Integer and combinatorial optimization
Integer and combinatorial optimization
Automatic Sensor Placement from Vision Task Requirements
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Automatic node selection for high performance applications on networks
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Random Structures & Algorithms
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Pattern Classification (2nd Edition)
Randomized pursuit-evasion with limited visibility
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Convex Optimization
Maximizing the functional lifetime of sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
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
Pareto Frontiers of Sensor Networks for Localization
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
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
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Pareto Frontiers of Sensor Networks for Localization
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
Impact of mobile node density on detection performance measures in a hybrid sensor network
IEEE Transactions on Wireless Communications
Signal recovery with cost-constrained measurements
IEEE Transactions on Signal Processing
Trustworthiness analysis of sensor data in cyber-physical systems
Journal of Computer and System Sciences
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In this article, we develop tractable mathematical models and approximate solution algorithms for a class of integer optimization problems with probabilistic and deterministic constraints, with applications to the design of distributed sensor networks that have limited connectivity. For a given deployment region size, we calculate the Pareto frontier of the sensor network utility at the desired probabilities for d-connectivity and k-coverage. As a result of our analysis, we determine (1) the number of sensors of different types to deploy from a sensor pool, which offers a cost vs. performance trade-off for each type of sensor, (2) the minimum required radio transmission ranges of the sensors to ensure connectivity, and (3) the lifetime of the sensor network. For generality, we consider randomly deployed sensor networks and formulate constrained optimization technique to obtain the localization performance. The approach is guided and validated using an unattended acoustic sensor network design. Finally, approximations of the complete statistical characterization of the acoustic sensor networks are given, which enable average network performance predictions of any combination of acoustic sensors.