Technological advances in the smartphone sector give rise to people-centric sensing that uses the sensing capabilities of mobile devices and the movement of their human carriers to satisfy the ever increasing demand for context information. The quick adoption of such pervasive and mobile services, however, increases the number of contributors, strains the device-to-server connections, and challenges the system’s scalability. Strategies that postpone load balancing to fixed infrastructure nodes miss the potential of mobile devices interconnecting to preprocess sensor data. This paper explores opportunistic service composition to coordinate in-network aggregation among autonomous mobile data providers. The composition protocol defers interaction with peers to the latest possible moment to accommodate for the dynamics in the operating environment. In simulations such an approach achieves a higher composition success ratio at similar or less delay and communication effort than an existing conventional composition solution.


Service Provider Mobile Device Wireless Sensor Network Service Composition Synchronisation Message 
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.
    Fasolo, E., Rossi, M., Widmer, J., Zorzi, M.: In-network aggregation techniques for wireless sensor networks: a survey. IEEE Wireless Communications 14(2), 70–87 (2007)CrossRefGoogle Scholar
  2. 2.
    Ferreira, H., Duarte, S., Preguiça, N., Navalho, D.: Scalable data processing for community sensing applications. In: Puiatti, A., Gu, T. (eds.) MobiQuitous 2011. LNICST, vol. 104, pp. 75–87. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Groba, C., Clarke, S.: Opportunistic composition of sequentially-connected services in mobile computing environments. In: International Conference on Web Services (ICWS), pp. 17–24. IEEE (2011)Google Scholar
  4. 4.
    Groba, C., Clarke, S.: Synchronising service compositions in dynamic ad hoc environments. In: International Conference on Mobile Services, pp. 56–63. IEEE (2012)Google Scholar
  5. 5.
    Gu, X., Nahrstedt, K., Yu, B.: Spidernet: An integrated peer-to-peer service composition framework. In: International Symposium on High performance Distributed Computing (HPDC), pp. 110–119. IEEE (2004)Google Scholar
  6. 6.
    Kamra, A., Misra, V., Rubenstein, D.: Counttorrent: ubiquitous access to query aggregates in dynamic and mobile sensor networks. In: International Conference on Embedded Networked Sensor Systems (SenSys), pp. 43–57. ACM (2007)Google Scholar
  7. 7.
    Kurkowski, S., Camp, T., Navidi, W.: Two standards for rigorous manet routing protocol evaluation. In: International Conference on Mobile Adhoc and Sensor Systems (MASS), pp. 256–266. IEEE (October 2006)Google Scholar
  8. 8.
    Lane, N., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., Campbell, A.: A survey of mobile phone sensing. IEEE Communications Magazine 48(9), 140–150 (2010)CrossRefGoogle Scholar
  9. 9.
    Loke, S.W.: Supporting ubiquitous sensor-cloudlets and context-cloudlets: Programming compositions of context-aware systems for mobile users. Future Generation Computer Systems 28(4), 619–632 (2012)CrossRefGoogle Scholar
  10. 10.
    Lu, H., Lane, N.D., Eisenman, S.B., Campbell, A.T.: Fast track article: Bubble-sensing: Binding sensing tasks to the physical world. Pervasive and Mobile Computing 6(1), 58–71 (2010)CrossRefGoogle Scholar
  11. 11.
    Park, E., Shin, H.: Recon gurable service composition and categorization for power-aware mobile computing. Transactions on Parallel and Distributed Systems 19(11), 1553–1564 (2008)CrossRefGoogle Scholar
  12. 12.
    Perez, A., Labrador, M., Barbeau, S.: G-sense: a scalable architecture for global sensing and monitoring. IEEE Network 24(4), 57–64 (2010)CrossRefGoogle Scholar
  13. 13.
    Perkins, C.E., Belding-Royer, E.M., Das, S.: Ad hoc on-demand distance vector (aodv) routing,
  14. 14.
    Prinz, V., Fuchs, F., Ruppel, P., Gerdes, C., Southall, A.: Adaptive and fault-tolerant service composition in peer-to-peer systems. In: Meier, R., Terzis, S. (eds.) DAIS 2008. LNCS, vol. 5053, pp. 30–43. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  15. 15.
    Sen, R., Roman, G.-C., Gill, C.: CiAN: A workflow engine for manets. In: Lea, D., Zavattaro, G. (eds.) COORDINATION 2008. LNCS, vol. 5052, pp. 280–295. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  16. 16.
    Wang, M., Li, B., Li, Z.: sflow: Towards resource-efficient and agile service federation in service overlay networks. In: International Conference on Distributed Computing Systems (ICDCS), pp. 628–635. IEEE (2004)Google Scholar
  17. 17.
    Yu, W.: Decentralized orchestration of BPEL processes with execution consistency. In: Li, Q., Feng, L., Pei, J., Wang, S.X., Zhou, X., Zhu, Q.-M. (eds.) APWeb/WAIM 2009. LNCS, vol. 5446, pp. 665–670. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  18. 18.
    Zaplata, S., Hamann, K., Kottke, K., Lamersdorf, W.: Flexible execution of distributed business processes based on process instance migration. Journal of Systems Integration 1(3), 3–16 (2010)Google Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2013

Authors and Affiliations

  • Christin Groba
    • 1
  • Siobhán Clarke
    • 1
  1. 1.Lero Graduate School of Software Engineering Distributed Systems Group School of Computer Science and StatisticsTrinity College DublinIreland

Personalised recommendations