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Smart Mobile-Internet of Things (M-IoT)

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 4345

Special Issue Editors


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Guest Editor
Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala-147004, Punjab, India
Interests: AI; machine learning; IoT

Special Issue Information

Dear Colleagues,

The increasing demand for connectivity has led to networking most of the devices around to form the Internet of Things (IoT). IoT has facilitated the modern day requirements of dense connectivity. However, at the same time, this exponential connectivity raises an astronomically large number of network issues related to performance, modelling, maintenance, and security. With the adaptation of Artificial Intelligence (AI), the focus has been on making IoT smarter and more effective, requiring better mobility management, adaptive connectivity, self-learning, and fault tolerance. However, attaining these features becomes critical when the network is diverse, distributed, and follows a hierarchical non-flat layout. Several dependencies in the network affect the operations of the IoT devices. Another issue to highlight is the computational complexity of the solutions operating on the IoT. Given the limitations of size, memory utilization, and battery consumption, the solutions built for IoT must be lightweight, flexible, and exceptionally reliable. Based on the current standings, we are inviting researchers to submit their original work to understand the impact and recent advances in efficient and smart mobile Internet of Things (M-IoT).

This Special Issue solicits the submission of high-quality unpublished papers that aim to solve and understand open technical problems and challenges typical of smart IoT. The main aim is to integrate solutions focusing on performance evaluation and comparison with existing solutions to resolve to attain efficient and smart M-IoT. High-quality review and survey papers are also welcomed. Topics of interest include but are not limited to:

  • Efficient computational schemes for smart M-IoT;
  • Transfer learning solutions in smart M-IoT;
  • Decentralized architectures for smart M-IoT;
  • Efficient and secure mobility management in M-IoT;
  • Verifiable mechanisms for Trust in M-IoT;
  • Blockchain-driven M-IoT architectures;
  • User-authentication and authorization in smart M-IoT;
  • AI solutions for adaptive security in smart M-IoT;
  • Flexible security for smart M-IoT;

Federated learning-based on-device computations in M-IoT

Dr. Vishal Sharma
Dr. Ravinder Kumar
Guest Editors

Manuscript Submission Information

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Keywords

  • Internet of Things
  • Security, privacy and trust
  • Wireless communications
  • Artificial intelligence
  • Decentralized architecture
  • Verifiable computation
  • IoT protocols

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Published Papers (1 paper)

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Research

15 pages, 16345 KiB  
Article
Enhanced Network Intrusion Detection System
by Ketan Kotecha, Raghav Verma, Prahalad V. Rao, Priyanshu Prasad, Vipul Kumar Mishra, Tapas Badal, Divyansh Jain, Deepak Garg and Shakti Sharma
Sensors 2021, 21(23), 7835; https://doi.org/10.3390/s21237835 - 25 Nov 2021
Cited by 17 | Viewed by 3292
Abstract
A reasonably good network intrusion detection system generally requires a high detection rate and a low false alarm rate in order to predict anomalies more accurately. Older datasets cannot capture the schema of a set of modern attacks; therefore, modelling based on these [...] Read more.
A reasonably good network intrusion detection system generally requires a high detection rate and a low false alarm rate in order to predict anomalies more accurately. Older datasets cannot capture the schema of a set of modern attacks; therefore, modelling based on these datasets lacked sufficient generalizability. This paper operates on the UNSW-NB15 Dataset, which is currently one of the best representatives of modern attacks and suggests various models. We discuss various models and conclude our discussion with the model that performs the best using various kinds of evaluation metrics. Alongside modelling, a comprehensive data analysis on the features of the dataset itself using our understanding of correlation, variance, and similar factors for a wider picture is done for better modelling. Furthermore, hypothetical ponderings are discussed for potential network intrusion detection systems, including suggestions on prospective modelling and dataset generation as well. Full article
(This article belongs to the Special Issue Smart Mobile-Internet of Things (M-IoT))
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