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Distributed Intelligence for IoT Data Analytics in Smart City Applications

Submission Deadline: 25 March 2023 (closed)

Guest Editors

Prof. P. Shanmugavadivu, The Gandhigram Rural Institute, India.
Dr. Hsin-Hsi Tsai, Foxconn, China (Taiwan).
Dr. Bharathi Raja Chakravarthi, National University of Ireland, Galway, Ireland.


In recent years, there has been hype revolving around the Internet of Things (IoT) among both service providers and consumers who strive to adopt technological advancements. This framework of connected devices may potentially create huge volumes of data which may be seen as an asset for many economic activities. Accordingly, to obtain meaningful data from this accumulated information, certain data analytics tools and technologies could be utilized. For instance, machine learning, data mining, deep learning, big data analytics, data visualization, etc. are some enabling technologies that could extract and uncover valuable insights from the raw data. Also, these data analytics tools according to their purposes could be prominently classified as descriptive, perspective, cognitive, diagnostic, and predictive analytics. Simply, this IoT analytics could efficiently create favourable conditions for various applications by optimizing their operations and control processes. As a result, this technology allows many beneficial factors like improved decision-making capability, enhanced operation automation and optimization, better human productivity, personalized customer experience, and much more.


Whereas, with the development of dedicated and distributed solutions, technologies like Distributed Intelligence (DI) play a crucial role in solving complex decision-making, planning, and learning problems. This also stays as a flexible tool that removes the complexities which are developed under a centralized system. With the ability to handle specialized tasks independently, these cooperative agents could greatly support these systems to achieve their goals. Integrating these technologies could achieve scalability, robustness, and reliability in various applications where they are employed. In that order, smart healthcare systems, intelligent traffic and transportation management, smart farming activities, industrial automation, digital education, intelligent waste management, etc are some smart city applications that are benefitted from these technologies. As well, this tech-empowered environment not only creates a safer community for the people but also emerges with new business opportunities. Despite having so many beneficial facts, there are still some disadvantages that are existing in this system. Starting from, security and privacy issues, inadequate data storage facilities, difficulties in integrating heterogeneous data, complexities in handling real-time data, over-reliance on technology, and much more are seen barriers in this system. Potentially, these blockades found in this technological infrastructure could reduce the efficacy of the system.


Aiming to improve the system efficiency with innovative solutions, we invite researchers, scholars, academicians, and other institutional participants who are related to this domain. To this end, this special issue welcomes submissions from various research participants across the globe for the upliftment of this field.


Topics that could be included in this special issue are listed below:


· Fog-empowered distributed intelligence for IoT data analytics in smart healthcare services

· Privacy protected distributed intelligence over IoT for smart cities using blockchain technology

· Distributed AI for machine learning-aided IoT system for robust industrial applications

· Heterogeneous IoT data analytics with fog computing for smart waste management system

· DI with visual analytics algorithms for emerging IoT applications

· IoT data analytics with DI and edge computing for emerging smart cities

· DI at the edge of IoT networks with deep learning capability for smart critical infrastructure management

· Adaptive federated learning with DI for IoT data analytics in smart city applications

· Decentralized data analytics in IoT networks with deep learning capabilities for smart transportation systems

· Mobile edge computing with IoT data analytics for connected critical infrastructure


IoT; Distributed Intelligence (DI); Smart; Data

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