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2nd International Conference on Internet of Vehicles (IOV)

作者： Yap, Chee Een Kim, Myung Ho Soongsil Univ Sch Comp Seoul South Korea Soongsil Univ Sch Software Seoul South Korea

ISBN: (纸本)9783319272931;9783319272924

Road accident becomes a threat to all drivers around the world. According to the study, fatigue or drowsiness is one of the causes to road accident. As the rapid development of the mobile devices and sensor networks, mobile based driver monitoring system has been widely proposed and discussed as an effort to reduce road accident rate around the world. sensors such as EEG, temperature or respiration sensor are used to collect the signal from the driver to alarm the driver if drowsiness is likely to happen. However, the sensor data management of the collected data(signals) is not being paid enough attention. In this paper, we propose a sensor data management mechanism for the mobile based driver monitoring system to handle the data in a more efficient manner.

关键词： sensor data management Driver monitoring Invertible bloom filter

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sensor data management in dynamic environments

Sensor data management in dynamic environments

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5th International Conference on Networked Sensing Systems

作者： Guergen, Levent Roncancio, Claudia Labbe, Cyril Olive, Vincent Donsez, Didier France Telecom R&D Orange Labs Grenoble France LIG Lab Grenoble France

ISBN: (纸本)9784907764319

sensor-based applications mostly need data from sensors of different types (temperature, pressure, location, camera, etc.) that may be managed by different software, e.g., proprietary software from manufacturers. This demonstration presents SStreaMWare, a service-oriented middleware for heterogeneous sensor data management. SStreaMWare's simple data schema allows data representation of various types of sensors in a common generic way. The service-oriented approach of SStreaMWare hides the heterogeneity of sensor software by adopting generic query services that can be discovered and used dynamically.

关键词： sensor data management declarative queries service oriented middleware heterogeneity dynamicity

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WikiSensing: An Online Collaborative Approach for sensor data management

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sensorS 2012年第10期12卷 13295-13332页

作者： Silva, Dilshan Ghanem, Moustafa Guo, Yike Univ London Imperial Coll Sci Technol & Med Dept Comp Sci London SW7 2AZ England

This paper presents a new methodology for collaborative sensor data management known as WikiSensing. It is a novel approach that incorporates online collaboration with sensor data management. We introduce the work on this research by describing the motivation and challenges of designing and developing an online collaborative sensor data management system. This is followed by a brief survey on popular sensor data management and online collaborative systems. We then present the architecture for WikiSensing highlighting its main components and features. Several example scenarios are described to present the functionality of the system. We evaluate the approach by investigating the performance of aggregate queries and the scalability of the system.

关键词： sensor data management online collaboration collaborative systems aggregate queries virtual sensors

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A sensor data management Scheme for Wireless sensor Networks

A Sensor Data Management Scheme for Wireless Sensor Networks

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International Conference on Materials, Mechatronics and Automation (ICMMA 2011)

作者： Zhang, Yuan Liu, Yue Jia, Zhongtian Univ Jinan Sch Informat Sci & Engn Jinan Peoples R China

ISBN: (纸本)9783037850176

One of the major difficulties of wireless sensor network (WSN) applications is how to efficiently manage the large amount of data produced by sensors. The differences from standard database source pose challenges of sensor data management. In this paper, we propose a novel sensor data management architecture based on our extensive discussion on existing works. The hierarchical system model consists of sensor network layer and proxy network layer. sensor network layer performs limited computation and communication while being managed intelligently by the proxy network. The proxy network receives sensor data, manages sensor data and processes queries in a distributed manner. We also provide insight into possible research directions in this area.

关键词： sensor data management Hierarchical Architecture Wireless sensor Network

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sensor data management in the cloud: data storage, data ingestion, and data retrieval

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2018年第1期30卷

作者： Sangat, Prajwol Indrawan-Santiago, Maria Taniar, David Monash Univ Fac Informat Technol Melbourne Vic Australia

sensors are widely used in the field of manufacturing, railways, aerospace, cars, medicines, robotics, and many other aspects of our everyday life. There is an increasing need to capture, store, and analyse the dynamic semi-structured data from those sensors. A similar growth of semi-structured data in the modern web has led to the creation of NoSQL data stores for scalability, availability, and performance, whereas large-scale data processing frameworks for parallel analysis. NoSQL data store such as MongoDB and data processing framework such as Apache Hadoop has been studied for scientific data analysis. However, there has been no study on MongoDB with Apache Spark, and there is a limited understanding of how sensor data management can benefit from these technologies, specifically for ingesting high-velocity sensor data and parallel retrieval of high volume data. In this paper, we evaluate the performance of MongoDB sharding and no-sharding databases with Apache Spark, to identify the right software environment for sensor data management.

关键词： Apache Spark data ingestion data retrieval data storage MongoDB sensor data management

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sensor data management in dynamic environments

Sensor data management in dynamic environments

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International Conference on Networked Sensing Systems

作者： Levent Gurgen Claudia Roncancio Cyril Labbe Vincent Olive Didier Donsez Orange Labs France Telecom R&D - Grenoble France

ISBN: (纸本)9784907764319

关键词： sensor data management Declarative queries Service oriented middleware Heterogeneity Dynamicity sensor data sensors data structures proprietary software Representation of data Heterogeneous Environments

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Noise Reduction Techniques in ADAS sensor data management: Methods and Comparative Analysis

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第8期15卷 679-695页

作者： Alami, Ahmed Belmajdoub, Fouad Univ Sidi Mohammed Ben Abdellah Fac Sci & Technol Fez Fes Morocco

This review examines noise reduction techniques in management, crucial for enhancing vehicle safety and performance. ADAS relies on real-time data from conventional critical functions like lane keeping, collision avoidance, and adaptive cruise control. These sensors are susceptible to thermal noise, mechanical vibrations, and environmental interferences, which degrade system performance. We explore filtering techniques including KalmanNet, Simple Moving Average and Low Pass Filtering (LPF), assessing their efficacy in noise reduction and data integrity improvement. These methods are compared using key performance metrics such as Signal-to-Noise Ratio (SNR), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Recent advancements in hybrid filtering approaches and adaptive algorithms are discussed, highlighting their strengths and limitations for different sensor types and ADAS functionalities. Findings demonstrate the superior performance of Wavelet Denoising for non-stationary signals, SMA and EMA's effectiveness for smoother signal variations, and LPF's excellence in high-frequency noise attenuation with careful tuning. KalmanNet showed significant improvements in noise reduction and data accuracy, particularly in complex and dynamic environments. Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) were especially effective for RADAR sensors, handling non-linearities and providing accurate bench testing to validate these techniques in real-world scenarios, this study underscores the importance of selecting appropriate methods based on specific noise characteristics and system requirements. This research provides valuable insights for ADAS and autonomous driving technologies development, emphasizing precise signal processing's critical role in ensuring accurate sensor data interpretation and decision-making.

关键词： ADAS sensor data management noise reduction KalmanNet Wavelet Denoising RADAR SMA EMA LPF HIL bench testing

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Dynamic energy-aware sensor configuration in multi-application monitoring systems

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PERVASIVE AND MOBILE COMPUTING 2017年 41卷 192-204页

作者： Pinarer, Ozgun Gripay, Yann Servigne, Sylvie Ozgovde, Atay Baskurt, Atilla Univ Lyon CNRS INSA Lyon LIRISUMR5205 F-69621 Villeurbanne France Galatasaray Univ Ciragan Cad 36 TR-34349 Istanbul Turkey

A typical pervasive monitoring system like a smart building depends on an infrastructure composed of hundreds of heterogeneous wireless sensor devices. Managing the energy consumption of these devices poses a challenging problem that affects the overall efficiency and usability. Existing approaches for sensor energy consumption typically assume a single monitoring application to consume sensor data and a static configuration for sensor devices. In this paper, we focus on a multi-application context with dynamic requirements and multi-modal sensor devices. We present 3SoSM, an approach to optimize interactions between application requirements and wireless sensor environment in real-time. It relies on an energy-aware dynamic configuration of sensor devices to lower energy consumption while fulfilling application requirements. To bind together sensor configuration and dynamic management of data streams, we design a sustainable multi-application monitoring system architecture for pervasive environments that collects application requirements for sensor data streams and optimizes them into sensor configurations. To demonstrate the effectiveness of our approach, a set of experiments are designed in the context of smart buildings. We comparatively evaluate our approach to show how dynamic sensor configuration for multiple monitoring applications indeed outperforms the mainstream duty-cycling method. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Pervasive environment sensor data management Continuous query processing Wireless sensor network

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SeaCloudDM: a database cluster framework for managing and querying massive heterogeneous sensor sampling data

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JOURNAL OF SUPERCOMPUTING 2013年第3期66卷 1260-1284页

作者： Ding, Zhiming Xu, Jiajie Yang, Qi Chinese Acad Sci Inst Software Beijing 100190 Peoples R China Natl Ctr ITS Engn & Technol Beijing 100088 Peoples R China

Recent advances in sensor networks and communication technologies have made the Internet of Things (IoT) a hot research issue. An IoT system can sample and manage the historical and present states of various kinds of physical and virtual objects such as vehicles, lakes, mountains, dams, city traffic conditions, atmosphere qualities, and so forth. It is well acknowledged that IoT will greatly change the way how people live and work. However, IoT also brings about great challenges to the data management community. For instance, the data to be managed in IoT are highly dynamic and heterogeneous. Meanwhile, since the sensor sampling data are managed in a centralized manner, the data size can be huge. Moreover, sensor data are intrinsically spatial-temporal data which may involve complicated spatial-temporal computations in query processing. To meet these challenges, we propose a novel Sea-Cloud-based data management (SeaCloudDM) mechanism in this paper. The experimental results show that the SeaCloudDM mechanism provides satisfactory performances in managing and querying massive sensor sampling data, and is thus a viable solution for IoT data management.

关键词： Internet of things sensor data management Massive data processing Sea-Cloud computing

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Language-Based Access to Large sensor Repositories

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sensorS 2009年第4期9卷 2926-2943页

作者： Baumann, Peter Jacobs Univ Bremen D-28759 Bremen Germany

sensor data have broadened their scope recently, ranging now from the simple time series measurements to, e. g., hyperspectral satellite image maps timeseries. In addition to observed data, simulation data increasingly have to be merged, for example 4-D ocean and atmospheric data. The majority of these data fall into the category of multidimensional rasters. However, when it comes to flexible retrieval, including sensor data search, aggregation, analysis, fusion, etc., standard query language support in the past has not kept up with the service level of, e. g., metadata retrieval. To close this gap, the Open GeoSpatial Consortium (OGC) has issued the Web Coverage Processing Service (WCPS) Standard in December 2008. WCPS defines a request language for multi-dimensional raster data, suitable for specifying navigation, download, and analysis of sensor, image, and statistics data. This contribution emphasises sensor data modeling and the perspectives for an integrated, cross-dimensional sensor data retrieval. Further, the WCPS reference implementation is briefly discussed.

关键词： sensor data management raster services standards OGC WCPS WCS

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