Adaptive Sampling for Wireless Household Water Consumption Monitoring

Abstract

Wireless sensing systems (WSS) are currently gaining wide use in data collection in various applications such as environment monitoring, energy consumption monitoring and control, and industrial condition monitoring. The WSS systems are predominantly battery driven with low data rates; therefore, it is undesirable to gather all available data without considering the dynamics of the monitored environments or processes. This study investigates an adaptive sampling strategy for WSS aimed at reducing the number of data samples by sensing data only when a significant change in these processes is detected. This detection strategy is based on an extension to Holt's Method and statistical model. To investigate this strategy, the water consumption in a household is used as a case study. A number of performance metrics are used to evaluate the proposed strategy, including sampling fraction, missing ratio and sampling performance. The experimental results show that the proposed strategy over-performs compared to two existing sampling algorithms.