RPL-OC: Extension of RPL Protocol for LLN Networks Based on the Operator Calculus Approach

Syarif, Abdusy; Brahmia, Mohamed-el-Amine; Dollinger, Jean-François; Abouaïssa, Abdelhafid; Idoumghar, Lhassane

doi:10.1007/978-981-16-2377-6_7

Cited by 4 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To overcome problem of frequent preferred parent changes, static and empirical thresholds are defined and its performance is superior to standard versions. In [37] the authors have proposed RPL-OC which utilizes four metrics Energy, RSSI, delay and hop count. Operator calculus is used to make the best route decision.…”

Section: Calculus Based Workmentioning

confidence: 99%

RPL protocol enhancement using K-Nearest Neighbor machine learning approach for IoT application

Kuwelkar¹,

Virani²

2023

Preprint

View full text Add to dashboard Cite

It is predicted that the coming decade will witness an upsurge of devices, mobile phones, sensors and actuators communicating over the Internet to enrich human lifestyle. The network design will have to ensure energy efficiency, speed and reliability for Internet of Things to be sustainable in long run. Routing of data, is one aspect which largely governs the quality of service in wireless networks. The IETF ROLL working group proposed the IPv6 Routing protocol for Low power Lossy Networks (RPL) as de-facto routing standard keeping in mind the sustainability of constrained IoT networks. The standard RPL is designed to select best route based on single parameter like hop count. The standard was kept open for researchers to experiment and augment it further. The main aim of this work is to improve the performance of RPL protocol by considering multiple metrics during selection of best route. Four routing parameters namely, hop count, delay, residual energy and link quality are chosen as criteria for deciding the best route. The K-Nearest Neighbor machine learning technique is proposed for multi criteria decision making. The K-NN computes the quality score for each candidate neighbor node and the one with highest score is selected as the most suitable parent in route towards the sink node This novel version, RPL-ML is tested in the COOJA simulator of Contiki OS. Deep dive analysis of RPL-ML by varying data rates and network density reveals that it lowers energy consumption by 18%, improves PDR by 3%, lowers delay by 20% and reduces the control overhead by 48% as compared to standard RPL implementations.

show abstract

Section: Calculus Based Workmentioning

confidence: 99%

RPL protocol enhancement using K-Nearest Neighbor machine learning approach for IoT application

Kuwelkar¹,

Virani²

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The RPL builds upon popular routing protocols, such as CTP and Hydro, used in the WSN domain, but is re-designed and extended to incorporate IPv6. Over a period, the RPL has attained maturity and been standardized as a routing protocol (Syarif et al 2022). The IP stack of many operating systems, such as Contiki OS, TinyOS, T-Kernel, EyeOS, LiteOS, and RIOT, includes the RPL as a standard routing protocol (Seyfollahi et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Transitioning to Sustainable Industry, Innovation and Infrastructure

2023

View full text Add to dashboard Cite

in Rzeszow (building engineering), the University of Rzeszow in Rzeszow (sociology), and doctoral studies at University of Physical Education in Warsaw (physical culture sciences). Since 2005, he has worked as a Professor at the University of Rzeszow and was appointed Head of Chair of the Socio-Cultural Foundation for Physical Education and Sports. He has published over 800 scientific works, including 20 books (monographs and manuals). His major scientific interests concern sociology (of culture, tourism and sport), coaching and pedagogy, and martial arts.

show abstract