On k-connectivity for a geometric random graph
Random Structures & Algorithms
Directed diffusion: a scalable and robust communication paradigm for sensor networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
On the minimum node degree and connectivity of a wireless multihop network
Proceedings of the 3rd ACM international symposium on Mobile ad hoc networking & computing
On deriving the upper bound of α-lifetime for large sensor networks
Proceedings of the 5th ACM international symposium on Mobile ad hoc networking and computing
Convex Optimization
TAG: a Tiny AGgregation service for Ad-Hoc sensor networks
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
Stochastic coverage in heterogeneous sensor networks
ACM Transactions on Sensor Networks (TOSN)
Introduction to Probability Models, Ninth Edition
Introduction to Probability Models, Ninth Edition
Collaborative in-network processing for target tracking
EURASIP Journal on Applied Signal Processing
Acoustic sensor network design for position estimation
ACM Transactions on Sensor Networks (TOSN)
Explicit Ziv-Zakai lower bound for bearing estimation
IEEE Transactions on Signal Processing
An application-specific protocol architecture for wireless microsensor networks
IEEE Transactions on Wireless Communications
Acoustic sensor network design for position estimation
ACM Transactions on Sensor Networks (TOSN)
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We develop a theory to predict the localization performance of randomly distributed sensor networks consisting of various sensor modalities when only a constant active subset of sensors that minimize localization error is used for estimation. The characteristics of the modalities include measurement type (bearing or range) and error, sensor reliability, FOV, sensing range, and mobility. We show that the localization performance of a sensor network is a function of a weighted sum of the total number of each sensor modality. We also show that optimization of this weighted sum is independent of how the sensor management strategy chooses the active sensors. We combine the utility objective with other objectives, such as lifetime, coverage and reliability to determine the best mix of sensors for an optimal sensor network design. The Pareto efficient frontier of the multi objectives are obtained with a dynamic program, which also accommodates additional convex constraints.