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Optimizing data Transmission from IoT Devices Through Weighted Online data Changing Detectors

ADVANCES IN data SCIENCE AND ADAPTIVE ANALYSIS 2020年第2期12卷

作者： Jose Divan, Mario Laura Sanchez-Reynoso, Maria Natl Univ La Pampa Econ Sch Coronel Gil 353 1st Floor RA-6300 Santa Rosa Argentina

The real-time data analysis requires an integrated approach to know the last known state of variables of a concept under monitoring. Thereby, the Internet-of-Thing (IoT) devices have provided alternatives to address distributed data collection strategies. However, the autonomy of IoT devices represents one of the main challenges to implement the collecting strategy. Battery autonomy is affected directly by the energy consumption derived from data transmissions. The data Stream processing Strategy (DSPS) is an architecture oriented to the implementation of measurement projects based on a measurement and evaluation framework. Its online processing is guided by the measurement metadata informed from IoT devices associated with a component named Measurement Adapter (MA). This paper presents a new data buffer organization based on measurement metadata articulated with online data filtering to optimize the data transmissions from MA. As contributions, a weighted data change detection approach is incorporated, while a new local buffer based on logical windows is proposed for MA. Also, an articulation among the data buffer, a temporal barrier, and data change detectors is introduced. The proposal was implemented and released on the pabmmCommons library. A discrete simulation on the library is here described to provide initial applicability patterns. The data buffer consumed 568 Kb for monitoring 100 simultaneous metrics. The online estimation of the mean and variance based on the Statistical Process Control consumed 238 ns. However, as a limitation, other scenarios need to be addressed before generalizing results. As future work, new alternatives to filter noise online will be addressed.

关键词： data transmission real-time data processing online data changing detector IoT data gathering

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real-time Fall Detection Using Microwave Doppler Sensor-Computational Cost Reduction Method Based on Genetic Algorithm

IEEE SENSORS LETTERS

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IEEE SENSORS LETTERS 2019年第3期3卷

作者： Kaburagi, Takashi Shiba, Kazuaki Kumagai, Satoshi Matsumoto, Toshiyuki Kurihara, Yosuke Aoyama Gakuin Univ Dept Sci & Engn Sagamihara Kanagawa 2525258 Japan

The risk of falling is a serious problem for the elderly. If the fall remains undetected for a considerable period, hypothermia and dehydration may occur, leading to death. Therefore, real-time detection of falls is critical. We previously proposed a fall-detection system based on a microwave Doppler sensor, which can capture the object's velocity. Our system performs template matching based on the dynamic time warping distance;hence, the processing time depends on the number of template datasets. Herein, we attempt a real-time fall detection by reducing the number of templates. We apply the genetic algorithm to select better templates. The fitness of the selected templates is evaluated by dividing the accuracy by the number of templates.

关键词： Sensor applications fall detection genetic algorithm (GA) microwave Doppler sensor real-time data processing

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A real-time Futures Information processing Strategy Based on Multi-level Cache and Multi-threading Cooperation

A Real-Time Futures Information Processing Strategy Based on...

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作者： Li-kai SU Center of Faculty Development & Educational Technology Guangdong University of Finances & Economics

Futures market data processing is a basic problem in the research of futures program trading. Because of the large amount of futures market data, there are many problems in the real-time data processing, such as high delay, low storage efficiency, poor stability and high input cost. In order to solve the above problems encountered in the development of futures program trading software, a non-blocking real-time market data processing strategy based on multi-level cache and multi-threaded collaboration is proposed and implemented, with the characteristic of low delay, high storage efficiency and low investment cost in market data processing, exhibits a high applicable value for the design of program trading software for small and medium-sized investment companies.

关键词： Futures program trading real-time data processing Multi-level cache Multi-threaded collaboration

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An approach to custom privacy policy violation detection problems using big social provenance data

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2018年第21期30卷

作者： Baeth, Mohamed Jehad Aktas, Mehmet S. Yildiz Teknik Univ Dept Comp Engn TR-34349 Istanbul Turkey

Social media software changes its system-wide privacy policies over time. Changes in such system-wide policies affect the privacy policies of the individual users. In turn, social media users lack the ability to configure and enforce customized privacy policies according to the precise privacy measure that they demand. To this end, we argue that there is an emerging need for third-party solutions that are independent of existing social media software and that can detect custom privacy policy violations. In order to address this need, we propose an approach to detecting custom privacy violations for social media users. We also introduce a generic software architecture that can be integrated with existing social media software to enable users to keep track of their data. The proposed solution utilizes social provenance data, which is defined as the metadata that describe the lifecycle of the data. To facilitate testing of the software architecture, we developed a prototype implementation and generated a large-scale synthetic provenance dataset. We discuss the details of the prototype implementation and the synthetic dataset. We evaluate the performance of the prototype under an increasing workload. We show the usability of the proposed architecture, as the initial performance testing results are promising.

关键词： big provenance data complex event processing privacy policy detection real-time data processing real-time monitoring

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Internet of Things Architecture for Handling Stream Air Pollution data 2

Internet of Things Architecture for Handling Stream Air Poll...

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2nd International Conference on Internet of Things, Big data and Security (IoTBDS)

作者： Kersting, Joschka Geierhos, Michaela Jung, Hanmin Kim, Taehong Univ Paderborn Heinz Nixdorf Inst Furstenallee 11 D-33102 Paderborn Germany Korea Inst Sci & Technol Informat Sci Data Res Ctr Daejeon South Korea

ISBN: (纸本)9789897582455

In this paper, we present an IoT architecture which handles stream sensor data of air pollution. Particle pollution is known as a serious threat to human health. Along with developments in the use of wireless sensors and the IoT, we propose an architecture that flexibly measures and processes stream data collected in real-time by movable and low-cost IoT sensors. Thus, it enables a wide-spread network of wireless sensors that can follow changes in human behavior. Apart from stating reasons for the need of such a development and its requirements, we provide a conceptual design as well as a technological design of such an architecture. The technological design consists of Kaa and Apache Storm which can collect air pollution information in real-time and solve various problems to process data such as missing data and synchronization. This enables us to add a simulation in which we provide issues that might come up when having our architecture in use. Together with these issues, we state reasons for choosing specific modules among candidates. Our architecture combines wireless sensors with the Kaa IoT framework, an Apache Kafka pipeline and an Apache Storm data Stream Management System among others. We even provide open-government data sets that are freely available.

关键词： Wireless Sensor Network Internet of Things Stream data Air Pollution DSMS real-time data processing

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Adaptive Optimized Pattern Extracting Algorithm for Forecasting Maximum Electrical Load Duration Using Random Sampling and Cumulative Slope Index

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ENERGIES 2018年第7期11卷 1723-1723页

作者： Kim, Jinseok Hong, Hyungseop Kim, Ki-Il KEPCO KDN Dept KDN Elect Power IT Res Inst Daejeon 34056 South Korea Chungnam Natl Univ Dept Comp Sci & Engn Daejeon 34134 South Korea

Load forecasting techniques can be an essential method to save energy and shave peak loads in order to improve energy efficiency and maintain the stability of a power grid. To achieve this goal, machine learning-based approaches have been proposed recently. Before moving toward the long-term and ultimate solution such as machine learning, we propose a simple and efficient method to forecast electricity usage patterns and the duration of maximum electrical load using a small data set. The proposed algorithm can forecast maximum electrical load duration using random sampling and a cumulative slope index. To verify the algorithm, we utilized electricity data (from 2015.11 to 2016.12) obtained from a building with a constant lifestyle and electricity pattern. The performance of the algorithm was evaluated using electricity bills, the discharging condition of an energy storage system, and the cumulative slope index. It was found that the proposed algorithm could provide electricity cost savings of 0.62-2.28% compared with other, conventional electricity prediction techniques, such as the moving average method and exponential smoothing. In near future research, it is expected that this algorithm could be applied to electrical big data to handle real-time data processing.

关键词： maximum electrical load duration pattern forecasting correlation analysis random sampling cumulative slope index big data real-time data processing

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British Columbia Smart Infrastructure Monitoring System

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CANADIAN JOURNAL OF CIVIL ENGINEERING 2017年第8期44卷 579-588页

作者： Kaya, Yavuz Ventura, Carlos Huffman, Sharlie Turek, Martin Univ British Columbia Fac Sci Appl Dept Civil Engn 6250 Appl Sci Lane Vancouver BC V6T 1Z4 Canada Huffman Engn Ltd 2180 Pentland Rd Victoria BC V8S 2W6 Canada

This paper describes the architecture of a comprehensive seismic monitoring system developed in 2009 by the British Columbia Ministry of Transportation and Infrastructure (MoT) and the University of British Columbia, Canada. The main purpose of the British Columbia Smart Infrastructure Monitoring System project is to instrument and monitor key structures to provide confirmation of their seismic capacity, assist in focusing retrofit efforts, detect structural damage, and provide rapid damage assessment of those structures, following a seismic event. The automatic creation of shakemaps following an earthquake provides information for the MoT's non-instrumented bridges. The public notification systems and web pages have been developed to display real-time seismic data both from the strong motion network and seismic structural health monitoring network. Tools and methods have been developed to help transform the current practice of inspecting and evaluating structures to a more rational and effective one that uses the up-to-date sensing technology with fast and efficient techniques.

关键词： seismic structural health monitoring strong motion network shakemap earthquake notification real-time data processing

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A distributed approach for real-time multi-camera multiple object tracking

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MACHINE VISION AND APPLICATIONS 2017年第3-4期28卷 421-430页

作者： Previtali, Fabio Bloisi, Domenico D. Iocchi, Luca Sapienza Univ Rome Dept Comp Control & Management Engn A Ruberti Via Ariosto 25 I-00185 Rome Italy

Estimating the positions of a set of moving objects captured from a network of cameras is still an open problem in Computer Vision. In this paper, a distributed and real-time approach for tracking multiple objects on multiple cameras is presented. A quantitative comparison with six state-of-the-art methods has been carried out on the publicly available PETS 2009 data set, demonstrating the effectiveness of the algorithm. Moreover, the proposed method has been tested also on a multi-camera soccer data set, showing its data fusion capabilities.

关键词： Distributed multiple object tracking real-time data processing Distributed data association

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Hybrid cloud computing monitoring software architecture

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2018年第21期30卷

作者： Aktas, Mehmet S. Yildiz Tech Univ Dept Comp Engn TR-34349 Istanbul Turkey

Cloud computing provides scalable computing resources on demand. Monitoring cloud computing resources is important so that resources can be dynamically allocated, migrated, or shut down to meet users' requirements. Challenges in cloud computing monitoring systems include detecting patterns that might lead to failure of the cloud system, detecting malfunctioning problems within the cloud platform after they occur, and issues related to the fact that existing monitoring solutions are tightly coupled to specific cloud platforms, while there is an emerging need for monitoring solutions that are independent of underlying subsystems. To address these challenges, we designed a hybrid cloud monitoring software architecture that can work as an add-on layer on top of existing cloud computing platforms. The proposed architecture is designed based on facade software design pattern and utilizes complex event processing concept in which data from various primitive metrics streams is processed to detect previously defined patterns. Prototype applications have been developed to demonstrate the architecture's usability. Performance tests were applied to prototype applications. Computation times required for the operation of the proposed architecture were negligible.

关键词： cloud computing cloud computing monitoring systems complex event processing hybrid software architecture real-time data processing

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Local Frechet Distance in Specific Emitter Identification 9

Local Frechet Distance in Specific Emitter Identification

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2017 9th IEEE International Conference on Communication Software and Networks (ICCSN 2017)

作者： Peibo Chen Ke Xu Gang Li Jianwei Wan Yong Chen College of Electronic Science and Engineering National University of Defense Technology College of Physics and Communication Electronics Jiangxi Normal University

ISBN: (纸本)9781509038237;9781509038220

Specific Emitter Identification(SEI) is one of most difficult tasks in modern electronic warfare. The principle function of the SEI is utilizing fingerprint features to identify different emitters of the same type by a precise measurement, which plays an important role in the recognition process. Under the premise that the time-domain waveforms such as the instantaneous amplitude and frequency are specified as the fingerprint features, the frechet distance can achieve preferable identification accuracy, compared to the Hausdorff and Dynamic time Warping(DTW) distance. Unfortunately, due to the disadvantage of its high time complexity, the frechet distance is unable to satisfy the requirement of real-time data processing in practical application, especially in the military field. Combining waveform structures of these fingerprint features, a local frechet distance is proposed in the paper. The new algorithm can provide a lower time complexity and hardly impact the identification accuracy.

关键词： specific emitter identification real-time data processing local frechet distance

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