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The latest proliferation of Internet of Things deployments and edge computing combined with artificial intelligence has led to new exciting application scenarios, where embedded digital devices are essential enablers. Moreover, new powerful and efficient devices are appearing to cope with workloads formerly reserved for the cloud, such as deep learning. These devices allow processing close to where data are generated, avoiding bottlenecks due to communication limitations. The efficient integration of hardware, software and artificial intelligence capabilities deployed in real sensing contexts empowers the edge intelligence paradigm, which will ultimately contribute to the fostering of the offloading processing functionalities to the edge. In this Special Issue, researchers have contributed nine peer-reviewed papers covering a wide range of topics in the area of edge intelligence. Among them are hardware-accelerated implementations of deep neural networks, IoT platforms for extreme edge computing, neuro-evolvable and neuromorphic machine learning, and embedded recommender systems.

high-level synthesis --- HLS --- SDSoC --- support vector machines --- SVM --- code refactoring --- Zynq --- ZedBoard --- extreme edge --- embedded edge computing --- internet of things deployment --- hardware design --- IoT security --- Contiki-NG --- trustability --- embedded systems --- collaborative filtering --- recommender systems --- parallelism --- reconfigurable hardware --- neuroevolution --- block-based neural network --- dynamic and partial reconfiguration --- scalability --- reinforcement learning --- embedded system --- artificial intelligence --- hardware acceleration --- neuromorphic processor --- power consumption --- harsh environment --- fog computing --- edge computing --- cloud computing --- IoT gateway --- LoRa --- WiFi --- low power consumption --- low latency --- flexible --- smart port --- quantisation --- evolutionary algorithm --- neural network --- FPGA --- Movidius VPU --- 2D graphics accelerator --- line-drawing --- Bresenham’s algorithm --- alpha-blending --- anti-aliasing --- field-programmable gate array --- deep learning --- performance estimation --- Gaussian process

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The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demands both in time and space. This flexibility cannot be achieved when the network’s infrastructure remains static. To this end, the topic of UAVs (unmanned aerial vehicles) have enabled wireless communications, and networking has received increased attention. As mentioned above, the network must serve a massive density of nodes that can be either human-held (user devices) or machine-type nodes (sensors). If we wish to properly serve these nodes and optimize their data, a proper wireless connection is fundamental. This can be achieved by using UAV-enabled communication and networks. This Special Issue addresses the many existing issues that still exist to allow UAV-enabled wireless communications and networking to be properly rolled out.

Technology: general issues --- History of engineering & technology --- unmanned aerial vehicle --- UAV positioning --- machine learning --- wireless communications --- drones --- network --- DTN --- mobility schedule --- routing algorithms --- data delivery --- Internet of drones --- communication --- security --- privacy --- UAV base station --- MIMO --- millimeter-wave band --- blind beamforming --- signal recovery --- UAV relay networks --- resource management --- transmit time allocation --- unmanned aerial vehicles --- dynamic spectrum access --- quality of service --- reinforcement learning --- multi-armed bandit --- aerial communication --- FANET --- not-spots --- stratospheric communication platform --- UAV --- UAV-assisted network --- 5G --- global positioning system --- GPS spoofing attacks --- detection techniques --- dynamic selection --- hyperparameter tuning --- IoT --- RF radio communication --- Wi-Fi direct --- D2D --- drone-based mobile secure zone --- friendly jamming --- mobility --- internet of things --- non-orthogonal multiple access --- resource allocation --- ultra reliable low latency communication --- uplink transmission --- Deep Q-learning (DQL) --- Double Deep Q-learning (DDQL) --- dynamic spectrum sharing --- High Altitude Platform Station (HAPS) --- cellular communications --- power control --- interference management --- cognitive UAV networks --- clustered two-stage-fusion cooperative spectrum sensing --- continuous hidden Markov model --- SNR estimation --- n/a

Choose an application

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

The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demands both in time and space. This flexibility cannot be achieved when the network’s infrastructure remains static. To this end, the topic of UAVs (unmanned aerial vehicles) have enabled wireless communications, and networking has received increased attention. As mentioned above, the network must serve a massive density of nodes that can be either human-held (user devices) or machine-type nodes (sensors). If we wish to properly serve these nodes and optimize their data, a proper wireless connection is fundamental. This can be achieved by using UAV-enabled communication and networks. This Special Issue addresses the many existing issues that still exist to allow UAV-enabled wireless communications and networking to be properly rolled out.

Technology: general issues --- History of engineering & technology --- unmanned aerial vehicle --- UAV positioning --- machine learning --- wireless communications --- drones --- network --- DTN --- mobility schedule --- routing algorithms --- data delivery --- Internet of drones --- communication --- security --- privacy --- UAV base station --- MIMO --- millimeter-wave band --- blind beamforming --- signal recovery --- UAV relay networks --- resource management --- transmit time allocation --- unmanned aerial vehicles --- dynamic spectrum access --- quality of service --- reinforcement learning --- multi-armed bandit --- aerial communication --- FANET --- not-spots --- stratospheric communication platform --- UAV --- UAV-assisted network --- 5G --- global positioning system --- GPS spoofing attacks --- detection techniques --- dynamic selection --- hyperparameter tuning --- IoT --- RF radio communication --- Wi-Fi direct --- D2D --- drone-based mobile secure zone --- friendly jamming --- mobility --- internet of things --- non-orthogonal multiple access --- resource allocation --- ultra reliable low latency communication --- uplink transmission --- Deep Q-learning (DQL) --- Double Deep Q-learning (DDQL) --- dynamic spectrum sharing --- High Altitude Platform Station (HAPS) --- cellular communications --- power control --- interference management --- cognitive UAV networks --- clustered two-stage-fusion cooperative spectrum sensing --- continuous hidden Markov model --- SNR estimation