Efficient Environmental Monitoring System with Data Fusion, Prediction & Fuzzy Logic

1. An Efficient Environmental Monitoring System adopting Data Fusion, Prediction& Fuzzy Logic K. Kolomvatsos1, C. Anagnostopoulos2, and S. Hadjiefthymiades1 1University of Athens, Department of Informatics & Telecommunications 2Department of Informatics, Ionian University 6th International Conference on Information, Intelligence, Systems and Applications July 2015 Corfu, Greece

2. Outline • Environmental Monitoring • The Proposed Mechanism • The Fusion Component • The Prediction Component • The Fuzzy Logic Controller • Experimental Results

3. Environmental Monitoring • Changes in the environment should be immediately identified and specific solutions should be applied. • Need: pro-active methods that immediately respond to any change in the environment’s characteristics. • A number of sensors could monitor specific areas and a Central System (CS) could reason over the observed values and support decision making. • Many research and commercial efforts adopt: • sensors observing a specific phenomenon (e.g., temperature, humidity, water level), and, • intelligent systems that respond to the identification of events (e.g., fire, flood)

4. The Proposed Mechanism (1/3) • We propose a mechanism that combines datafusion techniques, prediction(regression) and Fuzzy Logic (FL). • The proposed mechanism • is a decision making tool for the identification of environmental events. • builds on top of measurements performed by a number of sensors to provide immediate responses to any observed abnormality. • derives knowledge from the group of sensors. • aggregates the reported measurements and reasons over the opinion of the group (on the occurrence of the specific event).

5. The Proposed Mechanism (2/3) • We consider a group of N sensors. • Sensors observe the same phenomenon and their reports are TXed at pre-defined intervals to the CS. • When the CS receives sensor readings, it decides if an event has occurred and, then, triggers the respective alert. • The CS is not based only on a single sensor observations, as false alarms could undermine robustness. • Sensors could report invalid observations due to various reasons (intrinsic, extrinsic) .

6. The Proposed Mechanism (3/3) • In the proposed CS, a number of components are responsible to manage the incoming data and derive the appropriate decision. • The adopted components are: • a Fusion Component (FC) that undertakes the responsibility of eliminating the outlier data and providing the final fused measurement, • a Prediction Component (PC)that,based on the fused measurement historical values, derives the predicted future fused measurement and, • an FL Controller(FLC) that gets the results of FC and PC components and derives the Degree of Danger (DoD). • The DoD provides a view on the existing danger based on the current measurements and the predicted value.

7. The Fusion Component (1/4) • Data fusion combines contextual data from all sensors to derive reliable fused measurements. • We adopt the cumulative sum (CumSum) algorithm for outliers detection over all measurements and the linear opinion pool algorithm for deriving the final aggregated value. • CumSum detects if there is any change in the distribution of a contextual time series corresponding to a sensor. • The algorithm is a change-point detection technique based on the cumulative sum of the differences between the current value at instance t and the overall average up to t. • We adopt a 2-side detection scheme where an observation is inferred as an outlier when it lies above a target threshold h+ or below a target threshold h-

8. The Fusion Component (2/4) • Two signals are the output of the CumSum algorithm: the above detection signal and the below detection signal. • When the time series deviates from the thresholds, detection signals are set to 1. • The detected outliers are eliminated and, thus, the mechanism (at instancet) is based on the opinion (measurements) of those sensors that do not produce outliers. • The proposed MS adopts a linear opinion pool scheme for the remaining values.

9. The Fusion Component (3/4) • The linear opinion pool is a standard approach to combine experts’ opinion through a weighted linear average. • The final aim is to combine singe experts’ opinion to produce the opinion of the group. • We apply specific weights in each expert to pay more / less attention on each opinion affecting more / less the final result. • The fused measurement f = F(x1, x2, …, xN) is the opinion pool based on the pooling operator F over the measurements (opinions). • We adopt a weighted linear average i.e., , where wi is the weight associated with the opinion of sensor Si which does not produce an outlier. • m is the number of sensors derived by the CumSum algorithm..

10. The Fusion Component (4/4) • We define Ci as the confidence that the CS has on sensor Si in successfully fulfilling the assigned task. • Ci could be affected by the sensor state or by historical data. • We calculate each weight as follows: • The CS assigns more weight on the opinion of a sensor having a high confidence value.

11. The Prediction Component • For each sensor Si a time ordered set of past values is maintained. • History of the latest observations. • We predict the next measurement through a linear combination of the historical measurements with real-valued prediction coefficients. • The set of coefficients are estimated to minimize the prediction error between the predicted and the actual measurement. • We adopt the Levinson-Durbin* algorithm for coefficient estimation. * A)Durbin J., ‘The fitting of time series models’, Rev. Inst. Int. Stat., v. 28, 1960, pp. 233-243 B) Levinson N., ‘The Wiener RMS error criterion in filter design and prediction’, Journal Math. Phys., v. 25, 1947, pp. 261-278

12. The FL Controller • We developed an FL Controller (FLC), which is responsible for defining the CS’s reaction to the incoming data • The FL Controller has two inputs: • the fused measurement based on current sensors observations (fv) • the predicted measurement based on historical data (pv) • The output is the DoD value. • We consider that DoD 1 indicates that the danger is at high levels; the opposite stands when DoD  0. • We consider three linguistic values: Low, Medium, High. • A Low value represents that the variable takes values close to the lower limit while a High value depicts the case where the variable takes values close to the upper level. A Medium value depicts the case where the variable takes values close to the average. • We consider triangular membership functions.

13. Experimental Evaluation (1/3) • Performance Metrics • Rate of False Alerts (RFA). • It represents the rate of false alerts. • As RFA → 1, the system results a lot of false alerts. As RFA → 0, the system minimizes the rate of false alerts. • Index of Alert (IA). • It represents the index of the sensor report that triggers an alert. • Datasets • Intel Berkeley Research Lab dataset – Temperature values (Intel Lab Data, http://db.csail.mit.edu/labdata/labdata.html) • We inject faulty measurements to derive the RFA - P faulty measurements (P ∈ {1%, 5%, 10%, 20%, 40%}) of the total (15,000 measurements) • Real past flood event as reported by a number of water level sensors (http:// www.pegelonline.wsv.de)

14. Experimental Evaluation (2/3) • We compare the proposed FL system (FLS) with the following models: • The Single Sensor Alerting (SSA) mechanism • It delivers an alert when at least one sensor reports a value over a pre-defined threshold • The Average Measurements Alerting (AMA) mechanism • It produces alerts when the average value of the sensor reports is over the pre-defined threshold

15. Experimental Evaluation (3/3) • Experimental results for various P • Experimental results for various N (number of sensors) • Experimental results for IA (5 sensors) • Experimental results for IA (10 sensors)

16. Thank You!