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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.

Technology: general issues --- Internet of Things (IoT) --- ReRoute --- Multicast Repair (M-REP) --- internet of things (IoT) --- Fast Reroute --- bit repair (B-REP) --- failure repair --- WSN --- MANET --- DRONET --- multilayered network model --- 5G --- IoT --- smart sensors --- smart sensor --- IoT system --- Velostat --- pressure sensor --- convolutional neural network --- data classification --- position detection --- magnetometer --- traffic --- vehicle --- classification --- measurement --- detection --- Internet of Things --- Bluetooth --- indoor tracking --- mobile localization --- optical sensors --- vibration sensing --- quality of service differentiation --- wireless optical networks --- free space optics --- multiwavelength laser --- optical code division multiple access (OCDMA) --- underwater wireless sensor network --- energy-efficient --- clustering --- depth-based routing --- mm-wave radars --- GNSS-RTK positioning --- wireless technology --- electromagnetic scanning --- point cloud --- localization --- IMU --- Wi-Fi --- positioning --- dead reckoning --- particle filter --- fingerprinting --- Wi-Fi sensing --- human activity recognition --- location-independent --- meta learning --- metric learning --- few-shot learning --- ACR --- H.264/AVC --- H.265/HEVC --- QoE --- subjective assessment --- n/a

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• Reference Manager
• EndNote
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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.

Internet of Things (IoT) --- ReRoute --- Multicast Repair (M-REP) --- internet of things (IoT) --- Fast Reroute --- bit repair (B-REP) --- failure repair --- WSN --- MANET --- DRONET --- multilayered network model --- 5G --- IoT --- smart sensors --- smart sensor --- IoT system --- Velostat --- pressure sensor --- convolutional neural network --- data classification --- position detection --- magnetometer --- traffic --- vehicle --- classification --- measurement --- detection --- Internet of Things --- Bluetooth --- indoor tracking --- mobile localization --- optical sensors --- vibration sensing --- quality of service differentiation --- wireless optical networks --- free space optics --- multiwavelength laser --- optical code division multiple access (OCDMA) --- underwater wireless sensor network --- energy-efficient --- clustering --- depth-based routing --- mm-wave radars --- GNSS-RTK positioning --- wireless technology --- electromagnetic scanning --- point cloud --- localization --- IMU --- Wi-Fi --- positioning --- dead reckoning --- particle filter --- fingerprinting --- Wi-Fi sensing --- human activity recognition --- location-independent --- meta learning --- metric learning --- few-shot learning --- ACR --- H.264/AVC --- H.265/HEVC --- QoE --- subjective assessment --- n/a

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For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches.

CIELab --- component Substitution --- Pan sharpening --- Pléiades VHR Image --- coal --- inertinite macerals --- classification --- multifractal analysis --- support vector machine --- block-based coding --- video coding --- H.265/HEVC --- affine motion compensation --- image registration --- homography matrix --- local homography transformation --- convolutional neural network --- moving direct linear transformation --- super-resolution (SR) --- convolution neural network (CNN) --- Gene Expression Programming (GEP) --- deep learning --- image preclassification --- suspicious behavior detection --- motion --- magnitude --- gradient --- reactivity --- saliency --- haze removal --- dark channel --- atmospheric-light estimation --- coarse-to-fine search strategy --- sparse dictionary --- stable recovery --- frame --- RIP --- local dimming --- retinex theory --- bi-histogram equalization --- contrast ratio --- details preservation --- pansharpening --- image fusion --- image quality --- Satellite Pour l’Observation de la Terre (SPOT) 6 --- spectral consistency --- spatial consistency --- synthesis --- artificial intelligence --- dental application --- images --- detection --- parseval frame --- transform --- sparse representation --- octave convolution --- bilingual scene text reading --- Ethiopic script --- attention --- nasal cytology --- automatic cell segmentation --- rhinology --- image analysis --- feature extraction --- shape context --- plant recognition --- DPCNN --- BOF --- numeral spotting --- historical document analysis --- convolutional neural networks --- deep transfer learning --- handwritten digit recognition --- spectrum correction --- intensity correction --- compressed sensing --- tradeoff process --- IKONOS --- remote sensing --- fine-tuning --- learning rate scheduler --- cyclical learning rates --- label smoothing --- classification accuracy --- neural networks --- salient object detection --- RGB-D --- object detection --- small object --- multi-scale sampling --- balanced sampling --- texture --- structure --- optical --- coke --- iron ore --- sinter --- image processing --- segmentation --- identification --- action recognition --- silhouette sequences --- shape features --- ambient assisted living --- active ageing --- n/a --- Pléiades VHR Image --- Satellite Pour l'Observation de la Terre (SPOT) 6