Jren-Chit Chin

Identification of Low-Level Point Radiation Sources Using a Sensor Network

Identification of a low-level point radiation source amidst background radiation is achieved by a network of radiation sensors using a two-step approach. Based on measurements from three sensors, the geometric difference triangulation 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 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 radiation sources to emulate high intensity ones that cannot be easily and safely handled in laboratory experiments.

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

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.

An Experimental Low-Cost, Low-Data-Rate Rapid Structural Assessment Network

In this paper, we present the design, implementation, and experimental evaluation of a wireless sensor network for real-time structural ldquohealthrdquo monitoring. We use simple custom-built gages to detect cracks in critical structural elements. The main data reports require no structural analysis for interpretation, have a low data rate, and are naturally resilient to loss. We show how a variety of low-cost, off-the-shelf data acquisition/communication devices can be used to support remote monitoring by a control center. The heterogeneous hardware is accommodated by the use of open technology standards and a software architecture that is portable, modular, and highly configurable. We present an experimental evaluation of our structural-assessment network done using a full-scale three-story reinforced concrete building that was tested under lateral forces emulating forces induced by earthquakes. Our results show that a set of 12 strategically positioned sensors achieved a 100% detection rate for cracks crossing sensors and a zero false-alarm rate (in the sense that all signals exceeding a preset threshold were traced to cracks exceeding a specified total width).

Efficient and Robust Localization of Multiple Radiation Sources in Complex Environments

We present a robust localization algorithm for multiple radiation sources using a network of sensors under random underlying physical processes and measurement errors. The proposed solution uses a hybrid formulation of particle filter and mean-shift techniques to achieve several important features that address major challenges faced by existing localization algorithms. First, our algorithm is able to maintain a constant number of estimation (source) parameters even as the number of radiation sources K increases. In existing algorithms, the number of estimation parameters is proportional to K and thus the algorithm complexity grows exponentially with K. Second, to decide the number of sources K, existing algorithms either require the information to be known in advance or rely on expensive statistical estimations that do not scale well with K. Instead, our algorithm efficiently learns the number of sources from the estimated source parameters. Third, when obstacles are present, our algorithm can exploit the obstacles to achieve better isolation between the source signatures, thereby increasing the localization accuracy in complex deployment environments. In contrast, incompletely specified obstacles will significantly degrade the accuracy of existing algorithms due to their unpredictable effects on the source signatures. We present extensive simulation results to demonstrate that our algorithm has robust performance in complex deployment environments, and its efficiency is scalable to many radiation sources in these environments.

Matching and Fairness in Threat-Based Mobile Sensor Coverage

Mobile sensors can be used to effect complete coverage of a surveillance area for a given threat over time, thereby reducing the number of sensors necessary. The surveillance area may have a given threat profile as determined by the kind of threat, and accompanying meteorological, environmental, and human factors. In planning the movement of sensors, areas that are deemed higher threat should receive proportionately higher coverage. We propose a coverage algorithm for mobile sensors to achieve a coverage that will match - over the long term and as quantified by an RMSE metric - a given threat profile. Moreover, the algorithm has the following desirable properties: 1) stochastic, so that it is robust to contingencies and makes it hard for an adversary to anticipate the sensors movement, 2) efficient, and 3) practical, by avoiding movement over inaccessible areas. Further to matching, we argue that a fairness measure of performance over the shorter time scale is also important. We show that the RMSE and fairness are, in general, antagonistic, and argue for the need of a combined measure of performance, which we call efficacy. We show how a pause time parameter of the coverage algorithm can be used to control the trade-off between the RMSE and fairness, and present an efficient offline algorithm to determine the optimal pause time maximizing the efficacy. Finally, we discuss the effects of multiple sensors, under both independent and coordinated operation. Extensive simulation results - under realistic coverage scenarios - are presented for performance evaluation.

Distance Reduction in Mobile Wireless Communication: Lower Bound Analysis and Practical Attainment

The transmission energy required for a wireless communication increases superlinearly with the communication distance. In a mobile wireless network, nodal movement can be exploited to greatly reduce the energy required by postponing communication until the sender moves close to a target receiver, subject to application deadline constraints. In this paper, we characterize the fundamental performance limit, namely the lower bound expected communication distance, achievable by any postponement algorithm within given deadline constraints. Our analytical results concern mainly the random waypoint (RWP) model. Specifically, we develop a tight analytical lower bound of the achievable expected communication distance under the model. In addition, we define a more general map-based movement model, and characterize its lower bound distance by simulations. We also address the practical attainment of distance reduction through movement-predicted communication. Specifically, whereas prior work has experimentally demonstrated the effectiveness a least distance (LD) algorithm, we provide an absolute performance measure of how closely LD can match the theoretical optimum. We show that LD achieves an average reduction in the expected communication distance within 62% to 94% of the optimal, over a realistic range of nodal speeds, for both the RWP and map-based models.

Localization leads to improved distributed detection under non-smooth distributions

We consider a detection network of sensors that measure intensity levels due to a source amidst background inside a two-dimensional monitoring area. The source intensity decays away from it possibly in discrete jumps, and the corresponding sensor measurements could be random due to the nature of source and background, or due to sensor errors, or both. The detection problem is to infer the presence of a source based on sensor measurements. In the conventional decision/detection fusion approach, detection decisions are made at the individual sensors using Sequential Probability Ratio Test (SPRT), and are combined at the fusion center using a Boolean fusion rule. We show that better detection can be achieved by utilizing sensor measurements at the fusion center, by first localizing the source and then utilizing a more effective SPRT. This approach leads to the detection performance superior to any Boolean detection fuser, under fairly general conditions: (i) smooth and non-smooth source intensity functions and probability ratios, and (ii) a minimum packing number of the state-space. We apply this method to improve the detection of (a) low-level point radiation sources amidst background radiation under strong shielding conditions, and (b) the well-studied Gaussian source amidst Gaussian background.

Cyber-physical trade-offs in distributed detection networks

We consider a network of sensors that measure the scalar intensity due to the background or a source combined with background, inside a two-dimensional monitoring area. The sensor measurements may be random due to the underlying nature of the source and background or due to sensor errors or both. The detection problem is infer the presence of a source of unknown intensity and location based on sensor measurements. In the conventional approach, detection decisions are made at the individual sensors, which are then combined at the fusion center, for example using the majority rule. With increased communication and computation costs, we show that a more complex fusion algorithm based on measurements achieves better detection performance under smooth and non-smooth source intensity functions, Lipschitz conditions on probability ratios and a minimum packing number for the state-space. We show that these conditions for trade-offs between the cyber costs and physical detection performance are applicable for two detection problems: (i) Poisson radiation sources amidst background radiation, and (ii) sources and background with Gaussian distributions.

A low-cost, low-data-rate rapid structural assessment network: Design, implementation, and experimentation

We present the design, implementation, and experimental evaluation of a wireless sensor network for near real-time structural health monitoring. We use simple custom-built gages to unequivocally detect cracks in critical structural elements. The main data reports have a low data rate and are naturally resilient to loss. We show how a variety of low-cost, off-the-shelf data acquisition/communication devices can be used to support remote monitoring by a control center. The heterogeneous hardware is accommodated by the use of open technology standards, and a software architecture that is portable, modular, and highly configurable. We present an experimental evaluation of our structural assessment network, using a full-scale three-story reinforced concrete building, subjected to lateral forces emulating forces induced by earthquakes. Our results show that a set of 12 strategically positioned sensors on the three floors achieved a zero false-alarm rate, in the sense that each reported breakage can be traced to cracks exceeding the specified total width, and a 100% detection rate for cracks that are covered by a sensor.