1 / 19

Sensor Fusion-based Event Detection in Wireless Sensor Networks

Sensor Fusion-based Event Detection in Wireless Sensor Networks. Majid Bahrepour, Nirvana Meratnia, Paul Havinga Presented by: Stephan Bosch. Agenda. Introduction to wireless sensor networks (WSNs) Event detection definition Need of sensor fusion for event detection

flavio
Download Presentation

Sensor Fusion-based Event Detection in Wireless Sensor Networks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  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. Introduction to Wireless Sensor Networks (WSNs) P O W E R Sensors Storage Processor Radio • 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

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

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

  8. The Data-fusion Based approach

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

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

  11. Computational complexities

  12. Empirical ResultsData Preparation • Datasets recorded from ‘flaming fires’, ‘non-flaming fires’ and ‘nuisances’ were obtained from the NIST website (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. Empirical ResultsData Analysis Pattern of sensor data in a smoldering fire, a flaming fire and noise data sets: (a) (b) • Temperature • Ionazation sensor • Photoelectric sensor • CO sensor (c) (d)

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

  15. Empirical ResultsComparing the Results *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. Empirical ResultsTwo-level Approach (in the case some sensors drop)

  17. Empirical ResultsOne-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. 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. 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

More Related