Distributed spatiotemporal suppression for environmental data collection in real-world sensor networks

Environmental processes are often severely over-sampled. As sensor networks become more ubiquitous for this purpose, increasing network longevity becomes ever more important. Radio transceivers in particular are a great source of energy consumption, and many networking algorithms have been proposed that seek to minimize their use. Traditionally, such approaches are often data agnostic, i.e., their performance is not dependent on the properties of the data they transport. In this paper we explore algorithms that exploit environmental relationships in order to reduce the amount of transmitted data while maintaining expected levels of accuracy. We employ a realistic testing environment for evaluating the power savings brought by such algorithms, based on Sensorscope, a commercial sensor network product for environmental monitoring. We implement and test a suppression-based data collection algorithm from literature that to our knowledge has never been implemented on a real system, and propose modifications that make it more suitable for real-world conditions. Using a custom extension board developed for in situ power monitoring, we show that while the algorithms greatly reduce the amount of energy spent on transmitting packets, they have no effect on the real system's overall power consumption due to its preexisting network architecture.