Journal articles: 'Sensor data processing'

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., A. Hassini, N. Benabadji ., and A. H. Belbachir . "AVHRR Data Sensor Processing." Journal of Applied Sciences 6, no. 11 (May 15, 2006): 2501–5. http://dx.doi.org/10.3923/jas.2006.2501.2505.

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Guo, Yixuan, and Gaoyang Liang. "Perceptual Feedback Mechanism Sensor Technology in e-Commerce IoT Application Research." Journal of Sensors 2021 (September 28, 2021): 1–12. http://dx.doi.org/10.1155/2021/3840103.

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With the development of sensor technology and the Internet of Things (IoT) technology, the trend of miniaturization of sensors has prompted the inclusion of more sensors in IoT, and the perceptual feedback mechanism among these sensors has become particularly important, thus promoting the development of multiple sensor data fusion technologies. This paper deeply analyzes and summarizes the characteristics of sensory data and the new problems faced by the processing of sensory data under the new trend of IoT, deeply studies the acquisition, storage, and query of sensory data from the sensors of IoT in e-commerce, and proposes a ubiquitous storage method for massive sensory data by combining the sensory feedback mechanism of sensors, which makes full use of the storage resources of IoT storage network elements and maximally meets the massive. In this paper, we propose a ubiquitous storage method for massive sensing data, which makes full use of the storage resources of IoT storage network elements to maximize the storage requirements of massive sensing data and achieve load-balanced data storage. In this paper, starting from the overall development of IoT in recent years, the weak link of intelligent information processing is reinforced based on the sensory feedback mechanism of sensor technology.

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Yang, Yanning, Andrew May, and Shuang Hua Yang. "Sensor data processing for emergency response." International Journal of Emergency Management 7, no. 3/4 (2010): 233. http://dx.doi.org/10.1504/ijem.2010.037008.

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Ko, Dong-beom, Tae-young Kim, Jeong-Joon Kim, and Jeong-min Park. "Sensor Data Collecting and Processing System." Asia-pacific Journal of Multimedia services convergent with Art, Humanities, and Sociology 7, no. 9 (September 30, 2017): 259–69. http://dx.doi.org/10.14257/ajmahs.2017.09.28.

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Odeberg, Hans. "A tactile sensor data-processing system." Sensors and Actuators A: Physical 49, no. 3 (July 1995): 173–80. http://dx.doi.org/10.1016/0924-4247(95)01030-0.

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Liu, Yong, Baohua Liang, and Jiabao Jiang. "Information Processing and Data Management Technology in Wireless Sensor Networks." International Journal of Online Engineering (iJOE) 14, no. 09 (September 30, 2018): 66. http://dx.doi.org/10.3991/ijoe.v14i09.8270.

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The wireless sensor network is essentially a data-centric network that processes the continuous stream of data, which is collected by different sensors. Therefore, the existing data management technologies regard the wireless sensor network, which is named WSN as a distributed database, and it is composed of continuous data streams from the physical world. Wireless sensor networks are emerging next-generation sensor networks, but their transmission of information is highly dependent. The wireless sensor network processes the continuous stream of data collected by the sensor. Based on the features of wireless sensor networks, this paper presents a topology-dependent model of cluster evolution with fault tolerance. Through the limited data management, resources have reasonably configured, while also saving energy. The model is based on the energy-aware routing protocol in its network layer protocols. The key point is the energy routing principle. According to its own local view, the cluster head node builds the inter-cluster topology to achieve fault-tolerant and energy-saving goals. Simulation results show that the model has good fault tolerance and energy efficiency.

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Kammerer, Klaus, Rüdiger Pryss, Burkhard Hoppenstedt, Kevin Sommer, and Manfred Reichert. "Process-Driven and Flow-Based Processing of Industrial Sensor Data." Sensors 20, no. 18 (September 14, 2020): 5245. http://dx.doi.org/10.3390/s20185245.

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For machine manufacturing companies, besides the production of high quality and reliable machines, requirements have emerged to maintain machine-related aspects through digital services. The development of such services in the field of the Industrial Internet of Things (IIoT) is dealing with solutions such as effective condition monitoring and predictive maintenance. However, appropriate data sources are needed on which digital services can be technically based. As many powerful and cheap sensors have been introduced over the last years, their integration into complex machines is promising for developing digital services for various scenarios. It is apparent that for components handling recorded data of these sensors they must usually deal with large amounts of data. In particular, the labeling of raw sensor data must be furthered by a technical solution. To deal with these data handling challenges in a generic way, a sensor processing pipeline (SPP) was developed, which provides effective methods to capture, process, store, and visualize raw sensor data based on a processing chain. Based on the example of a machine manufacturing company, the SPP approach is presented in this work. For the company involved, the approach has revealed promising results.

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Tejero, S., U. Siart, and J. Detlefsen. "Coherent and non-coherent processing of multiband radar sensor data." Advances in Radio Science 4 (September 4, 2006): 73–78. http://dx.doi.org/10.5194/ars-4-73-2006.

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Abstract. Increasing resolution is an attractive goal for all types of radar sensor applications. Obtaining high radar resolution is strongly related to the signal bandwidth which can be used. The currently available frequency bands however, restrict the available bandwidth and consequently the achievable range resolution. As nowadays more sensors become available e.g. on automotive platforms, methods of combining sensor information stemming from sensors operating in different and not necessarily overlapping frequency bands are of concern. It will be shown that it is possible to derive benefit from perceiving the same radar scenery with two or more sensors in distinct frequency bands. Beyond ordinary sensor fusion methods, radar information can be combined more effectively if one compensates for the lack of mutual coherence, thus taking advantage of phase information. At high frequencies, complex scatterers can be approximately modeled as a group of single scattering centers with constant delay and slowly varying amplitude, i.e. a set of complex exponentials buried in noise. The eigenanalysis algorithms are well known for their capability to better resolve complex exponentials as compared to the classical spectral analysis methods. These methods exploit the statistical properties of those signals to estimate their frequencies. Here, two main approaches to extend the statistical analysis for the case of data collected at two different subbands are presented. One method relies on the use of the band gap information (and therefore, coherent data collection is needed) and achieves an increased resolution capability compared with the single-band case. On the other hand, the second approach does not use the band gap information and represents a robust way to process radar data collected with incoherent sensors. Combining the information obtained with these two approaches a robust estimator of the target locations with increased resolution can be built.

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Krishnamurthi, Rajalakshmi, Adarsh Kumar, Dhanalekshmi Gopinathan, Anand Nayyar, and Basit Qureshi. "An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques." Sensors 20, no. 21 (October 26, 2020): 6076. http://dx.doi.org/10.3390/s20216076.

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In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost. As such, this paper addresses how to process IoT sensor data, fusion with other data sources, and analyses to produce knowledgeable insight into hidden data patterns for rapid decision-making. This paper addresses the data processing techniques such as data denoising, data outlier detection, missing data imputation and data aggregation. Further, it elaborates on the necessity of data fusion and various data fusion methods such as direct fusion, associated feature extraction, and identity declaration data fusion. This paper also aims to address data analysis integration with emerging technologies, such as cloud computing, fog computing and edge computing, towards various challenges in IoT sensor network and sensor data analysis. In summary, this paper is the first of its kind to present a complete overview of IoT sensor data processing, fusion and analysis techniques.

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Manohar, Nathan, Abhishek Jain, and Amit Sahai. "Self-Processing Private Sensor Data via Garbled Encryption." Proceedings on Privacy Enhancing Technologies 2020, no. 4 (October 1, 2020): 434–60. http://dx.doi.org/10.2478/popets-2020-0081.

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AbstractWe introduce garbled encryption, a relaxation of secret-key multi-input functional encryption (MiFE) where a function key can be used to jointly compute upon only a particular subset of all possible tuples of ciphertexts. We construct garbled encryption for general functionalities based on one-way functions.We show that garbled encryption can be used to build a self-processing private sensor data system where after a one-time trusted setup phase, sensors deployed in the field can periodically broadcast encrypted readings of private data that can be computed upon by anyone holding function keys to learn processed output, without any interaction. Such a system can be used to periodically check, e.g., whether a cluster of servers are in an “alarm” state.We implement our garbled encryption scheme and find that it performs quite well, with function evaluations in the microseconds. The performance of our scheme was tested on a standard commodity laptop.

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Imai, Shintaro, Mariko Miyamoto, Mingrui Cai, Yoshikazu Arai, and Toshimitsu Inomata. "A Data Processing Method for Human Motion Estimation to Reduce Network and Sensor Node Loads." International Journal of Cognitive Informatics and Natural Intelligence 7, no. 1 (January 2013): 58–74. http://dx.doi.org/10.4018/jcini.2013010103.

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Systems for estimating human motion using acceleration sensors present the following two problems: 1) advanced analysis and processing of sensor data are difficult because of resource limitations of sensor nodes; and 2) such analyses and processes burden the network because numerous sensor data are sent to the network. The authors’ proposed method described herein for sensor data analysis and processing uses a host computer located near sensor nodes (neighborhood host). This method is intended to achieve a good balance between reduction of the network load and advanced sensor data analysis and processing. Moreover, this method incorporates reduction of the load to sensor nodes. To evaluate their method, the authors implement two prototype systems that use different machine learning methods. The authors conduct some experiments using these prototype systems. The experimentally obtained results demonstrate that the proposed method can resolve two problems.

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Bok, Kyoungsoo, Daeyun Kim, and Jaesoo Yoo. "Complex Event Processing for Sensor Stream Data." Sensors 18, no. 9 (September 13, 2018): 3084. http://dx.doi.org/10.3390/s18093084.

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As a large amount of stream data are generated through sensors over the Internet of Things environment, studies on complex event processing have been conducted to detect information required by users or specific applications in real time. A complex event is made by combining primitive events through a number of operators. However, the existing complex event-processing methods take a long time because they do not consider similarity and redundancy of operators. In this paper, we propose a new complex event-processing method considering similar and redundant operations for stream data from sensors in real time. In the proposed method, a similar operation in common events is converted into a virtual operator, and redundant operations on the same events are converted into a single operator. The event query tree for complex event detection is reconstructed using the converted operators. Through this method, the cost of comparison and inspection of similar and redundant operations is reduced, thereby decreasing the overall processing cost. To prove the superior performance of the proposed method, its performance is evaluated in comparison with existing methods.

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Santalov, G. D., and B. V. Artemiev. "Wireless Sensor Networks with Centralized Data Processing." INFORMACIONNYE TEHNOLOGII 24, no. 5 (May 8, 2018): 299–305. http://dx.doi.org/10.17587/it.24.299-305.

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Zhou, Guoqing, and Scott Reichle. "UAV-based multi-sensor data fusion processing." International Journal of Image and Data Fusion 1, no. 3 (September 2010): 283–91. http://dx.doi.org/10.1080/19479832.2010.497343.

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Yang, Menglu. "Unmanned Driving Infringement Judgment Based on Wireless Sensor Network Data Mining." Journal of Sensors 2021 (November 2, 2021): 1–11. http://dx.doi.org/10.1155/2021/1599330.

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Based on the wireless sensor network unmanned driving infringement identification system, this paper focuses on the application of data mining technology and state machine technology and designs and implements a set of practical and effective. Self-driving cars can reduce the frequency of traffic accidents, alleviate urban traffic congestion, improve people’s travel efficiency, and lower the threshold of driving and other social values. The data processing program and a number of algorithms are given, and a complete set of data processing procedures and algorithms are proposed, including the collection of raw sensor data, the preprocessing of the collected data, and the feature extraction of the processed data. In the experiment, the unmanned driving infringement monitoring network was first designed to conduct real-time monitoring of unmanned driving infringements during transportation and application. Aiming at the characteristics of unmanned driving infringements, a monitoring network platform was designed for remote control and large-scale monitoring. Secondly, according to the characteristics of the unmanned driving infringement monitoring sensor network, the unmanned driving infringement node monitoring terminal is designed. The monitoring terminal part mainly designs the sensor module, the wireless communication module, the display warning module power module, and the data mining processing module. The sensor modules, respectively, include temperature, humidity, and concentration sensors, and the communication mode in the communication module mainly adopts Wi-Fi. At the same time, the research is based on wireless sensor network, combined with data mining technology, puts forward a sensory data display system model based on data mining technology, and conducts an in-depth analysis of the sensory data display system model, including the logical level of the system, system architecture, and functional modules. Finally, it focuses on the specific application of data mining technology in environmental information analysis and prediction, uses JAVA programming and realizes a data analysis and display system based on wireless sensor network, and verifies the accuracy of the data mining algorithm. The experimental results analyze the application of data mining technology in the driverless infringement determination system and use a large number of unmanned driving infringements to analyze the determination rules, so as to realize the interaction between active people and driverless cars.

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Men, Shou Qiang, and Christian Resagk. "Data Acquisition and Processing of Weak Low-Frequency Magnetic Signals." Applied Mechanics and Materials 65 (June 2011): 299–302. http://dx.doi.org/10.4028/www.scientific.net/amm.65.299.

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A simple calibration system for magnetic field sensors was designed, and experiments were carried out to calibrate two-dimensional fluxgate sensors and a sensor ring composed of eight fluxgate sensors. Fast Fourier Transforms and trapezoidal numerical integrals were applied to deal with the raw signals. It is found that it is not suitable to apply fast Fourier Transforms only to deal with signals with several peaks close to each other, but trapezoidal numerical integrals should also be used in combination with the FFT method.

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Volpe, Gianluca, Simone Colella, Vittorio E. Brando, Vega Forneris, Flavio La Padula, Annalisa Di Cicco, Michela Sammartino, Marco Bracaglia, Florinda Artuso, and Rosalia Santoleri. "Mediterranean ocean colour Level 3 operational multi-sensor processing." Ocean Science 15, no. 1 (February 12, 2019): 127–46. http://dx.doi.org/10.5194/os-15-127-2019.

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Abstract. The Mediterranean near-real-time multi-sensor processing chain has been set up and is operational in the framework of the Copernicus Marine Environment Monitoring Service (CMEMS). This work describes the main steps operationally performed to enable single ocean colour sensors to enter the multi-sensor processing applied to the Mediterranean Sea by the Ocean Colour Thematic Assembly Centre within CMEMS. Here, the multi-sensor chain takes care of reducing the inter-sensor bias before data from different sensors are merged together. A basin-scale in situ bio-optical dataset is used both to fine tune the algorithms for the retrieval of phytoplankton chlorophyll and the attenuation coefficient of light, Kd, and to assess the uncertainty associated with them. The satellite multi-sensor remote sensing reflectance spectra agree better with the in situ observations than those of the single sensors. Here, we demonstrate that the operational multi-sensor processing chain compares sufficiently well with the historical in situ datasets to also confidently be used for reprocessing the full data time series.

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Namatame, Naoya, Jin Nakazawa, and Hideyuki Tokuda. "Logical Sensor Network: An Abstraction of Sensor Data Processing over Multidomain Sensor Network." ISRN Sensor Networks 2012 (December 31, 2012): 1–9. http://dx.doi.org/10.5402/2012/234251.

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This paper focuses on a sensor network virtualization over multidomain sensor network and proposes an abstraction called “logical sensor network (LSN)” for sensor data processing. In the proposed abstraction, processing is a directed acyclic graph that consists of nodes and streams, which represents a small data processor and communication rules between them, respectively. We have added a notion of a trigger to this graph. A trigger represents a timing of the process execution. We have implemented the middleware named LSN-Middle to run a virtualized sensor network and proved its feasibility.

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Borgmann, B., V. Schatz, H. Kieritz, C. Scherer-Klöckling, M. Hebel, and M. Arens. "DATA PROCESSING AND RECORDING USING A VERSATILE MULTI-SENSOR VEHICLE." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-1 (September 26, 2018): 21–28. http://dx.doi.org/10.5194/isprs-annals-iv-1-21-2018.

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Abstract. In this paper we present a versatile multi-sensor vehicle which is used in several research projects. The vehicle is equipped with various sensors in order to cover the needs of different research projects in the area of object detection and tracking, mobile mapping and change detection. We show an example for the capabilities of this vehicle by presenting camera- and LiDAR-based pedestrian detection methods. Besides this specific use case, we provide a more general in-depth description of the vehicle’s hard- and software design and its data-processing capabilities. The vehicle can be used as a sensor carrier for mobile mapping, but it also offers hardware and software components to allow for an adaptable onboard processing. This enables the development and testing of methods related to real-time applications or high-level driver assistance functions. The vehicle’s hardware and software layout result from several years of experience, and our lessons learned can help other researchers set up their own experimental platform.

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Zhou, Qian, Hua Dai, Jianguo Zhou, Rongqi Qi, Geng Yang, and Xun Yi. "EPCT: An Efficient Privacy-Preserving and Collusion-Resisting Top- k Query Processing in WSNs." Security and Communication Networks 2021 (November 15, 2021): 1–10. http://dx.doi.org/10.1155/2021/6234409.

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Data privacy threat arises during providing top- k query processing in the wireless sensor networks. This article presents an efficient privacy-preserving and collusion-resisting top- k (EPCT) query processing protocol. A minimized candidate encrypted dataset determination model is first designed, which is the foundation of EPCT. The model guides the idea of query processing and guarantees the correctness of the protocol. The symmetric encryption with different private key in each sensor is deployed to protect the privacy of sensory data even a few sensors in the networks have been colluding with adversaries. Based on the above model and security setting, two phases of interactions between the interested sensors and the sink are designed to implement the secure query processing protocol. The security analysis shows that the proposed protocol is capable of providing secure top- k queries in the manner of privacy protection and anticollusion, whereas the experimental result indicates that the protocol outperforms the existing works on communication overhead.

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Jovanovic, Zeljko. "Data stream management system for moving sensor object data." Serbian Journal of Electrical Engineering 12, no. 1 (2015): 117–27. http://dx.doi.org/10.2298/sjee1501117j.

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Sensor and communication development has led to the development of new types of applications. Classic database data storage becomes inadequate when data streams arrive from multiple sensors. Then, data querying and result presentation are not efficient. The desired results are obtained with a delay, and the database is filled with a large amount of unnecessary data. To adequately support the above applications, Data Stream Management System (DSMS) applications are needed. DSMSs provide real-time data stream processing. In this paper, a client-server system is presented with DSMS realized on the Java WebDSMS application server side. WebDSMS functionalities are tested with simulated data and in real-life usage.

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Liu, Ming Tang, Li Bin Fu, Yan Hui Xin, and Li Li. "Application of Data Fusion Based on Least Squares in Sediment Concentration Data Processing." Advanced Materials Research 181-182 (January 2011): 1064–68. http://dx.doi.org/10.4028/www.scientific.net/amr.181-182.1064.

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In the process of measuring the sediment concentration in flow-water, the temperature in the water will greatly influence the output of the capacitive differential pressure sensor. This paper uses least squares method to fuse the results of experiment to eliminate influence of the temperature and depth． The system uses value of the capacitive differential pressure sensor, the temperature sensor and the depth sensor as input . This paper particularly introduces principle of the least squares method, PLC hardware design and multi-channels data fusion technology. The results indicate that data fusion method based least squares can obtain accurate and steady outputs.

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Ma, Ya Jie, Zhi Jian Mei, and Xiang Chuan Tian. "Building Sensor Grid Architecture for Large-Scale Air Pollution Data Management." Advanced Materials Research 831 (December 2013): 276–81. http://dx.doi.org/10.4028/www.scientific.net/amr.831.276.

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Large-scale sensor networks are systems that a large number of high-throughput autonomous sensor nodes are distributed over wide areas. Much attention has paid to provide efficient data management in such systems. Sensor grid provides low cost and high performance computing to physical world data perceived through sensors. This article analyses the real-time sensor grid challenges on large-scale air pollution data management. A sensor grid architecture for pollution data management is proposed. The processing of the service-oriented grid management is described in psuedocode. A simulation experiment investigates the performance of the data management for such a system.

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Hu, Liang, Rui Sun, Feng Wang, Xiuhong Fei, and Kuo Zhao. "A Stream Processing System for Multisource Heterogeneous Sensor Data." Journal of Sensors 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/4287834.

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With the rapid development of the Internet of Things (IoT), a variety of sensor data are generated around everyone’s life. New research perspective regarding the streaming sensor data processing of the IoT has been raised as a hot research topic that is precisely the theme of this paper. Our study serves to provide guidance regarding the practical aspects of the IoT. Such guidance is rarely mentioned in the current research in which the focus has been more on theory and less on issues describing how to set up a practical system. In our study, we employ numerous open source projects to establish a distributed real time system to process streaming data of the IoT. Two urgent issues have been solved in our study that are (1) multisource heterogeneous sensor data integration and (2) processing streaming sensor data in real time manner with low latency. Furthermore, we set up a real time system to process streaming heterogeneous sensor data from multiple sources with low latency. Our tests are performed using field test data derived from environmental monitoring sensor data collected from indoor environment for system validation. The results show that our proposed system is valid and efficient for multisource heterogeneous sensor data integration and streaming data processing in real time manner.

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Sejdiu, Besmir, Florije Ismaili, and Lule Ahmedi. "IoTSAS: An Integrated System for Real-Time Semantic Annotation and Interpretation of IoT Sensor Stream Data." Computers 10, no. 10 (October 11, 2021): 127. http://dx.doi.org/10.3390/computers10100127.

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Sensors and other Internet of Things (IoT) technologies are increasingly finding application in various fields, such as air quality monitoring, weather alerts monitoring, water quality monitoring, healthcare monitoring, etc. IoT sensors continuously generate large volumes of observed stream data; therefore, processing requires a special approach. Extracting the contextual information essential for situational knowledge from sensor stream data is very difficult, especially when processing and interpretation of these data are required in real time. This paper focuses on processing and interpreting sensor stream data in real time by integrating different semantic annotations. In this context, a system named IoT Semantic Annotations System (IoTSAS) is developed. Furthermore, the performance of the IoTSAS System is presented by testing air quality and weather alerts monitoring IoT domains by extending the Open Geospatial Consortium (OGC) standards and the Sensor Observations Service (SOS) standards, respectively. The developed system provides information in real time to citizens about the health implications from air pollution and weather conditions, e.g., blizzard, flurry, etc.

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Kenchannavar, Harish H., Sushma S. Kudtarkar, and U. P. Kulkarni. "Energy Efficient Data Processing in Visual Sensor Network." International Journal of Computer Science and Information Technology 2, no. 5 (October 29, 2010): 151–60. http://dx.doi.org/10.5121/ijcsit.2010.2511.

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Shevchuk, Elena, and Yury Shevchuk. "Modern trends in sensor data storage and processing." Program Systems: Theory and Applications 6, no. 4 (2015): 157–76. http://dx.doi.org/10.25209/2079-3316-2015-6-4-157-176.

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Ferrari, José A., César D. Perciante, Alejandro Lagos, and Erna M. Frins. "Improved method for Faraday current sensor data processing." Optics Communications 199, no. 1-4 (November 2001): 77–81. http://dx.doi.org/10.1016/s0030-4018(01)01590-5.

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Polese, D., E. Martinelli, G. Magna, F. Dini, A. Catini, R. Paolesse, I. Lundstrom, and C. Di Natale. "Sharing data processing among replicated optical sensor arrays." Sensors and Actuators B: Chemical 179 (March 2013): 252–58. http://dx.doi.org/10.1016/j.snb.2012.10.032.

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Liu, Yun, Qing-An Zeng, Ying-Hong Wang, and Jan Holub. "Data Processing Techniques in Wireless Multimedia Sensor Networks." International Journal of Distributed Sensor Networks 11, no. 7 (January 2015): 260384. http://dx.doi.org/10.1155/2015/260384.

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Abe, Yuhei, Takaya Miyano, and Susumu Sugiyama. "Application of Collective Synchronization to Sensor Data Processing." IEEJ Transactions on Sensors and Micromachines 126, no. 5 (2006): 185–89. http://dx.doi.org/10.1541/ieejsmas.126.185.

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Baeten, Guido, Vincent Belougne, Tim Brice, Leendart Commbee, Ed Kragh, Andreas Laake, James Martin, Jacques Orban, Ali Özbek, and Peter Vermeer. "Acquisition and processing of single sensor seismic data." ASEG Extended Abstracts 2001, no. 1 (December 2001): 1–4. http://dx.doi.org/10.1071/aseg2001ab011.

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Jones, Amber Spackman, Tanner Lex Jones, and Jeffery S. Horsburgh. "Toward automating post processing of aquatic sensor data." Environmental Modelling & Software 151 (May 2022): 105364. http://dx.doi.org/10.1016/j.envsoft.2022.105364.

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Fleck, Sven, Benjamin May, Gwen Daniel, and Chris Davies. "Data driven degradation of automotive sensors and effect analysis." Electronic Imaging 2021, no. 17 (January 18, 2021): 180–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.17.avm-180.

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Autonomous driving plays a crucial role to prevent accidents and modern vehicles are equipped with multimodal sensor systems and AI-driven perception and sensor fusion. These features are however not stable during a vehicle’s lifetime due to various means of degradation. This introduces an inherent, yet unaddressed risk: once vehicles are in the field, their individual exposure to environmental effects lead to unpredictable behavior. The goal of this paper is to raise awareness of automotive sensor degradation. Various effects exist, which in combination may have a severe impact on the AI-based processing and ultimately on the customer domain. Failure mode and effects analysis (FMEA) type approaches are used to structure a complete coverage of relevant automotive degradation effects. Sensors include cameras, RADARs, LiDARs and other modalities, both outside and in-cabin. Sensor robustness alone is a well-known topic which is addressed by DV/PV. However, this is not sufficient and various degradations will be looked at which go significantly beyond currently tested environmental stress scenarios. In addition, the combination of sensor degradation and its impact on AI processing is identified as a validation gap. An outlook to future analysis and ways to detect relevant sensor degradations is also presented.

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Pires, Ivan, Nuno Garcia, Nuno Pombo, and Francisco Flórez-Revuelta. "From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices." Sensors 16, no. 2 (February 2, 2016): 184. http://dx.doi.org/10.3390/s16020184.

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This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device user’s daily activities. Sensor data fusion techniques are used to consolidate the data collected from several sensors, increasing the reliability of the algorithms for the identification of the different activities. However, mobile devices have several constraints, e.g., low memory, low battery life and low processing power, and some data fusion techniques are not suited to this scenario. The main purpose of this paper is to present an overview of the state of the art to identify examples of sensor data fusion techniques that can be applied to the sensors available in mobile devices aiming to identify activities of daily living (ADLs).

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Kodukula, Venkatesh, Saad Katrawala, Britton Jones, Carole-Jean Wu, and Robert LiKamWa. "Dynamic Temperature Management of Near-Sensor Processing for Energy-Efficient High-Fidelity Imaging." Sensors 21, no. 3 (January 30, 2021): 926. http://dx.doi.org/10.3390/s21030926.

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Vision processing on traditional architectures is inefficient due to energy-expensive off-chip data movement. Many researchers advocate pushing processing close to the sensor to substantially reduce data movement. However, continuous near-sensor processing raises sensor temperature, impairing imaging/vision fidelity. We characterize the thermal implications of using 3D stacked image sensors with near-sensor vision processing units. Our characterization reveals that near-sensor processing reduces system power but degrades image quality. For reasonable image fidelity, the sensor temperature needs to stay below a threshold, situationally determined by application needs. Fortunately, our characterization also identifies opportunities—unique to the needs of near-sensor processing—to regulate temperature based on dynamic visual task requirements and rapidly increase capture quality on demand. Based on our characterization, we propose and investigate two thermal management strategies—stop-capture-go and seasonal migration—for imaging-aware thermal management. For our evaluated tasks, our policies save up to 53% of system power with negligible performance impact and sustained image fidelity.

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Han, Xiao, Hongwei Yan, Baojian Liu, and Wen Liu. "Emotional Feeling Evaluation Model in Underwater Environment Based on Wearable Sensor." Mathematical Problems in Engineering 2022 (March 16, 2022): 1–12. http://dx.doi.org/10.1155/2022/2104465.

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Underwater sensor network technologies, as well as devices, are developing rapidly, and underwater IoT devices have been widely used in energy surveys, environmental indicator detection, military surveillance, and disaster event monitoring. The transmission of massive amounts of underwater data to the cloud for processing and analysis has become the dominant processing paradigm, and cloud computing has become a dominant computing paradigm. The preparation strategy of elastomer-coated hydrogel optical fibers for stable optical sensing proposed in this work opens up a new method and approach for developing low-cost and highly sensitive water flow sensors while analyzing the design of wearable smart devices to assess underwater environmental emotion perception evaluation schemes. In this paper, we propose a sensory data acquisition technique for event coverage detection of underwater environmental emotions, observing that an event may correspond to deviations from the normal sensory range of sensory data from multiple adjacent sensor nodes. Distributed edge computing is introduced to assume part of the cloud computing pressure, and an edge prediction-based data acquisition and sensing scheme for underwater sensor networks is proposed to realize the conversion of the acoustic communication transmission part of underwater data into data prediction transmission, thus reducing the energy consumption caused by acoustic communication. The model established in this paper effectively reduces sensor energy consumption while ensuring accurate data transmission and can respond to the underlying demand promptly, which is significantly better than the already existing schemes.

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Ríos, Lídice García, and José Alberto Incera Diéguez. "A Big Data Test-bed for Analyzing Data Generated by an Air Pollution Sensor Network." International Journal of Web Services Research 13, no. 4 (October 2016): 19–35. http://dx.doi.org/10.4018/ijwsr.2016100102.

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Sensor networks have perceived an extraordinary growth in the last few years. From niche industrial and military applications, they are currently deployed in a wide range of settings as sensors are becoming smaller, cheaper and easier to use. Sensor networks are a key player in the so-called Internet of Things, generating exponentially increasing amounts of data. Nonetheless, there are very few documented works that tackle the challenges related with the collection, manipulation and exploitation of the data generated by these networks. This paper presents a proposal for integrating Big Data tools (in rest and in motion) for gathering, storage and analysis of data generated by a sensor network that monitors air pollution levels in a city. The authors provide a proof of concept that combines Hadoop and Storm for data processing, storage and analysis, and Arduino-based kits for constructing their sensor prototypes.

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Syafrudin, Muhammad, Ganjar Alfian, Norma Fitriyani, and Jongtae Rhee. "Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing." Sensors 18, no. 9 (September 4, 2018): 2946. http://dx.doi.org/10.3390/s18092946.

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With the increase in the amount of data captured during the manufacturing process, monitoring systems are becoming important factors in decision making for management. Current technologies such as Internet of Things (IoT)-based sensors can be considered a solution to provide efficient monitoring of the manufacturing process. In this study, a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model is proposed. Firstly, an IoT-based sensor that collects temperature, humidity, accelerometer, and gyroscope data was developed. The characteristics of IoT-generated sensor data from the manufacturing process are: real-time, large amounts, and unstructured type. The proposed big data processing platform utilizes Apache Kafka as a message queue, Apache Storm as a real-time processing engine and MongoDB to store the sensor data from the manufacturing process. Secondly, for the proposed hybrid prediction model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection and Random Forest classification were used to remove outlier sensor data and provide fault detection during the manufacturing process, respectively. The proposed model was evaluated and tested at an automotive manufacturing assembly line in Korea. The results showed that IoT-based sensors and the proposed big data processing system are sufficiently efficient to monitor the manufacturing process. Furthermore, the proposed hybrid prediction model has better fault prediction accuracy than other models given the sensor data as input. The proposed system is expected to support management by improving decision-making and will help prevent unexpected losses caused by faults during the manufacturing process.

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Dybedal, Joacim, Atle Aalerud, and Geir Hovland. "Embedded Processing and Compression of 3D Sensor Data for Large Scale Industrial Environments." Sensors 19, no. 3 (February 2, 2019): 636. http://dx.doi.org/10.3390/s19030636.

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This paper presents a scalable embedded solution for processing and transferring 3D point cloud data. Sensors based on the time-of-flight principle generate data which are processed on a local embedded computer and compressed using an octree-based scheme. The compressed data is transferred to a central node where the individual point clouds from several nodes are decompressed and filtered based on a novel method for generating intensity values for sensors which do not natively produce such a value. The paper presents experimental results from a relatively large industrial robot cell with an approximate size of 10 m × 10 m × 4 m. The main advantage of processing point cloud data locally on the nodes is scalability. The proposed solution could, with a dedicated Gigabit Ethernet local network, be scaled up to approximately 440 sensor nodes, only limited by the processing power of the central node that is receiving the compressed data from the local nodes. A compression ratio of 40.5 was obtained when compressing a point cloud stream from a single Microsoft Kinect V2 sensor using an octree resolution of 4 cm.

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Manogaran, Gunasekaran, and Daphne Lopez. "Disease Surveillance System for Big Climate Data Processing and Dengue Transmission." International Journal of Ambient Computing and Intelligence 8, no. 2 (April 2017): 88–105. http://dx.doi.org/10.4018/ijaci.2017040106.

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Ambient intelligence is an emerging platform that provides advances in sensors and sensor networks, pervasive computing, and artificial intelligence to capture the real time climate data. This result continuously generates several exabytes of unstructured sensor data and so it is often called big climate data. Nowadays, researchers are trying to use big climate data to monitor and predict the climate change and possible diseases. Traditional data processing techniques and tools are not capable of handling such huge amount of climate data. Hence, there is a need to develop advanced big data architecture for processing the real time climate data. The purpose of this paper is to propose a big data based surveillance system that analyzes spatial climate big data and performs continuous monitoring of correlation between climate change and Dengue. Proposed disease surveillance system has been implemented with the help of Apache Hadoop MapReduce and its supporting tools.

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Sun, Yi Gang, Lei Wang, and Wei Xing Chen. "A Sensor of Aero-Engine Real-Time Fault Detection System Based on ARM9." Advanced Materials Research 591-593 (November 2012): 1470–74. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.1470.

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A system is designed to monitor fault of sensors for aircraft engine real-time. SCM C8051F120 is used to control sensor signal acquisition process, and after processing and storage, the data will be transferred to the data processing unit via Ethernet for analysis and detection. ARM9 embedded computer based on WinCE is used as a data processing core for the data processing unit, three layers BP neural network is used as a sensor fault detection algorithm and troubleshooting software with C++ is developed. It can handle large amounts of data and improve processing efficiency. It has a good interface as well. Compared with current systems, it has been greatly improved in real-time and accuracy. After verification, the system is accurate and strong real-time, and can monitor aircraft engine sensor faults correctly.

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Zhou, Liming, Yingzi Shan, and Lu Chen. "Secure and Efficient Cluster-Based Range Query Processing in Wireless Sensor Networks." Journal of Electrical and Computer Engineering 2018 (October 2, 2018): 1–8. http://dx.doi.org/10.1155/2018/9140937.

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In wireless sensor networks, preserving privacy is more important and has attracted more attentions. Protecting data and sensor privacy while collecting and computing query results is a challenge. In cluster-based sensor networks, when a user queries a sensitive data, the adversaries can monitor original node or gain the data in cluster node. To deal with this problem, we propose a secure and efficient scheme for cluster-based query processing in wireless sensor networks. To preserve location privacy of sensors, we use anonymity method to confuse adversaries. To protect the sensitive data, we use prefix membership verification method to prevent adversaries from gaining sensitive messages collected by sensor nodes. And we analyze the security and communication cost. The results show that our scheme can efficiently protect privacy in query processing.

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Zhang, Fu Xiang, and Wen Zhong Li. "Real-Time Signal Processing Method for Infrared Sensor Arrays." Applied Mechanics and Materials 105-107 (September 2011): 1835–38. http://dx.doi.org/10.4028/www.scientific.net/amm.105-107.1835.

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Infrared sensor arrays is a new type of apperceiving system of robots, which are made up of large-area and flexible sensors with data processing capabilities. In order to meet the requirement of real-time obstacle avoidance, sensors which can be covered on the surface of robots were designed to apperceive the environment and provide the environmental information by the infrared sensors and the controlling circuit on them. To get rid of the environmental disturbance to the infrared sensors, and to meet the requirement of system precision, real time and stability, a kind of reconfigurable architecture based on DSP+FPGA was designed and a kind of spectrum analyzing method based on FFT (Fast Fourier Transform) was used. Tests of the system with different color papers as the measured subjects showed that the output of sensor arrays had low sensitivity.

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El Mabrouk, Marouane, and Salma Gaou. "Proposed Intelligent Pre-Processing Model of Real-Time Flood Forecasting and Warning for Data Classification and Aggregation." International Journal of Online Engineering (iJOE) 13, no. 11 (November 22, 2017): 4. http://dx.doi.org/10.3991/ijoe.v13i11.7382.

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A wireless sensor network is a network that can design a self-organizing structure and provides effective support for several protocols such as routing, locating, discovering services, etc. It is composed of several nodes called sensors grouped together into a network to communicate with each other and with the base stations. Nowadays, the use of Wireless sensor networks increased considerably. It can collect physical data and transform it into a digital values in real-time to monitor in a continuous manner different disaster like flood. However, due to various factors that can affect the wireless sensor networks namely, environmental, manufacturing errors hardware and software problems etc... It is necessary to carefully select and filter the data from the wireless sensors since we are providing a decision support system for flood forecasting and warning. In this paper, we presents an intelligent Pre-Processing model of real-time flood forecasting and warning for data classification and aggregation. The proposed model consists on several stages to monitor the wireless sensors and its proper functioning, to provide the most appropriate data received from the wireless sensor networks in order to guarantee the best accuracy in terms of real-time data and to generate a historical data to be used in the further flood forecasting.

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Cheung, Edward, and Vladimir Lumelsky. "Real-time path planning procedure for a whole-sensitive robot arm manipulator." Robotica 10, no. 4 (July 1992): 339–49. http://dx.doi.org/10.1017/s0263574700008171.

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SUMMARYWe consider the problem of sensor-based motion planning for a three-dimensional robot arm manipulator operating among unknown obstacles. When every point of the robot body is subject to potential collision. The corresponding planning system must include these four basic components: sensor hardware; real-time signal/sensory data processing hardware/software; a local step planning subsystem that works at the basic sample rate of the arm; and finally, a subsystem for global planning. The arm sensor system developed at Yale University presents a proximity sensitive skin that covers the whole body of the arm and consists of an array of discrete active infrared sensors that detect obstacles by processing reflected light. The sensor data then undergoes low level processing via a step planning procedure, which converts sensor information into local normals at the contact points in the configuration space of the robot. This paper presents preliminary results on the fourth component, a real-time algorithm that realizes the upper, global level of planning. Based on the current collection of local normals, the algorithm generates preferable directions of motion around obstacles, so as to guarantee reaching the target position if it is reachable. Experimental results from testing the developed system are also discussed.

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Testoni, Nicola, Cristiano Aguzzi, Valentina Arditi, Federica Zonzini, Luca De Marchi, Alessandro Marzani, and Tullio Salmon Cinotti. "A Sensor Network with Embedded Data Processing and Data-to-Cloud Capabilities for Vibration-Based Real-Time SHM." Journal of Sensors 2018 (July 2, 2018): 1–12. http://dx.doi.org/10.1155/2018/2107679.

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This work describes a network of low power/low-cost microelectromechanical- (MEMS-) based three-axial acceleration sensors with local data processing and data-to-cloud capabilities. In particular, the developed sensor nodes are capable to acquire acceleration time series and extract their frequency spectrum peaks, which are autonomously sent through an ad hoc developed gateway device to an online database using a dedicated transfer protocol. The developed network minimizes the power consumption to monitor remotely and in real time the acceleration spectra peaks at each sensor node. An experimental setup in which a network of 5 sensor nodes is used to monitor a simply supported steel beam in free vibration conditions is considered to test the performance of the implemented circuitry. The total weight and energy consumption of the entire network are, respectively, less than 50 g and 300 mW in continuous monitoring conditions. Results show a very good agreement between the measured natural vibration frequencies of the beam and the theoretical values estimated according to the classical closed formula. As such, the proposed monitoring network can be considered ideal for the SHM of civil structures like long-span bridges.

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Cai, Saihua, Jinfu Chen, Baoquan Yin, Ruizhi Sun, Chi Zhang, Haibo Chen, Jingyi Chen, and Min Lin. "An Efficient Outlier Detection Approach for Streaming Sensor Data Based on Neighbor Difference and Clustering." Security and Communication Networks 2022 (February 27, 2022): 1–14. http://dx.doi.org/10.1155/2022/3062541.

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In wireless sensor networks (WSNs), the widely distributed sensors make the real-time processing of data face severe challenges, which prompts the use of edge computing. However, some problems that occur during the operation of sensors will cause unreliability of the collected data, which can result in inaccurate results of edge computing-based processing; thus, it is necessary to detect potential abnormal data (also known as outliers) in the sensor data to ensure their quality. Although the clustering-based outlier detection approaches can detect outliers from the static data, the feature of streaming sensor data requires the detection operation in a one-pass fashion; in addition, the clustering-based approaches also do not consider the time correlation among the streaming sensor data, which leads to its low detection accuracy. To solve these problems, we propose an efficient outlier detection approach based on neighbor difference and clustering, namely, ODNDC, which not only quickly and accurately detects outliers but also identifies the source of outliers in the streaming sensor data. Experiments on a synthetic dataset and a real dataset show that the proposed ODNDC approach achieves great performance in detecting outliers and identifying their sources, as well as the low time consumption.

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Nguyen Mau Quoc, Hoan, Martin Serrano, Han Mau Nguyen, John G. Breslin, and Danh Le-Phuoc. "EAGLE—A Scalable Query Processing Engine for Linked Sensor Data." Sensors 19, no. 20 (October 9, 2019): 4362. http://dx.doi.org/10.3390/s19204362.

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Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio–temporal correlations. Most semantic approaches do not have spatio–temporal support. Some of them have attempted to provide full spatio–temporal support, but have poor performance for complex spatio–temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this article, we introduce EAGLE, a spatio–temporal query engine for querying sensor data based on the linked data model. The ultimate goal of EAGLE is to provide an elastic and scalable system which allows fast searching and analysis with respect to the relationships of space, time and semantics in sensor data. We also extend SPARQL with a set of new query operators in order to support spatio–temporal computing in the linked sensor data context.

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Ehala, Johannes, Jaanus Kaugerand, Raido Pahtma, Sergei Astapov, Andri Riid, Timo Tomson, Jürgo-Sören Preden, and Leo Mõtus. "Situation awareness via Internet of things and in-network data processing." International Journal of Distributed Sensor Networks 13, no. 1 (January 2017): 155014771668657. http://dx.doi.org/10.1177/1550147716686578.

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Computing on the edge of the Internet of things comprises among other tasks in-sensor signal processing and performing distributed data fusion and aggregation at network nodes. This poses a challenge to distributed sensor networks of low computing power devices that have to do complex fusion, aggregation and signal processing in situ. One of the difficulties lies in ensuring validity of data collected from heterogeneous sources. Ensuring data validity, for example, the temporal and spatial correctness of data, is crucial for correct in-network data fusion and aggregation. The article considers wireless sensor technology in military domain with the aim of improving situation awareness for military operations. Requirements for contemporary intelligence, surveillance and reconnaissance applications are explored and an experimental wireless sensor network, designed to enhance situation awareness to both in-the-field units and remote intelligence operatives, is described. The sensor nodes have the capability to perform in-sensor signal processing and distributed in-network data aggregation and fusion complying with edge computing paradigm. In-network data processing is supported by service-oriented middleware which facilitates run-time sensor discovery and tasking and ad hoc (re)configuration of the network links. The article describes two experiments demonstrating the ability of the wireless sensor network to meet intelligence, surveillance and reconnaissance requirements. The efficiency of distributed data fusion is evaluated and the importance and effect of establishing data validity is shown.

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Journal articles on the topic 'Sensor data processing'

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