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ML-RPL: Machine Learning-Based Routing Protocol for Wireless Smart Grid Networks
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DOI：10.1109/ACCESS.2023.3283208.png)
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摘要
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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.
Keyword:
Routing
Smart grids
Measurement
Wireless communication
Routing protocols
Wireless sensor networks
Load modeling
Machine learning
wireless smart grid networks
neighbourhood area networks (NAN)
routing protocol for low-power and lossy networks (RPL)
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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