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.

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 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.

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.

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 sensor's 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.

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.

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.

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 m 2 in a surveillance area.

No abstract available.

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. We consider a realistic map based stochastic movement model, of which the well known random waypoint model is a special case. For the random waypoint model, we develop a tight analytical lower bound of the achievable expected communication distance. For the general map-based model, we characterize the lower bound distance experimentally. We also address the practical attainment of distance reduction (and hence, energy savings) through movement predicted communication. Specifically, whereas prior work has presented a least distance (LD) postponement algorithm and established its effectiveness experimentally, 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 map based and random waypoint models. Moreover, the algorithm's absolute performance increases as the nodal speed or the allowable postponement delay increases.

We study the problem of a mobile target (the mouse) trying to evade detection by one or more mobile sensors (we call such a sensor a cat) in a closed network area. We view our problem as a game between two players: the mouse, and the collection of cats forming a single (meta-)player. The game ends when the mouse falls within the sensing range of one or more cats. A cat tries to determine its optimal strategy to minimize the worst case expected detection time of the mouse. The mouse tries to determine an optimal counter movement strategy to maximize the expected detection time. We divide the problem into two cases based on the relative sensing capabilities of the cats and the mouse. When the mouse has a larger sensing range than the cats, we show how the mouse can determine its optimal movement strategy based on local observations of the cats' movements. When the mouse has a sensing range smaller than or equal to the cats', we develop a dynamic programming solution for the mouse's optimal strategy, assuming high level information about the cats' movement model. We discuss how the cats' chosen movement model will affect its presence matrix in the network, and hence its payoff in the game. Extensive experimental results verify and illustrate the analytical results, and evaluate the game's payoffs as a function of several important system parameters.