Parameter-Based Data Aggregation for Statistical Information Extraction in Wireless Sensor Networks.
Wireless sensor networks (WSNs) have a broad range of applications, such as battlefield surveillance, environmental monitoring, and relief. These networks usually have stringent constraints on the system resources, making data-extraction and aggregation techniques critically important. However, accurate data extraction and aggregation is difficult, due to significant variations in sensor readings and frequent link and node failures. To address these challenges, we propose data-aggregation techniques based on statistical information extraction that capture the effects of aggregation over different scales. We also design, in this paper, an accurate estimation of the distribution parameters of sensory data using the expectation–maximization (EM) algorithm. We demonstrate that the proposed techniques not only greatly reduce the communication cost but also retain valuable statistical information that is otherwise lost in many existing data-aggregation approaches for sensor networks. Moreover, simulation results show that the proposed techniques are robust against link and node failures and perform consistently well in broad scenarios with various network configurations. [ABSTRACT FROM PUBLISHER]/nCopyright of IEEE Transactions on Vehicular Technology is the property of IEEE and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Comparison with other algorithms
Simulation and theoretical analysis
Node failuresEnergy consumption
Theoretical approximation, simulation development, experimental
spatially correlated elevation data released by the U.S. Geological Survey. We select a 100 × 100 spatial subset of the original data as our data sets. This subset size can be easily scaled to cover the square where all nodes are.
We demonstrate that the proposed techniques not only greatly reduce the communication cost but also retain valuable statistical information that is otherwise lost in many existing dataaggregation approaches for sensor networks. Moreover, simulation results show that the proposed techniques are robust against link and node failures and perform consistently well in broad scenarios with various network configurations.The proposed scheme exploits an unbiased loss-tolerant multipath routing for data aggregation. It strives to extract the statistical information of the original data distribution but preserve the accuracy of estimation and avoid the loss of valuable statistical information.
Accurate data extraction and aggregation of Wireless sensor networks (WSNs) is difficult, due to significant variations in sensor readings and frequent link and node failures. We propose data-aggregation techniques based on statistical information extraction that capture the effects of aggregation over different scales. We also design, in this paper, an accurate estimation of the distribution parameters of sensory data using the expectation–maximization (EM) algorithm.
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