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The synergy between artificial intelligence and power and energy systems is providing promising solutions to deal with the increasing complexity of the energy sector. Multi-agent systems, in particular, are widely used to simulate complex problems in the power and energy domain as they enable modeling dynamic environments and studying the interactions between the involved players. Multi-agent systems are suitable for dealing not only with problems related to the upper levels of the system, such as the transmission grid and wholesale electricity markets, but also to address challenges associated with the management of distributed generation, renewables, large-scale integration of electric vehicles, and consumption flexibility. Agent-based approaches are also being increasingly used for control and to combine simulation and emulation by enabling modeling of the details of buildings’ electrical devices, microgrids, and smart grid components. This book discusses and highlights the latest advances and trends in multi-agent energy systems simulation. The addressed application topics include the design, modeling, and simulation of electricity markets operation, the management and scheduling of energy resources, the definition of dynamic energy tariffs for consumption and electrical vehicles charging, the large-scale integration of variable renewable energy sources, and mitigation of the associated power network issues.

EV charging --- multi-agent system --- digital twin --- customer satisfaction indicator --- smart microgrid --- energy management system --- real-time optimization --- immune system algorithm --- economic dispatch --- energy consumption --- wireless sensor network --- cooperation --- collaboration --- ontology --- energy sector --- scoping review --- decision-aid --- distributed energy resources --- distribution system operator --- reactive power management --- uncertainty --- day-ahead market --- balancing market --- bilateral trading --- market design --- variable renewable energy --- agent-based simulation --- MATREM system --- congestion management --- dynamic tariff --- agent-based distribution networks --- demand response --- routing protocols --- performance parameters --- Wireless Sensor Network (WSN)

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A smart city is a modern technology-driven urban area which uses sensing devices, information, and communication technology connected to the internet of things (IoTs) for the optimum and efficient utilization of infrastructures and services with the goal of improving the living conditions of citizens. Increasing populations, lower budgets, limited resources, and compatibility of the upgraded technologies are some of the few problems affecting the implementation of smart cities. Hence, there is continuous advancement regarding technologies for the implementation of smart cities. The aim of this Special Issue is to report on the design and development of integrated/smart sensors, a universal interfacing platform, along with the IoT framework, extending it to next-generation communication networks for monitoring parameters of interest with the goal of achieving smart cities. The proposed universal interfacing platform with the IoT framework will solve many challenging issues and significantly boost the growth of IoT-related applications, not just in the environmental monitoring domain but in the other key areas, such as smart home, assistive technology for the elderly care, smart city with smart waste management, smart E-metering, smart water supply, intelligent traffic control, smart grid, remote healthcare applications, etc., signifying benefits for all countries.

n/a --- data mining algorithms --- pressure sensors --- proactive content delivery --- Elman neural network --- cockroaches --- capacitive sensor --- renewable energy --- indoor comfort --- impedance measurement --- Internet of things (IoT) --- context awareness --- redundant capacity --- city behavior --- secondary traffic --- SDN --- ontology --- bi-reflector solar PV system (BRPVS) --- air quality --- ontology development --- assistive living --- sol-gel technique --- decision support system --- ambient assisted living --- LCC converter --- insect surveillance --- sensitivity --- wireless sensor node (WSN) --- unpowered --- load balancing --- wireless sensor network --- dynamic range --- solar --- anomaly detection --- location-based social networks --- real-time assessment --- porous alumina --- IoT --- building integrated photovoltaics (BIPV) --- carbon nanotubes --- six-port structure --- domestic environment reconfiguration --- half bridge --- smart mat --- cloud computing --- differentiated services --- reflection-based --- nanocomposite sensor --- ppm --- chemical sensors --- sensor systems and applications --- tensile testing --- WSN --- smart traps --- ontology-based application --- hotel room comfort

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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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In the last decade, we have witnessed the rapid development of electronic technologies that are transforming our daily lives. Such technologies are often integrated with various sensors that facilitate the collection of human motion and physiological data and are equipped with wireless communication modules such as Bluetooth, radio frequency identification, and near-field communication. In smart healthcare applications, designing ergonomic and intuitive human–computer interfaces is crucial because a system that is not easy to use will create a huge obstacle to adoption and may significantly reduce the efficacy of the solution. Signal and data processing is another important consideration in smart healthcare applications because it must ensure high accuracy with a high level of confidence in order for the applications to be useful for clinicians in making diagnosis and treatment decisions. This Special Issue is a collection of 10 articles selected from a total of 26 contributions. These contributions span the areas of signal processing and smart healthcare systems mostly contributed by authors from Europe, including Italy, Spain, France, Portugal, Romania, Sweden, and Netherlands. Authors from China, Korea, Taiwan, Indonesia, and Ecuador are also included.

smart homes --- Internet of Things (IoT) --- Wi-Fi --- human monitoring --- behavioral analysis --- ambient assisted living --- intelligent luminaires --- wireless sensor network --- indoor localisation --- indoor monitoring --- Graphics Processing Units (GPUs) --- CUDA --- OpenMP --- OpenCL --- K-means --- brain cancer detection --- hyperspectral imaging --- unsupervised clustering --- impaired sensor --- Structural Health Monitoring --- Time of Flight --- subharmonics --- Cascaded-Integrator-Comb (CIC) filter --- FPGA --- fixed point math --- data adaptive demodulator --- motion estimation --- inertial sensors --- simulation --- spline function --- Kalman filter --- eHealth --- software engineering --- gesture recognition --- Dynamic Time Warping --- Hidden Markov Model --- usability --- Cramér–Rao lower bound (CRLB) --- human motion --- Inertial Measurement Unit (IMU) --- Time of Arrival (TOA) --- wearable sensors --- endothelial dysfunction --- photoplethysmography --- machine learning --- computer-assisted screening --- sleep pose recognition --- keypoints feature matching --- Bayesian inference --- near-infrared images --- scale invariant feature transform --- heartbeat classification --- arrhythmia --- denoising autoencoder --- autoencoder --- deep learning --- auditory perception --- biometrics --- computer vision --- web control access --- web security --- human–computer interaction --- n/a --- Cramér-Rao lower bound (CRLB) --- human-computer interaction

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Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones.

wildfire --- satellite vegetation indices --- live fuel moisture --- empirical model function --- Southern California --- chaparral ecosystem --- forest fire --- forest recovery --- satellite remote sensing --- vegetation index --- burn index --- gross primary production --- South Korea --- land subsidence --- PS-InSAR --- uneven settlement --- building construction --- Beijing urban area --- floodplain delineation --- inaccessible region --- machine learning --- flash flood --- risk --- LSSVM --- China --- Himawari-8 --- threshold-based algorithm --- remote sensing --- dryness monitoring --- soil moisture --- NIR–Red spectral space --- Landsat-8 --- MODIS --- Xinjiang province of China --- SDE --- PE --- groundwater level --- compressible sediment layer --- tropical cyclone formation --- WindSat --- disaster monitoring --- wireless sensor network --- debris flow --- anomaly detection --- deep learning --- accelerometer sensor --- total precipitable water --- Himawari-8 AHI --- random forest --- deep neural network --- XGBoost --- n/a --- NIR-Red spectral space