Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Efficient numerical methods in non-uniform sampling theory
Numerische Mathematik
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Coping with irregular spatio-temporal sampling in sensor networks
ACM SIGCOMM Computer Communication Review
Localization for mobile sensor networks
Proceedings of the 10th annual international conference on Mobile computing and networking
Robust distributed network localization with noisy range measurements
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Random matrix theory and wireless communications
Communications and Information Theory
Perturbation of Regular Sampling in Shift-Invariant Spaces for Frames
IEEE Transactions on Information Theory
Data fusion improves the coverage of wireless sensor networks
Proceedings of the 15th annual international conference on Mobile computing and networking
Signal reconstruction in sensor networks with flat and clustered topologies
Computer Networks: The International Journal of Computer and Telecommunications Networking
Asymptotic analysis of multidimensional jittered sampling
IEEE Transactions on Signal Processing
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We consider wireless sensor networks whose nodes are randomly deployed and, thus, provide an irregular sampling of the sensed field. The field is assumed to be bandlimited; a sink node collects the data gathered by the sensors and reconstructs the field by using a technique based on linear filtering. By taking the mean square error (MSE) as performance metric, we evaluate the effect of quasi-equally spaced sensor layouts on the quality of the reconstructed signal. The MSE is derived through asymptotic analysis for different sensor spatial distributions, and for two of them we are able to obtain an approximate closed form expression. The case of uniformly distributed sensors is also considered for the sake of comparison. The validity of our asymptotic analysis is shown by comparison against numerical results and it is proven to hold even for a small number of nodes. Finally, with the help of a simple example, we show the key role that our results play in the deployment of sensor networks.