24,405 research outputs found
Rate-distortion Balanced Data Compression for Wireless Sensor Networks
This paper presents a data compression algorithm with error bound guarantee
for wireless sensor networks (WSNs) using compressing neural networks. The
proposed algorithm minimizes data congestion and reduces energy consumption by
exploring spatio-temporal correlations among data samples. The adaptive
rate-distortion feature balances the compressed data size (data rate) with the
required error bound guarantee (distortion level). This compression relieves
the strain on energy and bandwidth resources while collecting WSN data within
tolerable error margins, thereby increasing the scale of WSNs. The algorithm is
evaluated using real-world datasets and compared with conventional methods for
temporal and spatial data compression. The experimental validation reveals that
the proposed algorithm outperforms several existing WSN data compression
methods in terms of compression efficiency and signal reconstruction. Moreover,
an energy analysis shows that compressing the data can reduce the energy
expenditure, and hence expand the service lifespan by several folds.Comment: arXiv admin note: text overlap with arXiv:1408.294
Tandem: A Context-Aware Method for Spontaneous Clustering of Dynamic Wireless Sensor Nodes
Wireless sensor nodes attached to everyday objects and worn by people are able to collaborate and actively assist users in their activities. We propose a method through which wireless sensor nodes organize spontaneously into clusters based on a common context. Provided that the confidence of sharing a common context varies in time, the algorithm takes into account a window-based history of believes. We approximate the behaviour of the algorithm using a Markov chain model and we analyse theoretically the cluster stability. We compare the theoretical approximation with simulations, by making use of experimental results reported from field tests. We show the tradeoff between the time history necessary to achieve a certain stability and the responsiveness of the clustering algorithm
Cram\'er-Rao Bounds for Polynomial Signal Estimation using Sensors with AR(1) Drift
We seek to characterize the estimation performance of a sensor network where
the individual sensors exhibit the phenomenon of drift, i.e., a gradual change
of the bias. Though estimation in the presence of random errors has been
extensively studied in the literature, the loss of estimation performance due
to systematic errors like drift have rarely been looked into. In this paper, we
derive closed-form Fisher Information matrix and subsequently Cram\'er-Rao
bounds (upto reasonable approximation) for the estimation accuracy of
drift-corrupted signals. We assume a polynomial time-series as the
representative signal and an autoregressive process model for the drift. When
the Markov parameter for drift \rho<1, we show that the first-order effect of
drift is asymptotically equivalent to scaling the measurement noise by an
appropriate factor. For \rho=1, i.e., when the drift is non-stationary, we show
that the constant part of a signal can only be estimated inconsistently
(non-zero asymptotic variance). Practical usage of the results are demonstrated
through the analysis of 1) networks with multiple sensors and 2) bandwidth
limited networks communicating only quantized observations.Comment: 14 pages, 6 figures, This paper will appear in the Oct/Nov 2012 issue
of IEEE Transactions on Signal Processin
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