A Multisensor Data Fusion Technique for Multiapplication Wireless Sensor Networks Based on Overlapping Intervals

Abstract

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. With an increasing number of sharing applications, a growing amount of sensor-generated data will be produced, from which useful information can be extracted. However, wireless sensors and actuators commonly rely on batteries as their energy sources, whose replacement is undesirable or unfeasible. Therefore, in order to reduce the amount of data to be transmitted in the wireless channel, thus saving energy, Multisensor Data Fusion Methods (MDF) can be employed. MDF can also enhance data accuracy in the multiapplication scenario and make inferences that are not feasible from a single sensor or data source. As the application data ranges overlap it becomes more complex to identify the origin of each data sample and thus delivering data to the correct application, consequently reducing data accuracy. In this paper we propose a MDF technique that considers both the application's range and frequency to achieve better accuracy. In our solution we divide the monitored interval into a set of intervals (application exclusive intervals and overlapped intervals) and attribute each interval to a virtual sensor. In application exclusive intervals we apply traditional MDFs and in overlapped intervals we will apply a new MDF that will consider the different application data ranges and frequencies. We will extend a traditional method known as Moving Average Filter as a proof of concept.