Improving the Accuracy of Indoor Object Localization in the Internet of Things Using Recurrent Neural Networks (LSTM) in Challenging Environments

Indoor localization is an essential part of many applications of the Internet of Things, and to track an object within a building with the required precision and reliability one needs to give guarantee to accurate indoor location-based services. This study proposes a new mixture of latest deep learning with sparse feature selection to further increase accuracy in the localization of objects in complex indoor environments. The approach uses nearly all the standard techniques to find best signal processing concepts and classification methods, successfully merging them together. The process integrates. Fhor signal noise reduction, statistical, entropic, and stochastic differential modeling methods to identify the features, and then, selects the optimized features to correct the inputs. The Long Short-Term Memory (LSTM) neural network is then trained on the selected features and excels in location classification with an extremely high precision. The experimental results, however, show that the proposed new compact method achieved a higher classification accuracy at 97.5% which was better than that obtained by such classic approaches as CNNs, RNNs, and Random Forest classifiers. Increased optimization is a positive side-effect of harmonizing sparse autoencoders based on a feature selection process, and deep learning classifiers. The discovery must be the result of a cheap and deployable information technology, apart from crucial issues such as energy, noise, and the behavior of the moving objects, and actuators.