Distributed Spatiotemporal Suppression for Environmental Data Collection in Real-World Sensor Networks

Abstract

Abstract-Environmental processes are often severely oversampled. 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.

Data Suppression Algorithms for Surveillance Applications of Wireless Sensor and Actor Networks

“…In such applications, a sink node needs to collect information describing a given set of parameters with a defined precision or recognize predetermined events. These state-of-the-art suppression methods are based on an assumption that a large subset of sensor readings does not need to be reported to the sink as these readings can be inferred from the other transferred data [10,11,12,13]. In order to infer suppressed data, the sink uses a predictive model of the monitored phenomena.…”

“…Parameters monitored by WSNs usually exhibit correlations in both time and space [15]. Thus, several more sophisticated spatiotemporal suppression methods were proposed that combine the basic temporal suppression with detection of spatially correlated data from nearby nodes [12,13,16]. According to the spatiotemporal approach, sensor nodes are clustered based on spatial correlations.…”