A Convergence Time Predictive Model using Machine Learning for LLN

HARI MOHAN PANDEY, Sakshi Garg, Deepti Mehrotra, Sujata Pandey

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Abstract

The need to interface Low power and Lossy Network (LLN) to the web acquired notoriety with the rise of Internet of Things (IoT). Accordingly, IETF ROLL working group proposed a de-facto IPv6 routing protocol called RPL. RPL provisions 6LoWPAN (IPv6 over Low power and Wireless Personal Area Network) and has been the profound interest among researchers, primarily because of its flexibility to cope with the topology changes and its ability to auto-configure, detect and avoids loops. Since, motes that are deployed in IoT network are battery driven and lossy in nature, network execution is strongly influenced. Consequently, if the network convergence for scalable network can be foreseen, it can be utilized to upgrade the network performance. The idea behind
this article is to propose a predictive model that gauges Convergence Time (CT) by performing feature selection by
utilizing Machine Learning (ML) strategy for RPL and IoTorii.
IoTorii is another such convention proposed in recent literature
that scales network better. RPL and IoTorii protocols are
simulated on Contiki OS/Cooja simulator using Sky motes.
Further, RPL execution precision is tried for Storing and NonStoring modes both. Similarly, IoTorii performance accuracy is
tested for both of its proposed variations: nHLMAC1 and
nHLMAC3 addresses. Additionally, the network parameters
obtained from the simulation are used for feature selection in
predictive modelling. The experiment shows that the prediction
model gives the best forecast with 93.619%, 96.962%, 93.112%
and 92.635% accuracy for both the protocols with different
modes and addresses respectively.
Original languageEnglish
Title of host publication8th "IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering
Subtitle of host publicationUPCON-2021
PublisherIEEE Explore
Publication statusAccepted/In press - 4 Oct 2021
Event8th "IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering - India, Dehradun, India
Duration: 11 Nov 202113 Nov 2021
http://upcon2021.in/index.html

Conference

Conference8th "IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering
Abbreviated titleUPCON-2021
Country/TerritoryIndia
CityDehradun
Period11/11/2113/11/21
Internet address

Keywords

  • Routing protocol
  • Low power and Lossy Network
  • Internet of Things
  • Convergence Time
  • Linear Regression

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