CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Dynamic fine-grained localization in Ad-Hoc networks of sensors
Proceedings of the 7th annual international conference on Mobile computing and networking
Clustering Algorithms
Range-free localization schemes for large scale sensor networks
Proceedings of the 9th annual international conference on Mobile computing and networking
Sensor network-based countersniper system
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
iTPS: an improved location discovery scheme for sensor networks with long-range beacons
Journal of Parallel and Distributed Computing
Identification of Low-Level Point Radiation Sources Using a Sensor Network
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
A computational geometry method for localization using differences of distances
ACM Transactions on Sensor Networks (TOSN)
Estimation of Pollutant-Emitting Point-Sources Using Resource-Constrained Sensor Networks
GSN '09 Proceedings of the 3rd International Conference on GeoSensor Networks
Identification of low-level point radioactive sources using a sensor network
ACM Transactions on Sensor Networks (TOSN)
Complex Event Detection in Extremely Resource-Constrained Wireless Sensor Networks
Mobile Networks and Applications
Accuracy-aware aquatic diffusion process profiling using robotic sensor networks
Proceedings of the 11th international conference on Information Processing in Sensor Networks
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The localization of a radioactive source can be solved in closed-form using 4 ideal sensors and the Apollonius circle in a noise- and error-free environment. When measurement errors and noise such as background radiation are considered, a larger number of sensors is needed to produce accurate results, particularly for extremely low source intensities. In this paper, we present an efficient fusion algorithm that can exploit measurements from n sensors to improve the localization accuracy, and show how the accuracy scales with n. We report testbed results for a 0.911 μCi source to illustrate the effectiveness of our algorithm, in particular performance comparisons with state-of-the-art fusion algorithms based on Mean of Estimates (MoE) and Maximum Likelihood Estimation (MLE). We show that ITP is more accurate than MoE, whereas the choice between ITP and MLE is generally a tradeoff between accuracy and run time efficiency. Higher-intensity radioactive sources are not safe for actual experiments. In this case, we present simulation results based on a validated simulation model. We show that a low-intensity 400 μCi source, similar to the radioactivity of a concealed dirty bomb, can be localized to within 32.5 m using a sensor density of about 1 per 1100 m2 in a surveillance area.