Efficient and complete data collection is one of the most important tasks in wireless ad-hoc sensor networks. Additionally, the collection of the full data set should be performed in the most resource efficient way, thus prolonging the battery lifetime of the network. We introduce a new approach for energy efficient data collection through the use of staggered sampling. Staggered sampling means that at each sampling moment (epoch) only a small percentage of sensors collect (sample) data. The proposed approach leverages on statistical relationships between samples taken from different sensors and/or at different epochs for the prediction of the non-sampled sensor data.
The main goal of the approach is to ensure complete collection of data during a periodic cycle while minimizing the number of sensor readings collected at any point in time. Complete data collection is confirmed by ensuring that each sensor is either sampled at each epoch or the data sample can be accurately recovered though model prediction of the sampled sensors. The proposed approach consists of two main phases. First, efficient modeling of the prediction relationship between two sensors using kernel smoothing over different time lags is performed. Second, the selection of epochs at which each sensor is to sample the data is determined. A 0-1 integer linear programming formulation is used to address this NP-complete assignment problem optimally on relatively large instances. We demonstrate the effectiveness of the approach on traces from actually deployed networks for sensor of two modalities: temperature and humidity.
Recently, several studies have analyzed the statistical properties of low power wireless links in real environments, clearly demonstrating the differences between experimentally observed communication properties and widely used simulation models. However, most of these studies have not performed in depth analysis of the temporal properties of wireless links. These properties have high impact on the performance of routing algorithms.
Our first goal is to study the statistical temporal properties of links in low power wireless communications. We study short term temporal issues, like lagged autocorrelation of individual links, lagged correlation of reverse links, and consecutive same path links. We also study long term temporal aspects, gaining insight on the length of time the channel needs to be measured and how often we should update our models.
Our second objective is to explore how statistical temporal properties impact routing protocols. We studied one-to-one routing schemes and developed new routing algorithms that consider autocorrelation, and reverse link and consecutive same path link lagged correlations. We have developed two new routing algorithms for the cost link model: (i) a generalized Dijkstra algorithm with centralized execution, and (ii)a localized distributed probabilistic algorithm.
Recently, several wireless sensor network studies demonstrated large discrepancies between experimentally observed communication properties and properties produced by widely used simulation models. Our first goal is to provide sound foundations for conclusions drawn from these studies by extracting relationships between location (e.g distance) and communication properties (e.g. reception rate) using non-parametric statistical techniques. The objective is to provide a probability density function that completely characterizes the relationship. Furthermore, we study individual link properties and their correlation with respect to common transmitters, receivers and geometrical location.
The second objective is to develop a series of wireless network models that produce networks of arbitrary sizes with realistic properties. We use an iterative improvement-based optimization procedure to generate network instances that are statistically similar to empirically observed networks. We evaluate the accuracy of our conclusions using our models on a set of standard communication tasks, like connectivity maintenance and routing.
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