"Enhanced Indoor localization using CNN and LSTM"

In this project, a complete indoor positioning system is proposed, which utilizes Bluetooth Low Energy (BLE) beacon with iBeacon technology for location-based services in the buildings.The proliferation of tracking devices (smartphones with GPS embedded) equipped with certain low-power sensors such as accelerometers has transformed many aspects of human lives, also creating new opportunities.GPS technology works great for outdoor positioning but falters indoors due to satellite signal restrictions.The purpose of the project is to use iBeacon (one workflow, with more location power) technology and solve the problems of indoor positioning in a creative way.Low Energy Beacons (BLE) that come with iBeacon satisfy such characteristic and represent a built-in cross-platform technology for Android and iOS devices in the long run.The iBeacon technology offers a range of significant benefits that make it a valuable tool for various sectors, including retail, event management, and education.Its advantages include cost-effective hardware, lower energy consumption, independence from internet connectivity, and the capability to send notifications in the background.These features enhance communication and improve user experiences in indoor environments.Recent advancements in iBeacon projects have focused on integrating both X and Y coordinate predictions into a unified model.This approach employs a valid time series data split for training and testing, utilizing Convolutional Neural Networks (CNNs) to analyze sensor data spatially.A novel method transforms sensor readings into an image-like format, allowing CNNs to effectively capture spatial relationships.Additionally, unsupervised pretraining with autoencoders is leveraged to utilize unlabeled data, which can minimize the need for manual measurements in real-world settings.Initially, a Multilayer Perceptron (MLP) was used for position prediction, establishing a foundational understanding of how sensor inputs relate to coordinates.The transition to CNNs enhances spatial comprehension by treating sensor data as images, thereby improving generalizability across varying environments.