Accurate localization of low-level radioactive source under noise and measurement errors

Chin, Jren-Chit; Yau, David K. Y.; Rao, Nageswara S. V.; Yang, Yong; Y.T., Chris; Shankar, Mallikarjun

doi:10.1145/1460412.1460431

Cited by 54 publications

(33 citation statements)

References 15 publications

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“…The passing route is determined according to various estimation performance metrics including CRB. In geometric trilateration approach [14,4], the measurement of a sensor is mapped to the distance from the sensor to the diffusion source. The source location can then be estimated by trilateration among multiple sensors.…”

Section: Related Workmentioning

confidence: 99%

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Accuracy-aware aquatic diffusion process profiling using robotic sensor networks

Wang¹,

Tan²,

Xing³

et al. 2012

2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN)

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Water resources and aquatic ecosystems are facing increasing threats from climate change, improper waste disposal, and oil spill incidents. It is of great interest to deploy mobile sensors to detect and monitor certain diffusion processes (e.g., chemical pollutants) that are harmful to aquatic environments. In this paper, we propose an accuracy-aware diffusion process profiling approach using smart aquatic mobile sensors such as robotic fish. In our approach, the robotic sensors collaboratively profile the characteristics of a diffusion process including source location, discharged substance amount, and its evolution over time. In particular, the robotic sensors reposition themselves to progressively improve the profiling accuracy. We formulate a novel movement scheduling problem that aims to maximize the profiling accuracy subject to limited sensor mobility and energy budget. We develop an efficient greedy algorithm and a more complex near-optimal radial algorithm to solve the problem. We conduct extensive simulations based on real data traces of robotic fish movement and wireless communication. The results show that our approach can accurately profile dynamic diffusion processes under tight energy budgets. Moreover, a preliminary evaluation based on the implementation on TelosB motes validates the feasibility of deploying our movement scheduling algorithms on mote-class robotic sensor platforms.

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Section: Related Workmentioning

confidence: 99%

“…The source location can then be estimated by trilateration among multiple sensors. Such an approach incurs low computational complexities, but suffers lower estimation accuracy compared with more advanced approaches such as MLE [4].…”

Section: Related Workmentioning

confidence: 99%

Accuracy-aware aquatic diffusion process profiling using robotic sensor networks

Wang¹,

Tan²,

Xing³

et al. 2012

2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN)

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“…Figure 1b shows a chemical dispersion event that spans the 2-dimensional space and comprises approximately 10,000 points. It is constructed by the model proposed by Ishida et al [22] and is similar to target events considered by applications such as Gunatilaka et al [16] and Chin et al [9]. Finally, Fig.…”

Section: Problem Statement and Contributionsmentioning

confidence: 99%

Complex Event Detection in Extremely Resource-Constrained Wireless Sensor Networks

Zoumboulakis

Roussos

2010

Mobile Netw Appl

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Complex Events are sequences of sensor measurements indicating interesting or unusual activity in the monitored process. Such events are ubiquitous in a wide range of Wireless Sensor Network (WSN) applications, yet there does not exist a common mechanism that addresses both the considerable constraints of WSNs and the specific properties of Complex Events. We argue that Complex Events cannot be described using standard threshold-based or composite logic approaches and attempting to represent them as such can lead to unpredictable execution cost while detection accuracy suffers from erroneous recording of observations which are common in WSNs. To address this, we develop a family of Complex Event Detection (CED) algorithms based on online symbolic conversion of sensor readings. With fixed execution cost and modest resource requirements, the CED algorithms cater for exact, approximate, non-parametric, multiple and probabilistic detection that is neither application nor data dependent. Overall, full implementation and simulations provide experimental evidence of the advantages of the proposed approach. We find that the proposed algorithms minimise configuration, promote

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“…The source strength is estimated using a linear combination of the estimates from individual sensors. The N-sensor localization method is based on the iterative pruning (ITP) algorithm in Chin et al [2008]. ITP increases the robustness of the estimation in the face of noise and errors in the measurement process.…”

Section: Introductionmentioning

confidence: 99%

Identification of low-level point radioactive sources using a sensor network

Chin

Rao

Yau

et al. 2010

ACM Trans. Sen. Netw.

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Identification of a low-level point radioactive source amidst background radiation is achieved by a network of radiation sensors using a two-step approach. Based on measurements from three or more sensors, a geometric difference triangulation method or an N-sensor localization method is used to estimate the location and strength of the source. Then a sequential probability ratio test based on current measurements and estimated parameters is employed to finally decide: (1) the presence of a source with the estimated parameters, or (2) the absence of the source, or (3) the Author's address: J.-C. Chin: email: jcchin@cs.purdue.edu. c 2010 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by a contractor or affiliate of the U.S. Government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 ( insufficiency of measurements to make a decision. This method achieves specified levels of false alarm and missed detection probabilities, while ensuring a close-to-minimal number of measurements for reaching a decision. This method minimizes the ghost-source problem of current estimation methods, and achieves a lower false alarm rate compared with current detection methods. This method is tested and demonstrated using: (1) simulations, and (2) a test-bed that utilizes the scaling properties of point radioactive sources to emulate high intensity ones that cannot be easily and safely handled in laboratory experiments.

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Accurate localization of low-level radioactive source under noise and measurement errors

Cited by 54 publications

References 15 publications

Accuracy-aware aquatic diffusion process profiling using robotic sensor networks

Accuracy-aware aquatic diffusion process profiling using robotic sensor networks

Complex Event Detection in Extremely Resource-Constrained Wireless Sensor Networks

Identification of low-level point radioactive sources using a sensor network

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