Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Atmospheric Modeling, Data Assimilation, and Sensor Networks for Detection and Analysis of Airborne Agent Spread. Part II: Sensor Networks and Data Stream Integration Dr. Michael Murphy Dr. Boris Khattatov Prof. Leo Franca

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 CO emitted by factories in Colorado on December

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Wireless Sensor Networks Low cost per unit. Low cost per unit. Amenable to “Deploy ‘em and leave ‘em” situations” e.g., air dropping into a hot zone. Amenable to “Deploy ‘em and leave ‘em” situations” e.g., air dropping into a hot zone. Nanotechnology-based sensors are being developed that use far less power than traditional sensors. Nanotechnology-based sensors are being developed that use far less power than traditional sensors. However, sensors can produce signals with high signal-to-noise ratios. However, sensors can produce signals with high signal-to-noise ratios. Have sensitivity to biases and bias drifts. Have sensitivity to biases and bias drifts. Have severe power and communications constraints. Have severe power and communications constraints.

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Sensor-Stream Analysis New methods for calculating sensor bias using data assimilation techniques. New methods for calculating sensor bias using data assimilation techniques. New programming methods for sensor networks to adapt to communications and power limitations. New programming methods for sensor networks to adapt to communications and power limitations. Adding novel methods for “event triggering” and for summarizing massive data streams. Adding novel methods for “event triggering” and for summarizing massive data streams.

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Sensor Network Application A contamination event occurs, resulting in a developing plume. A contamination event occurs, resulting in a developing plume. A semi-autonomous swarm of actuated sensors is deployed to collect contaminant samples. A semi-autonomous swarm of actuated sensors is deployed to collect contaminant samples.

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Wireless Sensor Nets (from Wireless Sensor Nets (from Kevin L. Moore, Director Center for Self-Organizing and Intelligent Systems Utah State University)

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Wireless Sensor Nets (from Wireless Sensor Nets (from Kevin L. Moore, Director Center for Self-Organizing and Intelligent Systems Utah State University) Analysis Model

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Experiments with Sensor Streams We have a simulation of an airborne contaminant event and therefore can simulate sensor readings. We have a simulation of an airborne contaminant event and therefore can simulate sensor readings.

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Sensitivity and Selectivity Issues

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 The Bias problem – Bias Drifts

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Solution, use model together with Kalman filter to compute biases. Here, y is the observations from the sensors. The operator H relates model observations and biases from the previous time step.

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Solution, use model together with Kalman filter to compute biases. We used this method to successfully estimate satellite clock and receiver error.

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Improving models and integration Integration of GIS into the plume model – possibly incorporating terrain features and buildings. Integration of GIS into the plume model – possibly incorporating terrain features and buildings. At what level should the model be integrated with the sensor network and how can they be used? At what level should the model be integrated with the sensor network and how can they be used? Correcting biases, identifying miscalibrated sensors, detecting important events… Correcting biases, identifying miscalibrated sensors, detecting important events…

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 TinyDB:Acquisitional Query Language For Sensor Networks (Madden et al) When should samples for a particular query be taken? When should samples for a particular query be taken? What sensor nodes have data relevant to a particular query? What sensor nodes have data relevant to a particular query? In what order should samples for this query be taken, and how should sampling be interleaved with other operations? In what order should samples for this query be taken, and how should sampling be interleaved with other operations? Is it worth expending computational power or bandwidth to process and relay a particular sample? Is it worth expending computational power or bandwidth to process and relay a particular sample?

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Acquisitional Query Language For Sensor Networks (Madden) (Inputs) Power-consumption profile such as this one from [Mainwaring], might be given as follows: Power-consumption profile such as this one from [Mainwaring], might be given as follows: Topology of the sensor network is usually inferred upon initialization and updated. Topology of the sensor network is usually inferred upon initialization and updated.

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Example Acquisitional Queries The basic query: The basic query: SELECT nodeid, light, temp FROM sensors FROM sensors SAMPLE INTERVAL 1s FOR 10s SAMPLE INTERVAL 1s FOR 10s Consumes too much power Consumes too much power

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Better Acquisitional Queries Landmark query: Output a stream of counts indicating the number of recent light readings that were brighter that the current reading. Landmark query: Output a stream of counts indicating the number of recent light readings that were brighter that the current reading. Sliding-window queries: Report the average volume over the last 30 seconds once every 5 seconds, sampling once per second. Sliding-window queries: Report the average volume over the last 30 seconds once every 5 seconds, sampling once per second. STOP-ON events. Continue reporting light readings every second until the level falls below a certain threshold STOP-ON events. Continue reporting light readings every second until the level falls below a certain threshold

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Event-Based Acquisitional Queries Event based queries: Report the average light and temperature level at sensors near a bird nest where a bird has just been detected. Every time a bird-detect event occurs, the query is issued from the detecting node and the average light and temperature are collected from nearby nodes once every 2 seconds for 30 seconds. Event based queries: Report the average light and temperature level at sensors near a bird nest where a bird has just been detected. Every time a bird-detect event occurs, the query is issued from the detecting node and the average light and temperature are collected from nearby nodes once every 2 seconds for 30 seconds.

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Improving Acquisitional Processing TinyDB would benefit from an improved means of sensor “performance” description (sensitivity, selectivity, stability, lifetime, ruggedness, etc.) This would allow for more sophisticated query processing. TinyDB would benefit from an improved means of sensor “performance” description (sensitivity, selectivity, stability, lifetime, ruggedness, etc.) This would allow for more sophisticated query processing.

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Sensor Response Examples

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Improving Acquisitional Processing We’d like to investigate other event-triggering algorithms. We’d like to investigate other event-triggering algorithms. Assimilated models diverging from observations Assimilated models diverging from observations Wavelet techniques Wavelet techniques Streaming Singular Value Decomposition Streaming Singular Value Decomposition

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Wavelet Analysis For Sensor Streams (Mattias) Suppose we have a signal S = [2,2,7,9] Suppose we have a signal S = [2,2,7,9] We first average the cumulative frequencies, pairwise to get the new lower-resolution signal [2,8]. We first average the cumulative frequencies, pairwise to get the new lower-resolution signal [2,8]. We continue recursively until we get the average of [5]. We continue recursively until we get the average of [5]. To reconstruct the signal, we need detail coefficients – With Haar Wavelets, we store the pairwise distances of the original values [0,2] and [6]. To reconstruct the signal, we need detail coefficients – With Haar Wavelets, we store the pairwise distances of the original values [0,2] and [6].

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Wavelet Analysis For Sensor Streams Suppose we have a signal S = [2,2,7,9] Suppose we have a signal S = [2,2,7,9] The wavelet transform, W is thus [5,6,0,2]. The wavelet transform, W is thus [5,6,0,2]. We can then reconstruct the signal using: We can then reconstruct the signal using: S(0) = W(0) – ½ W(1) – ½ W(2) S(0) = W(0) – ½ W(1) – ½ W(2) S(1) = W(0) – ½ W(1) + ½ W(2) S(1) = W(0) – ½ W(1) + ½ W(2) S(2) = W(0) + ½ W(1) – ½ W(3) S(2) = W(0) + ½ W(1) – ½ W(3) S(3) = W(0) + ½ W(1) + ½ W(3) S(3) = W(0) + ½ W(1) + ½ W(3)

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Wavelet Analysis For Sensor Streams The advantage of using wavelets is that a large number of detail coefficients are very small in magnitude. The advantage of using wavelets is that a large number of detail coefficients are very small in magnitude. Truncating these coefficients introduces very small errors in the signal. (JPEG2000 image compression works this way.) Truncating these coefficients introduces very small errors in the signal. (JPEG2000 image compression works this way.) We can approximate the original data distribution efficiently by keeping only the most significant coefficients. We can approximate the original data distribution efficiently by keeping only the most significant coefficients.

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Wavelet Analysis For Sensor Streams Histograms

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Wavelet Analysis For Sensor Streams Streaming wavelet coefficients are easy to compute. Streaming wavelet coefficients are easy to compute. Thus, we can track changes in significant wavelet coefficients very efficiently. Thus, we can track changes in significant wavelet coefficients very efficiently. This gives rise to event-detection methods. Identifies spikes in the data. This gives rise to event-detection methods. Identifies spikes in the data. Can also be used to support “aggregate” and “point” queries when searching in history. Can also be used to support “aggregate” and “point” queries when searching in history.

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Streaming SVD

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Streaming SVD Singular Value Decomposition has enjoyed a wide-variety of data analysis applications for static data sets (face recognition software, search engines, pattern recognition.) Singular Value Decomposition has enjoyed a wide-variety of data analysis applications for static data sets (face recognition software, search engines, pattern recognition.) In a plume-tracking scenario, small eigenvalues would suggest that sensors are not well-placed. In a plume-tracking scenario, small eigenvalues would suggest that sensors are not well-placed. There are new and promising algorithms for computing SVD over data streams [M. Brand 2003] [S. Guha 2003]. There are new and promising algorithms for computing SVD over data streams [M. Brand 2003] [S. Guha 2003].

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Dual Space Algorithms for Plume tracking Guibas et al. The transformation between points and lines in the primal space and lines and points in the dual space. The transformation between points and lines in the primal space and lines and points in the dual space.

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Dual Space Algorithms for Plume tracking A primal and dual space representation for a set of points and a line. A primal and dual space representation for a set of points and a line.

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Dual Space Algorithms for Plume tracking A trace of a shadow passing over a sensor network. Far fewer sensors are used to track it. A trace of a shadow passing over a sensor network. Far fewer sensors are used to track it.

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Dual Space Algorithms for Plume tracking The dual-space approach seems useful for Plume- tracking applications in sensor networks but needs improvements. The dual-space approach seems useful for Plume- tracking applications in sensor networks but needs improvements. It only works for shadow-detection and convex plumes at the moment. It only works for shadow-detection and convex plumes at the moment. We would like to investigate its performance with a model-based approach for activating sensors using TinyDB query framework. We would like to investigate its performance with a model-based approach for activating sensors using TinyDB query framework.

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 GPS Issues in Wireless Sensor Networks For mobile or air-dropped sensors, knowing positions is crucial. For mobile or air-dropped sensors, knowing positions is crucial. However, a GPS chip on-board a fixed sensor can be helpful with synchronization issues in communications protocols. (Spread-spectrum in particular.) However, a GPS chip on-board a fixed sensor can be helpful with synchronization issues in communications protocols. (Spread-spectrum in particular.) Low cost: $3.50/unit when purchased in volume. Low cost: $3.50/unit when purchased in volume. Currently unclear what’s the best way to integrate GPS and maximize its benefits. Currently unclear what’s the best way to integrate GPS and maximize its benefits.

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Low-Power Communications Protocols Most sensor network protocols are designed so that the sensors do not require a controlling base station to function (e.g., directed diffusion). Most sensor network protocols are designed so that the sensors do not require a controlling base station to function (e.g., directed diffusion). In the case where base stations are fixed and reliable, communications protocols could be improved by harnessing the base station’s extra power and computational ability to optimize routing, while at the same time remaining autonomous, if necessary. In the case where base stations are fixed and reliable, communications protocols could be improved by harnessing the base station’s extra power and computational ability to optimize routing, while at the same time remaining autonomous, if necessary.

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah September 2003 Conclusions TinyDB needs more robust methods for incorporating models into its reporting rules. TinyDB needs more robust methods for incorporating models into its reporting rules. Query optimization is required for sensors with complicated properties Query optimization is required for sensors with complicated properties Low-power plume-tracking algorithms for sensor networks need refinement Low-power plume-tracking algorithms for sensor networks need refinement Streaming wavelet analysis and SVD could offer improved decision support Streaming wavelet analysis and SVD could offer improved decision support How to integrate GPS into sensor nodes? How to integrate GPS into sensor nodes?