Smart Anything Everywhere: New Frontiers, Solutions, Issues & Challenges

Dear Colleagues,

‘Smart anything everywhere’ is the next wave of products that integrate digital technology. The main challenge is to accelerate the design, development and uptake of advanced digital technologies in products that include innovative electronic components, software and systems, and especially in sectors where digital technologies are underexploited. To disseminate best practice, to coordinate access to technology, resources, demonstrators and open platforms, and to facilitate the cross development of platforms.

‘Smart anything everywhere’ (Healthcare, Infrastructure Inspection and Maintenance, Agri-Food and Agile Production, Emerging ICT solutions, smart cities and buildings, Internet of Everything (IoE), Internet of things (IoT) Agriculture, Next generation networks, Smart computing, Software defined networking, Grid, Embedded systems, Cyber Physical systems, Home, Hospital, largescale distributed systems (i.e., Cloud, Fog, Edge), Robotics, and Future factories and Industry etc.) has revolutionized the world by mainly bringing smartness to the digital world. Machine learning, Artificial Intelligence (AI), potentials of the Internet of Things (IoT) and Internet of Everything (IoE) together can build an intelligent, adaptive, cost effective, sustainable and smart societies, enterprise and varied future factories and business. Largescale distributed systems such as Cloud Computing, and Fog Computing, plays a vital role to satisfy the computational and storage requirements of the exponentially growing IoT devices. Edge computing can crucially offer a highly scalable infrastructure with delay-aware computing at the edge of the network. The IoT data streams forwarded to the cloud result in high bandwidth usage, long-way data traveling, high traffic on the Internet backbone, unnecessary loop delays, limited mobility support, and higher reliability concerns for successful communication. Hence, the edge resources for data storage and computing services potentially resolve these issues via micro datacenters in the geographical proximity of end devices. In this perspective, edge computing can play a key role to smartly address the real-time computational challenges and issues in cloud-IoT/ Cloud-IoE paradigm.

AI and machine learning together enables smartness by developing intelligent behavior through the processes of learning, reasoning, and self-correction. To make this happen, a substantial amount of processing and storage capabilities are required. Although IoT devices are capable to store and compute; their resource-constrained nature is insufficient to completely leverage the benefits of AI-based and learning mechanisms such as machine learning, deep learning, and reinforcement learning. Moreover, most AI-based solutions are computationally intensive in nature. Additionally, deploying an intelligent learning-based solution at the edge of the network is extremely challenging.

The Special Issue aims to acquire varied comprehensive state-of-the-art unpublished contributions from both industry and academia to meticulously address the new frontiers, novel solutions, issues, challenges surrounded by implementing ‘Smart anything everywhere’. This Special Issue equally encourages the proposition and demonstration of use cases, frameworks, applications, and testbeds for Smart X. The topics of interest include, but are not limited to:

Intelligent edge-IoT computing for Smart X

• Energy efficient Smart X
• Cyber-physical and embedded systems
• Artificial Intelligence (AI) based solutions
• Lightweight cryptographic solutions
• Customized low energy computing powering CPS and the IoT
• Flexible and Wearable Electronics
• Widening Digital Innovation Hubs
• Data privacy and protection, cyber-security issues (including security by design)
• Heterogeneous smart resource management at the edge networks
• Security, trust, and privacy solutions for Smart X
• Deep learning solutions for edge-IoT
• Smart solutions for big data analytics
• Edge-IoT content placement and delivery for Smart X
• AI-based techniques for Smart X
• SDN-enabled intelligent edge-IoT
• Blockchain for intelligent edge-IoT
• Service provisioning for Smart X
• Resource management for Smart edge-IoT
• Data Analytics for intelligent edge-IoT
• Deep and reinforcement learning based smart solutions
• Machine learning at the edge for smart X
• Intelligent Network management for edge-IoT
• Context Aware Security and Privacy of cyber-physical systems
• Intelligent adaptive object interaction for Industrial Internet of Things
• Big Data architectures and solutions for smart manufacturing, supply chain management & co-design
• Knowledge discovery in decentralized and collaborative Industry 4.0 platforms
• Predictive maintenance techniques for industrial settings using Artificial Intelligence
• Energy efficient cyber-physical systems and industry 4.0
• QoS-aware cyber-physical systems for industry 4.0
• Future Architectures of Secure cyber-physical systems & IIoT
• Context-Aware Privacy of cyber-physical systems & IIoT ecosystems
• QoE-aware cyber-physical systems for industry 4.0
• Secure & dependable cyber-physical systems for industry 4.0
• Secure and smart software defined architectures for cyber-physical systems & industry 4.0
• Novel and innovative intelligent applications for Industrial Internet of Things (IIoT)
• Novel mechanisms to encounter dynamic and zero-day attacks on cyber-physical systems & IIoT ecosystems
• Secure by design mobile computing applications for cyber-physical systems & IIoT
• The application of blockchain technologies in securing cyber-physical systems & IIoT
• Decentralized Trust management in IIoT ecosystems
• Machine Learning techniques in enabling trust management in cyber-physical systems & IIoT

Dr. Adnan Akhunzada
Prof. Dr. Philippe Roose
Dr. André Fonteles
Guest Editors

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