Skip to main content

Advertisement

SpringerLink
Log in

Energy-efficient multiple itinerary planning for mobile agents-based data aggregation in WSNs

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

Data aggregation is recognized as a key method for reducing the amount of network traffic and the energy consumption on wireless sensor network nodes. Mobile agent (MA) technology represents a distributed computing paradigm which has been proposed as a means for increasing the energy efficiency of data aggregation tasks and addressing the scalability problems of centralized methods. Nevertheless, the itineraries followed by travelling MAs largely determine the overall performance of the data aggregation applications. Along this line, this article introduces a novel algorithmic approach for energy-efficient itinerary planning of MAs engaged in data aggregation tasks. Our algorithm adopts an iterated local search approach in deriving the hop sequence of multiple travelling MAs over the deployed source nodes. Simulation results demonstrate the performance gain of our method against existing multiple MA itinerary planning methods.

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Notes

  1. Iterated Local Search is based on building a sequence of locally optimal solutions by: (a) perturbing the current local minimum; (b) applying local search after starting from the modified solution.

  2. The impact factor \(G_{ij}\) between two nodes i and j is given by the following equation, with \(H_{j}^{i}\) denoting the estimated hop count between nodes: \(G_{ij}=e^{-\frac{\left( {H_j^i -1} \right) ^{2}}{2\cdot \sigma ^{2}}}\)

  3. In our implementation we assume that every node i in V is a source node. Nevertheless, our modeling also suits scenarios wherein the source nodes comprise a subset of V. Note that a source node may be visited multiple times. Once to actually retrieve sensory data and the remainder times in the process if migrating from one node to another (in the latter case, the node acts as intermediate node).

  4. https://github.com/ivenetis/Castalia-3.2-MIP.

  5. In the OP the objective is to determine a path, limited in length that visits some vertices and maximizes the sum of the collected scores.

References

  1. Abdul-Salaam, G., Abdullah, A. H., Anisi, M. H., Gani, A., & Alelaiwi, A. (2016). A comparative analysis of energy conservation approaches in hybrid wireless sensor networks data collection protocols. Telecommunication Systems, 61(1), 159–179.

    Article  Google Scholar 

  2. Aiello, F., Fortino, G., Gravina, R., & Guerrieri, A. (2011). A Java-based agent platform for programming wireless sensor networks. The Computer Journal, 54(3), 439–454.

    Article  Google Scholar 

  3. Biswas, P. K., Qi, H., & Xu, Y. (2008). Mobile-agent-based collaborative sensor fusion. Information Fusion, 9(3), 399–411.

    Article  Google Scholar 

  4. Castalia, http://castalia.research.nicta.com.

  5. Cao, J., & Das, S. K. (2012). Mobile agents in networking and distributed computing. New York: Wiley.

    Book  Google Scholar 

  6. Chen, M., Cai, W., González, S. & Leung V.C.M. (2010). Balanced itinerary planning for multiple mobile agents in wireless sensor networks. Proceedings of the second international conference in ad hoc networks (ADHOCNETS’2010) (pp. 416–428). Springer, Berlin.

  7. Chen, M., González, S., Zhang, Y. & Leung, V.C.M. (2009). Multi-agent itinerary planning for wireless sensor networks. Proceedings of the IEEE 2009 international conference on heterogeneous networking for quality, reliability, security and robustness (QShine’2009) (pp. 584–597).

  8. Chen, M., Kwon, T., Yuan, Y., Choi, Y., & Leung, V. C. M. (2007). Mobile agent-based directed diffusion in wireless sensor networks. EURASIP Journal on Applied Signal Processing, 2007(1), 219–219.

    Google Scholar 

  9. Chen, M., Yang, L. T., Kwon, T., Zhou, L., & Jo, M. (2011). Itinerary planning for energy-efficient agent communications in wireless sensor networks. IEEE Transactions on Vehicular Technology, 60(7), 3290–3299.

    Article  Google Scholar 

  10. Dong, M., Ota, K., Yang, L. T., Chang, S., Zhu, H., & Zhou, Z. (2014). Mobile agent-based energy-aware and user-centric data collection in wireless sensor networks. Computer Networks, 74, 58–70.

    Article  Google Scholar 

  11. Esau, L., & Williams, K. (1966). On teleprocessing system design, Part II—a method for approximating the optimal network. IBM Systems Journal, 5(3), 142–147.

    Article  Google Scholar 

  12. Fok, C. L., Roman, G. C., & Lu, C. (2009). Agilla: A mobile agent middleware for self-adaptive wireless sensor networks. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 4(3), 16.

    Google Scholar 

  13. Gavalas, D. (2001). Mobile software agents for network monitoring and performance management. PhD Thesis, University of Essex.

  14. Gavalas, D., Mpitziopoulos, A., Pantziou, G., & Konstantopoulos, C. (2010). An approach for near-optimal distributed data fusion in wireless sensor networks. Wireless Networks, 16(5), 1407–1425.

    Article  Google Scholar 

  15. Gupta, G. P., Misra, M., & Garg, K. (2014). Energy and trust aware mobile agent migration protocol for data aggregation in wireless sensor networks. Journal of Network and Computer Applications, 41, 300–311.

    Article  Google Scholar 

  16. Khaleghi, B., Khamis, A., Karray, F. O., & Razavi, S. N. (2013). Multisensor data fusion: A review of the state-of-the-art. Information Fusion, 14(1), 28–44.

    Article  Google Scholar 

  17. Konstantopoulos, C., Mpitziopoulos, A., Gavalas, D., & Pantziou, G. (2010). Effective determination of mobile agent itineraries for data fusion tasks on sensor networks. IEEE Transactions on Knowledge and Data Engineering, 22(12), 1679–1693.

    Article  Google Scholar 

  18. Kwon, Y., Mechitov, K. & Agha, G. (2014). Design and implementation of a mobile actor platform for wireless sensor networks. In: Concurrent objects and beyond (pp. 276–316). Springer, Berlin.

  19. Lourenço, H. R., Martin, O. C., & Stützle, T. (2003). Iterated local search. Handbook of Metaheuristics (pp. 320–353). Springer, Berlin.

  20. Mercadal, E., Vidueira, C., Sreenan, C. J., & Borrell, J. (2013). Improving the dynamism of mobile agent applications in wireless sensor networks through separate itineraries. Computer Communications, 36(9), 1011–1023.

    Article  Google Scholar 

  21. Mpitziopoulos, A., Gavalas, D., Konstantopoulos C. & Pantziou, G. (2009). Mobile agent middleware for autonomic data fusion in wireless sensor networks. In: Denko, M.K., Yang, L.T., & Zhang, Y. (eds.), Autonomic computing and networking. Chapter 3 (pp. 57–81). Springer, Berlin.

  22. Paul, T. & Stanley, K. G. (2014). Data collection from wireless sensor networks using a hybrid mobile agent-based approach. Proceedings of the 2014 IEEE 39th conference on local computer networks (LCN’2014) (pp. 288–295).

  23. Shakshuki, E., Malik, H., & Denko, M. K. (2008). Software agent-based directed diffusion in wireless sensor network. Telecommunication Systems, 38(3–4), 161–174.

    Article  Google Scholar 

  24. Shim, Y. & Kim, Y. (2014). Data aggregation with multiple sinks in information-centric wireless sensor network. Proceedings of the 2014 international conference on information networking (ICOIN’2014) (pp. 13–17).

  25. Vansteenwegen, P., Souffriau, W., & Oudheusden, D. V. (2011). The orienteering problem: A survey. European Journal of Operational Research, 209(1), 1–10.

    Article  Google Scholar 

  26. Venetis, I. E., Pantziou, G., Gavalas, D. & Konstantopoulos, C. (2014). Benchmarking mobile agent itinerary planning algorithms for data aggregation on WSNs. Proceedings of the 6th international conference on ubiquitous and future networks (ICUFN’2014) (pp. 105–110).

  27. Wang, X., Chen, M., Kwon, T., & Chao, H. C. (2011). Multiple mobile agents’ itinerary planning in wireless sensor networks: Survey and evaluation. IET Communications, 5(12), 1769–1776.

    Article  Google Scholar 

  28. Wang, J., Zhang, Y., Cheng, Z., & Zhu, X. (2015). EMIP: Energy-efficient itinerary planning for multiple mobile agents in wireless sensor network. Telecommunication Systems, in press.

  29. Wu, Q., Rao, N. S., Barhen, J., Iyenger, S. S., Vaishnavi, V. K., Qi, H., et al. (2004). On computing mobile agent routes for data fusion in distributed sensor networks. IEEE Transactions on Knowledge and Data Engineering, 16(6), 740–753.

    Article  Google Scholar 

  30. Yadav, S. S., Chitra, A., & Deepika, C. L. (2015). Reviewing the process of data fusion in wireless sensor network: A brief survey. International Journal of Wireless and Mobile Computing, 8(2), 130–140.

    Article  Google Scholar 

  31. Zaslavsky, A. (2004). Mobile agents: Can they assist with context awareness?. Proceedings of the 2013 IEEE 14th international conference on mobile data management (MDM’13) (pp. 304–304).

Download references

Acknowledgments

This research has been co-financed by the European Union (European Social Fund–ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF)—Research Funding Program: Archimedes III. Investing in knowledge society through the European Social Fund.

Author information

Authors and Affiliations

  1. Department of Cultural Technology and Communication, University of the Aegean, Mytilene, Greece

    Damianos Gavalas

  2. Department of Informatics, Technological Educational Institution of Athens, Athens, Greece

    Ioannis E. Venetis & Grammati Pantziou

  3. Department of Informatics, University of Piraeus, Piraeus, Greece

    Charalampos Konstantopoulos

Authors
  1. Damianos Gavalas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ioannis E. Venetis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Charalampos Konstantopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Grammati Pantziou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Damianos Gavalas.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gavalas, D., Venetis, I.E., Konstantopoulos, C. et al. Energy-efficient multiple itinerary planning for mobile agents-based data aggregation in WSNs. Telecommun Syst 63, 531–545 (2016). https://doi.org/10.1007/s11235-016-0140-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-016-0140-z

Keywords

Search

Navigation