second stage. An enhanced artificial bee colony algorithm is developed to solve the first-stage model and the Monte Carlo Simulation method is adopted to solve the second-stage model. Numerical results are presented to illustrate the effectiveness and efficiency of the strategy and the effect of prediction errors.

An Optimized Beluga Whale Approach for Migration to Reduce Power and Service Level Agreement in Real-Time System

Managing power consumption in cloud data centers has become a critical challenge. Live container migration is a technology supporting energy efficiency in this context. To address the hurdles of power consumption management, thereby mitigating carbon emissions and minimizing service level agreement (SLA) violations, we propose an approach utilizing a real-time server with the Beluga Whale Optimization Algorithm (BWOA) for container migration. The proposed approach aims to optimize energy consumption while ensuring SLA compliance. The BWOA, a machine learning-based method, is employed to predict the resource requirements of containers and migrate them to hosts with sufficient resources. We implemented the proposed approach in a real-time cloud server and compared its performance with other algorithms in terms of response time. The results demonstrate a remarkable 30% improvement in response time, leading to reduced SLA violations and optimized power consumption in containerized data centers.

A Survey on Routing Algorithms Based on Machine Learning

With the rapid development of the Internet in today's world, many computer applications have emerged, including many new network applications, such as real-time multimedia streaming services. However, traditional computer routing algorithms have defects in these emerging network applications, so we urgently need to develop new routing algorithms to compensate for them. Machine learning technology has achieved considerable results in computer vision, image generation, and game processing. In order to further improve network performance, some research groups have introduced machine learning techniques into routing problems. Unlike traditional routing algorithms, machine learning-based routing algorithms are typically driven by data. Hence, they are more adaptable to changes in network status and can make decisions promptly. Current intelligent routing algorithms demonstrate their potential through the application of machine learning, and this idea is likely to become an indispensable part of the Internet in the future. This article first introduces machine learning technology, then introduces routing algorithms using different machine learning technologies, and finally points out the development prospects of routing algorithms based on machine learning technology.

IOT Based Car Parking Using Edge Computing

The promise of autonomous driving is that edge computing can deliver real time services. Using a variety of sensors and communication modules, autonomous cars may function both indoors including in parking lots and outdoors. But because the interior station's ability to communicate with the outer world is limited globe, and the present underpowered autos don't really work; they could have to handle all the data. Autonomously, hence a control mechanism must be put in place. Completely self-sufficient car in the surroundings. In this research, we suggest an edge computing based smart parking system for self driving cars. Smart parking has resolved Boundaries and traffic, and parking spaces are managed using a grid. To assess the effectiveness of the smart parking system, In the current parking system, we contrasted the journey time with the parking time. Furthermore, the resource to make sure that the service quality margin number is offered and maintained; expenses and transaction data are examined.

Distribution System Security Region with Energy Storage Systems

The high penetration of distributed energy resources (DERs) in distribution systems calls for advanced security management techniques. Hence, this paper proposes the model of the distribution system security region with energy storage systems (DSSR-ESS). The N-1 secure operation range of distribution systems integrating ESS is characterized. Further research reveals the effect and mechanism of ESS on the DSSR properties. Firstly, the ESS in distribution systems is clarified from the perspective of security, and the security service time and real-time security service capability are defined. Secondly, the DSSR-ESS model is formulated by: 1) selecting load and distributed generation (DG) as the state variables, 2) identifying the normal operation security and N-1 security scenarios, and 3) determining the complete security constraints. Then, to solve the time-varying and nonlinear N-1 DSSR-ESS model, an algorithm is developed. The analytical expressions of the region boundaries considering voltage constraints, line loss, and ESS SOC are further obtained. Finally, a modified IEEE-RBTS-BUS4 system and its expanded 104-node test system are used to verify the proposed model. Comparing with the existing research, the proposed DSSR-ESS model considers ESS. This paper concludes that ESS bidirectionally adjusts the power flow of its upstream branches, thereby expanding the security region both in positive and negative directions. But limited by state space boundaries and security boundaries unaffected by ESS, increasing ESS capacity may not lead to DSSR-ESS expansion. Based on this discovery, a sitting strategy for ESS is developed to expand the security region, which demonstrates the practical application of DSSR-ESS in the security management of distribution systems.

An Online Knowledge-Based Support System

Technology advances have touched all the fields of human life, and the educational system is not an exception. Students in Nigerian universities experience challenges in their early or later stages of school due to a lack of a knowledge-based support system. This results in massive failure and confusion in later days in school. A knowledge-based online support system has been developed and implemented in this paper to improve academic support services for students and faculty members in the university. The need for such a support system stems from the various academic problems that the students often experience, including a lack of answers to certain questions, which can result in academic failure, taking too long to graduate, or dropping out of the university altogether. The system is web-based; therefore, users can access the necessary information when needed, and, in most cases, it solves common academic issues in a short time using the web. Following the Agile software development methodology and employing a client-server architecture, the system was designed in a user-friendly and efficient manner. Highlights of the system include an extensive database of FAQs, real-time interactive services, and multi-typed query handling. Testing phases were performed and integrated, and modular testing showed that the system worked as intended, providing valid inputs and also flagging any invalid inputs without causing the system failures, and performing provisions for user corrections. The results suggest that the online support system could enhance the academic environment at AE-FUNAI and therefore would benefit students and staff members alike. Nevertheless, certain limitations still prevail, such as poor internet access, which calls for constant enhancement and provision of support to the system.

Bringing Collaborative Positioning to Native 5G Systems for Enhanced 2D & 3D Location Services

5G's large bandwidths and ubiquity make it a promising candidate for catalyzing future mobile services (emergency, V2X, XR, etc.) that benefit from accurate positioning over a wide area. However, we demonstrate that its heavy reliance on infrastructure base stations significantly limits its positioning ability in urban environments, where multi-story buildings and urban canyons are the norm, resulting in only a small fraction of devices being positioned, often only in 2D. To this end, we propose CAMP, a novel, collaboration-augmented paradigm for 2D and 3D positioning with cellular networks, where UEs leverage 5G NR's peer-peer Sidelink protocol to directly enable positioning measurements between each other in a scalable manner. Leveraging better connectivity (higher degree and SNR) between devices, CAMP overcomes the challenges inherent to infrastructure approaches. It brings algorithmic innovations in distributed, stochastic access and dynamic resource scheduling in a 5G-compliant manner to Sidelink measurements that are otherwise not scalable in conventional gNB-driven Sidelink operation. CAMP is implemented as a near real-time xApp service in the Open-RAN framework. Evaluations in a small cell 5G test-bed and a large-scale RAN on Colosseum, highlight its ability to position over 80% (72%) UEs with under 3m accuracy in 2D (3D) in real-world urban scenarios (compared to 30% with infrastructure approaches) and with 7-25x less overhead compared to baseline collaborative schemes.

Toward real-time operations of modular-vehicle transit services: From rolling horizon control to learning-based approach

Recent technological advancements have opened doors for real-time adjustments and controls during public transport operations. In particular, the introduction of modular vehicles has the potential to significantly enhance public transit service quality. This innovative public transit service with modular vehicles, characterized by its flexible schedules and vehicle formations, allows for the dynamic management of transit capacity to meet the fluctuating passenger demands. This paper proposes to schedule the flexible modular-vehicle transit service in real time considering the varying demands. To jointly optimize the service schedule and vehicle formations, we propose the rolling horizon control approach to decompose the complex problem into subproblems that can be solved efficiently during the process. On top of this, we introduce a learning-based optimization proxy to streamline the optimization process within the rolling horizon framework, enabling near-optimal decisions to be made with minimal execution time without directly solving the optimization problem. Through numerical studies, we demonstrate the effectiveness and efficiency of the proposed methods in terms of solution quality and efficiency. Furthermore, our case studies show that modular vehicles can adapt to the changing demand and effectively reduce the total costs in the transit system.

Flexible Hyper-Distributed IoT–Edge–Cloud Platform for Real-Time Digital Twin Applications on 6G-Intended Testbeds for Logistics and Industry

This paper presents the design and development of a flexible hyper-distributed IoT–Edge–Cloud computing platform for real-time Digital Twins in real logistics and industrial environments, intended as a novel living lab and testbed for future 6G applications. It expands the limited capabilities of IoT devices with extended Cloud and Edge computing functionalities, creating an IoT–Edge–Cloud continuum platform composed of multiple stakeholder solutions, in which vertical application developers can take full advantage of the computing resources of the infrastructure. The platform is built together with a private 5G network to connect machines and sensors on a large scale. Artificial intelligence and machine learning are used to allocate computing resources for real-time services by an end-to-end intelligent orchestrator, and real-time distributed analytic tools leverage Edge computing platforms to support different types of Digital Twin applications for logistics and industry, such as immersive remote driving, with specific characteristics and features. Performance evaluations demonstrated the platform’s capability to support the high-throughput communications required for Digital Twins, achieving user-experienced rates close to the maximum theoretical values, up to 552 Mb/s for the downlink and 87.3 Mb/s for the uplink in the n78 frequency band. Moreover, the platform’s support for Digital Twins was validated via QoE assessments conducted on an immersive remote driving prototype, which demonstrated high levels of user satisfaction in key dimensions such as presence, engagement, control, sensory integration, and cognitive load.

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.