Enhancement of sensor data transmission by inference and efficient data processing

When wearable and personal health device and sensors capture data such as heart rate and body temperature for fitness tracking and health services, they simply transfer data without filtering or optimising. This can cause over-loading to the sensors as well as rapid battery consumption when they interact with Internet of Things (IoT) networks, which are expected to increase and de-mand more health data from device wearers. To solve the problem, this paper proposes to infer sensed data to reduce the data volume, which will affect the bandwidth and battery power reduction that are essential requirements to sensor devices. This is achieved by applying beacon data points after the inferencing of data processing utilising variance rates, which compare the sensed data with ad-jacent data before and after. This novel approach verifies by experiments that data volume can be saved by up to 99.5% with a 98.62% accuracy. Whilst most existing works focus on sensor network improvements such as routing, operation and reading data algorithms, we efficiently reduce data volume to reduce band-width and battery power consumption while maintaining accuracy by implement-ing intelligence and optimisation in sensor devices.