Location-enabled IoT (LE-IoT): Indoor Localization for IoT Environments using Machine Learning

In recent years, the concept of the Internet of things (IoT) has become ubiquitous among several applications. However, traditional localization methods face challenges in accurately measuring sensor node positions, particularly in scenarios involving wireless signal instability, energy harvesting, anchor mobility, latency, and dense environmental conditions. However, these problems open the new ways to utilize machine learning (ML) and self-calibration for localization. To defeat these problems, in this paper, we have proposed a new localization technique, which reduces the localization error caused by the iterations in a triangulation method and drastically shortens the computation time for full network coverage despite noisy environments. First, we present a localization technique based on a looped network. The link quality induction (LQI) is used to formulate multiple feature vectors for the localization problem, solved using linear regression. Second, we examine the appropriateness of a few algorithms under different tradeoffs by testing the localization accuracy for different network parameters, including the anchor node density, additive noise, and quality of the wireless channel. The simulation achieves exciting results that the adoption of a support vector machine (SVM) and nonlinear regression model applied with a radial basis function (RBF) kernel leads to an enhanced localization error with Root-Mean Square Error (RMSE) of 0.25m. Furthermore, we gain 48.5% accuracy in the localization error by implementing two models.