Sigma Routing Metric for RPL Protocol
This paper presents the adaptation of a specific metric for the RPL protocol in the objective function MRHOF. Among the functions standardized by IETF, we find OF0, which is based on the minimum hop count, as well as MRHOF, which is based on the Expected Transmission Count (ETX). However, when the n...
- Autores:
-
Sanmartin, Paul
Rojas, Aldo
Fernandez, Luis
Avila, Karen
Jabba, Daladier
Valle, Sebastian
- Tipo de recurso:
- Fecha de publicación:
- 2018
- Institución:
- Universidad Simón Bolívar
- Repositorio:
- Repositorio Digital USB
- Idioma:
- eng
- OAI Identifier:
- oai:bonga.unisimon.edu.co:20.500.12442/2108
- Acceso en línea:
- http://hdl.handle.net/20.500.12442/2108
- Palabra clave:
- LLN
RPL
Objective function
Routing metric
- Rights
- License
- Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional
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Sigma Routing Metric for RPL Protocol |
| title |
Sigma Routing Metric for RPL Protocol |
| spellingShingle |
Sigma Routing Metric for RPL Protocol LLN RPL Objective function Routing metric |
| title_short |
Sigma Routing Metric for RPL Protocol |
| title_full |
Sigma Routing Metric for RPL Protocol |
| title_fullStr |
Sigma Routing Metric for RPL Protocol |
| title_full_unstemmed |
Sigma Routing Metric for RPL Protocol |
| title_sort |
Sigma Routing Metric for RPL Protocol |
| dc.creator.fl_str_mv |
Sanmartin, Paul Rojas, Aldo Fernandez, Luis Avila, Karen Jabba, Daladier Valle, Sebastian |
| dc.contributor.author.none.fl_str_mv |
Sanmartin, Paul Rojas, Aldo Fernandez, Luis Avila, Karen Jabba, Daladier Valle, Sebastian |
| dc.subject.eng.fl_str_mv |
LLN RPL Objective function Routing metric |
| topic |
LLN RPL Objective function Routing metric |
| description |
This paper presents the adaptation of a specific metric for the RPL protocol in the objective function MRHOF. Among the functions standardized by IETF, we find OF0, which is based on the minimum hop count, as well as MRHOF, which is based on the Expected Transmission Count (ETX). However, when the network becomes denser or the number of nodes increases, both OF0 and MRHOF introduce long hops, which can generate a bottleneck that restricts the network. The adaptation is proposed to optimize both OFs through a new routing metric. To solve the above problem, the metrics of the minimum number of hops and the ETX are combined by designing a new routing metric called SIGMA-ETX, in which the best route is calculated using the standard deviation of ETX values between each node, as opposed to working with the ETX average along the route. This method ensures a better routing performance in dense sensor networks. The simulations are done through the Cooja simulator, based on the Contiki operating system. The simulations showed that the proposed optimization outperforms at a high margin in both OF0 and MRHOF, in terms of network latency, packet delivery ratio, lifetime, and power consumption. |
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2018 |
| dc.date.accessioned.none.fl_str_mv |
2018-05-23T19:35:31Z |
| dc.date.available.none.fl_str_mv |
2018-05-23T19:35:31Z |
| dc.date.issued.none.fl_str_mv |
2018-04 |
| dc.type.eng.fl_str_mv |
article |
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http://purl.org/coar/resource_type/c_6501 |
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14248220 |
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http://hdl.handle.net/20.500.12442/2108 |
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14248220 |
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http://hdl.handle.net/20.500.12442/2108 |
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eng |
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eng |
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http://purl.org/coar/access_right/c_abf2 |
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Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional |
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Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional http://purl.org/coar/access_right/c_abf2 |
| dc.publisher.eng.fl_str_mv |
MDPI. |
| dc.source.eng.fl_str_mv |
Sensors |
| dc.source.spa.fl_str_mv |
Vol. 18, No.4 (2018) |
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Universidad Simón Bolívar |
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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948651/ |
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Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Sanmartin, Paul637b2108-8720-4718-8d08-92bb119cc9e4-1Rojas, Aldoafca9230-9b36-46d0-9855-35ff5b0ae425-1Fernandez, Luisea76802d-6eaf-4e86-b6f2-25bf5081887a-1Avila, Karen65cd10ac-1d49-4844-b959-df97ab73c242-1Jabba, Daladier19d0263d-ac39-4417-b735-1297cec1f8e6-1Valle, Sebastian0881a978-bf0a-4ff3-ad92-dbbb3f90e990-12018-05-23T19:35:31Z2018-05-23T19:35:31Z2018-0414248220http://hdl.handle.net/20.500.12442/2108This paper presents the adaptation of a specific metric for the RPL protocol in the objective function MRHOF. Among the functions standardized by IETF, we find OF0, which is based on the minimum hop count, as well as MRHOF, which is based on the Expected Transmission Count (ETX). However, when the network becomes denser or the number of nodes increases, both OF0 and MRHOF introduce long hops, which can generate a bottleneck that restricts the network. The adaptation is proposed to optimize both OFs through a new routing metric. To solve the above problem, the metrics of the minimum number of hops and the ETX are combined by designing a new routing metric called SIGMA-ETX, in which the best route is calculated using the standard deviation of ETX values between each node, as opposed to working with the ETX average along the route. This method ensures a better routing performance in dense sensor networks. The simulations are done through the Cooja simulator, based on the Contiki operating system. The simulations showed that the proposed optimization outperforms at a high margin in both OF0 and MRHOF, in terms of network latency, packet delivery ratio, lifetime, and power consumption.engMDPI.SensorsVol. 18, No.4 (2018)https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948651/LLNRPLObjective functionRouting metricSigma Routing Metric for RPL Protocolarticlehttp://purl.org/coar/resource_type/c_6501Kim, T.-H.; Ramos, C.; Mohammed, S. Smart City and IoT; Elsevier: New York, NY, USA, 2017.Ang, L.-M.; Seng, K.P.; Zungeru, A.M.; Ijemaru, G.K. Big sensor data systems for smart cities. IEEE Internet Things J. 2017, 4, 1259–1271. [CrossRef]Abdelgadir, M.; Saeed, R.A.; Babiker, A. Mobility routing model for vehicular Ad-Hoc networks (VANETS), smart city scenarios. Veh. Commun. 2017, 9, 154–161. [CrossRef]Peixoto, J.P.J.; Costa, D.G.Wireless visual sensor networks for smart city applications: A relevance-based approach for multiple sinks mobility. Future Gener. Comput. Syst. 2017, 76, 51–62. [CrossRef]Boukerche, A.; Turgut, B.; Aydin, N.; Ahmad, M.Z.; Bölöni, L.; Turgut, D. Routing protocols in Ad Hoc networks: A survey. Comput. Netw. 2011, 55, 3032–3080. [CrossRef]Ana De Pablo, E. Development of aWireless Sensor Network with 6LoWPAN Support. Available online: https://upcommons.upc.edu/bitstream/handle2099.1/7806/memoria.pdf?sequense=1&isAlloweb=y (accessed on 13 October 2017).Ergen, S.C. ZigBee/IEEE 802.15. 4 Summary; UC Berkeley: Berkeley, CA, USA, 2004; Volume 10, p. 17.Al-Turjman, F. Cognitive routing protocol for disaster-inspired internet of things. Future Gener. Comput. Syst. 2017. [CrossRef]Chejerla, B.K.; Madria, S.K. QoS guaranteeing robust scheduling in attack resilient cloud integrated cyber physical system. Future Gener. Comput. Syst. 2017, 75, 145–157. [CrossRef]Vasseur, J.-P.; Kim, M.; Pister, K.; Dejean, N.; Barthel, D. Routing Metrics Used for Path Calculation in Low-Power and Lossy Networks; 2070-1721; IETF: Fremont, CA, USA, 2012.Thubert, P. Objective Function Zero for the Routing Protocol for Low-Power and Lossy Networks (RPL). Available online: https://tools.ietf.org/html/rfc6552 (accessed on 15 September 2017).Karkazis, P.; Leligou, H.-C.; Sarakis, L.; Zahariadis, T.; Trakadas, P.; Velivassaki, T.H.; Capsalis, C. Design of primary and composite routing metrics for RPL-compliant wireless sensor networks. In Proceedings of the 2012 International Conference on Telecommunications and Multimedia (TEMU), Chania, Greece, 30 July–1 August 2012; pp. 13–18.Park, J.; Kim, K.-H.; Kim, K. An algorithm for timely transmission of solicitation messages in RPL for energy-efficient node mobility. Sensors 2017, 17, 899. [CrossRef] [PubMed]Gnawali, O. The Minimum Rank with Hysteresis Objective Function. Available online: https://tools.ietf. org/html/rfc6719 (accessed on 11 September 2017).Zhao, M.; Nawaz, A.; Rongxing, L. An energy-efficient and self-regioning-based-based RPL for low-power and lossy networks. In Proceedings of the 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall), Montreal, QC, Canada, 18–21 September 2016.Kim, S.; Kawahara, Y.; Georgiadis, A.; Collado, A.; Tentzeris, M.M. Low-cost inkjet-printed fully passive rfid tags for calibration-free capacitive/haptic sensor applications. IEEE Sens. J. 2015, 15, 3135–3145. [CrossRef]Xiao,W.; Liu, J.; Jiang, N.; Shi, H. An optimization of the object function for routing protocol of low-power and lossy networks. In Proceedings of the 2014 2nd International Conference on Systems and Informatics (ICSAI), Shanghai, China, 15–17 November 2014; pp. 515–519.Sanmartin, P.; Cardona, J.; Jabba, D. QoS in heterogeneous networks based on based on dwdm technology. In Proceedings of the 2015 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), Santiago, Chile, 28–30 October 2015; pp. 339–344.Trakadas, P.; Zahariadis, T. Design Guidelines for Routing Metrics Composition in LLN. Available online: https://tools.ietf.org/html/draft-zahariadis-roll-metrics-composition-03 (accessed on 15 September 2017).De Couto, D.S.J.; Aguayo, D.; Bicket, J.; Morris, R. A high-throughput path metric for multi-hop wireless routing. Wirel. Netw. 2005, 11, 419–434. [CrossRef]Javaid, N.; Javaid, A.; Khan, I.A.; Djouani, K. Performance study of etx-based-based wireless routing metrics. In Proceedings of the 2009 2nd International Conference on Computer, Control and Communication (IC4 2009), Karachi, Pakistan, 17–18 February 2009; pp. 1–7.Kim, H.-S.; Ko, J.; Culler, D.E.; Paek, J. Challenging the IPv6 routing protocol for low-power and lossy networks (RPL): A survey. IEEE Commun. Surv. Tutor. 2017, 19, 2502–2525. [CrossRef]Kamgueu, P.O.; Nataf, E.; Ndié, T.D.; Festor, O. Energy-Based Routing Metric for RPL; INRIA: Roquecourbe, France, 2013.Thulasiraman, P. RPL routing for multigateway AMI networks under interference constraints. In Proceedings of the 2013 IEEE International Conference on Communications (ICC), Budapest, Hungary, 9–13 June 2013; pp. 4477–4482.Morton, A. Framework for Metric Composition; Framework; IETF: Fremont, CA, USA, 2010.Silva, M.D.; Senne, E.L.F.; Vijaykumar, N.L. An optimization model to minimize the expected end-to-end transmission time in wireless mesh networks. Pesq. Oper. 2017, 37, 209–227. [CrossRef]Aguirre, E.A.; Pedraza, L.F.; Puerta, G.A. WCETT routing-metric setup assessment on a cognitive radio network. Tecnura 2013, 17, 55–62. [CrossRef]Hinton, K.; Jalali, F. A survey of internet energy efficiency metrics. In Proceedings of the 2016 5th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS), Rome, Italy, 23–25 April 2016; pp. 1–9.Yang, Y.;Wang, J. Design guidelines for routing metrics in multihop wireless networks. In Proceedings of the IEEE INFOCOM, Phoenix, AZ, USA, 13–18 April 2008; pp. 1615–1623.Fotouhi, H.; Moreira, D.; Alves, M. MRPL: Boosting mobility in the Internetthe Internet of things. Ad Hoc Netw. 2015, 26, 17–35. [CrossRef]Todolí-Ferrandis, D.; Santonja-Climent, S.; Sempere-Payá, V.; Silvestre-Blanes, J. RPL routing in a real life scenario with an energy efficient objective function. In Proceedings of the 2015 23rd Telecommunications Forum Telfor (TELFOR), Belgrade, Serbia, 24–26 November 2015; pp. 285–288.Kim, H.-S.; Cho, H.; Kim, H.; Bahk, S. DT-RPL: Diverse bidirectional traffic delivery through RPL routing protocol in low power and lossy networks. Comput. Netw. 2017, 126, 150–161. [CrossRef]Duquennoy, S.; Eriksson, J.; Voigt, T. Five-nines reliable downward routing in RPL. arXiv, 2017.Khallef, W.; Molnar, M.; Benslimane, A.; Durand, S. Multiple constrained qos routing with RPL. In Proceedings of the 2017 IEEE International Conference on Communications (ICC), Paris, France, 21–25 May 2017; pp. 1–6.Kim, H.S.; Paek, J.; Bahk, S. QU-RPL: Queue utilization-based-based RPL for load balancing in large scale industrial applications. In Proceedings of the 2015 12th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), Seattle, WA, USA, 22–25 June 2015; pp. 265–273.Zhao, M.; Kumar, A.; Chong, P.H.J.; Lu, R. A comprehensive study of RPL and P2P-RPL routing protocols: Implementation, challenges and opportunities. Peer-to-Peer Netw. Appl. 2017, 10, 1232–1256. [CrossRef]Díaz, J.M.; Mendoza, P.S.; Céspedes, J.D. Modelado y Simulación de Redes: Aplicación de los QoS con Opnet Modeler; Universidad del Norte: Barranquilla, DC, USA, 2013.ORIGINALPDF.pdfPDF.pdfPDFapplication/pdf5706991https://bonga.unisimon.edu.co/bitstreams/166b5b96-43aa-4478-91d1-829dd5ecd9b7/download26ca891bb32eb82abc29046f5290a259MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-8368https://bonga.unisimon.edu.co/bitstreams/3fcdf0f2-a97a-408d-87c8-57c76da2e291/download3fdc7b41651299350522650338f5754dMD52TEXTsensors Sigma Routing Metric.pdf.txtsensors Sigma Routing Metric.pdf.txtExtracted texttext/plain63054https://bonga.unisimon.edu.co/bitstreams/42c6650d-d54c-4ad5-bea6-154c6ac66193/download1932426e1d2ab1cd7ac21d21282f1ec1MD53PDF.pdf.txtPDF.pdf.txtExtracted texttext/plain67335https://bonga.unisimon.edu.co/bitstreams/ee6ec974-583e-4c5a-b24c-21691fe85cf7/download069d6d50a489adb5fa8c76e8acdc4301MD55THUMBNAILsensors Sigma Routing Metric.pdf.jpgsensors Sigma Routing Metric.pdf.jpgGenerated Thumbnailimage/jpeg1585https://bonga.unisimon.edu.co/bitstreams/56003618-f004-41fb-b1e6-7dda39f72743/download675d7cdc8a1c2064e3265390a1a0d9a5MD54PDF.pdf.jpgPDF.pdf.jpgGenerated Thumbnailimage/jpeg5297https://bonga.unisimon.edu.co/bitstreams/61fbc834-4253-486d-b3e3-3ceaf142f539/download3d805b1d8f5e879696a005058a7c308aMD5620.500.12442/2108oai:bonga.unisimon.edu.co:20.500.12442/21082024-07-25 04:16:08.47open.accesshttps://bonga.unisimon.edu.coRepositorio Digital Universidad Simón Bolívarrepositorio.digital@unisimon.edu.coPGEgcmVsPSJsaWNlbnNlIiBocmVmPSJodHRwOi8vY3JlYXRpdmVjb21tb25zLm9yZy9saWNlbnNlcy9ieS1uYy80LjAvIj48aW1nIGFsdD0iTGljZW5jaWEgQ3JlYXRpdmUgQ29tbW9ucyIgc3R5bGU9ImJvcmRlci13aWR0aDowIiBzcmM9Imh0dHBzOi8vaS5jcmVhdGl2ZWNvbW1vbnMub3JnL2wvYnktbmMvNC4wLzg4eDMxLnBuZyIgLz48L2E+PGJyLz5Fc3RhIG9icmEgZXN0w6EgYmFqbyB1bmEgPGEgcmVsPSJsaWNlbnNlIiBocmVmPSJodHRwOi8vY3JlYXRpdmVjb21tb25zLm9yZy9saWNlbnNlcy9ieS1uYy80LjAvIj5MaWNlbmNpYSBDcmVhdGl2ZSBDb21tb25zIEF0cmlidWNpw7NuLU5vQ29tZXJjaWFsIDQuMCBJbnRlcm5hY2lvbmFsPC9hPi4= |