Special Issue Smart Anything Everywhere: New Frontiers, Solutions, Issues & Challenges
Résumé
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.
