Skip to main content

Advertisement

SpringerLink
Log in

Bacterial Foraging Optimization Scheme for Mobile Sensing in Wireless Sensor Networks

  • Published:
International Journal of Wireless Information Networks Aims and scope Submit manuscript

Abstract

Future generations of radio-based networks promise new timeliness for collaborative low-power sensing schemes in wireless sensor networks. Due to the hostile and inaccessible environment in which sensors are deployed, collect and transfer data in such networks is not an easy task. An effective data gathering can be improved by introducing unmanned aerial vehicles called drones, which act as mobile sinks and can autonomously fly over the network with the primary goal of collecting data from sensors. This paper presents a biologically inspired scheme of collaborative mobile sensing. The proposal has been designed in such a way that the coverage, the energy efficiency and a high network availability are maintained. Social foraging behaviors of the Escherichia coli bacteria modeled in the bacterial foraging optimization have been used to achieve these goals, especially the chemotaxis and the swarming features that allow bacteria to move. After a description, a formalization of the problem of mobile sensing is presented. Then, models that allow mobile sinks to move in a self-organized and self-adaptive way is proposed. In order to highlight the impact of mobility on energy consumption, delay, network coverage and successful amount of delivered data, intensive experiments have been done. Results demonstrate the effectiveness of the approach.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. G. Anastasi, E. Borgia, M. Conti and E. Gregori, A hybrid adaptive protocol for reliable data delivery in wsns with multiple mobile sinks, The Computer Journal, Vol. 54, No. 2, pp. 213–229, 2011.

    Article  Google Scholar 

  2. F.A. Aoudia, M. Gautier, and O. Berder, GRAPMAN: gradual power manager for consistent throughput of energy harvesting wireless sensor nodes. In Personal, Indoor, and Mobile Radio Communications (PIMRC), 2015 IEEE 26th Annual International Symposium on, pp. 954–959. IEEE 2015.

  3. A. A. A. Ari, A. Gueroui, N. Labraoui and B. O. Yenké, Concepts and evolution of research in the field of wireless sensor networks, International Journal of Computer Networks & Communications, Vol. 7, No. 1, pp. 81–98, 2015.

    Article  Google Scholar 

  4. A. A. A. Ari, A. Gueroui, N. Labraoui, B.O. Yenké, C. Titouna, and I. Damakoa, Adaptive scheme for collaborative mobile sensing in wireless sensor networks: Bacterial foraging optimization approach. In: Personal, Indoor, and Mobile Radio Communications (PIMRC), 2016 IEEE 27th Annual International Symposium on, pp. 1–6. IEEE, 2016.

  5. A. A. A. Ari, A. Gueroui, B.O. Yenké, and N. Labraoui, Energy efficient clustering algorithm for wireless sensor networks using the abc metaheuristic. In: 2016 International Conference on Computer Communication and Informatics (ICCCI), pp. 1–6. IEEE, 2016.

  6. A. A. A. Ari, N. Labraoui, B.O. Yenké, and A. Gueroui, Clustering algorithm for wireless sensor networks: the honeybee swarms nest-sites selection process based approach. International Journal of Sensor Networks, 2017.

  7. A. A. A. Ari, B. O. Yenké, N. Labraoui, I. Damakoa and A. Gueroui, A power efficient cluster-based routing algorithm for wireless sensor networks: Honeybees swarm intelligence based approach, Journal of Network and Computer Applications, Vol. 69, pp. 77–97, 2016.

    Article  Google Scholar 

  8. M. Azharuddin, P. Kuila and P. K. Jana, Energy efficient fault tolerant clustering and routing algorithms for wireless sensor networks, Computers & Electrical Engineering, Vol. 41, pp. 177–190, 2015.

    Article  Google Scholar 

  9. A. Barberis, L. Barboni, and M. Valle, Evaluating energy consumption in wireless sensor networks applications. In: Digital System Design Architectures, Methods and Tools, 2007. DSD 2007. 10th Euromicro Conference on, pp. 455–462. IEEE, 2007.

  10. E.T. Fute, and E. Tonye, Modelling and self-organizing in mobile wireless sensor networks: application to fire detection. International Journal of Applied Information Systems, IJAIS, Vol. 5, No. 3, 2013.

  11. K. I. Gandhi and P. Narayanasamy, A cluster-based quad-tree partitioning for scheduling the mobile element in wireless sensor networks, International Journal of Wireless Information Networks, Vol. 18, No. 1, pp. 50–55, 2011.

    Article  Google Scholar 

  12. S.K. Gupta, P. Kuila, and P. K. Jana, Genetic algorithm for k-connected relay node placement in wireless sensor networks. In: Proceedings of the Second International Conference on Computer and Communication Technologies, pp. 721–729. Springer, Berlin, 2016.

  13. W. B. Heinzelman, A. P. Chandrakasan and H. Balakrishnan, An application-specific protocol architecture for wireless microsensor networks, Wireless Communications, IEEE Transactions on, Vol. 1, No. 4, pp. 660–670, 2002.

    Article  Google Scholar 

  14. K. S. Kumar and T. Jayabarathi, Power system reconfiguration and loss minimization for an distribution systems using bacterial foraging optimization algorithm, International Journal of Electrical Power & Energy Systems, Vol. 36, No. 1, pp. 13–17, 2012.

    Article  Google Scholar 

  15. N. Labraoui, M. Gueroui, and L. Sekhri, On-off attacks mitigation against trust systems in wireless sensor networks. In: Computer Science and Its Applications, pp. 406–415. Springer, Berlin, 2015.

  16. Y. Liu and K. Passino, Biomimicry of social foraging bacteria for distributed optimization: models, principles, and emergent behaviors, Journal of Optimization Theory and Applications, Vol. 115, No. 3, pp. 603–628, 2002.

    Article  MathSciNet  MATH  Google Scholar 

  17. R. Loomba, R. de Fréin, and B. Jennings, Selecting energy efficient cluster-head trajectories for collaborative mobile sensing. In: 2015 IEEE Global Communications Conference (GLOBECOM), pp. 1–7. IEEE, 2015.

  18. J. Luo, J. Panchard, M. Piórkowski, M. Grossglauser, and J. P. Hubaux, Mobiroute: Routing towards a mobile sink for improving lifetime in sensor networks. In: International Conference on Distributed Computing in Sensor Systems, pp. 480–497. Springer, 2006.

  19. Z. S. Ma and A. W. Krings, Insect sensory systems inspired computing and communications, Ad Hoc Networks, Vol. 7, No. 4, pp. 742–755, 2009.

    Article  Google Scholar 

  20. A. Mazayev, N. Correia and G. Schütz, Data gathering in wireless sensor networks using unmanned aerial vehicles, International Journal of Wireless Information Networks, Vol. 23, No. 4, pp. 297–309, 2016.

    Article  Google Scholar 

  21. O. Moussaoui, A. Ksentini, M. Naimi, and Gueroui, A novel clustering algorithm for efficient energy saving in wireless sensor networks. In: Computer Networks, 2006 International Symposium on, pp. 66–72. IEEE, 2006.

  22. S. Panda, B. Mohanty and P. Hota, Hybrid BFOA-PSO algorithm for automatic generation control of linear and nonlinear interconnected power systems, Applied Soft Computing, Vol. 13, No. 12, pp. 4718–4730, 2013.

    Article  Google Scholar 

  23. K. M. Passino, Biomimicry of bacterial foraging for distributed optimization and control, Control Systems, IEEE, Vol. 22, No. 3, pp. 52–67, 2002.

    Article  Google Scholar 

  24. N. Rajasekar, N. K. Kumar and R. Venugopalan, Bacterial foraging algorithm based solar pv parameter estimation, Solar Energy, Vol. 97, pp. 255–265, 2013.

    Article  Google Scholar 

  25. A. A. Somasundara, A. Ramamoorthy and M. B. Srivastava, Mobile element scheduling with dynamic deadlines, IEEE transactions on Mobile Computing, Vol. 6, No. 4, pp. 395–410, 2007.

    Article  Google Scholar 

  26. Texas Instruments: Chipcon as smartrf®  CC2420 preliminary datasheet (rev 1.2), 2004-06-09. Tech. rep., http://www.ti.com/lit/ds/symlink/cc2420.pdf (2014 [accessed on 28.02.2016])

  27. C. Titouna, M. Aliouat and M. Gueroui, FDS: fault detection scheme for wireless sensor networks, Wireless Personal Communications, Vol. 86, No. 2, pp. 549–562, 2016.

    Article  Google Scholar 

  28. C. Titouna, A. Gueroui, M. Aliouat, and A. A. A. Ari, Adouane, Distributed Fault-Tolerant Algorithm for Wireless Sensor Networks. International Journal of Communication Networks and Information Security, 2017

  29. X. Wang, D. Le and H. Cheng, Mobility management for 6lowpan wireless sensor networks in critical environments, International Journal of Wireless Information Networks, Vol. 22, No. 1, pp. 41–52, 2015.

    Article  Google Scholar 

  30. G. Werner-Allen, K. Lorincz, M. Ruiz, O. Marcillo, J. Johnson, J. Lees and M. Welsh, Deploying a wireless sensor network on an active volcano, IEEE internet computing, Vol. 10, No. 2, pp. 18–25, 2006.

    Article  Google Scholar 

  31. G. Xing, T. Wang, Z. Xie and W. Jia, Rendezvous planning in wireless sensor networks with mobile elements, IEEE Transactions on Mobile Computing, Vol. 7, No. 12, pp. 1430–1443, 2008.

    Article  Google Scholar 

  32. B. O. Yenké, D. W. Sambo, A. A. A. Ari and A. Gueroui, MMEDD: Multithreading Model for an Efficient Data Delivery in wireless sensor networks, International Journal of Communication Networks and Information Security, Vol. 8, No. 3, pp. 179–186, 2016.

    Google Scholar 

  33. J. Yick, B. Mukherjee and D. Ghosal, Wireless sensor network survey, Computer networks, Vol. 52, No. 12, pp. 2292–2330, 2008.

    Article  Google Scholar 

  34. A. M. Zungeru, L. M. Ang and K. P. Seng, Termite-hill: Performance optimized swarm intelligence based routing algorithm for wireless sensor networks, Journal of Network and Computer Applications, Vol. 35, No. 6, pp. 1901–1917, 2012.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LI-PaRAD Laboratory, Université Paris Saclay, University of Versailles Saint-Quentin-en-Yvelines, Versailles, France

    Ado Adamou Abba Ari & Abdelhak Gueroui

  2. LASE Laboratory, University of Ngaoundéré, Ngaoundéré, Cameroon

    Irépran Damakoa & Blaise Omer Yenké

  3. Department of Mathematics and Computer Science, Faculty of Science, University of Maroua, Maroua, Cameroon

    Ado Adamou Abba Ari & Guidedi Kaladzavi

  4. Computer Science Department, University of M’Sila, M’Sila, Algeria

    Chafiq Titouna

  5. STIC Laboratory, University of Tlemcen, Tlemcen, Algeria

    Nabila Labraoui

Authors
  1. Ado Adamou Abba Ari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Irépran Damakoa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abdelhak Gueroui
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chafiq Titouna
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nabila Labraoui
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Guidedi Kaladzavi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Blaise Omer Yenké
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ado Adamou Abba Ari.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ari, A.A.A., Damakoa, I., Gueroui, A. et al. Bacterial Foraging Optimization Scheme for Mobile Sensing in Wireless Sensor Networks. Int J Wireless Inf Networks 24, 254–267 (2017). https://doi.org/10.1007/s10776-017-0359-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10776-017-0359-y

Keywords

Search

Navigation