Adaptive Aggregation of Redundant Sensor Data in the Internet of Things

2016 ◽  
Author(s):  
Stephan SchmeiBer ◽  
Gregor Schiele
Keyword(s):  
Internet Of Things ◽  
Sensor Data ◽  
The Internet ◽  
Adaptive Aggregation ◽  
The Internet Of Things

scholarly journals An Improved Unsupervised Single-Channel Speech Separation Algorithm for Processing Speech Sensor Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Dazhi Jiang ◽  
Zhihui He ◽  
Yingqing Lin ◽  
Yifei Chen ◽  
Linyan Xu
Keyword(s):  
Internet Of Things ◽  
Speech Signal ◽  
Single Channel ◽  
Low Noise ◽  
Sensor Data ◽  
The Internet ◽  
Speech Separation ◽  
Adaptive Wavelet ◽  
Enhancement Technique ◽  
The Internet Of Things

As network supporting devices and sensors in the Internet of Things are leaping forward, countless real-world data will be generated for human intelligent applications. Speech sensor networks, an important part of the Internet of Things, have numerous application needs. Indeed, the sensor data can further help intelligent applications to provide higher quality services, whereas this data may involve considerable noise data. Accordingly, speech signal processing method should be urgently implemented to acquire low-noise and effective speech data. Blind source separation and enhancement technique refer to one of the representative methods. However, in the unsupervised complex environment, in the only presence of a single-channel signal, many technical challenges are imposed on achieving single-channel and multiperson mixed speech separation. For this reason, this study develops an unsupervised speech separation method CNMF+JADE, i.e., a hybrid method combined with Convolutional Non-Negative Matrix Factorization and Joint Approximative Diagonalization of Eigenmatrix. Moreover, an adaptive wavelet transform-based speech enhancement technique is proposed, capable of adaptively and effectively enhancing the separated speech signal. The proposed method is aimed at yielding a general and efficient speech processing algorithm for the data acquired by speech sensors. As revealed from the experimental results, in the TIMIT speech sources, the proposed method can effectively extract the target speaker from the mixed speech with a tiny training sample. The algorithm is highly general and robust, capable of technically supporting the processing of speech signal acquired by most speech sensors.

scholarly journals A Novel Sensor Data Pre-Processing Methodology for the Internet of Things Using Anomaly Detection and Transfer-By-Subspace-Similarity Transformation

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4536 ◽  
Author(s):  
Yan Zhong ◽  
Simon Fong ◽  
Shimin Hu ◽  
Raymond Wong ◽  
Weiwei Lin
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Anomaly Detection ◽  
Sensor Data ◽  
Detection Methods ◽  
The Internet ◽  
Quality Of Data ◽  
Data Mining Algorithms ◽  
Sensing Applications ◽  
The Internet Of Things

The Internet of Things (IoT) and sensors are becoming increasingly popular, especially in monitoring large and ambient environments. Applications that embrace IoT and sensors often require mining the data feeds that are collected at frequent intervals for intelligence. Despite the fact that such sensor data are massive, most of the data contents are identical and repetitive; for example, human traffic in a park at night. Most of the traditional classification algorithms were originally formulated decades ago, and they were not designed to handle such sensor data effectively. Hence, the performance of the learned model is often poor because of the small granularity in classification and the sporadic patterns in the data. To improve the quality of data mining from the IoT data, a new pre-processing methodology based on subspace similarity detection is proposed. Our method can be well integrated with traditional data mining algorithms and anomaly detection methods. The pre-processing method is flexible for handling similar kinds of sensor data that are sporadic in nature that exist in many ambient sensing applications. The proposed methodology is evaluated by extensive experiment with a collection of classical data mining models. An improvement over the precision rate is shown by using the proposed method.

scholarly journals Sensor Data

2021 ◽  
pp. 41-48
Author(s):  
Savvas Rogotis ◽  
Fabiana Fournier ◽  
Karel Charvát ◽  
Michal Kepka
Keyword(s):  
Internet Of Things ◽  
Sensor Data ◽  
The Internet ◽  
The Internet Of Things

AbstractThe chapter describes the key role that sensor data play in the DataBio project. It introduces the concept of sensing devices and their contribution in the evolution of the Internet of Things (IoT). The chapter outlines how IoT technologies have affected bioeconomy sectors over the years. The last part outlines key examples of sensing devices and IoT data that are exploited in the context of the DataBio project.

A Semantic Sensor Web Architecture in the Internet of Things

2019 ◽  
pp. 75-97
Author(s):  
Eliot Bytyçi ◽  
Besmir Sejdiu ◽  
Arten Avdiu ◽  
Lule Ahmedi
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Sensor Data ◽  
Wireless Sensor ◽  
The Internet ◽  
Web Architecture ◽  
Hidden Knowledge ◽  
Semantic Sensor Web ◽  
The Internet Of Things ◽  
The Web

The Internet of Things (IoT) vision is connecting uniquely identifiable devices to the internet, best described through ontologies. Furthermore, new emerging technologies such as wireless sensor networks (WSN) are recognized as essential enabling component of the IoT today. Hence, the interest is to provide linked sensor data through the web either following the semantic web enablement (SWE) standard or the linked data approach. Likewise, a need exists to explore those data for potential hidden knowledge through data mining techniques utilized by a domain ontology. Following that rationale, a new lightweight IoT architecture has been developed. It supports linking sensors, other devices and people via a single web by mean of a device-person-activity (DPA) ontology. The architecture is validated by mean of three rich-in-semantic services: contextual data mining over WSN, semantic WSN web enablement, and linked WSN data. The architecture could be easily extensible to capture semantics of input sensor data from other domains as well.

scholarly journals Optimization of Water Microbial Concentration Monitoring System Based on Internet of Things

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Miaomiao Zheng ◽  
Shanshan Zhang ◽  
Yidan Zhang ◽  
Baozhong Hu
Keyword(s):  
Wireless Sensor Network ◽  
Internet Of Things ◽  
Environmental Monitoring ◽  
Data Acquisition ◽  
Development Trend ◽  
Sensor Data ◽  
Wireless Sensor ◽  
The Internet ◽  
Microbial Sensor ◽  
The Internet Of Things

The Internet of Things is an emerging information industry. Applying the information collection, transmission, and processing technologies in the Internet of Things technology to environmental monitoring, environmental emergency, and other environmental protection supervision fields will greatly improve the speed and accuracy of environmental supervision and facilitate the scientific development of environmental protection. Through the Internet of Things, people can obtain a large amount of reliable real-time information, and it is not easy to be affected by time, place, and environment, while the wireless sensor network has the advantages of easy installation and low cost, so environmental monitoring through the Internet of Things is the future development trend. In this paper, in view of the current situation of water scarcity and serious water pollution in China, combined with the development trend and advantages of the Internet of Things (IoT), and based on the inadequacy of the existing microbial sensor data collection equipment, we propose a design scheme of microbial concentration monitoring system for waters based on IoT. The system is based on Zig Bee wireless sensor network to build a common data acquisition platform and design special hardware to carry out high-precision microbial sensor data acquisition in water and through the PC to complete the real-time measurement data storage, waveform display, and data processing. In this paper, the schematic diagram and PCB board design of the system hardware module NUC120 main control board, CC2530 RF board, Wi-Fi wireless communication module, and high-precision ADC acquisition module are completed and fabricated. Then, the four modules are combined to realize the development of the data aggregation node and data acquisition node of the dedicated Zig Bee wireless network hardware device.

scholarly journals The Infusion of Notification Design With an Application of Social Media Based on a Internet of Things (IOT)

SinkrOn ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. 129
Author(s):  
Rudi Arif Candra ◽  
Devi Satria Saputra ◽  
Dirja Nur Ilham ◽  
Herry Setiawan ◽  
Hardisal Hardisal
Keyword(s):  
Internet Of Things ◽  
Network Connectivity ◽  
Load Cell ◽  
Sensor Data ◽  
The Internet ◽  
Hospital Nurses ◽  
Test Results ◽  
Iot Platform ◽  
The Internet Of Things ◽  
Internet Network

This study discusses the infusion detection device in a hospital room. This tool is designed to help hospital nurses to cope more quickly to avoid problems due to the infusion. Load cell sensors are used as heavy detectors that send notifications to the nurses through the telegram application that has been installed. The nurse will get a notification message sent to the telegram if the sensor has read the weight. The tool is made using a load cell sensor and NodeMCU Wi-FiESP866 which functions to send notification of the results of sensor data input to the Internet of Things (IOT) platform namely Telegram. Nurses need to be connected to the internet network to get notifications on the telegram. Test results show that the time needed to send and receive notifications on Telegram takes about 2-5 seconds. The message will be sent 3 times, first the infusion WARNING is almost exhausted (alert), second the infusion WARNING is almost exhausted (standby) and the infusion WARNING is almost exhausted (please replace). If the infusion is not replaced by the nurse, it will be warned by Buzzer. However, time can be influenced by the available internet network connectivity. However, time can be affected by the available internet network.

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