MiniMax Methods for Image Reconstruction
MiniMax Methods for Image Reconstruction
Boundary estimation in sensor networks: theory and methods
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Estimating inhomogeneous fields using wireless sensor networks
IEEE Journal on Selected Areas in Communications
Active learning for adaptive mobile sensing networks
Proceedings of the 5th international conference on Information processing in sensor networks
Design and implementation of a wireless sensor network for intelligent light control
Proceedings of the 6th international conference on Information processing in sensor networks
Information fusion for wireless sensor networks: Methods, models, and classifications
ACM Computing Surveys (CSUR)
Future scenarios of parallel computing: Distributed sensor networks
Journal of Visual Languages and Computing
Summarizing Distributed Data Streams for Storage in Data Warehouses
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Energy conservation in wireless sensor networks: A survey
Ad Hoc Networks
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Decentralized control of adaptive sampling in wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
FloodNet: coupling adaptive sampling with energy aware routing in a flood warning system
Journal of Computer Science and Technology
Coordinating recharging of large scale robotic teams
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Energy-efficient data gathering in wireless sensor networks with asynchronous sampling
ACM Transactions on Sensor Networks (TOSN)
A utility-based adaptive sensing and multihop communication protocol for wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
DCOSS'07 Proceedings of the 3rd IEEE international conference on Distributed computing in sensor systems
Level set estimation using uncoordinated mobile sensors
ADHOC-NOW'07 Proceedings of the 6th international conference on Ad-hoc, mobile and wireless networks
SILENCE: distributed adaptive sampling for sensor-based autonomic systems
Proceedings of the 8th ACM international conference on Autonomic computing
Energy-aware data processing techniques for wireless sensor networks: a review
Transactions on large-scale data- and knowledge-centered systems III
Information quality model and optimization for 802.15.4-based wireless sensor networks
Journal of Network and Computer Applications
Routing explicit side information for data compression in wireless sensor networks
DCOSS'05 Proceedings of the First IEEE international conference on Distributed Computing in Sensor Systems
Energy-aware sparse approximation technique (EAST) for rechargeable wireless sensor networks
EWSN'10 Proceedings of the 7th European conference on Wireless Sensor Networks
Autonomous Agents and Multi-Agent Systems
Value of information and mobility constraints for sampling with mobile sensors
Computers & Geosciences
Scalaness/nesT: type specialized staged programming for sensor networks
Proceedings of the 12th international conference on Generative programming: concepts & experiences
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Wireless sensor networks provide an attractive approach to spatially monitoring environments. Wireless technology makes these systems relatively exible, but also places heavy demands on energy consumption for communications. This raises a fundamental trade-off: using higher densities of sensors provides more measurements, higher resolution and better accuracy, but requires more communications and pro-cessing. This paper proposes a new approach, called "back-casting," which can significantly reduce communications and energy consumption while maintaining high accuracy. Back-casting operates by first having a small subset of the wireless sensors communicate their information to a fusion center. This provides an initial estimate of the environment being sensed, and guides the allocation of additional network resources. Specifically, the fusion center backcasts information based on the initial estimate to the network at large, selectively activating additional sensor nodes in order to achieve a target error level. The key idea is that the initial estimate can detect correlations in the environment, indicating that many sensors may not need to be activated by the fusion center. Thus, adaptive sampling can save energy compared to dense, non-adaptive sampling. This method is theoretically analyzed in the context of field estimation and it is shown that the energy savings can be quite significant compared to conventional approaches. For example, when sensing a piecewise smooth field with an array of 100 -- 100 sensors, adaptive sampling can reduce the energy consumption by roughly a factor of 10 while providing the same accuracy achievable if all sensors were activated.