Advertisement

SERAPH: Service Allocation Algorithm for the Execution of Multiple Applications in Heterogeneous Shared Sensor and Actuator Networks

  • Claudio M. de Farias
  • Wei Li
  • Flávia C. Delicato
  • Luci Pirmez
  • Paulo F. Pires
  • Albert Y. Zomaya
Chapter
Part of the Internet of Things book series (ITTCC)

Abstract

Shared Sensor and Actuator Networks (SSAN) represent a new design trend in the field of Wireless Sensor Networks (WSNs) that allows the sensing and communication infrastructure to be shared among multiple applications submitted by different users, instead of the original application-specific WSN design. In this paper, with the goal of fully utilising the network infrastructure and inspired by a service-oriented architecture, we modeled applications as sets of primitive services to be provided by sensor nodes. By using such approach, sensor nodes can perform different roles according to the services they offer and it is possible to identify common services required by different applications so that leveraging service sharing and optimizing the use of the network resources. With these premises, we propose an adaptive service selection and allocation algorithm called SERAPH that can efficiently utilise the underlying heterogeneous hardware resources, and yet provide the desired QoS level for multiple applications. Experimental results show that SERAPH provides competitive performance regarding energy efficiency, making it a promising task allocation algorithm for SSANs.

Keywords

Wireless sensor networks Shared sensor networks Task allocation 

References

  1. 1.
    Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of things (IoT): a vision, architectural elements, and future directions. Future Gener. Comput. Syst. 29(7), 1645–1660 (2013)CrossRefGoogle Scholar
  2. 2.
    Billet, B., Issarny, V.: From task graphs to concrete actions: a new task mapping algorithm for the future internet of things. In: 11th IEEE International Conference on Mobile Ad Hoc and Sensor Systems (MASS), pp. 470–478, Oct 2014Google Scholar
  3. 3.
    Fortino, G., Guerrieri, A., Russo, W., Savaglio, C.: Middlewares for smart objects and smart environments: overview and comparison. In: Internet of Things Based on Smart Objects, pp. 1–27. Springer, Berlin (2014)Google Scholar
  4. 4.
    Li, W., Delicato, F.C., Zomaya, A.Y.: Adaptive energy-efficient scheduling for hierarchical wireless sensor networks. ACM Trans. Sen. Netw. 9(3), 1–34 (2013)CrossRefzbMATHGoogle Scholar
  5. 5.
    Fortino, G., Giannantonio, R., Gravina, R., Kuryloski, P., Jafari, R.: Enabling effective programming and flexible management of efficient body sensor network applications. Hum. Mach. Syst. IEEE Trans. 43(1), 115–133 (2013)CrossRefGoogle Scholar
  6. 6.
    Leontiadis, I., Efstratiou, C., Mascolo, C., Crowcroft, J.: SenShare: transforming sensor networks into multi-application sensing infrastructures. In: Picco, G., Heinzelman, W. (eds.) Wireless Sensor Networks, pp. 65–81. Springer, Berlin (2012)Google Scholar
  7. 7.
    de Farias, C.M., Pirmez, L., Delicato, F.C., Li, W., Zomaya, A.Y., De Souza, J.N.: A scheduling algorithm for shared sensor and actuator networks. In: IEEE International Conference on Information Networking (ICOIN), pp. 648–653, Jan 2013Google Scholar
  8. 8.
    Li, W., Delicato, F.C., Pires, P.F., Zomaya, A.Y.: Energy-efficient three-phase service scheduling heuristic for supporting distributed applications in cyber-physical systems. In: Proceedings of the 15th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, Paphos, Cyprus, pp. 229–238 (2012)Google Scholar
  9. 9.
    Geyik, S., Szymanski, B., Zerfos, P.: Robust dynamic service composition in sensor networks. Serv. Comput. IEEE Trans. 6(4), 1–1 (2012)Google Scholar
  10. 10.
    Bell, M.: SOA Modeling Patterns for Service Oriented Discovery and Analysis. Wiley, London (2010)Google Scholar
  11. 11.
    Cheng, B.C., Lemos, R., Giese, H., Inverardi, P., Magee, J., Andersson, J., Becker, B., Bencomo, N., Brun, Y., Cukic, B., Marzo Serugendo, G., Dustdar, S., Finkelstein, A., Gacek, C., Geihs, K., Grassi, V., Karsai, G., Kienle, H., Kramer, J., Litoiu, M., Malek, S., Mirandola, R., Müller, H., Park, S., Shaw, M., Tichy, M., Tivoli, M., Weyns, D., Whittle, J.: Software engineering for self-adaptive systems: a research roadmap. In: Cheng, B.C., Lemos, R., Giese, H., Inverardi, P., Magee, J. (eds.) Software Engineering for Self-Adaptive Systems, pp. 1–26. Springer, Berlin (2009)Google Scholar
  12. 12.
    Rouvoy, R., Eliassen, F., Floch, J., Hallsteinsen, S., Stav, E.: Composing components and services using a planning-based adaptation middleware. In: Pautasso, C., Tanter, É. (eds.) Software Composition, pp. 52–67. Springer, Berlin (2008)Google Scholar
  13. 13.
    Fortino, G., Guerrieri, A., Russo, W.: Agent-oriented smart objects development. In: IEEE 16th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 907–912, May 2012Google Scholar
  14. 14.
    Rao, J., Su, X.: A survey of automated web service composition methods. In: Cardoso, J., Sheth, A. (eds.) Semantic Web Services and Web Process Composition, pp. 43–54. Springer, Berlin (2005)Google Scholar
  15. 15.
    Fok, C.-L., Roman, G.-C., Lu, C.: Adaptive service provisioning for enhanced energy efficiency and flexibility in wireless sensor networks. Sci. Comput. Program. 78(2), 195–217 (2013)CrossRefzbMATHGoogle Scholar
  16. 16.
    Paschoalino, R., Madeira, E.R.M.: A scalable link quality routing protocol for multi-radio wireless mesh networks. In: Proceedings of 16th International Conference on Computer Communications and Networks (ICCCN), pp. 1053–1058, 13–16 Aug 2007Google Scholar
  17. 17.
    Efstratiou, C., Leontiadis, I., Mascolo, C., Crowcroft, J.: A shared sensor network infrastructure. In: Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems (SenSys’10), pp. 367–368. ACM, New York (2010). doi: 10.1145/1869983.1870026
  18. 18.
    Xu, Y., Saifullah, A., Chen, Y., Lu, C., Bhattacharya, S.: Near optimal multi-application allocation in shared sensor networks. In: Proceedings of the Eleventh ACM International Symposium on Mobile Ad Hoc Networking and Computing, Chicago, pp. 181–190 (2010)Google Scholar
  19. 19.
    Bhattacharya, S., Saifullah, A., Lu, C., Roman, G.-C.: Multi-application deployment in shared sensor networks based on quality of monitoring. In: 2010 16th IEEE Real-Time and Embedded Technology and Applications Symposium, pp. 259–268 (2010). doi: 10.1109/RTAS.2010.20
  20. 20.
    Wu, C., Xu, Y., Chen, Y., Lu, C.: Submodular game for distributed application allocation in shared sensor networks. In: Proceedings of IEEE Conference on INFOCOM, pp. 127–135, Mar 2012Google Scholar
  21. 21.
    Edalat, N., Xiao, W., Motani, M., Roy, N., Das, S.K.: Auction-based task allocation with trust management for shared sensor networks. Secur. Commun. Netw. 5(11), 1223–1234 (2012)Google Scholar
  22. 22.
    Siu-Nam, C., Chan, A.T.S.: Dynamic QoS adaptation for mobile middleware. Softw. Eng. IEEE Trans. 34(6), 738–752 (2008)CrossRefGoogle Scholar
  23. 23.
    Floch, J., Hallsteinsen, S., Stav, E., Eliassen, F., Lund, K., Gjorven, E.: Using architecture models for runtime adaptability. Softw. IEEE 23(2), 62–70 (2006)CrossRefGoogle Scholar
  24. 24.
    Geihs, K., Barone, P., Eliassen, F., Floch, J., Fricke, R., Gjorven, E., Hallsteinsen, S., Horn, G., Khan, M.U., Mamelli, A., Papadopoulos, G.A., Paspallis, N., Reichle, R., Stav, E.: A comprehensive solution for application-level adaptation. Softw. Pract. Experience 39(4), 385–422 (2009)Google Scholar
  25. 25.
    Fortino, G., Guerrieri, A., O’Hare, G.M., Ruzzelli, A.: A flexible building management framework based on wireless sensor and actuator networks. J. Netw. Comput. Appl. 35(6), 1934–1952 (2012)CrossRefGoogle Scholar
  26. 26.
    Li, W., Delicato, F.C., Pires, P.F., Lee, Y.C., Zomaya, A.Y., Miceli, C., Pirmez, L.: Efficient allocation of resources in multiple heterogeneous wireless sensor networks. J. Parallel Distrib. Comput. 74(1), 1775–1788 (2014)CrossRefGoogle Scholar
  27. 27.
    Li, W., Vesilo, R.: Modeling of session persistence in web server systems. In: Australian Telecommunications Networks and Application Conference, Melbourne (2006)Google Scholar
  28. 28.
    Delicato, F., Protti, F., Pirmez, L., de Rezende, J.F.: An efficient heuristic for selecting active nodes in wireless sensor networks. Comput. Netw. 50(18), 3701–3720 (2006)CrossRefzbMATHGoogle Scholar
  29. 29.
    Garlan, D., Shang-Wen, C., An-Cheng, H., Schmerl, B., Steenkiste, P.: Rainbow: architecture-based self-adaptation with reusable infrastructure. Computer 37(10), 46–54 (2004)CrossRefGoogle Scholar
  30. 30.
    Herrera, F., Herrera-Viedma, E.: Linguistic decision analysis: steps for solving decision problems under linguistic information. Fuzzy Sets Syst. 115(1), 67–82 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  31. 31.
    Reyes, J.A.G., Robles, R.S., Recéndez, B.E.S., Olague, J.G.A.: Implementation of a timestamping service for SunSPOT sensors. Procedia Technol. 7, 4–10 (2013). ISSN 2212-0173, http://dx.doi.org/10.1016/j.protcy.2013.04.001
  32. 32.
    Rowaihy, H., Johnson, M.P., Liu, O., Bar-Noy, A., Brown, T., Porta, T.L.: Sensor-mission assignment in wireless sensor networks. ACM Trans. Sen. Netw. 6(4), 1–33 (2010)CrossRefGoogle Scholar
  33. 33.
    Noh, A.S.-I., Lee, W.J., Ye, J.Y.: Comparison of the mechanisms of the zigbee’s indoor localization algorithm. In: International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2008 (SNPD’08), pp. 13, 18, 6–8 Aug 2008. Ninth ACISGoogle Scholar
  34. 34.
    Xiong, S., Li, J., Li, M., Wang, J., Liu, Y.: Multiple task scheduling for low-duty-cycled wireless sensor networks. In: INFOCOM’11Google Scholar
  35. 35.
    Ting, Z., Mohaisen, A., Yi, P., Towsley, D.: DEOS: dynamic energy-oriented scheduling for sustainable wireless sensor networks. In: Proceedings of IEEE on INFOCOM, pp. 2363–2371, 25–30 Mar 2012Google Scholar
  36. 36.
    Shuguang, X., Jianzhong, L., Zhenjiang, L., Jiliang, W., Yunhao, L.: Multiple task scheduling for low-duty-cycled wireless sensor networks. In: Proceedings of IEEE on INFOCOM, pp. 1323–1331, 10–15 April 2011Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Claudio M. de Farias
    • 1
  • Wei Li
    • 2
  • Flávia C. Delicato
    • 1
  • Luci Pirmez
    • 1
  • Paulo F. Pires
    • 1
  • Albert Y. Zomaya
    • 2
  1. 1.PPGI-iNCE, DCC-IMUniversidade Federal do Rio de JaneiroRio de JaneiroBrazil
  2. 2.Centre for Distributed and High Performance Computing, School of Information TechnologiesThe University of SydneySydneyAustralia

Personalised recommendations