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Increasing the Life-time of Wireless Sensor Networks Using Data Prediction

Inanloo, Mahdieh | 2012

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 43462 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Hemmatyar, Afshin
  7. Abstract:
  8. Wireless sensor networks (WSNs) can be used in a variety of applications. The prime shortcoming of these networks is their energy constraint. The main energy consumer in a sensor node is its radio transmitter. Therefore data prediction is one of the most effective methods to reduce the data transmission rate. By data prediction, a large amount of energy is saved; which results in the longevity of the network life. Environmental variations almost have similar effects on different sensor sources in a sensor device. So, considering the correlation between different sources reduces the noise impact and increases data prediction accuracy. In this thesis, we use temporal and multisource correlations to reduce data transmission in WSNs. For this purpose, we have used item-based collaborative filtering for data prediction. Collaborative filtering can be used to extract the relationship between different phenomena sensed by sensors in consequent time points. The corresponding information is used to predict data instances of the following time points. We conducted our simulations on the actual data collected from 54 sensors deployed in the Intel Berkeley Research lab. According to the simulation results, collaborative filtering reduces transmission rate and computational cost, in comparison to the other state of the art methods. When the error threshold is greater than 0.5, it can decrease more than 98% data transmissions.

  9. Keywords:
  10. Wireless Sensor Network ; Network Lifetime ; Data Flow Prediction ; Collaborative Filtering

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