Backcasting: adaptive sampling for sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Call and response: experiments in sampling the environment
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Spatio-temporal sampling rates and energy efficiency in wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Proceedings of the 5th international conference on Information processing in sensor networks
Analytic modeling of detection latency in mobile sensor networks
Proceedings of the 5th international conference on Information processing in sensor networks
Sensing the channel: sensor networks with shared sensing and communications
Proceedings of the 5th international conference on Information processing in sensor networks
Matched source-channel communication for field estimation in wireless sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
A spatial sampling scheme based on innovations diffusion in sensor networks
Proceedings of the 6th international conference on Information processing in sensor networks
Power scheduling for wireless sensor and actuator networks
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)
Data fusion and topology control in wireless sensor networks
WSEAS Transactions on Signal Processing
Distributed field estimation with randomly deployed, noisy, binary sensors
IEEE Transactions on Signal Processing
Active wireless sensing: a versatile framework for information retrieval in sensor networks
IEEE Transactions on Signal Processing
Innovations diffusion: a spatial sampling scheme for distributed estimation and detection
IEEE Transactions on Signal Processing
Field estimation from randomly located binary noisy sensors
IEEE Transactions on Information Theory
Coordinating recharging of large scale robotic teams
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
ACM Transactions on Sensor Networks (TOSN)
Low-complexity one-dimensional edge detection in wireless sensor networks
EURASIP Journal on Wireless Communications and Networking - Special issue on signal processing-assisted protocols and algorithms for cooperating objects and wireless sensor networks
Dual-decomposition approach for distributed optimization in wireless sensor networks
WASA'11 Proceedings of the 6th international conference on Wireless algorithms, systems, and applications
Evaluating local contributions to global performance in wireless sensor and actuator networks
DCOSS'06 Proceedings of the Second IEEE international conference on Distributed Computing in Sensor Systems
Adaptive edge detection with distributed behaviour-based agents in WSNs
International Journal of Sensor Networks
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Sensor networks have emerged as a fundamentally new tool for monitoring spatial phenomena. This paper describes a theory and methodology for estimating inhomogeneous, two-dimensional fields using wireless sensor networks. Inhomogeneous fields are composed of two or more homogeneous (smoothly varying) regions separated by boundaries. The boundaries, which correspond to abrupt spatial changes in the field, are nonparametric one-dimensional curves. The sensors make noisy measurements of the field, and the goal is to obtain an accurate estimate of the field at some desired destination (typically remote from the sensor network). The presence of boundaries makes this problem especially challenging. There are two key questions: 1) Given n sensors, how accurately can the field be estimated? 2) How much energy will be consumed by the communications required to obtain an accurate estimate at the destination? Theoretical upper and lower bounds on the estimation error and energy consumption are given. A practical strategy for estimation and communication is presented. The strategy, based on a hierarchical data-handling and communication architecture, provides a near-optimal balance of accuracy and energy consumption.