1. Sensor Fusion-based Event Detection in Wireless Sensor Networks Majid Bahrepour, Nirvana Meratnia, Paul Havinga Presented by: Stephan Bosch

2. 2 Agenda Introduction to wireless sensor networks (WSNs) Event detection definition Need of sensor fusion for event detection The proposed, data fusion based, approach Empirical results Conclusion

3. 3 Introduction to Wireless Sensor Networks (WSNs) Low-power processor & limited memory – Limited processing – Limited memory – 16 bit processor, 2KB RAM, 60KB FLASH Radio. – Low data rate (max 100 kbits/sec) – Limited range (a few hundred meters) Power – Usually small batteries – Needs to operate for long period of time Sensors – Scalar sensors: temperature, light, etc. – Cameras, microphones Sensors Processor Radio Storage POWERPOWER

4. 4 Challenges – Dynamics: environment, network topology and resources can change at any time – Unreliability: regarding sensor data, communication, hardware failure, etc. – Large-scale: autonomous, self-configuration, scalability, robustness – Dependability

5. 5 Event Detection Continuous monitoring and event detection are two major distinct classes of applications for WSNs. Continuous monitoring: continuously reports sensor readings at a regular basis. Event detection: reads sensory information and detects and reports any event of interest – Yields output only if an event has been occurred.

6. 6 Approaches to Event Detection Centralized approach: All sensory information gets gathered at a base station. In the base station event detection is conducted. Traditional New Trend In-network Approach: Event detection is carried out inside the network.

7. 7 Need for Sensor Fusion There might be several issues happening in WSN such as: 1. Sensor-node failures 2. Problems with communication 3. Faulty event reports by individual sensor nodes 4. Unavailability of particular sensors on some sensor nodes

8. 8 The Data-fusion Based approach

9. 9 The Proposed Approach Data Fuser Event/ No-event Signal Node #1 Sensor A Sensor … Sensor M Node #N Sensor A Sensor … Sensor M Node #... Sensor A Sensor … Sensor M The likelihood of a specific event........ Firstly N sensor nodes classify whether the sensory input is an event (each node usually has multiple sensors) The received data are fused to see whether any events occurred Event likelihoods are sent to a higher level (such as a cluster head)

10. 10 Proposed Classifiers Feed Forward Neural Networks (FFNN) Naïve Bayes Because they are 1- Computationally light 2- Accurate 3- Simple/ possible to be programmed into sensor nodes

11. 11 Computational complexities

12. 12 Empirical Results Data Preparation Datasets recorded from ‘flaming fires’, ‘non-flaming fires’ and ‘nuisances’ were obtained from the NIST website (http://smokealarm.nist.gov/).http://smokealarm.nist.gov/ The aim is to discriminate these three situations. Among a number of available features ‘temperature’, ‘ionization’, ‘photoelectric’ and ‘CO’ were chosen. These sensors have previously been shown as the optimal and robust sensors for residential fire detection in classical fire detection literature.

13. 13 Empirical Results Data Analysis (a)(b) (c) (d) a)Temperature b)Ionazation sensor c)Photoelectric sensor d)CO sensor Pattern of sensor data in a smoldering fire, a flaming fire and noise data sets:

14. 14 Empirical Results Experiment Method Both classifier types are trained and tested as follows: Training Data (900)Test Data (500) 1400 rows (total data) Train Data (350)Test Data (150) 500 rows Second Level Training First Level Training

15. 15 Empirical Results Comparing the Results Two Level Approach in Comparison with a Related Study Naïve BayesFFNND-FLER[1] * Accuracy Rate99.33%98.93%98.67% *D-FLER uses fuzzy logic which detects events in a distributed fashion. [1]-Marin-Perianu, M. and P. Havinga, D-FLER – A Distributed Fuzzy Logic Engine for Rule-Based Wireless Sensor Networks Lecture Notes in Computer Science. Vol. 4836. 2008, Heidelberg: Springer Berlin.

16. 16 Empirical Results Two-level Approach (in the case some sensors drop)

17. 17 Empirical Results One-level Approach (Contributions of each Sensor) * It appears that CO is contributing the most which is inline with classical fire detection experiments as well.

18. 18 Conclusions A new event detection approach based on data-fusion is proposed. Data fusion helps with accuracy and robustness of event detection. The proposed approach is a general concept that can be used for any kind of events.

19. 19 Thank you for your attention Any question ? The authors would be delighted to take your questions. Please make a contact at: m.bahrepour@utwente.nl m.bahrepour@utwente.nl