1. K. Kolomvatsos 1, C. Anagnostopoulos 2, and S. Hadjiefthymiades 1 An Efficient Environmental Monitoring System adopting Data Fusion, Prediction & Fuzzy Logic 1 University of Athens, Department of Informatics & Telecommunications 2 Department of Informatics, Ionian University 6 th 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 data fusion 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 instance t) 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(x 1, x 2, …, x N ) is the opinion pool based on the pooling operator F over the measurements (opinions). We adopt a weighted linear average i.e.,, where w i is the weight associated with the opinion of sensor S i 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 C i as the confidence that the CS has on sensor S i in successfully fulfilling the assigned task. C i 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 S i 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 (f v ) the predicted measurement based on historical data (p v ) 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) ModelIA Real Event45 FLC44 AMA88 ModelIA Real Event45 FLC40 AMA45 SSA34, 35, 36, 39

16. Thank You!