A threshold of ln n for approximating set cover
Journal of the ACM (JACM)
Approximation algorithms for directed Steiner problems
Journal of 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
Approximating minimum size weakly-connected dominating sets for clustering mobile ad hoc networks
Proceedings of the 3rd ACM international symposium on Mobile ad hoc networking & computing
On the interdependence of routing and data compression in multi-hop sensor networks
Proceedings of the 8th annual international conference on Mobile computing and networking
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Simultaneous optimization for concave costs: single sink aggregation or single source buy-at-bulk
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
The impact of spatial correlation on routing with compression in wireless sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Gathering correlated data in sensor networks
Proceedings of the 2004 joint workshop on Foundations of mobile computing
Snapshot Queries: Towards Data-Centric Sensor Networks
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Efficient gathering of correlated data in sensor networks
Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing
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
On optimal communication cost for gathering correlated data through wireless sensor networks
Proceedings of the 12th annual international conference on Mobile computing and networking
ACM Transactions on Sensor Networks (TOSN)
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Predictive modeling-based data collection in wireless sensor networks
EWSN'08 Proceedings of the 5th European conference on Wireless sensor networks
Networked Slepian-Wolf: theory, algorithms, and scaling laws
IEEE Transactions on Information Theory
Minimum-hot-spot query trees for wireless sensor networks
Proceedings of the Ninth ACM International Workshop on Data Engineering for Wireless and Mobile Access
K2: a system for campaign deployments of wireless sensor networks
REALWSN'10 Proceedings of the 4th international conference on Real-world wireless sensor networks
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We address the problem of efficiently gathering correlated data from a wireless sensor network, with the aim of designing algorithms with provable optimality guarantees, and understanding how close we can get to the known theoretical lower bounds. Our proposed approach is based on finding an optimal or a near-optimal compression tree for a given sensor network: a compression tree is a directed tree over the sensor network nodes such that the value of a node is compressed using the value of its parent. We focus on broadcast communication model in this paper, but our results are more generally applicable to a unicast communication model as well. We draw connections between the data collection problem and a previously studied graph concept called weakly connected dominating sets, and we use this to develop novel approximation algorithms for the problem. We present comparative results on several synthetic and real-world datasets showing that our algorithms construct near-optimal compression trees that yield a significant reduction in the data collection cost.