Real-time System Architecture Research Articles

Research on the Architectural Design and Performance Optimization of Large Scale Real-Time Chat Systems

With the rapid development of internet technology, large-scale real-time chat systems have become an essential tool for connecting users worldwide. These systems not only support the core functions of social communication but also serve as key platforms for business, education, and remote collaboration. However, the surge in user numbers and the explosive growth in message volume pose unprecedented technical challenges to real-time chat systems, including high concurrency processing, low latency communication, data consistency assurance, and system scalability. This study aims to explore and implement an efficient, stable, and scalable architecture for large-scale real-time chat systems and propose corresponding performance optimization strategies. The research employs literature review, system analysis, prototype design, and performance evaluation methods. It first analyzes the design limitations and performance bottlenecks of existing real-time chat systems. Based on this analysis, a microservices-based system design scheme is proposed, which enhances system performance and reliability through componentized services, message queues, load balancing, and caching mechanisms. In terms of performance optimization, this paper focuses on key technologies such as load balancing algorithms, asynchronous processing mechanisms of message queues, caching strategies, and data storage optimization. Experimental evaluations have confirmed the significant effects of the proposed scheme in handling high-concurrency requests, reducing system latency, and improving data throughput.

Architecture and Algorithm Design for Civil Aviation Data Real-Time Analysis System

Architecture and Algorithm Design for Civil Aviation Data Real-Time Analysis System

ROS2 Real-time Performance Optimization and Evaluation

Real-time interaction with uncertain and dynamic environments is essential for robotic systems to achieve functions such as visual perception, force interaction, spatial obstacle avoidance, and motion planning. To ensure the reliability and determinism of system execution, a flexible real-time control system architecture and interaction algorithm are required. The ROS framework was designed to improve the reusability of robotic software development by providing a distributed structure, hardware abstraction, message-passing mechanism, and application prototypes. Rich ecosystems for robotic development have been built around ROS1 and ROS2 architectures based on the Linux system. However, because of the fairness scheduling principle of the default Linux system design and the complexity of the kernel, the system does not have real-time computing. To achieve a balance between real-time and non-real-time computing, this paper uses the transmission mechanism of ROS2, combines it with the scheduling mechanism of the Linux operating system, and uses Preempt_RT to enhance the real-time computing of ROS1 and ROS2. The real-time performance evaluation of ROS1 and ROS2 is conducted from multiple perspectives, including throughput, transmission mode, QoS service quality, frequency, number of subscription nodes and EtherCAT master. This paper makes two significant contributions: firstly, it employs Preempt_RT to optimize the native ROS2 system, effectively enhancing the real-time performance of native ROS2 message transmission; secondly, it conducts a comprehensive evaluation of the real-time performance of both native and optimized ROS2 systems. This comparison elucidates the benefits of the optimized ROS2 architecture regarding real-time performance, with results vividly demonstrated through illustrative figures.

Hybrid Learning Models for IMU-Based HAR with Feature Analysis and Data Correction.

This paper proposes a novel approach to tackle the human activity recognition (HAR) problem. Four classes of body movement datasets, namely stand-up, sit-down, run, and walk, are applied to perform HAR. Instead of using vision-based solutions, we address the HAR challenge by implementing a real-time HAR system architecture with a wearable inertial measurement unit (IMU) sensor, which aims to achieve networked sensing and data sampling of human activity, data pre-processing and feature analysis, data generation and correction, and activity classification using hybrid learning models. Referring to the experimental results, the proposed system selects the pre-trained eXtreme Gradient Boosting (XGBoost) model and the Convolutional Variational Autoencoder (CVAE) model as the classifier and generator, respectively, with 96.03% classification accuracy.

A Real-time Solar Array Monitoring System Architecture Design Based on FBG

The solar array is a key part of spacecraft, which is the main power supply. With the trend of spacecraft bus development, the size of the solar array panel attached to the spacecraft becomes larger and larger to provide more load power. Large scale, low stiffness, and flexibility affect the attitude control of the spacecraft directly. Monitoring and analyzing the solar array dynamics behavior will be the key part of the overall design of the spacecraft’s control system. The purpose of monitoring is to verify the dynamic modeling and health diagnosis for a typical spacecraft flexible solar array. In this paper, Fiber Bragg Grating transducers (FBGs) and PXI-E Bus technology are used to perform online monitoring of 3D shapes through the strain profile. Using NI DAQ hardware and the FPGA Module to implement a DAQ system that can real-time and accurately perform online analysis and secondary processing of parameters from a system using NI LabVIEW software and RTOS to provide a complete, systematic solution to acquire data from FBGs. In this paper, the design of system architecture is described.

Communication architecture for real-time decision support systems in railway nodes

Rail transport nodes are complex systems providing transport for goods and people. The dispatcher providing operational control is essential for the proper functioning of these nodes. His role mainly consists of decision-making connected with the allocation of limited resources such as tracks or personnel. These decisions are complicated because of many unexpected influences, e.g., train delays. Recognizing the importance of decision support for the dispatcher's decision-making process, we had designed a real-time decision support system with a simulation model at its core. Such a system requires a high-quality information source for its operation, that needs to be efficiently acquired, processed, and used. In this paper, therefore, we address the possibilities of obtaining up-to-date information about the railway system. Moreover, we designed a communication architecture that allows the distribution of information to the designed decision support system with a simulation model in the core.

Validation of Embedded State Estimator Modules for Decentralized Monitoring of Power Distribution Systems Using IoT Components.

Recent theoretical studies demonstrate the advantages of using decentralized architectures over traditional centralized architectures for real-time Power Distribution Systems (PDSs) operation. These advantages include the reduction of the amount of data to be transmitted and processed when performing state estimation in PDSs. The main contribution of this paper is to provide lab validation of the advantages and feasibility of decentralized monitoring of PDSs. Therefore, this paper presents an advanced trial emulating realistic conditions and hardware setup. More specifically, the paper proposes: (i) The laboratory development and implementation of an Advanced Measurement Infrastructure (AMI) prototype to enable the simulation of a smart grid. To emulate the information traffic between smart meters and distribution operation centers, communication modules, that enable the use of wireless networks for sending messages in real-time, are used, bridging concepts from both IoT and Edge Computing. (ii) The laboratory development and implementation of a decentralized architecture based on Embedded State Estimator Modules (ESEMs) are carried out. ESEMs manage information from smart meters at lower voltage networks, performing real-time state estimation in PDSs. Simulations performed on a real PDS with 208 buses (considering both medium and low voltage buses) have met the aims of this paper. The results show that by using ESEMs in a decentralized architecture, both the data transit through the communication network, as well as the computational requirements involved in monitoring PDSs in real-time, are reduced considerably without any loss of accuracy.

A New Low-Cost Acoustic Beamforming Architecture for Real-Time Marine Sensing: Evaluation and Design

This paper mainly studies the performance of an acoustic beamforming technique applied to a low-cost hydrophone in a linear array of two to four elements for the detection and localization of underwater acoustic sound waves. It also evaluates the integration of the array in an energy-efficient real-time monitoring system architecture, allowing marine sensing to be conducted without human intervention. Such architecture would consist of vertical linear arrays of two or four RHSA-10 hydrophones models attached to a buoy or a vessel for sound detection; a frequency domain beamformer (FDB) technique implemented in a Xilinx Spartan-7 field programmable gate array (FPGA) for sound source localization; a LoRa wireless sensor network mote to provide convenient access from a base center. The architecture aims to alleviate sea traffic control for countries that lack the financial resources to properly address illegal fishing or piracy issues, mostly committed in small fast motorized boats. In our experiment, the sound waves emitted by a small motorized boat were successfully detected and tracked by three data acquisitions at a 1 km range. It is demonstrated that a system using a small number of hydrophones is capable of producing robust accuracy over a large band frequency in the presence of noise interference.

Real-time system architecture design practices

In this paper we give an overview of both hardware and software architectures of real time systems used in devices for various purposes — from lab bench contraptions to cars. The goal of the paper is to reveal separate classes of such architectures as well as to define preconditions for choosing a particular architecture.

Energy-Efficient Task Scheduling in Design of Multithread Time Predictable Real-Time Systems

The paper presents balanced heuristic techniques of static tasks scheduling in multi-core real-time system architecture. The main objective was to minimize the energy consumed by the system without causing deadlines to be missed. The authors proposed a few scheduling scenarios based on modifications of different parameters of the system and defined appropriate algorithms. The methodology was verified and tested on the original hardware platform of the system developed by the authors. The system consists of the reconfigurable set of cores based on an interleaved pipeline processing scheme. The entire system was modeled as original IP at the RTL level in VERILOG and implemented on a Virtex7 FPGA platform. The tasks were executed as a set of programs based on Malardalen WCET benchmarks; commonly used by the PRET community for validation time predictable systems and worst-case analyses. Many series of experiments were carried out and the results validated the approach and showed that it is possible to radically reduce the energy consumed by the system while retaining timing conditions (i.e., deadlines). The authors gathered and discussed results and formulated a set of recommendations for the safety real-time system’s design flowchart aimed to minimize the energy consumed by the system.

Development and Validation of an EEG-Based Real-Time Emotion Recognition System Using Edge AI Computing Platform With Convolutional Neural Network System-on-Chip Design

This study proposed an electroencephalogram (EEG)-based real-time emotion recognition hardware system architecture based on multiphase convolutional neural network (CNN) algorithm implemented on a 28-nm technology chip and on field programmable gate array (FPGA) for binary and quaternary classification. Sample entropy, differential asymmetry, short-time Fourier transform, and a channel reconstruction method were used for emotion feature extraction. In this work, six EEG channels were selected (FP1, FP2, F3, F4, F7, and F8), and EEG images were generated from spectrogram fusions. The complete CNN architecture included training and acceleration for efficient artificial intelligence (AI) edge application, and we proposed a multiphase CNN execution method to accommodate hardware resource constraints. Datasets of 32 subjects from the DEAP database were used to validate the proposed design, exhibiting mean accuracies for valance binary classification and valance-arousal quaternary classification of 83.36% and 76.67%, respectively. The core area and total power consumption of the CNN chip were $1.83 \,\, \times \,\, 1.83~mm^{2}$ , respectively, and $76.61~mW$ . The chip operation was validated using ADVANTEST V93000 PS1600, and the training process and real-time classification processing time took 0.12495 ms and 0.02634 ms for each EEG image, respectively. The proposed EEG-based real-time emotion recognition system included a dry electrode EEG headset, feature extraction processor, CNN chip platform, and graphical user interface, and the execution time costed $450~ms$ for each emotional state recognition.

Ethernet-based Network Architectures for Future Real-time Systems in the Car

Ethernet-based Network Architectures for Future Real-time Systems in the Car

Design and Implementation of Real-Time Management System Architecture based on GraphQL

In times of big data, the increasing growth of data sets is a big challenge for both data storage and data access, especially the management system with high real-time requirements. For real-time management system, there are some performance problems in the traditional Web service architecture based on REST style. In this paper,it proposes to establish GraphQL server in the real-time subsystem and integrate it into the existing systems.Through this architecture, the client can obtain the specified data from the server side, thus reducing the number of network requests and avoiding excessive capture or capture of data. Experiments are carried out in a real-time monitoring system. The experimental results show that the proposed architecture reduces the transmission data packets by 57.38% in rea-time monitoring and management system, and reduces the average time of data transmission by 8.9%.

Scheduling computation and communication on a software-defined photonic Network-on-Chip architecture for high-performance real-time systems

Abstract This paper presents a novel scheduling approach that efficiently schedules the computational and the communicational resources of a Software-Defined Photonic Network-on-Chip (SD-PNoC) Architecture designed for high-performance real-time systems. The proposed scheduling approach extends the conventional cyclic executive scheduling algorithm from the one that schedules solely the computational resources into a general one that schedules both the computational and the communicational resources together in a synchronized manner. The proposed SD-PNoC architecture employs Software Defined Network (SDN) approach by using application specific conflict-free and contention-free communication patterns to solve the routing and wavelength assignment (RWA) problem exists in photonic networks. The implementations of the collective communication primitives such as broadcast and all-to-broadcast are also presented to demonstrate the system as a whole.

Multi-Axis Force Sensor for Human-Robot Interaction Sensing in a Rehabilitation Robotic Device.

Human–robot interaction sensing is a compulsory feature in modern robotic systems where direct contact or close collaboration is desired. Rehabilitation and assistive robotics are fields where interaction forces are required for both safety and increased control performance of the device with a more comfortable experience for the user. In order to provide an efficient interaction feedback between the user and rehabilitation device, high performance sensing units are demanded. This work introduces a novel design of a multi-axis force sensor dedicated for measuring pelvis interaction forces in a rehabilitation exoskeleton device. The sensor is conceived such that it has different sensitivity characteristics for the three axes of interest having also movable parts in order to allow free rotations and limit crosstalk errors. Integrated sensor electronics make it easy to acquire and process data for a real-time distributed system architecture. Two of the developed sensors are integrated and tested in a complex gait rehabilitation device for safe and compliant control.

An Intelligent Distributed Embedded System Architecture for Real-Time traffic control system

An Intelligent Distributed Embedded System Architecture for Real-Time traffic control system

Evaluation of a low overhead predication system for a deterministic VLIW architecture targeting real-time applications

Recently, there is a considerable research effort on providing time-predictable architectures for real-time systems. The correct functioning of them depends not only on the logically correct response, but also the time when it is given. To provide such determinism, the use of VLIW (Very Long Instruction Word) architecture with in-order pipelines and predication support became attractive. Predication is an architecture technique which helps the compiler to translate control dependencies into data dependencies reducing control flow overhead and enhancing the worst-case timing analysis. Since hardware predication support does not come for free, the goal of this work is to investigate a low overhead predication system for multi-issue VLIW machines targeting real-time applications. This study was conducted using a VLIW 4-issue prototype based on the VLIW HP ST231 ISA describing the hardware implementation and the benefits on the worst-case performance.

Right-time vs real-time pro-active construction safety and health system architecture

Purpose The purpose of this paper is to investigate the critical time window for pro-active construction accident prevention and response. Large to small organisations throughout the entire construction supply chain continue to be challenged to adequately prevent accidents. Construction worker injuries and fatalities represent significant waste of resources. Although the five C’s (culture, competency, communication, controls and contractors) have been focusing on compliance, good practices and best-in-class strategies, even industry leaders have only marginal improvements in recorded safety statistics for many years. Design/methodology/approach Right-time vs real-time construction safety and health identifies three major focus areas to aid in the development of a strategic, as opposed to tactical, response. Occupational safety and health by design, real-time safety and health monitoring and alerts and education, training and feedback leveraging state-of-the-art technology provide meaningful predictive, quantitative and qualitative measures to identify, correlate and eliminate hazards before workers get injured or incidents cause collateral damage. Findings The current state and development of existing innovative initiatives in the occupational construction safety and health domain are identified. A framework for right-time vs real-time construction safety and health presents the specific focus on automated safety and health data gathering, analysis and reporting to achieve better safety performance. The developed roadmap for right-time vs real-time safety and health is finally tested in selected application scenarios of high concern in the construction industry. Originality/value A strategic roadmap to eliminate hazards and accidents through right-time vs real-time automation is presented that has practical as well as social implications on conducting a rigorous safety culture and climate in a construction business and its entire supply chain.

Evaluating the Design of a VLIW Processor for Real-Time Systems

Nowadays, many real-time applications are very complex and as the complexity and the requirements of those systems become more demanding, more hardware processing capacity is necessary. Unfortunately, the correct functioning of real-time systems depends not only on the logically correct response but also on the time when it is produced. General-purpose processor design fails to deliver analyzability due to their nondeterministic behavior caused by the use of cache memories, dynamic branch prediction, speculative execution, and out-of-order pipelines. In this article, we investigate the pipeline performance of Very Long Instruction Word (VLIW) architectures for real-time systems with an in-order pipeline considering Worst-Case Execution Time (WCET) performance. Techniques on obtaining the WCET of VLIW machines are also considered and we make a quantification on how important are hardware techniques such as static branch prediction, predication, and pipeline speed of complex operations such as memory access and multiplication for high-performance real-time systems. The memory hierarchy is out of the scope of this article and we used a classic deterministic structure formed by a direct mapped instruction cache and a data scratchpad memory. A VLIW prototype was implemented in VHDL from scratch considering the HP VLIW ST231 ISA. We also show some compiler insights and we use a representative subset of the Mälardalen’s WCET benchmarks for validation and performance quantification.

Sistema Distribuido de Detección de Sismos Usando una Red de Sensores Inalámbrica para Alerta Temprana.

Detecting disruptive events using COTS sensors like the ones embedded in smartphones is a difficult challenge but also an interesting opportunity. In this paper, we present a distributed, reliable, hierarchical and hard real-time system architecture of smartphones acting as opportunistic sensor nodes. Using a low energy-consumption application, we have used the smartphones inertial sensor as an accelerograph. The deployed smartphones and the application form a low-cost wireless sensor network, that detects, analyses and notifies a seismic-peak. The systems optimizes the distributed calculations in the smartphones; communication capabilities and integration in order to provide extra time for early warning in disaster scenarios (e.g. earthquakes), although the architecture may be extended to other disruptive and rare events. We propose an innovative real-time solution which considers time and spatial analyses, not present in another works, making it more precise and customizable, coupling it to the features of the geographical zone, network and resources, so as providing evidence of the feasibility of earthquake early warning using a distributed network of cell phones. The architecture has been validated by extensive evaluation and the most relevant result has been the improvement in notifications delivery about a seismic-peak 12seconds earlier than previous works in the epicenter zone, and a reduction in the number of false positives. Additionally the proposed architecture includes a post-event management to help users and strengthen coordination between aid-agencies in order to optimize human resources and time to implement measures in order to eliminate negative effects on the population.