Differential Evolution-Based Sensor Allocation for Target Tracking Application in Sensor-Cloud

  • Sangeeta Kumari
  • Govind P. Gupta
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 695)


In a sensor-cloud system, an optimal set of sensor nodes are generally allocated to complete the subsequent target tracking task. In this kind of system, allocation of an optimal number of sensor nodes for target tracking application is a NP-hard problem. In this paper, a meta-heuristic optimization-based scheme is used, called differential evolution-based sensor allocation scheme (DESA) for allocation of optimal sensor nodes to attain efficient target tracking. DESA uses a novel fitness function which comprises three parameters such as dwelling time, detection probability of the sensor node, and competency of the sensor. Simulation results show that proposed scheme allocates approximately 40–48% less number of sensor nodes for covering the target for its efficient tracking.


  1. 1.
    Díaz, M., Martín, C., Rubio, B.: State-of-the-art, challenges, and open issues in the integration of Internet of things and cloud computing. J. Netw. Comput. Appl. 67, 99–117 (2016)CrossRefGoogle Scholar
  2. 2.
    Yuriyama, M., Kushida, T.: Sensor-cloud infrastructure-physical sensor management with virtualized sensors on cloud computing. In: 13th IEEE International Conference on Network-Based Information Systems (NBiS) (2010)Google Scholar
  3. 3.
    Yuriyama, M., Kushida, T., Itakura, M.: A new model of accelerating service innovation with sensor-cloud infrastructure. In: SRII Global Conference (SRII), 2011 Annual. IEEE (2011)Google Scholar
  4. 4.
    Misra, S., Chatterjee, S., Obaidat, M.S.: On theoretical modeling of sensor cloud: a paradigm shift from wireless sensor network. IEEE Syst. J. 11(2), 1084–1093 (2017)CrossRefGoogle Scholar
  5. 5.
    Madria, S., Kumar, V., Dalvi, R.: Sensor cloud: a cloud of virtual sensors. IEEE Softw. 31(2), 70–77 (2014)CrossRefGoogle Scholar
  6. 6.
    Kim, M., et al.: Developing an on-demand cloud-based sensing-as-a-service system for internet of things. J. Comput. Netw. Commun. (2016)Google Scholar
  7. 7.
    Alamri, A., et al.: A survey on sensor-cloud: architecture, applications, and approaches. Int. J. Distrib. Sens. Netw. 9(2), 917923 (2013)CrossRefGoogle Scholar
  8. 8.
    Chatterjee, S., Misra, S., Khan, S.: Optimal data center scheduling for quality of service management in sensor-cloud. IEEE Trans. Cloud Comput. (2015)Google Scholar
  9. 9.
    Chatterjee, S., Misra, S.: Target tracking using sensor-cloud: sensor-target mapping in presence of overlapping coverage. IEEE Commun. Lett. 18(8), 1435–1438 (2014)CrossRefGoogle Scholar
  10. 10.
    Wang, H., Yao, K., Estrin, D.: Information-theoretic approaches for sensor selection and placement in sensor networks for target localization and tracking. J. Commun. Netw. 7(4), 438–449 (2005)CrossRefGoogle Scholar
  11. 11.
    Hamouda, Y.E.M., Phillips, C.: Adaptive sampling for energy-efficient collaborative multi-target tracking in wireless sensor networks. IET Wirel. Sens. Syst. 1(1), 15–25 (2011)CrossRefGoogle Scholar
  12. 12.
    Aeron, S., Saligrama, V., Castanon, David A.: Efficient sensor management policies for distributed target tracking in multihop sensor networks. IEEE Trans. Signal Process. 56(6), 2562–2574 (2008)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Maheswararajah, S., Halgamuge, Saman K., Premaratne, M.: Sensor scheduling for target tracking by suboptimal algorithms. IEEE Trans. Veh. Technol. 58(3), 1467–1479 (2009)CrossRefGoogle Scholar
  14. 14.
    Huber, M.F.: Optimal pruning for multi-step sensor scheduling. IEEE Trans. Autom. Control 57(5), 1338–1343 (2012)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Zhou, L., Wang, H.: Toward blind scheduling in mobile media cloud: Fairness, simplicity, and asymptotic optimality. IEEE Trans. Multimed. 15(4), 735–746 (2013)CrossRefGoogle Scholar
  16. 16.
    Zhou, L., et al.: Exploring blind online scheduling for mobile cloud multimedia services. IEEE Wirel. Commun. 20(3), 54–61 (2013)CrossRefGoogle Scholar
  17. 17.
    Misra, S., et al.: QoS-aware sensor allocation for target tracking in sensor-cloud. Ad Hoc Netw. 33, 140–153 (2015)CrossRefGoogle Scholar
  18. 18.
    Kurschl, W., Beer, W.: Combining cloud computing and wireless sensor networks. In: Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services. ACM (2009)Google Scholar
  19. 19.
    Dash, S.K., Mohapatra, S., Pattnaik, P.K.: A survey on applications of wireless sensor network using cloud computing. Int. J. Comput. Sci. Emerg. Technol. 1(4), 50–55 (2010)Google Scholar
  20. 20.
    Iqbal, M., et al.: Multi-objective optimization in sensor networks: Optimization classification, applications and solution approaches. Comput. Netw. 99, 134–161 (2016)CrossRefGoogle Scholar
  21. 21.
    Fazio, M., Puliafito, A.: Cloud4sens: A cloud-based architecture for sensor controlling and monitoring. IEEE. Commun. Mag. 53(3), 41–47 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Information TechnologyNational Institute of TechnologyRaipurIndia

Personalised recommendations