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Predation and scavenging are pervasive ecological interactions in both terrestrial and aquatic environments. The ecology, evolution and conservation of predators and scavengers have received wide scientific attention and public awareness. However, the close connection that exists between predation and scavenging has not been emphasized until very recently. The recognition that carnivorous animals may obtain meat by either hunting prey or scavenging their carcasses has profound implications from individual behavior to population, community and ecosystem levels. However, many relevant questions still remain unexplored. This book deals with some of these questions, with the final aim to definitively dismiss the traditional view that predation and scavenging are disconnected ecological processes. This compendium of science may help to inspire ecologists, evolutionary biologists, paleontologists, anthropologists, epidemiologists, forensic scientists, anatomists, and, of course, conservation biologists in their stimulating and promising endeavor of achieving a more comprehensive understanding of carnivory in a rapidly changing world.

caching --- Capreolus capreolus --- carrion --- Eurasian lynx --- Lynx lynx --- Norway --- predation --- roe deer --- scavenging --- decomposition --- nitrogen --- nutrient recycling --- trophic cascade --- apex predators --- bear --- interspecific interactions --- moose --- wolf --- birds of prey --- foraging --- predators --- scavengers --- vision --- carcass --- confrontational scavenging --- disease risk --- facultative scavenger --- landscape of peril --- marine ecosystems --- parasite risk --- predator risk --- terrestrial ecosystems --- anthropogenic food --- diet --- urban habitats --- ecological functions --- carnivorous --- n/a

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The Internet-of-Things is becoming an established technology, with devices being deployed in homes, workplaces, and public areas at an increasingly rapid rate. IoT devices are the core technology of smart-homes, smart-cities, intelligent transport systems, and promise to optimise travel, reduce energy usage and improve quality of life. With the IoT prevalence, the problem of how to manage the vast volumes of data, wide variety and type of data generated, and erratic generation patterns is becoming increasingly clear and challenging. This Special Issue focuses on solving this problem through the use of edge computing. Edge computing offers a solution to managing IoT data through the processing of IoT data close to the location where the data is being generated. Edge computing allows computation to be performed locally, thus reducing the volume of data that needs to be transmitted to remote data centres and Cloud storage. It also allows decisions to be made locally without having to wait for Cloud servers to respond.

Technology: general issues --- History of engineering & technology --- Energy industries & utilities --- hierarchical edge computing --- WSN --- rapid response strategy --- edge node --- fog access points --- cache memory --- convolutional neural network --- proactive caching --- fog computing --- Internet of Things --- service placement --- fog service orchestration --- spectral classification --- portable optical fiber spectrometers --- dew computing --- edge computing --- smartphone --- job scheduling --- scheduling heuristics --- mobile edge computing --- mobile edge server placement --- multiagent RL --- edge security --- offloading computation --- distributed collaboration --- data processing --- dynamic offloading --- IoT --- gateways --- Internet of Things (IoT) --- failure recovery --- FP-Growth algorithm --- association rules --- frequency pattern analysis --- computational offloading --- orchestration --- functional programming --- n/a

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The Internet-of-Things is becoming an established technology, with devices being deployed in homes, workplaces, and public areas at an increasingly rapid rate. IoT devices are the core technology of smart-homes, smart-cities, intelligent transport systems, and promise to optimise travel, reduce energy usage and improve quality of life. With the IoT prevalence, the problem of how to manage the vast volumes of data, wide variety and type of data generated, and erratic generation patterns is becoming increasingly clear and challenging. This Special Issue focuses on solving this problem through the use of edge computing. Edge computing offers a solution to managing IoT data through the processing of IoT data close to the location where the data is being generated. Edge computing allows computation to be performed locally, thus reducing the volume of data that needs to be transmitted to remote data centres and Cloud storage. It also allows decisions to be made locally without having to wait for Cloud servers to respond.

Technology: general issues --- History of engineering & technology --- Energy industries & utilities --- hierarchical edge computing --- WSN --- rapid response strategy --- edge node --- fog access points --- cache memory --- convolutional neural network --- proactive caching --- fog computing --- Internet of Things --- service placement --- fog service orchestration --- spectral classification --- portable optical fiber spectrometers --- dew computing --- edge computing --- smartphone --- job scheduling --- scheduling heuristics --- mobile edge computing --- mobile edge server placement --- multiagent RL --- edge security --- offloading computation --- distributed collaboration --- data processing --- dynamic offloading --- IoT --- gateways --- Internet of Things (IoT) --- failure recovery --- FP-Growth algorithm --- association rules --- frequency pattern analysis --- computational offloading --- orchestration --- functional programming --- hierarchical edge computing --- WSN --- rapid response strategy --- edge node --- fog access points --- cache memory --- convolutional neural network --- proactive caching --- fog computing --- Internet of Things --- service placement --- fog service orchestration --- spectral classification --- portable optical fiber spectrometers --- dew computing --- edge computing --- smartphone --- job scheduling --- scheduling heuristics --- mobile edge computing --- mobile edge server placement --- multiagent RL --- edge security --- offloading computation --- distributed collaboration --- data processing --- dynamic offloading --- IoT --- gateways --- Internet of Things (IoT) --- failure recovery --- FP-Growth algorithm --- association rules --- frequency pattern analysis --- computational offloading --- orchestration --- functional programming

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The recent years have witnessed tremendous growth in connected vehicles due to major interest in vehicular ad hoc networks (VANET) technology from both the research and industrial communities. VANET involves the generation of data from onboard sensors and its dissemination in other vehicles via vehicle-to-everything (V2X) communication, thus resulting in numerous applications such as steep-curve warnings. However, to increase the scope of applications, VANET has to integrate various technologies including sensor networks, which results in a new paradigm commonly referred to as vehicular sensor networks (VSN). Unlike traditional sensor networks, every node (vehicle) in VSN is equipped with various sensing (distance sensors, GPS, and cameras), storage, and communication capabilities, which can provide a wide range of applications including environmental surveillance and traffic monitoring. VSN has the potential to improve transportation technology and the transportation environment due to its unlimited power supply and resulting minimum energy constraints. However, VSN faces numerous challenges in terms of its design, implementation, network scalability, reliability, and deployment over large-scale networks, which need to be addressed before it is realized. This book comprises 12 outstanding research works related to vehicular sensor networks, addressing various aspects such as security, routing, SDN, and NDN.

Information technology industries --- barrier control --- sensors platform --- vehicle detection --- license plate recognition --- raspberry-pi --- features extraction --- machine learning algorithms --- connected vehicles, internet of vehicles --- security --- IoT --- blockchain --- vehicular ad-hoc network --- wireless sensor networks --- wake-up radio --- medium access control protocol --- receiver-initiated MAC protocol --- traffic adaptation --- software-defined vehicular network --- vehicle-to-everything (V2X) --- modeling and implementation --- software defined network --- information-centric networking (ICN) --- client-cache (CC) --- video on demand (VoD) --- vehicular sensor network (VSN) --- smart city --- delay tolerant network --- infrastructure offloading --- opportunistic network --- vehicular mobility --- energy consumption --- carbon emission --- V2V communication --- message contents plausibility --- power control --- vehicle edge computing --- 5G cellular networks --- multi-receiver signcryption --- privacy --- PSO --- genetic algorithm --- ITS --- UAV --- simulation --- dynamic positioning --- 3D placement --- vehicular communications --- cross-validation --- anti-collaborative attack --- resource-saving --- trust computing --- Caching --- Named Data Networking --- Information Centric Networking --- Vehicular Ad Hoc Networks --- 5G --- D2D communication --- vehicle-to-vehicle communication --- mode selection --- vehicular social network --- vehicular sensor networks (VSN) --- vehicular ad-hoc networks (VANET) --- privacy and trust --- cyber security --- multimedia and cellular communication --- emerging IoT applications in VANET and VSN --- blockchain within VANET and VSN --- barrier control --- sensors platform --- vehicle detection --- license plate recognition --- raspberry-pi --- features extraction --- machine learning algorithms --- connected vehicles, internet of vehicles --- security --- IoT --- blockchain --- vehicular ad-hoc network --- wireless sensor networks --- wake-up radio --- medium access control protocol --- receiver-initiated MAC protocol --- traffic adaptation --- software-defined vehicular network --- vehicle-to-everything (V2X) --- modeling and implementation --- software defined network --- information-centric networking (ICN) --- client-cache (CC) --- video on demand (VoD) --- vehicular sensor network (VSN) --- smart city --- delay tolerant network --- infrastructure offloading --- opportunistic network --- vehicular mobility --- energy consumption --- carbon emission --- V2V communication --- message contents plausibility --- power control --- vehicle edge computing --- 5G cellular networks --- multi-receiver signcryption --- privacy --- PSO --- genetic algorithm --- ITS --- UAV --- simulation --- dynamic positioning --- 3D placement --- vehicular communications --- cross-validation --- anti-collaborative attack --- resource-saving --- trust computing --- Caching --- Named Data Networking --- Information Centric Networking --- Vehicular Ad Hoc Networks --- 5G --- D2D communication --- vehicle-to-vehicle communication --- mode selection --- vehicular social network --- vehicular sensor networks (VSN) --- vehicular ad-hoc networks (VANET) --- privacy and trust --- cyber security --- multimedia and cellular communication --- emerging IoT applications in VANET and VSN --- blockchain within VANET and VSN

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The recent years have witnessed tremendous growth in connected vehicles due to major interest in vehicular ad hoc networks (VANET) technology from both the research and industrial communities. VANET involves the generation of data from onboard sensors and its dissemination in other vehicles via vehicle-to-everything (V2X) communication, thus resulting in numerous applications such as steep-curve warnings. However, to increase the scope of applications, VANET has to integrate various technologies including sensor networks, which results in a new paradigm commonly referred to as vehicular sensor networks (VSN). Unlike traditional sensor networks, every node (vehicle) in VSN is equipped with various sensing (distance sensors, GPS, and cameras), storage, and communication capabilities, which can provide a wide range of applications including environmental surveillance and traffic monitoring. VSN has the potential to improve transportation technology and the transportation environment due to its unlimited power supply and resulting minimum energy constraints. However, VSN faces numerous challenges in terms of its design, implementation, network scalability, reliability, and deployment over large-scale networks, which need to be addressed before it is realized. This book comprises 12 outstanding research works related to vehicular sensor networks, addressing various aspects such as security, routing, SDN, and NDN.

barrier control --- sensors platform --- vehicle detection --- license plate recognition --- raspberry-pi --- features extraction --- machine learning algorithms --- connected vehicles, internet of vehicles --- security --- IoT --- blockchain --- vehicular ad-hoc network --- wireless sensor networks --- wake-up radio --- medium access control protocol --- receiver-initiated MAC protocol --- traffic adaptation --- software-defined vehicular network --- vehicle-to-everything (V2X) --- modeling and implementation --- software defined network --- information-centric networking (ICN) --- client-cache (CC) --- video on demand (VoD) --- vehicular sensor network (VSN) --- smart city --- delay tolerant network --- infrastructure offloading --- opportunistic network --- vehicular mobility --- energy consumption --- carbon emission --- V2V communication --- message contents plausibility --- power control --- vehicle edge computing --- 5G cellular networks --- multi-receiver signcryption --- privacy --- PSO --- genetic algorithm --- ITS --- UAV --- simulation --- dynamic positioning --- 3D placement --- vehicular communications --- cross-validation --- anti-collaborative attack --- resource-saving --- trust computing --- Caching --- Named Data Networking --- Information Centric Networking --- Vehicular Ad Hoc Networks --- 5G --- D2D communication --- vehicle-to-vehicle communication --- mode selection --- vehicular social network --- vehicular sensor networks (VSN) --- vehicular ad-hoc networks (VANET) --- privacy and trust --- cyber security --- multimedia and cellular communication --- emerging IoT applications in VANET and VSN --- blockchain within VANET and VSN