A multi-sensor data fusion technique using data correlations among multiple applications

While wireless sensor networks (WSNs) have been traditionally tasked with single applications, in recent years we have witnessed the emergence of WSNs that allow the sensing and communication infrastructure to be shared among multiple applications thus optimizing the use of resources. As the number of applications in a WSN increases, a growing amount of sensor-generated data will be produced, from which useful information can be extracted. A major requirement in these networks is to save energy in order to extend their operational lifetime. However, wireless sensors and actuators commonly rely on batteries as their energy sources, whose replacement is undesirable or unfeasible. Among the methods employed to extend network lifetime, Multisensor data fusion (MDF) is one of the most widely used. Traditional MDFs are not able to identify different contexts, since they are designed using an application-specific design for the network. As the number of applications increases, the application data ranges overlap and it becomes more complex to identify the origin of each data sample to deliver data to the correct application, with the consequence of reducing data accuracy. In order to overcome these limitations, we propose a MDF technique that divides the monitored interval into a set of intervals (non-overlapped intervals and overlapped intervals) and attributes each interval to an abstract sensor. Then we use MDFs to identify (hidden) correlations in abstract sensors and to exploit such knowledge to monitor the behavior of sensors during their working life. Our proposal is validated through simulations and tests on real nodes.