Condition numbers of random matrices
Journal of Complexity
Condition Numbers of Random Triangular Matrices
SIAM Journal on Matrix Analysis and Applications
An analysis of a large scale habitat monitoring application
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Prediction-based monitoring in sensor networks: taking lessons from MPEG
ACM SIGCOMM Computer Communication Review - Special issue on wireless extensions to the internet
Decentralized compression and predistribution via randomized gossiping
Proceedings of the 5th international conference on Information processing in sensor networks
Proceedings of the 5th international conference on Information processing in sensor networks
Proceedings of the 5th international conference on Information processing in sensor networks
Approximate Data Collection in Sensor Networks using Probabilistic Models
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Network correlated data gathering with explicit communication: NP-completeness and algorithms
IEEE/ACM Transactions on Networking (TON)
Constraint chaining: on energy-efficient continuous monitoring in sensor networks
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
ACM Transactions on Sensor Networks (TOSN)
Distributed sparse random projections for refinable approximation
Proceedings of the 6th international conference on Information processing in sensor networks
Efficient gathering of correlated data in sensor networks
ACM Transactions on Sensor Networks (TOSN)
Correlated data gathering in wireless sensor networks based on distributed source coding
International Journal of Sensor Networks
International Journal of Distributed Sensor Networks
Spatially-Localized Compressed Sensing and Routing in Multi-hop Sensor Networks
GSN '09 Proceedings of the 3rd International Conference on GeoSensor Networks
Compressive data gathering for large-scale wireless sensor networks
Proceedings of the 15th annual international conference on Mobile computing and networking
EWSN'07 Proceedings of the 4th European conference on Wireless sensor networks
Predictive modeling-based data collection in wireless sensor networks
EWSN'08 Proceedings of the 5th European conference on Wireless sensor networks
Exact signal recovery from sparsely corrupted measurements through the pursuit of justice
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Decoding by linear programming
IEEE Transactions on Information Theory
Stable recovery of sparse overcomplete representations in the presence of noise
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
An efficient compressive data gathering routing scheme for large-scale wireless sensor networks
Computers and Electrical Engineering
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We proposed compressive data gathering (CDG) that leverages compressive sampling (CS) principle to efficiently reduce communication cost and prolong network lifetime for large scale monitoring sensor networks. The network capacity has been proven to increase proportionally to the sparsity of sensor readings. In this paper, we further address two key problems in the CDG framework. First, we investigate how to generate RIP (restricted isometry property) preserving measurements of sensor readings by taking multi-hop communication cost into account. Excitingly, we discover that a simple form of measurement matrix [I R] has good RIP, and the data gathering scheme that realizes this measurement matrix can further reduce the communication cost of CDG for both chain-type and tree-type topology. Second, although the sparsity of sensor readings is pervasive, it might be rather complicated to fully exploit it. Owing to the inherent flexibility of CS principle, the proposed CDG framework is able to utilize various sparsity patterns despite of a simple and unified data gathering process. In particular, we present approaches for adapting CS decoder to utilize cross-domain sparsity (e.g. temporal-frequency and spatial-frequency). We carry out simulation experiments over both synthesized and real sensor data. The results confirm that CDG can preserve sensor data fidelity at a reduced communication cost.