The Next Generation Internet of Things Architecture Towards Distributed Intelligence: Reviews, Applications, and Research Challenges

Authors

  • Baha Rababah Department of Computer Science, University of Manitoba, Canada
  • Tanweer Alam Faculty of Computer and Information Systems. Islamic University of Madinah, Saudi Arabia.
  • Rasit Eskicioglu Department of Computer Science, University of Manitoba, Canada

Keywords:

Cloud Computing, Distributed Intelligence, Edge Computing, Internet of Things, Machine Learning, Smart Gateway,

Abstract

Increasing the implication of growing data generated by the Internet of Things (IoT) brings the focus toward extracting knowledge from the raw data derived from sensors. In the current cloud computing architecture, all the IoT raw data are transmitted to the cloud for processing, storage, and controlling things. Nevertheless, the scenario of sending all raw data to the cloud is inefficient as it wastes the bandwidth and increases the network load. This problem can be solved by providing IoT Gateway at the edge layer with the required intelligence to gain the knowledge from raw data to decide whether to actuate or offload complicated tasks to the cloud. This collaboration between the cloud and the edge is called distributed intelligence. This work highlights the distributed intelligence concept in IoT. It presents a deep investigation of distributed intelligence between the cloud and the edge layers under IoT architecture, with an emphasis on its vision, applications, and research challenges. This work aims to bring the attention of IoT specialists to distributed intelligence and its role to deduce current IoT challenges such as availability, mobility, energy efficiency, security, scalability, interoperability, and reliability.

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2020-06-30

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Rababah, B., Alam, T., & Eskicioglu, R. (2020). The Next Generation Internet of Things Architecture Towards Distributed Intelligence: Reviews, Applications, and Research Challenges. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 12(2), 11–19. Retrieved from https://jtec.utem.edu.my/jtec/article/view/5535

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