How does the Selection of Wireless Technology Impact the Performance of the Smart Grid? A Simulation Approach

León, Juan Pablo Astudillo; Santos, Carlos Lester Dueñas; Mezher, Ahmad Mohamad; Barrera, Julián Cárdenas; Meng, Julian; Guerra, Eduardo Castillo

doi:10.1145/3551663.3558673

Cited by 2 publications

(1 citation statement)

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“…The three subnetworks have their own requirements and are important for the correct functioning of the smart grid network. However, the NAN has attracted the attention of academia and industry since it supports the transmission of a considerable volume of data and distributes necessary control signals between many end devices and utility control centers [1]. VOLUME 11, 2023 This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.…”

Section: Introductionmentioning

confidence: 99%

ML-RPL: Machine Learning-Based Routing Protocol for Wireless Smart Grid Networks

et al. 2023

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This research explores the potential of Machine Learning (ML) to enhance wireless communication networks, specifically in the context of Wireless Smart Grid Networks (WSGNs). We integrated ML into the well-established Routing Protocol for Low-Power and Lossy Networks (RPL), resulting in an advanced version called ML-RPL. This novel protocol utilizes CatBoost, a Gradient Boosted Decision Trees (GBDT) algorithm, to optimize routing decisions. The ML model, trained on a dataset of routing metrics, predicts the probability of successfully reaching a destination node. Each node in the network uses the model to choose the route with the highest probability of effectively delivering packets. Our performance evaluation, carried out in a realistic scenario and under various traffic loads, reveals that ML-RPL significantly improves the packet delivery ratio and minimizes end-to-end delay, making it a promising solution for more efficient and responsive WSGNs.INDEX TERMS Machine learning, wireless smart grid networks, neighbourhood area networks (NAN), routing protocol for low-power and lossy networks (RPL).

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Section: Introductionmentioning

confidence: 99%