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LSTM-Based Congestion Detection in Named Data Networks

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Intelligent Systems Design and Applications (ISDA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 717))

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Abstract

Nowadays, Named Data Network (NDN) presents the most famous paradigm based on Information Centric Network (ICN). Known as the Internet of the future, it searches on data content rather than its localisation. Congestion control is still a research focus in NDN. Seen in-network caching and since interests can be satisfied at different nodes, the end to end TCP congestion control doesn’t work. Congestion control protocols must be adapted to the specific features of NDN. Also, congestion detection presents an essential step in congestion control procedure. Some non-intelligent techniques only detect congestion when it occurs and then flag it. In this paper, we propose an intelligent consumer-based detection method to improve the congestion control efficiency by giving the consumer the ability of perception and speeding up detection, so that network overload could be known in advance. To detect congestion, the proposed scheme integrates a perception andprediction strategy based on a type of Recurrent Neural Network, namely Long Short Term Memory (LSTM).

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Author information

Authors and Affiliations

  1. Hatem Bettahar IResCoMath Lab, University of Gabes, Gabes, Tunisia

    Salwa Abdelwahed & Haifa Touati

  2. Faculty of Sciences of Gabes, Gabes, Tunisia

    Salwa Abdelwahed & Haifa Touati

Authors
  1. Salwa Abdelwahed
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  2. Haifa Touati
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Corresponding author

Correspondence to Salwa Abdelwahed .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Data Science, FLAME University, Pune, Maharashtra, India

    Ajith Abraham

  2. Center for Smart Computing Continuum, Burgenland, Austria

    Sabri Pllana

  3. University of Bari, Bari, Italy

    Gabriella Casalino

  4. University of Jinan, Jinan, Shandong, China

    Kun Ma

  5. Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Anu Bajaj

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Abdelwahed, S., Touati, H. (2023). LSTM-Based Congestion Detection in Named Data Networks. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 717. Springer, Cham. https://doi.org/10.1007/978-3-031-35510-3_14

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