Mutual Information-Based Sensor Positioning for Car Cabin Comfort Control

  • Diana Hintea
  • James Brusey
  • Elena Gaura
  • Neil Beloe
  • David Bridge
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6883)


Car cabins are transient, non-uniform thermal environments, both with respect to time and space. Identifying representative locations for the Heating, Ventilation and Air Conditioning (HVAC) system sensors is an open research problem. Common sensor positioning approaches are driven by considerations such as cost or aesthetics, which may impact on the performance/outputs of the HVAC system and thus occupants’ comfort. Based on experimental data, this paper quantifies the spacial-temporal variations in the cabin’s environment by using Mutual Information (MI) as a similarity measure. The overarching aim for the work is to find optimal (but practical) locations for sensors that: i) can produce accurate estimates of temperature at locations where sensors would be difficult to place, such as on an occupant’s face or abdomen and ii) thus, support the development of occupant rather than cabin focused HVAC control algorithms. When applied to experimental data from stable and hot/cold soaking scenarios, the method proposed successfully identified practical sensor locations which estimate face and abdomen temperatures of an occupant with less than 0.7°C and 0.5°C error, respectively.


Mutual Information Thermal Comfort Sensor Location Fuzzy Neural Network Joint Entropy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cameron, A., Durrant-Whyte, H.: A bayesian approach to optimal sensor placement. The International Journal of Robotics Research 9 (1990)Google Scholar
  2. 2.
    Cover, T.M., Thomas, J.A.: Elements of Information Theory. John Wiley & Sons, Inc., Chichester (1991)CrossRefzbMATHGoogle Scholar
  3. 3.
    Fanger, P.O.: Thermal Comfort. PhD thesis, Technical University of Denmark (1970)Google Scholar
  4. 4.
    Guestrin, C., Krause, A., Singh, A.P.: Near-optimal sensor placements in gaussian processes. In: ICML 2005 Proceedings of the 22nd International Conference on Machine Learning (2005)Google Scholar
  5. 5.
    Gutierrez, J.M., Kreinovich, V., Osegueda, R., Ferregut, C., George, M.J.: Maximum entropy approach to optimal sensor placement for aerospace non-destructive testing. Maximum Entropy and Bayesian Methods (1998)Google Scholar
  6. 6.
    Huizenga, C., Zhang, H., Duan, T., Arens, E.: An improved multinode model of human physiology and thermal comfort. Building Simulation (1999)Google Scholar
  7. 7.
    Luo, X., Hou, W., Li, Y., Wang, Z.: A fuzzy neural network model for predicting clothing thermal comfort. Computers & Mathematics with Applications 53, 1840–1846 (2007)CrossRefGoogle Scholar
  8. 8.
    Paninski, L.: Estimation of entropy and mutual information. Neural Computation 15, 1191–1253 (2003)CrossRefzbMATHGoogle Scholar
  9. 9.
    Papadimitriou, C., Beck, J.L., Au, S.-K.: Entropy-based optimal sensor location for structural model updating. Journal of Vibration and Control 6, 781–802 (2000)CrossRefGoogle Scholar
  10. 10.
    RadTherm©. ThermoAnalytics Inc., Heat Transfer Analysis Software, (accessed on the April 6, 2011),
  11. 11.
    Shah, P.C., Udwadia, F.E.: A methodology for optimal sensor locations for identification of dynamic systems. Journal of Applied Mechanics 45, 188–197 (1978)CrossRefGoogle Scholar
  12. 12.
    Stephen, E.A., Shnathi, M., Rajalakshmy, P., Melbern Parthido, M.: Application of fuzzy logic in control of thermal comfort. International Journal of Computational and Applied Mathematics 5, 289–300 (2010)Google Scholar
  13. 13.
    Torres, J.L., Martin, M.L.: Adaptive control of thermal comfort using neural networks. In: Argentine Symposium on Computing Technology (2008)Google Scholar
  14. 14.
    Zhang, H.: Human Thermal Sensation and Comfort in Transient and Non-Uniform Thermal Environments. PhD thesis, University of California, Berkeley (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Diana Hintea
    • 1
  • James Brusey
    • 1
  • Elena Gaura
    • 1
  • Neil Beloe
    • 2
  • David Bridge
    • 3
  1. 1.Coventry UniversityPriory LaneUK
  2. 2.Jaguar Land Rover Ltd.WhitleyUK
  3. 3.MIRA Ltd.NuneatonUK

Personalised recommendations