Wireless Interference Prediction for Embedded Health Devices

Embedded healthcare devices are becoming increasingly popular. A device could use a low-power sensor to send data. These low-power sensors typically use ZigBee wireless signals; however, these signals are easily masked by stronger WiFi signals. If a sensor is sending data at the same time as a WiFi-enabled device, the sensor data is lost and needs to be retransmitted. This causes a loss in efficiency and energy usage of the sensor. We proposed two algorithms, based on a Hidden Markov Model and concordance, that are able to predict when a WiFi interference will occur, which ensures with high accuracy that the ZigBee sensor can send data without any risk of wireless interference. Concordance is based on the premise that past history repeats itself. Our experimental results are based on real datasets. Our Hidden Markov Model has a prediction accuracy rate of over 78% and our concordance algorithm has a prediction accuracy rate of over 70%. Our algorithms can also predict longer periods of time and achieve a higher prediction accuracy than current algorithms. Our concordance algorithm has a very low processing overhead, taking microseconds to make a prediction. The throughput improvement gain of our proposed algorithms over current algorithms is a factor of 2.5.