Dear Colleagues,

The nature of fog and edge computing devices is evolving as a result of developments in the Internet of Things (IoT). A perfect storm has formed within the IoT ecosystem due to the availability of novel sensor interfaces, effective low-power digital processors, and high-bandwidth low-power communication protocols. A growing breadth of capabilities needs to be supported by next-generation Internet of Things end-nodes, including multisensory data processing and analysis, complicated system control schemes, and ultimately artificial intelligence. The development of wearable and implantable biomedical devices as well as autonomous, insect-sized drones and nanoscale devices for environmental sensing and continuous monitoring of structures, machinery, and power grids will be made possible by these new capabilities. As a result, computationally demanding jobs are increasingly being performed on extremely energy-efficient small-form-factor devices.

Additionally, the AI revolution is posing new, intriguing challenges and necessitates the investigation of novel HW–SW codesign methodologies and advanced optimization techniques for AI frameworks on resource-constrained processors. This revolution gains traction from the widespread use of deep learning. The huge volume of MAC operations and high-bandwidth data transfers needed for deep learning inference make digital architectures—especially those employed in edge devices—critical. Because of this, tools for adjusting and analyzing the performance of neural networks as well as architectural optimizations are crucial for the development of next-generation edge devices.

Modern smart digital objects are networked and distributed systems that make up the Internet of Things systems, made possible by the development of 5G technology. A smart electronic device is an IoT node that is connected to the outside world via a communication infrastructure, or network. IoT applications place heavy demands on a network’s computing and communication resources, which are limited in terms of bandwidth for device-to-cloud connection and in-device processing power. The goal of edge computing is to use computing power located close to IoT nodes to deliver services quickly and with fewer accesses to the cloud, which may be slow or even intermittent. Edge computing allows low-latency service delivery for both safety and mission-critical applications like autonomous decision-making and non-critical applications like infotainment by bringing computer resources to the edge in closer proximity to devices. This Special Issue seeks to give a place for discussing various facets of edge computing and IoT systems. Subjects of interest include, but are not restricted to:

• Framework, and models for edge-computing-enabled IoT systems;
• Frameworks and models for fog-computing-enabled IoT systems;
• Resource management and computational offloading for edge-computing-enabled IoT systems;
• Machine learning, deep learning and federated learning for edge-computing-enabled IoT systems;
• Security and privacy for edge-computing-enabled IoT systems;
• Energy-efficient and green computing for edge-computing-enabled IoT systems;
• Autonomous driving assisted by edge-computing-enabled IoT systems;
• Traffic monitoring and video analytics with edge-computing-enabled IoT systems;
• Application case studies for edge-computing-enabled IoT systems;
• Application case studies for fog-computing-enabled IoT systems.

Dr. Subramaniyaswamy V.
Dr. Logesh Ravi 
Dr. Luca Davoli
Dr. Laura Belli
Dr. Redowan Mahmud
Guest Editors

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• Internet of Things
• fog computing
• edge computing
• intelligent systems
• machine learning
• deep learning
• federated learning
• computational intelligence