IoTorii: Outperforming RPL with scalable routing

Citation Author(s):
Elisa
Rojas
Departamento de Automatica, University of Alcala, Alcala de Henares (Madrid), Spain. e-mails: elisa.rojas@uah.es
Hedayat
Hosseini
Computer Engineering and Information Technology Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran. e-mail: h.hosseini@aut.ac.ir
Carles
Gomez
Dept. of Network Engineering, Universitat Politècnica de Catalunya, Spain. e-mail: carlesgo@entel.upc.edu
David
Carrascal
Departamento de Automatica, University of Alcala, Alcala de Henares (Madrid), Spain. e-mails: davidcawork@gmail.com
Jeferson
Rodrigues Cotrim
Engineering, Modeling and Social Science Center, Federal University of ABC, Santo André (São Paulo), Brazil. e-mail: jeferson.cotrim@ufabc.edu.br
Submitted by:
SeyedHedayat Ho...
Last updated:
Thu, 04/23/2020 - 10:08
DOI:
10.21227/cjw4-kr75
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Abstract 

RPL is the de-facto IPv6-based routing protocol for the Internet of Things (IoT), where networks are mainly formed by sensors and other low capacity devices. However, RPL lacks scalability and is inefficient in any-to-any communications. In this article, we present IoTorii, a layer-two hierarchical protocol that creates routes based on a probe frame, instead of the computation of a distance vector, as in RPL. We implemented a proof-of-concept of IoTorii and compared it with RPL, proving lower number of table entries, exchanged control messages and convergence time, with similar creation of shortest paths, which is particularly promising in the IoT field.

Instructions: 

To evaluate IoTorii, we implemented RPL and IoTorii in the OMNeT++ 5.2.1 simulator, with the INET 3.6.3 library. After validating IoTorii in OMNeT++, we developed both routing protocols using Contiki-NG which is a real Operating System (OS) for IoT devices. This Dataset includes data collected by OMNeT++ and Cooja simulators.

The Dataset includes two folders to store raw and chart data. The folder of raw data includes all data collected by the simulators. The folder of chart data includes data selected/aggregated by the raw data. Sub folders in this folder are categorized and named based on the simulations. Each sub folder includes three excel files to aggregate the raw data, and 3_plotData.xlsx includes the final data in the excel format, and all text files include the final data to be used as data tables in the TiKZ library of LaTeX.

Dataset Files

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