In Industry 4.0, predictive maintenance (PdM) is one of the most important applications
pertaining to the Internet of Things (IoT). Machine learning is used to predict the possible
failure of a machine before the actual event occurs. However, main challenges in PdM
are:(a) lack of enough data from failing machines, and (b) paucity of power and bandwidth to
transmit sensor data to cloud throughout the lifetime of the machine. Alternatively, edge
computing approaches reduce data transmission and consume low energy. In this paper, we …

To overcome the energy and bandwidth limitations of traditional IoT systems,“edge
computing” or information extraction at the sensor node has become popular. However, now
it is important to create very low energy information extraction or pattern recognition systems.
In this paper, we present an approximate computing method to reduce the computation
energy of a specific type of IoT system used for anomaly detection (eg in predictive
maintenance, epileptic seizure detection, etc). Termed as Anomaly Detection Based Power …