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The Integration of Stream Data Models in Modern Transportation networks 3

The Integration of Stream Data Models in Modern Transportati...

引用

3rd IEEE International conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Naval, Preeti Jain, Sandeep Kumar Krishna, K. Gopala Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Electrical Engineering Jaipur India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka562112 India

ISBN: (纸本)9798350343274

The study proposal proposes an innovative strategy for managing modern transport networks, with an emphasis on the coordinated use of analytics, real-time data processing, and dynamic decision-making utilizing stream data models. To create a solid infrastructure for real-time data analysis, it is required to use a diverse set of data sources, such as sensors, GPS devices, traffic cameras, and mobile application software. A strategy for reaching this goal is described below. The real-time system that integrates transportation models, artificial intelligence, and data mining is at the core of this strategy. In addition to monitoring and assessment, one of the key responsibilities of this system is to develop the transportation network. Without this framework as a foundation, the method would be worthless. This collaborative effort provides proactive traffic control, efficient route planning, and event prediction in real time. The application of machine learning in predictive analytics and traffic analysis, which together give solid traffic forecasts, prompts the creation of tactics that cut wait times and enhance productivity. Event detection algorithms are critical for ensuring commuter safety and reducing wait times in the event of an emergency. Using cloud-hosted data processing technologies, the idea manages massive amounts of real-time data. This is critical for data collection, processing, and storage methods. Finally, we believe that the development of an intelligent transportation networkone that can continually learn, adapt, and drive itselfwill result in less traffic, more safety, shorter travel times, and environmentally friendly legislation and practices. To address the ever-increasing demands placed on dynamic urban settings, it is believed that the technique developed would transform current transport networks into more responsive, user-friendly, and efficient systems. © 2023 IEEE.

关键词： Real time systems

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On Integrating Robotic Data with GIS Tools in a Cloud Environment (Application Paper)

On Integrating Robotic Data with GIS Tools in a Cloud Enviro...

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Workshops of the EDBT/ICDT 2025 Joint conference, EDBT/ICDT-WS 2025

作者： Wrembel, Robert Kasprzyk, Jean-Paul Billen, Roland Bimonte, Sandro d'Orazio, Laurent Sacharidis, Dimitris Skrzypczyński, Piotr Université de Liège Liège Belgium INRAE Clermont-Ferrand France Université de Rennes Rennes France Université Libre de Bruxelles Bruxelles Belgium Poznan University of Technology Poznań Poland

Merging robotic technologies, sensor networks, and Geographic information Systems (GIS) offers significant potential across various domains, including agriculture and urban planning. However, a critical challenge lies in the lack of interoperability between data generated by these technologies and existing GIS tools. The EU-funded GIS4IoRT project addresses this gap by developing a plug-and-play and cloud-based middleware. This middleware facilitates seamless integration and visualization of multi-dimensional and multi-modal data within GIS environments. Key GIS4IoRT components include: a middleware architecture, a scalable cloud-based infrastructure, real-time robot querying capabilities, data quality assurance, spatio-temporal query support within the cloud, integration with GIS tools, and adherence to relevant standards. The middleware supports diverse data types, including LiDaR, imagery, and sensor data. This paper (1) presents an initial data integration architecture specifically designed for the sustainable architecture domain, (2) outlines the challenges encountered in designing such an architecture, and (3) explores novel data processing paradigms enabled by the architecture. © 2025 Copyright © 2025 for this paper by its authors.

关键词： data integration geographical information system images LiDaR robots sensors sustainable agriculture

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Agent-based Orchestration on a Swarm of Edge Devices 23

Agent-based Orchestration on a Swarm of Edge Devices

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17th ACM International conference on Distributed and Event-based Systems, DEBS 2023

作者： Anuraj, Banani University of Applied Sciences and Arts Western Switzerland HES-SO Sierre Switzerland

ISBN: (纸本)9798400701221

The proliferation of smart devices, sensors, autonomous robots, drones, and other similar instruments have profoundly changed the way of implementing and deploying systems in industrial and home environments, for diverse scenarios such as smart agriculture, healthcare, or manufacturing. Devices in these settings are not limited to simply observe and acquire data for monitoring, but they are also equipped with actuation capabilities, as well as the possibility of autonomously processing the incoming data through various techniques. However, given their intrinsic limitations regarding the capacity to store and process computations, it is often necessary to delegate some of these processing tasks to intermediary edge nodes in the network. These nodes, given their unique position can act as orchestrators guiding the decentralized work of the interconnected autonomous devices. Beyond static and pre-defined organization structures, in this work we propose the usage of agent and multi-agent-based models for designing and implementing swarms of edge nodes, conceived to dynamically orchestrate other devices, while meeting quality of service conditions. Allowing the control of intelligent edge nodes as conveyors and orchestrators on swarms of devices, we aim at providing intelligence to the self-organization of edge nodes, which may interchange streaming data, and represent their own capabilities through semantic models. Swarm-inspired behavioral patterns would guide the collaborative distribution of their computational tasks. Finally, we will implement and demonstrate the proposed technologies in an elderly home environment powered with a host of edge computing, sensing, and actuating devices. © 2023 Owner/Author(s).

关键词： sensor networks

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IoT-Enabled Automated Radish Care System: intelligent Detection and Response for Watering and Sunlight Needs 9

IoT-Enabled Automated Radish Care System: Intelligent Detect...

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9th International conference on Communication and Electronics Systems, ICCES 2024

作者： MacAranas, Nathaniel R. Mandariaga, Joseph Andrei A. Pablo, Carlos Miguel D. Dioses, Raymund M. Co, Joseph Darwin C. Mercado, Mark Anthony S. College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Manila Philippines

ISBN: (纸本)9798350377972

One of the critical issues, both in households and agriculture, is the ability to take care of radish plants due to their complexity. Traditional techniques, on the other hand, have problems such as inconsistent monitoring, high labor costs, wastage of resources, and improper timing of harvesting. Nowadays, sensor-based monitoring and automated irrigation systems are examples of novel methods that have tried to solve these problems but are mostly poorly integrated and do not respond smartly. In this paper, an intelligent system and an IoT-based system for the automated care of radish vegetables where the system can detect and watering the plants or exposing them to sunlight whenever need arises is presented. Proposed solutions to the challenges are based on modern aggregate sensor technology and automatic control, cloud communication, and predictive analysis. The system smartly incorporates multiple environmental sensors networks, data storage, and analytics in the cloud, and takes advantage of pre-energy for automated operations to ensure proper care of plants with little human input and resource usage. PlanterBot, a mobile app, allows users to control and monitor system activities remotely, integrating machine learning to perform pre-harvest assessments. Evaluation using ISO/IEC 25010 standards showed excellent performance in all targeted Functional Suitability (1.32), Usability (1.31), Reliability (1.40), and Performance Efficiency (1.37). The findings prove that the current device is functional in solving the existing problems while developing the concept of smart farming. © 2024 IEEE.

关键词： Smart agriculture

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Using LSTM to Accurately Predict the Ammonia Concentration Detected by Graphene sensors Affected by Temperature and Humidity

Using LSTM to Accurately Predict the Ammonia Concentration D...

引用

6th International conference on intelligent Computing and Signal processing (ICSP)

作者： Keyifan Yang Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

In response to the issue of graphene ammonia gas sensors being highly susceptible to temperature and humidity interference, this paper proposes a novel method for detecting ammonia gas concentration. This method incorporates both a temperature and humidity compensation algorithm and a Long Short Term Memory network (LSTM) to achieve highly accurate predictions of ammonia gas concentration under temperature and humidity interference. Firstly, collect the response data of a 4-unit Pt NPs/GO/graphene gas sensor array to different concentrations of ammonia in different testing environments (50% RH-100% RH, 20 °C to 60 °C, 0-100ppm). Subsequently, temperature and humidity compensation is applied to adjust the sensor response data, mitigating the impact of temperature and humidity variations. Additionally, dimensionality reduction is employed through downsampling. Finally, LSTM is constructed to train and predict the response data collected by the sensor array. The experimental results show that LSTM has high accuracy in predicting the ammonia concentration detected by the sensor array, with an average absolute error (MAE) value of 0.916. In addition, a decrease in the number of sensors will reduce the prediction accuracy, and different materials modified with graphene also have an impact on the prediction accuracy. The research in this work provides an effective solution for graphene sensors to resist temperature and humidity interference and has high application potential.

关键词： Temperature sensors Ammonia Accuracy Machine learning algorithms Graphene Humidity Interference Long short term memory Gas detectors sensor arrays

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Optimizing Human Motion Posture Detection and Humanized Computing for Smart Health Applications

Optimizing Human Motion Posture Detection and Humanized Comp...

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Digital Analysis and processing, intelligent Computation (DAPIC), International conference on

作者： Weiwei Wang Jingshan Zhang School of Sports Medicine and Rehabilitation North Sichuan Medical College Nanchong Sichuan China Department of Sports Humanities and Social Sciences Sichuan Sports College Chengdu Sichuan China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

Existing human motion detection technologies have problems such as insufficient accuracy and poor real-time performance, which makes it difficult to meet the needs of health management. This paper proposes a human motion posture detection and humanized calculation method based on multi-sensor fusion and LSTM. This paper first deploys IMU sensors at key parts of the human body, and uses visual sensors to capture the three-dimensional posture information of human motion, and obtains physiological data such as heart rate and number of steps through wearable devices such as smart bracelets. Secondly, the IMU data is denoised and calibrated to eliminate sensor errors. Next, the fused data is trained using a long short-term memory network (LSTM). The rationality of the motion posture is then analyzed, and the user's exercise intensity and health status are evaluated based on the motion data and physiological data. In terms of posture recognition accuracy, the average value of this method is 92.61 %. In terms of abnormal posture detection rate, the detection rate of some data points of this method is close to 100%. At the same time, the maximum recognition time of this method is only 789 milliseconds, and the minimum recognition time is as low as 503 milliseconds, which is better than the traditional method. The optimized human motion posture detection and humanized calculation method proposed in this paper realizes high-precision detection of human motion and intelligent health management through multi-source data fusion, LSTM construction, human kinematics analysis and personalized health assessment.

关键词： Training Accuracy Soft sensors Data integration Real-time systems Physiology Data models Long short term memory intelligent sensors Wearable sensors

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Research on Design and Optimization Methods of intelligent sensors Based on Optoelectronic Detection Technology

Research on Design and Optimization Methods of Intelligent S...

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Electrical Drives, Power Electronics & Engineering (EDPEE), International conference on

作者： Dinghe Zhou Junbo Huang Harbin Engineering University Harbin Heilongjiang China Hebei University of Technology Tianjin China

ISBN: (数字)9798350395631

ISBN: (纸本)9798350395648

In optoelectronic information science and engineering technology, optoelectronic detection technology is a very important technology. Photoelectric sensor (PS) refers to the process of using a photoelectric element as the basic detection component, converting the measured changes into changes in other signals, and then using the photoelectric element to convert the optical signal into an electrical signal. With the gradual improvement of modern science and technology, optoelectronic detection technology has further integrated modern technologies such as automation control systems and digital information processing technology, effectively combining optical and electronic technologies, greatly improving the accuracy and speed of optoelectronic detection. This article designs an intelligent sensor based on photoelectric detection technology. Through in-depth research on the basic principles of photoelectric detection technology, combined with microelectronics technology and integrated circuit design, a miniaturized and low-power intelligent sensor is achieved. Meanwhile, utilizing deep learning (DL) algorithms to optimize and calibrate sensors, improving their performance and accuracy. The experimental results show that the sensor designed in this article has high measurement accuracy and good application prospects.

关键词： Integrated circuit synthesis Performance evaluation Biomedical optical imaging Optimization methods Optical variables measurement Market research Microelectronics

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Cloud-Enhanced Wireless sensor networks for Air Quality Monitoring

Cloud-Enhanced Wireless Sensor Networks for Air Quality Moni...

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Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), International conference on

作者： Shreenidhi H S Trapty Agrawal Kiran Kale Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

A growing trend in environmental monitoring involves the use of inexpensive wireless sensors to create densely dispersed networks that provide higher spatial resolution than traditional air quality control systems. In this study, a novel air quality monitoring system—a wireless sensors network, or WSN, made up of a dispersed sensor network connected to a cloud system—is presented. The sensor nodes use low-power ZigBee motes to send field measurements in real time to the cloud via a dedicated gateway. An optimally calibrated cloud computing framework has been instituted to effectively retain, oversee, handle, and present the data originating from the sensor system. Optimised chemical and contaminant identification is made possible by using artificial intelligence tools in the cloud for sophisticated data handling and analysis. This system is unique in that it is small, light, and energy-efficient, allowing for the deployment of several nodes for thorough air quality evaluations in various locations. The effectiveness of the method in identifying common volatile organic chemicals including the compounds benzene, toluene, ethane, and xylene to is validated by a practical example in a laboratory environment. Principal component analysis, perceptron networks with backpropagation learning, and help vector computation methods are all used in the data processing. The results show that the volatile organic compound concentrations may be distinguished and quantified with good accuracy. Cloud computing, air quality, chemical sensors, and wireless sensor networks are among the key themes.

关键词： Volatile organic compounds Wireless communication Wireless sensor networks Cloud computing Chemical sensors Air quality Data processing sensor systems sensors Testing

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A Task Bundling based Multi-Platform Cooperation Mechanism for Mobile Crowdsensing 20

A Task Bundling based Multi-Platform Cooperation Mechanism f...

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20th IEEE International Wireless Communications and Mobile Computing (IEEE IWCMC) - Green and intelligent Communications

作者： Zhao, Zixing Zhang, Baoxian Liu, Chen Yao, Zheng Li, Cheng Univ Chinese Acad Sci Res Ctr Ubiquitous Sensor Networks Beijing 100049 Peoples R China Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada Simon Fraser Univ Sch Engn Sci Burnaby BC V5A 1S6 Canada Mem Univ St John NL Canada

ISBN: (纸本)9798350361261;9798350361278

Mobile crowdsensing (MCS) is a cost-effective sensing paradigm by incentivizing mobile users to perform sensing tasks using their smartphones with rich embedded sensors. An important problem in MCS is how to achieve high task completion rate for location dependent tasks since some of them can be far away from potential users. Most existing work in this aspect assumes that there is only one service platform without consideration of existence of multiple platforms and also impact of their cooperation on task completion rate. In this paper, we design a task bundling based multi-platform cooperation mechanism (TBMCM) for completion of location dependent sensing tasks. The design objective is to maximize the system profit while improving the task completion rate. TBMCM works in a slot-by-slot manner. In each slot, each platform first assigns its tasks and task bundles to its registered users through reverse auctions and then submits information about its idle users and not-assigned-yet unpopular tasks to a cross-platform cooperation managing entity (CCM) for cross-platform cooperation. Then, the CCM entity releases the tasks and task bundles created using its collected tasks to the idle users. Meanwhile, each platform has the option to bundle its own tasks with the tasks provided by the CCM entity. A task bundling method is then designed to perform effective bundling between unpopular and popular tasks for improved task completion rate. We show the proposed mechanism satisfies individual and cooperative rationality. Extensive simulation results show the high performance of TBMCM in terms of task completion rate and system profit.

关键词： Mobile crowdsensing multi-platform cooperation task bundling

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Research on the Application of Lora Mesh Technology in the Field of Service Performance Monitoring for Highway Infrastructure

Research on the Application of Lora Mesh Technology in the F...

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sensors, Electronics and Computer Engineering (ICSECE), IEEE International conference on

作者： Yongkang Hu Dantao Han Zhen Wang Qiuping Zheng Network and Sensor Center Instrumentation Technology and Economy Institute Beijing China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

With the development of sensors and communication technology, the service performance monitoring system of highway traffic infrastructure based on intelligent sensors has been widely used. By arranging sensors to obtain information such as strain, load, displacement and deformation of highway infrastructure, we can timely grasp its structural changes and health status, which is of great significance for improving the maintenance level of highway infrastructure. However, the current monitoring system mainly uses wired sensor network, which has a series of problems such as complex wiring, high cost and poor scalability. Therefore, in this paper, a highway traffic infrastructure service performance monitoring system based on wireless communication technology is designed by integrating LoRa and Mesh AD hoc networking technology. The system has AD hoc networking, multi-hop transmission, network self-healing and other functions. The experimental results show that the LoRa Mesh monitoring system using multi-hop transmission has obvious advantages in coverage and communication reliability, and has broad application prospects in the field of highway traffic infrastructure performance monitoring.

关键词： Road transportation Wireless communication Wireless sensor networks Power demand Computer network reliability LoRa Spread spectrum communication sensor systems Monitoring intelligent sensors

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