An in-network reduction algorithm for real-time wireless sensor network applications
Proceedings of the 4th ACM workshop on Wireless multimedia networking and performance modeling
DCOSS '09 Proceedings of the 5th IEEE International Conference on Distributed Computing in Sensor Systems
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
Energy-efficient data gathering in wireless sensor networks with asynchronous sampling
ACM Transactions on Sensor Networks (TOSN)
Maximizing lifetime of sensor-target surveillance in wireless sensor networks
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Optimal data compression and forwarding in wireless sensor networks
IEEE Communications Letters
ICACT'10 Proceedings of the 12th international conference on Advanced communication technology
DPLC: dynamic packet length control in wireless sensor networks
INFOCOM'10 Proceedings of the 29th conference on Information communications
Information Sciences: an International Journal
A survey of visual sensor network platforms
Multimedia Tools and Applications
International Journal of Ad Hoc and Ubiquitous Computing
Prolonging wireless sensor network lifetime in stealth mode through intelligent data compression
Proceedings of the 10th ACM symposium on Performance evaluation of wireless ad hoc, sensor, & ubiquitous networks
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We study the problem of constructing a data gathering tree over a wireless sensor network in order to minimize the total energy for compressing and transporting information from a set of source nodes to the sink. This problem is crucial for advanced computation-intensive applications, where traditional "maximum" in-network compression may result in significant computation energy. We investigate a tunable data compression technique that enables effective tradeoffs between the computation and communication costs. We derive the optimal compression strategy for a given data gathering tree and then investigate the performance of different tree structures for networks deployed on a grid topology as well as general graphs. Our analytical results pertaining to the grid topology and simulation results pertaining to the general graphs indicate that the performance of a simple greedy approximation to the Minimal Steiner Tree (MST) provides a constantfactor approximation for the grid topology and good average performance on the general graphs. Although theoretically, a more complicated randomized algorithm offers a poly-logarithmic performance bound, the simple greedy approximation of MST is attractive for practical implementation.