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Biogeographic-Based Temporal Prediction of Link Stability in Mobile Ad Hoc Networks

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Abstract

A set of moving nodes communicating with each other without any infrastructure is considered a mobile ad hoc network (MANET). Stability is a big problem with this type of network due to its variable location and variable speed with respect to time. As a result, link failure is a big problem in MANET. When the link fails, the network faces high packet drop and higher delay in delivery of the packets due to a new routing setup in most cases. In this paper, we have proposed a method to frame up a stable link network using a temporal data analysis model. In this model, we first analyzed the mobility and position of neighbor nodes with respect to each node from the temporal snapshot of the network. The statistical model ARMA (Auto Regressive Moving Average) is used for predicting the stable neighbors of each node in a future time frame. These stable neighbors can be used for creating a link between different nodes. The combination between different nodes builds a path between the source and destination. We applied a BBO (Biogeographic-based optimization) technique to estimate parameters relevant to the optimal path from source to destination nodes. This optimal link offers a stable and reliable connection for the remaining lifetime of the data transfer for the said network.

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Authors and Affiliations

  1. Academy of Technology, Hooghly, West Bengal, India

    Arindrajit Pal & Shayak Sadhu

  2. Visva-Bharati University, Bolpur, India

    Paramartha Dutta

  3. A.K.Choudhury School of IT, University of Calcutta, Kolkata, India

    Amlan Chakrabarti

  4. National Institute of Technology Patna, Patna, India

    Jyoti Prakash Singh

Authors
  1. Arindrajit Pal
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  2. Paramartha Dutta
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  3. Amlan Chakrabarti
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  4. Jyoti Prakash Singh
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  5. Shayak Sadhu
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Correspondence to Arindrajit Pal.

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Pal, A., Dutta, P., Chakrabarti, A. et al. Biogeographic-Based Temporal Prediction of Link Stability in Mobile Ad Hoc Networks. Wireless Pers Commun 104, 217–233 (2019). https://doi.org/10.1007/s11277-018-6016-7

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  • DOI: https://doi.org/10.1007/s11277-018-6016-7

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