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A deep analysis of object capabilities for intelligence considering wireless IoT devices with the DNN approach

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Abstract

The Internet of Things (IoT) represents a potential paradigm increase in the number of linked devices, referred to as things. Administering these things remains a difficulty. The advancement of Internet of Things technology has generated innovative techniques to resolve these issues. Within the context of the low speed, low fusion precision, and other issues of present Internet of Things data fusion techniques, an Internet of Things data fusion procedure based on an intelligent optimization method is developed with the goal of improving the Internet of Things heterogeneous data fusion effect. Although IoT systems truly provide the potential to enhance efficiency, ownership, transparency, and efficacy, they also experience a larger number of potential flaws. This study examines the security of IoT systems under the framework of smart-world critical setups. This study specifically performs an in-depth analysis of the risks in IoT-based critical infrastructures from the viewpoint of apps, networking, operating systems, software, rmware, and hardware. Additionally, this study highlights the three essential IoT-based cyber-physical infrastructures, viz. intelligent passage, intelligent build-up, and intelligent gridding. The current state of IoT research focuses mostly on the processing of scalar sensor data events and pays scant attention to the issues faced in multimedia-based events. In addition, this work conducts a comparative analysis of various ML techniques, such as the support vector machine, convolutional neural network, naive Bayes, and k-nearest neighbor techniques. By assessing sensors, networking, services, and implementation services offered by the IoT, this study analyzes the existing methods for the Internet of Multimedia Things.

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Authors and Affiliations

  1. Department of Information Technology, Kalasalingam Academy of Research and Education, Krishnankovil, Tamil Nadu, India

    S. Senthil Kumaran & S. P. Balakannan

  2. Guangzhou University, Guangzhou, China

    Jun Li

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  1. S. Senthil Kumaran
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  2. S. P. Balakannan
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  3. Jun Li
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Correspondence to S. Senthil Kumaran.

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Kumaran, S.S., Balakannan, S.P. & Li, J. A deep analysis of object capabilities for intelligence considering wireless IoT devices with the DNN approach. J Supercomput 78, 4745–4758 (2022). https://doi.org/10.1007/s11227-021-04064-0

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  • DOI: https://doi.org/10.1007/s11227-021-04064-0

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