Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book focuses on all the technologies involved in improving the teaching and learning process of some of the sensor-based IoT topics, such as virtual sensors, simulated data acquisition, virtual and remote labs for IoT sensing, gamification experiences and innovative teaching materials, among others. In particular, the articles inside the book show excellent works about hot topics, such as: - Remote labs for IoT teaching, including the full development cycle. - Practical guides for IoT cybersecurity. - Innovative multimodal learning analytics architecture that builds on software-defined networks and network function virtualization principles. - Problem-based learning experiences using designed complex sensor-based IoT ecosystems with sensors, actuators, microcontrollers, plants, soils and irrigation systems. - Block-based programming extensions to facilitate the creation of mobile apps for smart learning experiences. The articles published in this book present only some of the most important topics about sensor-based IoT learning and teaching. However, the selected papers offer significant studies and promising environments.

History of engineering & technology --- Internet of Things (IoT) --- mobile apps --- end-user development --- App Inventor --- block-based languages --- map-reduce --- computer-based systems --- environmental awareness --- Industry 4.0 --- Internet of Things --- irrigation systems --- planter --- project-based-learning --- smart learning environments --- teamwork --- smart classrooms --- educational technology --- multimodal learning analytics --- internet of things --- multisensorial networks --- IoT --- cybersecurity --- Shodan --- teaching methodology --- use case based learning --- security audit --- vulnerabilities --- cyber-attacks --- vulnerability assessment --- web of things --- IoT learning --- cloud computing --- protocols --- virtualization --- instructional design

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book focuses on all the technologies involved in improving the teaching and learning process of some of the sensor-based IoT topics, such as virtual sensors, simulated data acquisition, virtual and remote labs for IoT sensing, gamification experiences and innovative teaching materials, among others. In particular, the articles inside the book show excellent works about hot topics, such as: - Remote labs for IoT teaching, including the full development cycle. - Practical guides for IoT cybersecurity. - Innovative multimodal learning analytics architecture that builds on software-defined networks and network function virtualization principles. - Problem-based learning experiences using designed complex sensor-based IoT ecosystems with sensors, actuators, microcontrollers, plants, soils and irrigation systems. - Block-based programming extensions to facilitate the creation of mobile apps for smart learning experiences. The articles published in this book present only some of the most important topics about sensor-based IoT learning and teaching. However, the selected papers offer significant studies and promising environments.

History of engineering & technology --- Internet of Things (IoT) --- mobile apps --- end-user development --- App Inventor --- block-based languages --- map-reduce --- computer-based systems --- environmental awareness --- Industry 4.0 --- Internet of Things --- irrigation systems --- planter --- project-based-learning --- smart learning environments --- teamwork --- smart classrooms --- educational technology --- multimodal learning analytics --- internet of things --- multisensorial networks --- IoT --- cybersecurity --- Shodan --- teaching methodology --- use case based learning --- security audit --- vulnerabilities --- cyber-attacks --- vulnerability assessment --- web of things --- IoT learning --- cloud computing --- protocols --- virtualization --- instructional design

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Digital Twins in Industry is a compilation of works by authors with specific emphasis on industrial applications. Much of the research on digital twins has been conducted by the academia in both theoretical considerations and laboratory-based prototypes. Industry, while taking the lead on larger scale implementations of Digital Twins (DT) using sophisticated software, is concentrating on dedicated solutions that are not within the reach of the average-sized industries. This book covers 11 chapters of various implementations of DT. It provides an insight for companies who are contemplating the adaption of the DT technology, as well as researchers and senior students in exploring the potential of DT and its associated technologies.

Technology: general issues --- digital twin --- manufacturing --- tolerancing --- geometry assurance --- digital thread --- virtual reality --- augmented reality --- 360 modules --- YouTube --- online App --- construction --- building --- digital pedagogy --- role play --- e-learning --- risk management. --- internet of things --- interoperability --- standardization --- engine block --- industrial process --- steady state simulation --- directed graph --- piping and instrumentation diagram --- Balas® --- ergonomics --- production process --- Digital Twin --- digital twins --- crane --- machine design --- integration --- maintenance --- operation --- API --- open source --- framework --- shipyard --- industry 4.0 --- digital-twin --- gerotor pump --- hydraulic-systems --- simulation --- computer-aided design --- safety --- smart operator --- smart manufacturing --- Industry 4.0 --- digital twins (DTs) --- Internet of Things (IoT) --- sustainability requirements --- sustainable development --- product design --- n/a