Environment monitoring and automatic control of a building is a vital application of wireless sensor network, however, to maximize the network lifetime is a key challenge. The investigation of designing an efficient sensor network that minimizes energy dissipation in a battery of the sensor node, with limited battery power, is a vital consideration for the sensor network lifetime. Battery lifetime greatly affects the overall network communication performance, hence, the careful management of communication distance is very important. In this paper we propose a model to estimate the mean square distance from the sensor to the cluster head in sensor fields, such as the ones used for monitoring humidity, temperature, light intensity and air quality (CO and CO2 level), considering three dimensional building structures. We use experimental datasets of the link quality distribution in an indoor building environment (single storey as well as multi-storey buildings) to investigate the possible building length of the different clusters and the data success rates. We then statistically analysed the data success rate of the experimental datasets using the Wilcoxon Rank Sum test and found that there was no statistically significant difference (p > 0.05). Our results show that the clustering is important for the single storey and multi-storey building sensor networks, however, after a certain size of the building it is unimportant. Our results also demonstrate that we can save sensor battery energy, significantly, by optimizing the distance from the sensor to the cluster head, while obtaining a high data success rate. The results over different clusters of sensor networks suggest its applicability for different building sizes. Based on this paper the designers can optimize energy e±ciency subject to the required specifications.
1. Shu, F., M. N. Halgamuge, and W. Chen, "Building automation systems using wireless sensor networks: Radio characteristics and energy e±cient communication protocols," EJSE Special Issue: Sensor Network on Building Monitoring: From Theory to Real Application, 66-73, 2009.
2. Boulis, A., S. Ganeriwal, and M. B. Srivastava, "Aggregation in sensor networks: An energy-accuracy trade-off," Proc. Int. Sensor Network Protocols and Applications, 128-138, 2003.
3. Cayirci, E., "Data aggregation and dilution by modulus addressing in wireless sensor networks," IEEE Commun. Lett., Vol. 7, No. 8, 355-357, Aug. 2003. doi:10.1109/LCOMM.2003.815663
4. Sankarasubramaniam, Y., I. F. Akyildiz, and S. W. McLaughlin, "Energy efficiency based packet size optimization in wireless sensor networks," Proc. IEEE Int. Sensor Network Protocols and Applications Conf., 1-8, 2003.
5. Halgamuge, M. N., S. M. Guru, and A. Jennings, "Energy efficient cluster formation in wireless sensor networks," Proc. IEEE Int. Telecommunications Conf., ICT'03, Vol. 2, 1571-1576, Tahity, French Polynesia, Feb.-Mar. 2003.
6. Halgamuge, M. N., S. M. Guru, and A. Jennings, "Centralised strategies for cluster formation in sensor networks," Classi¯cation and Clustering for Knowledge Discovery, 315-334, Springer-Verlag, Aug. 2005, ISBN: 3-540-26073-0.
7. Zou, Y. and K. Chakrabarty, "Target localization based on energy considerations in distributed sensor networks," Proc. IEEE Int. Sensor Network Protocols and Applications Conf., 51-58, May 2003.
8. Halgamuge, M. N., "Efficient battery management for sensor lifetime," Proc. IEEE AINAW Conf., Vol. 1, 56-61, Niagara Falls, Canada, May 2007.
9. Heinzelman, W. R., A. Chandrakasan, and H. Balakrishnan, "An application-specific protocol architecture for wireless microsensor networks," IEEE Tran. on Wireless Comm., Vol. 1, No. 4, 660-670, Oct. 2002. doi:10.1109/TWC.2002.804190
10. Halgamuge, M. N., K. Ramamohanarao, and M. Zukerman, "High powered cluster heads for extending sensor network lifetime," Proceedings of 6th IEEE International Symposium on Signal Processing and Information Technology (ISSPIT06), 64-69, Vancouver, Canada, Aug. 2006.
11. Intanagonwiwat, C., R. Govindan, and D. Estrin, "Directed diffusion: A scalable and robust communication paradigm for sensor networks,", Tech. Rep. 00-732, University of Southern California, Los Angeles, 2000.
12. Halgamuge, M. N., Resource Allocation in Wireless Networks: Cellular and Sensor Networks, 268, Lambert Academic Publishing, Germany, 2009, ISBN: 978-3-8383-2117-2.
13. Halgamuge, M. N., "Performance evaluation and enhancement of mobile and sensor networks,", Ph.D. Dissertation, Department of Electrical and Electronic Engineering, The University of Melbourne, Melbourne, Australia, 2007.
14. Halgamuge, M. N., "Design factors for sustainable sensor networks," Proc. 3rd Int. Conf. on Information and Automation for Sustainability, 106-110, Melbourne, Australia, Dec. 2007.
15. Halgamuge, M. N., M. Zukerman, K. Ramamohanarao, and H. L. Vu, "An estimation of sensor energy consumption," Progress In Electromagnetics Research B, Vol. 12, 259-295, 2009. doi:10.2528/PIERB08122303
16. Heinzelman, W. B., A. P. Chandrakasan, and H. Balakrishnan, "An application-specific protocol architecture for wireless microsensor networks," IEEE Trans. Wireless Commun., Vol. 1, No. 4, 660-670. doi:10.1109/TWC.2002.804190
17. Halgamuge, M. N., T. K. Chan, and P. Mendis, "Experimental study of link quality distribution in sensor network deployment for building environment," EJSE Special Issue: Sensor Network on Building Monitoring: From Theory to Real Application, 28-34, 2009.
18. Halgamuge, M. N., T. K. Chan, and P. Mendis, "Experiences of deploying an indoor building sensor network," Third International Conference on Sensor Technologies and Applications, SENSORCOMM'09, 378-381, Athens, Greece, Jun. 2009.
19. Halgamuge, M. N., T. K. Chan, and P. Mendis, "Improving multi-story building sensor network with an external hub," World Academy of Science, Engineering and Technology, Vol. 52, 420-423, Apr. 2009.