A Clustering Topology for Wireless Sensor Networks - New Semantics over Network Topology

Paul Cotofrei, Ionel Tudor Calistru, Kilian Stoffel


Sensor networks are a primary source of massive amounts of data about the real world that surrounds us, measuring a wide range of physical parameters in real time. Given the hardware limitations and physical environment in which the sensors must operate, along with frequent changes of network topology, algorithms and protocols must be designed to provide a robust and energy efficient communications mechanism. With a view to addressing these constraints, this paper proposes a routing technique that is based on density based spatial clustering of applications with noise (DBSCAN) algorithm. This technique reveals several network topology semantics, enables the splitting of sensors responsibilities (communication/routing and sensing/monitoring), reduces the level of energy wasted on sending messages through the network by data aggregation only in cluster-head nodes and last but not the least, brings along very good results prolonging the network lifetime.


  1. Abbasi, A. and Younis, M. (2007). A survey on clustering algorithms for wireless sensor networks. Comput. Commun, 30(14):2826-2841.
  2. Akkaya, K. and Younis, M. (2005). A survey on routing protocols for wireless sensor networks. Ad hoc networks, 3:325-349.
  3. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., and Cayirci, E. (2002). A survey on sensor networks. IEEE Communications Magazine, 40:102-114.
  4. Almuzaini, K. and Gulliver, T. (2011). Range-based localization in wireless networks using the dbscan clustering algorithm. In Vehicular Technology Conference (VTC Spring), 2011 IEEE 73rd, pages 1-7.
  5. Apiletti, D., Baralis, E., and Cerquitelli, T. (2011). Energysaving models for wireless sensor networks. Knowl. Inf. Syst., 28:615-644.
  6. Bajaber, F. and Awan, I. (2010). Energy efficient clustering protocol to enhance lifetime of wireless sensor network. Journal of Ambient Intelligence and Humanized Computing, 1:239-248.
  7. Baker, D. and Ephremides, A. (1981). The architectural organization of a mobile radio network via a distributed algorithm. IEEE Trans. on Communications, 29(11):1694-1701.
  8. Bandyopadhyay, S. and Coyle, E. (2003). An energy efficient hierarchical clustering algorithm for wireless sensor networks. In Proc. of the 22nd Annual Joint Conf. of the IEEE Computer and Communications Societies (INFOCOM), volume 3, pages 1713-1723.
  9. Calbimonte, J.-P., Jeung, H., Corcho, O., , and Aberer, K. (2011). Semantic sensor data search in a large-scale federated sensor network. 4th International Workshop on Semantic Sensor Networks 2011, 11:23-38.
  10. Cardei, M., MacCallum, D., Cheng, M. X., Min, M., Jia, X., Li, D., and Du, D.-Z. (2002). Wireless sensor networks with energy efficient organization. Journal of Interconnection Networks, 3:213-229.
  11. Cardei, M. and Wu, J. (2006). Energy-efficient coverage problems in wireless ad-hoc sensor networks. Computer Communications, 29:413-420.
  12. Collier, N. and North, M. (2012). Parallel agent-based simulation with repast for high performance computing. Simulation: Transactions of the Society for Modeling and Simulation International.
  13. Ester, M., Kriegel, H. P., Sander, J., and Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Second International Conference on Knowledge Discovery and Data Mining, pages 226-231.
  14. Estrin, D., Govindan, R., Heidemann, J., and Kumar., S. (1999). Next century challenges: Scalable coordination in sensor networks. In Proceedings of the fifth annual ACM/IEEE international conference on Mobile computing and networking, pages 263-270.
  15. Heinzelman, W., Chandrakasan, A., and Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Trans. on Wireless Communications, 1(4):660-670.
  16. Intanagonwiwat, C., Govindan, R., Estrin, D., Heidemann, J., and Silva, F. (2003). Directed diffusion for wireless sensor networking. IEEE/ACM Transactions on Networking (TON), 11:2-16.
  17. Nagpal, R. and Coore, D. (1998). An algorithm for group formation in an amorphous computer. In In Proc. of the 10th Int. Conf. on Parallel and Distributed Computing Systems (PDCS).
  18. North, M., Collier, N., Ozik, J., Tatara, E., Altaweel, M., Macal, C., Bragen, M., and Sydelko, P. (2013). Complex adaptive systems modeling with repast simphony. Complex Adaptive Systems Modeling, 1(3):1-26.
  19. Sander, J., Ester, M., Kriegel, H.-P., and Xu, X. (1998). Density-based clustering in spatial databases:the algorithm gdbscan and its applications. Data Mining and Knowledge Discovery, 2:169-194.
  20. Sheth, A., Henson, C., and Sahoo, S. (2008). Semantic sensor web. IEEE Internet Computing, 12:78-83.
  21. Shin, K., Abraham, A., and Han, S. Y. (2006). Self organizing sensors by minimization of cluster heads using intelligent clustering. Journal of Digital Information Managenment, 4:87-923.
  22. Stojmenovic, I. (2005). Handbook of Sensor Networks: Algorithms and Architectures. John Wiley & Sons.
  23. Tripathi, R., Singh, Y., and Verma, N. (2012). N-leach, a balanced cost cluster-heads selection algorithm for wireless sensor network. In In National Conference on Communications (NCC).
  24. Younis, O. and Fahmy, S. (2003). Distributed clustering for scalable, long-lived sensor networks. Technical report, Purdue University.
  25. Younis, O. and Fahmy, S. (2004). Heed: a hybrid, energyefficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans. on Mobile Computing, 3(4):366-379.

Paper Citation

in Harvard Style

Cotofrei P., Calistru I. and Stoffel K. (2013). A Clustering Topology for Wireless Sensor Networks - New Semantics over Network Topology . In Proceedings of the 2nd International Conference on Data Technologies and Applications - Volume 1: DATA, ISBN 978-989-8565-67-9, pages 153-160. DOI: 10.5220/0004423101530160

in Bibtex Style

author={Paul Cotofrei and Ionel Tudor Calistru and Kilian Stoffel},
title={A Clustering Topology for Wireless Sensor Networks - New Semantics over Network Topology},
booktitle={Proceedings of the 2nd International Conference on Data Technologies and Applications - Volume 1: DATA,},

in EndNote Style

JO - Proceedings of the 2nd International Conference on Data Technologies and Applications - Volume 1: DATA,
TI - A Clustering Topology for Wireless Sensor Networks - New Semantics over Network Topology
SN - 978-989-8565-67-9
AU - Cotofrei P.
AU - Calistru I.
AU - Stoffel K.
PY - 2013
SP - 153
EP - 160
DO - 10.5220/0004423101530160