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ORuML: Optimized Routing in wireless networks using Machine Learning
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DOI：10.1002/dac.4394.png)
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摘要
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Routing is a process of selecting a path in a network for delivering a packet from source node to destination node. Successful delivery of a message is a challenge, and therefore, this paper proposes an algorithm for a wireless network called Optimized Routing in wireless networks using Machine Learning (ORuML), which uses machine learning algorithm namely, K-nearest neighbor (KNN), Support Vector Machine (SVM), and Multinomial Logistic Regression (MLR), to predict the network type of the source and destination nodes. The ML model is trained by using characteristic features of a node collected in real time such as battery power utilization, available internal storage, IP address, and range of a node. Intuitively, the MLR should outperform KNN and SVM in terms of accuracy and Area under ROC Curve (AUC). The proposed algorithm determines whether the source and destination nodes are co-located and also, determines the best neighboring hop for efficient routing.
Keyword:
accuracy
machine learning
multinomial logistic regression
wireless network
optimized routing
K-nearest neighbor
support vector machine
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