Anomaly Detection in Battery Devices: Identifying Unusual Battery Voltage Patterns
In modern security systems, the widespread deployment of battery-powered IoT devices creates a critical need for accurate battery replacement strategies. Premature replacement leads to unnecessary waste, while delayed replacement risks system failure in the case of an incident. This thesis presents the workflow building on limited knowledge of the data in an unsupervised learning setting to a work
