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.

Efficient and reliable data routing is critical in Advanced Metering Infrastructure (AMI) within Smart Grids, dictating the overall network performance and resilience. This paper introduces Q-RPL, a novel Q-learning-based Routing Protocol designed to enhance routing decisions in AMI deployments based on wireless mesh technologies. Q-RPL leverages the principles of Reinforcement Learning (RL) to dynamically select optimal next-hop forwarding candidates, adapting to changing network conditions. The protocol operates on top of the standard IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL), integrating it with intelligent decision-making capabilities. Through extensive simulations carried out in real map scenarios, Q-RPL demonstrates a significant improvement in key performance metrics such as packet delivery ratio, end-to-end delay, and compliant factor compared to the standard RPL implementation and other benchmark algorithms found in the literature. The adaptability and robustness of Q-RPL mark a significant advancement in the evolution of routing protocols for Smart Grid AMI, promising enhanced efficiency and reliability for future intelligent energy systems. The findings of this study also underscore the potential of Reinforcement Learning to improve networking protocols.

This study investigates the potential of utilizing the Gaussian Naive Bayes algorithm for enhancing the performance of the Wireless Smart Grid Networks (WSGNs). We have incorporated the Gaussian Naive Bayes algorithm into the widely used Routing Protocol for Low-Power and Lossy Networks (RPL), resulting in an advanced variant named as GNB-RPL. This innovative protocol leverages the Naive Bayes algorithm to optimize routing decisions. Training a Naive Bayes classifier model on a data set of routing metrics enables us to make predictions about the probability of successfully reaching a destination node. Each network node utilizes this classifier to select the route with the highest probability of delivering packets effectively. Our findings demonstrate that GNB-RPL significantly enhances the packet delivery ratio while minimizing end-to-end delay through a comprehensive performance evaluation conducted in a realistic scenario and across different traffic loads. These results show the potential of GNB-RPL as a promising solution for achieving greater efficiency in WSGNs.

At the present time, a great interest has been shown by the research and the industrial community concerning smart grid communications where important technical advances have arisen as a consequence. Concretely, one of the most important goals of RF mesh network is the design and development of an efficient routing protocol to connect different devices of the network between each other. Nevertheless, the overall performance of the RF Mesh network communications depends in high measure on the positions of key devices in Radio Frequency (RF) Mesh network such as routers and collectors. With this in mind, we focus our work in this paper to design a strategy to optimize the positions of the key devices of RF Mesh network such as routers and collectors to achieve the highest possible connectivity between different components of RF Mesh network. To do so, we have used a well-known clustering algorithm called Lloyd's algorithm, to design our strategy, with the clear objective of allocating routers and collectors in their optimal positions in a smart grid scenario. Extensive simulations have been carried out with the proposed strategy, and good results have been obtained.

This study aims to achieve two objectives. Firstly, it evaluates the performance of various traditional machine learning (ML) techniques using two datasets containing Quality of Service (QoS) metrics obtained from real-world and synthetic Grid scenarios, both utilizing the Routing Protocol for Low-Power and Lossy Networks (RPL) in Wireless Smart Grid Networks (WSGNs). Secondly, it investigates how different scenarios impact the performance of ML models, considering their potential integration within the widely adopted RPL. Initially, the performance of multiple traditional ML techniques was assessed to determine the most effective one. Subsequently, two distinct models were created, one for each generated dataset, using the most effective technique. The findings highlight that the Long Short-Term Memory (LSTM) technique outperforms all other techniques, achieving an AUC (Area Under the Curve) value of >=0.98. Furthermore, an important discovery emerged from this research, indicating that the performance of the ML model diminishes when applied to a scenario different from the one it was trained on. This effect is particularly notable when transitioning from a real-world context to a simplified grid scenario.