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A supervised active learning method for identifying critical nodes in IoT networks

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Abstract

The energy efficiency of wireless sensor networks (WSNs) as a key feature of the Internet of Things (IoT) and fifth-generation (5G) mobile networks is determined by several key characteristics, such as hop count, user’s location, allocated power, and relay. Identifying important nodes, known as critical nodes, in IoT networks that involve a massive number of interconnected devices and sensors significantly affects these characteristics. However, it also requires a significant computational overhead and energy consumption. To address this issue, we introduce a novel supervised active learning method for identifying critical nodes in IoT networks aimed at enhancing the energy efficiency of WSNs in 5G environments. Our experimental results, designed to closely replicate varied and complex IoT network scenarios focusing on mission-critical multi-hop IoT applications, demonstrate the proposed method’s capability to improve adaptability and computational efficiency. These results suggest a strong potential for mission-critical applications in real-world large-scale multi-hop WSN environments in 5G, as well as massively distributed IoT.

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Data availability

No datasets were generated or analyzed during the current study.

Notes

  1. General Algebraic Modeling System (GAMS). Available: https://www.gams.com/.

  2. C Programming Language EXtended (CPLEX). Available: https://www.gams.com/latest/docs/S_CPLEX.html.

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Acknowledgment

This work has been partially supported by the BODYinTRANSIT project as part of the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101002711 and the projects AEON-ZERO (TSI-063000-2021-52), FREE-6G (TSI-063000-2021-144), AROMA3D (TSI-063000-2021-70/71), 6G-OASIS (TSI-063000-2021-24), SUCCESS-6G (TSI-063000-2021-39/40/41) and ADV5GTWN (TSI-063000-2021-112/113/114) under the UNICO5G-RPTR programme.

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Authors and Affiliations

  1. Centre Tecnologic de Telecomunicacions de Catalunya (CTTC/CERCA), Castelldefels, Spain

    Behnam Ojaghi

  2. Department of Computer Science and Engineering, Universidad Carlos III de Madrid, Madrid, Spain

    Mohammad Mahdi Dehshibi

  3. Nearby Computing S.L., Barcelona, Spain

    Angelos Antonopoulos

Authors
  1. Behnam Ojaghi
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  2. Mohammad Mahdi Dehshibi
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  3. Angelos Antonopoulos
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Contributions

Behnam proposes the main idea and methods. Mohammad implements experiments and writes this article. Angelos analyzes the experimental data and revises the structure of manuscript.

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Correspondence to Behnam Ojaghi.

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Ojaghi, B., Dehshibi, M.M. & Antonopoulos, A. A supervised active learning method for identifying critical nodes in IoT networks. J Supercomput (2024). https://doi.org/10.1007/s11227-024-06103-y

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