Smart Sensor Technologies for IoT

The recent development in wireless networks and devices has led to novel services that will utilize wireless communication on a new level. Much effort and resources have been dedicated to establishing new communication networks that will support machine-to-machine communication and the Internet of T...

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Year of Publication:2021
Language:English
Physical Description:1 electronic resource (270 p.)
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520 |a The recent development in wireless networks and devices has led to novel services that will utilize wireless communication on a new level. Much effort and resources have been dedicated to establishing new communication networks that will support machine-to-machine communication and the Internet of Things (IoT). In these systems, various smart and sensory devices are deployed and connected, enabling large amounts of data to be streamed. Smart services represent new trends in mobile services, i.e., a completely new spectrum of context-aware, personalized, and intelligent services and applications. A variety of existing services utilize information about the position of the user or mobile device. The position of mobile devices is often achieved using the Global Navigation Satellite System (GNSS) chips that are integrated into all modern mobile devices (smartphones). However, GNSS is not always a reliable source of position estimates due to multipath propagation and signal blockage. Moreover, integrating GNSS chips into all devices might have a negative impact on the battery life of future IoT applications. Therefore, alternative solutions to position estimation should be investigated and implemented in IoT applications. This Special Issue, “Smart Sensor Technologies for IoT” aims to report on some of the recent research efforts on this increasingly important topic. The twelve accepted papers in this issue cover various aspects of Smart Sensor Technologies for IoT. 
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653 |a smart sensors 
653 |a smart sensor 
653 |a IoT system 
653 |a Velostat 
653 |a pressure sensor 
653 |a convolutional neural network 
653 |a data classification 
653 |a position detection 
653 |a magnetometer 
653 |a traffic 
653 |a vehicle 
653 |a classification 
653 |a measurement 
653 |a detection 
653 |a Internet of Things 
653 |a Bluetooth 
653 |a indoor tracking 
653 |a mobile localization 
653 |a optical sensors 
653 |a vibration sensing 
653 |a quality of service differentiation 
653 |a wireless optical networks 
653 |a free space optics 
653 |a multiwavelength laser 
653 |a optical code division multiple access (OCDMA) 
653 |a underwater wireless sensor network 
653 |a energy-efficient 
653 |a clustering 
653 |a depth-based routing 
653 |a mm-wave radars 
653 |a GNSS-RTK positioning 
653 |a wireless technology 
653 |a electromagnetic scanning 
653 |a point cloud 
653 |a localization 
653 |a IMU 
653 |a Wi-Fi 
653 |a positioning 
653 |a dead reckoning 
653 |a particle filter 
653 |a fingerprinting 
653 |a Wi-Fi sensing 
653 |a human activity recognition 
653 |a location-independent 
653 |a meta learning 
653 |a metric learning 
653 |a few-shot learning 
653 |a ACR 
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653 |a QoE 
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