Energy Efficiency in W-Grid Data-Centric Sensor Networks via Workload Balancing

  • Alfredo Cuzzocrea
  • Gianluca Moro
  • Claudio Sartori
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7808)


Wireless sensor networks are usually composed by small units able to sense and transmit to a sink elementary data which are then processed by an external machine. However, recent improvements in the memory and computational power of sensors, together with the reduction of energy consumption, are rapidly changing the potential of such systems, moving the attention towards data-centric sensor networks. In this kind of networks, nodes are smart enough either to store some data and to perform basic processing allowing the network itself to supply higher level information closer to the network user expectations. In other words, sensors no longer transmit each elementary data sensed, rather they cooperate in order to assemble them in more complex and synthetic information, which will be locally stored and transmitted according to queries and/or events defined by users and external applications. Recently, we proposed W-Grid, a fully decentralized cross-layer infrastructure for self-organizing data-centric sensor networks where wireless communication occur through multi-hop routing among devices. In this paper, we show that network traffic, and thus the energy consumption, can be balanced among sensors by assigning multiple virtual coordinates to nodes trough a fully decentralized workload balancing algorithm, which extends W-Grid.


Sensor Network Wireless Sensor Network Network Load Average Path Length High Level Information 
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.


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  1. 1.
    Cerroni, W., Moro, G., Pirini, T., Ramilli, M.: Peer-to-peer data mining classifiers for decentralized detection of network attacks. In: Wang, H., Zhang, R. (eds.) ADC 2013, Adelaide, South Australia. CRPIT, pp. 1–8. ACS (2013)Google Scholar
  2. 2.
    Cuzzocrea, A.: CAMS: OLAPing multidimensional data streams efficiently. In: Pedersen, T.B., Mohania, M.K., Tjoa, A.M. (eds.) DaWaK 2009. LNCS, vol. 5691, pp. 48–62. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  3. 3.
    Cuzzocrea, A.: Retrieving accurate estimates to OLAP queries over uncertain and imprecise multidimensional data streams. In: Bayard Cushing, J., French, J., Bowers, S. (eds.) SSDBM 2011. LNCS, vol. 6809, pp. 575–576. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Cuzzocrea, A., Chakravarthy, S.: Event-based lossy compression for effective and efficient OLAP over data streams. Data Knowl. Eng. 69(7), 678–708 (2010)CrossRefGoogle Scholar
  5. 5.
    Cuzzocrea, A., Furfaro, F., Greco, S., Masciari, E., Mazzeo, G.M., Saccà, D.: A distributed system for answering range queries on sensor network data. In: PerCom Workshops, pp. 369–373 (2005)Google Scholar
  6. 6.
    Cuzzocrea, A., Furfaro, F., Mazzeo, G.M., Saccá, D.: A grid framework for approximate aggregate query answering on summarized sensor network readings. In: Meersman, R., Tari, Z., Corsaro, A. (eds.) OTM Workshops 2004. LNCS, vol. 3292, pp. 144–153. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Cuzzocrea, A., Furfaro, F., Saccà, D.: Enabling OLAP in mobile environments via intelligent data cube compression techniques. J. Intell. Inf. Syst. 33(2), 95–143 (2009)CrossRefGoogle Scholar
  8. 8.
    El-Moukaddem, F., Torng, E., Xing, G.: Mobile relay configuration in data-intensive wireless sensor networks. IEEE Trans. Mob. Comput. 12(2), 261–273 (2013)CrossRefGoogle Scholar
  9. 9.
    Greenstein, B., Estrin, D., Govindan, R., Ratnasamy, S., Shenker, S.: Difs: A distributed index for features in sensor networks. In: Proceedings of First IEEE WSNA, pp. 163–173. IEEE Computer Society (2003)Google Scholar
  10. 10.
    Intanagonwiwat, C., Govindan, R., Estrin, D., Heidemann, J., Silva, F.: Directed diffusion for wireless sensor networking. IEEE/ACM Trans. Netw. 11(1), 2–16 (2003)CrossRefGoogle Scholar
  11. 11.
    Karp, B., Kung, H.: GPRS: greedy perimeter stateless routing for wireless networks. In: MobiCom 2000, pp. 243–254. ACM Press (2000)Google Scholar
  12. 12.
    Li, X., Kim, Y., Govindan, R., Hong, W.: Multi-dimensional range queries in sensor networks. In: SenSys 2003, pp. 63–75. ACM Press, New York (2003)Google Scholar
  13. 13.
    Li, Z., Liu, Y., Li, M., Wang, J., Cao, Z.: Exploiting ubiquitous data collection for mobile users in wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 24(2), 312–326 (2013)CrossRefGoogle Scholar
  14. 14.
    Liu, B., Dousse, O., Nain, P., Towsley, D.: Dynamic coverage of mobile sensor networks. IEEE Trans. Parallel Distrib. Syst. 24(2), 301–311 (2013)CrossRefGoogle Scholar
  15. 15.
    Monti, G., Moro, G., Sartori, C.: WR-Grid: A scalable cross-layer infrastructure for routing, multi-dimensional data management and replication in wireless sensor networks. In: Min, G., Di Martino, B., Yang, L.T., Guo, M., Rünger, G. (eds.) ISPA 2006 Ws. LNCS, vol. 4331, pp. 377–386. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Moro, G., Monti, G.: W-Grid: a self-organizing infrastructure for multi-dimensional querying and routing in wireless ad-hoc networks. In: IEEE P2P 2006 (2006)Google Scholar
  17. 17.
    Moro, G., Monti, G.: W-grid: A scalable and efficient self-organizing infrastructure for multi-dimensional data management, querying and routing in wireless data-centric sensor networks. Journal of Network and Computer Applications 35(4), 1218–1234 (2012), CrossRefGoogle Scholar
  18. 18.
    Ouksel, A.M., Moro, G.: G-Grid: A class of scalable and self-organizing data structures for multi-dimensional querying and content routing in P2P networks. In: Moro, G., Sartori, C., Singh, M.P. (eds.) AP2PC 2003. LNCS (LNAI), vol. 2872, pp. 123–137. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  19. 19.
    Ratnasamy, S., Karp, B., Shenker, S., Estrin, D., Govindan, R., Yin, L., Yu, F.: Data-centric storage in sensornets with ght, a geographic hash table. Mob. Netw. Appl. 8(4), 427–442 (2003)CrossRefGoogle Scholar
  20. 20.
    Xiao, L., Ouksel, A.: Tolerance of localization imprecision in efficiently managing mobile sensor databases. In: ACM MobiDE 2005, pp. 25–32. ACM Press, New York (2005)Google Scholar
  21. 21.
    Ye, F., Luo, H., Cheng, J., Lu, S., Zhang, L.: A two-tier data dissemination model for large-scale wireless sensor networks. In: MobiCom 2002, pp. 148–159. ACM Press, New York (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alfredo Cuzzocrea
    • 1
  • Gianluca Moro
    • 2
  • Claudio Sartori
    • 3
  1. 1.ICAR-CNR and University of CalabriaItaly
  2. 2.DISI Department – Cesena BranchUniversity of BolognaItaly
  3. 3.DISI Department – Bologna BranchUniversity of BolognaItaly

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