Internet of Raspberry Things (IoRT): Applications, Challenges and Opportunities

Dear Colleagues,

The Internet of things (IoT) encompasses a variety of devices, which has amble of computational capabilities. IoT will make a world global village through intelligent connectivity to maximally analyze and utilize information technology to enable realistic, effective, context aware and scalable data computations, storage and analysis; anywhere and anytime. The presence of context awareness will show the true vision of IoT in multiple disciplines, e.g., health, transportation, smart grid, smart cities, logistics, etc.

However, presently almost 3.5% of the electricity around the globe is being consumed by IoT devices and this figure will be 20% in 2040. Therefore, a need emerges for hardware technologies, platforms, and software architectures to employ IoT to make context based decisions for reliable and precise information about the physical world. In this regard, Raspberry Pi is the most feasible platform which allows low powered operations in IoT devices. This will envision the era of IoT with small, cheap and flexible computer hardware which support end-users to program the devices using Ruby, PHP, JAVA, Python, etc.

Raspberry Pi is, not only the most effective platform to build IoT networks, but also a superb platform to learn about IoT applications. One of the significant factors of Raspberry Pi in IoT is its connectivity space which has a very low power consumption which makes it friendly for battery-operated devices. With the exponential growth of IoT devices across the world, Raspberry Pi will be the best competitor to entertain IoT applications with efficient power consuming hardware.

Therefore, this Special Issue invites research about Raspberry Pi in IoT to make it more user friendly and effective. Topics of interest for the Special Issue include, but are not limited to, the following:

• Intelligent storage of big data using Raspberry Pi
• Energy management in residential sector
• Minimizing energy dissipation in electricity supply side, transmission, and distribution
• Deep learning for IoRT
• Artificial intelligence for IoRT connectivity
• Machine learning for heterogeneous resource based IoRT
• Pervasive networks and communications
• IoRT based wireless sensor networks
• Data Science, data analytics and applications
• Context aware computing for energy efficiency using IoRT
• Ubiquitous intelligence based computing
• Intelligent interfacing for IoRT

Dr. Nadeem Javaid
Prof. Dr. Leonard Barroli
Guest Editors

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