A novel opportunistic Fuzzy logic based objective function for the Routing Protocol for Low-Power and Lossy Networks

Kechiche, Ines; Bousnina, Ines; Samet, Abdelaziz

doi:10.1109/iwcmc.2019.8766691

Cited by 10 publications

(7 citation statements)

References 15 publications

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“…In [24] the authors propose an opportunistic fuzzy-logic based objective function. A novel metric, known as Children Number is derived using fuzzy logic approach to combine the conventional Hop Count (HC) and expected transmission count (ETX) metrics.…”

Section: Fuzzy Logic Based Workmentioning

confidence: 99%

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

Kuwelkar¹,

Virani²

2023

Preprint

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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.

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Section: Fuzzy Logic Based Workmentioning

confidence: 99%

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

Kuwelkar¹,

Virani²

2023

Preprint

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“…The routing process should be sensitive to the battery levels of nodes in order to extend the lifetime. For any given network, energy efficiency can be determined by computing residual energy, power consumption (average), and network lifetime (Kechiche et al 2019) Reliability: In a network, sensor nodes collect crucial information and transmit it to the sink. A good route should ensure that all of this information reaches the gateway or sink without any loss.…”

Section: Qos Requirements For Sustainable Networkmentioning

confidence: 99%

Transitioning to Sustainable Industry, Innovation and Infrastructure

2023

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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.

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“…The packets overhead represents the summation of ICMPv6 respectively DIO, DAO and DIS packets [23]. From Fig.…”

Section: Evaluation Of Packets Overheadmentioning

confidence: 99%

Towards an Enhanced Minimum Rank Hysteresis Objective Function for RPL IoT Routing Protocol

Hassani

Sahel

Badri

2021

Lecture Notes in Electrical Engineering

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RPL is designed as a routing protocol dedicated to Internet of Things. RPL is based on an objective function to build a Destination Oriented Directed Acyclic Graph that connects the leaf nodes in the wireless sensor network to the central node which collects all the informations. In its path selection, RPL does not offer the best network quality of service due to the unoptimized standard metrics. In this paper, we propose an improvement of Minimum Rank Hysteresis Objective function called E-MRHOF based on a new method of calculating the link metric. The simulations show that E-MRHOF can increase the number of packets delivered to the sink with low latency and power consumption, while offering a network convergence time and ICMPv6 packets inferior than the classical MRHOF based on ETX and Energy.

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A novel opportunistic Fuzzy logic based objective function for the Routing Protocol for Low-Power and Lossy Networks

Cited by 10 publications

References 15 publications

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

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

Transitioning to Sustainable Industry, Innovation and Infrastructure

Towards an Enhanced Minimum Rank Hysteresis Objective Function for RPL IoT Routing Protocol

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