Skip to main content

Advertisement

SpringerLink
Log in

Adaptive data aggregation with probabilistic routing in wireless sensor networks

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Periodical extraction of raw sensor readings is one of the most representative and comprehensive applications in Wireless sensor networks. In order to reduce the data redundancy and the communication load, in-network data aggregation is usually applied to merge the packets during the routing process. Aggregation protocols with deterministic routing pre-construct the stationary structure to perform data aggregation. However, the overhead of construction and maintenance always outweighs the benefits of data aggregation under dynamic scenarios. This paper proposes an Adaptive Data Aggregation protocol with Probabilistic Routing for the periodical data collection events. The main idea is to encourage the nodes to use an optimal routing structure for data aggregation with certain probability. The optimal routing structure is defined as a Multi-Objective Steiner Tree, which can be explored and exploited by the routing scheme based on the Ant Colony Optimization and Genetic Algorithm hybrid approach. The probabilistic routing decision ensures the adaptability for some topology transformations. Moreover, by using the prediction model based on the sliding window for future arriving packets, the adaptive timing policy can reduce the transmission delay and can enhance the aggregation probability. Therefore, the packet transmission converges from both spatial and temporal aspects for the data aggregation. Finally, the theoretical analysis and the simulation results validate the feasibility and the high efficiency of the novel protocol when compared with other existing approaches.

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

Similar content being viewed by others

References

  1. Li, M., Li, Z., & Vasilakos, A. V. (2013). A survey on topology control in wireless sensor networks: Taxonomy, comparative study, and open issues. Proceedings of the IEEE, 101(12), 2538–2557.

    Article  Google Scholar 

  2. Fasolo, E., Rossi, M., Widmer, J., & Zorzi, M. (2007). In-network aggregation techniques for wireless sensor networks: A survey. IEEE Wireless Communications, 14(2), 70–87.

    Article  Google Scholar 

  3. Xiang, L., Luo, J., & Vasilakos, A. (2011). Compressed data aggregation for energy efficient wireless sensor networks. In IEEE communications society conference on sensor, mesh and Ad hoc communications and networks (SECON) (pp. 46–54).

  4. Laukik, C., Alin, D., & Sanjay, R. (2008). Aggregation methods for large-scale sensor networks. ACM Transactions on Sensor Networks (TOSN), 4(2), 1–29.

    Google Scholar 

  5. Wei, G., Ling, Y., Guo, B., Xiao, B., & Vasilakos, A. V. (2011). Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman Filter. Computer Communications, 34(6), 793–802.

    Article  Google Scholar 

  6. Xu, X., Ansari, R., Khokhar, A., & Vasilakos, A. V. (2015). Hierarchical data aggregation using compressive sensing (HDACS) in WSNs. ACM Transactions on Sensor Networks (TOSN), 11, 45.

    Google Scholar 

  7. Chilamkurti, N., Zeadally, S., Vasilakos, A., & Sharma, V. (2009). Cross-layer support for energy efficient routing in wireless sensor networks. Journal of Sensors, 2009, 1–9.

    Article  Google Scholar 

  8. Dijun, L., Xiaojun, Z., Xiaobing, W., & Guihai, C. (2011). Maximizing lifetime for the shortest path aggregation tree in wireless sensor networks. In IEEE international conference on computer communications (INFOCOM) (1566–1574).

  9. Chao, C.-M., & Hsiao, T.-Y. (2009). Design of structure-free and energy-balanced data aggregation in wireless sensor networks. In IEEE international conference on high performance computing and communications (HPCC) (222–229).

  10. Lu, Y., Chen, J. P., Comsa, I. S., Kuonen, P., & Hirsbrunner, B. (2014). Construction of data aggregation tree for multi-objectives in wireless sensor networks through jump particle swarm optimization. In International conference on knowledge-based and intelligent information & engineering systems (KES) (73–82).

  11. Lee, M., & Wong, V. W. S. (2005). An energy-aware spanning tree algorithm for data aggregation in wireless sensor networks. In IEEE pacific rim conference on communications, computers and signal processing (PACRIM) (300–303).

  12. Malhotra, B., Nikolaidis, I., & Nascimento, M. (2010). Aggregation convergecast scheduling in wireless sensor networks. Springer Wireless Networks, 17(2), 319335.

    Google Scholar 

  13. Fan, K.-W., Liu, S., & Sinha, P. (2007). Structure-free data aggregation in sensor networks. IEEE Transactions on Mobile Computing, 6(8), 929–942.

    Article  Google Scholar 

  14. Vass, D., & Vidacs, A., (2007). Distributed data agregation with geographical routing in wireless sensor Networks. IEEE international conference on pervasive services (68–71).

  15. Bagaa, M., Challal, Y., Ksentini, A., Derhab, A., & Badache, N. (2014). Data aggregation scheduling algorithms in wireless sensor networks: Solutions and challenges. IEEE Communications Surveys & Tutorials, 16(3), 1339–1368.

    Article  Google Scholar 

  16. Lu, Y., Comsa, I.S., Kuonen, P., & Hirsbrunner, B. (2015). Dynamic data aggregation protocol based on multiple objective tree in wireless sensor networks. In IEEE international conference on intelligent sensors, sensor networks and information processing (ISSNIP) (1–7).

  17. Meng, T., Wu, F., Yang, Z., Chen, G., & Vasilakos, A. (2015). Spatial reusability-aware routing in multi-hop wireless networks. IEEE Transactions on Computers, 99, 1–1.

    MathSciNet  Google Scholar 

  18. Song, Y., Liu, L., Ma, H., & Vasilakos, A. V. (2014). A biology-based algorithm to minimal exposure problem of wireless sensor networks. IEEE Transactions on Network and Service Management, 11(3), 417–430.

    Article  Google Scholar 

  19. Yao, Y., Cao, Q., & Vasilakos, A. V. (2015). EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks. IEEE/ACM Transactions on Networking, 23(3), 810–823.

    Article  Google Scholar 

  20. Solis, I., & Obraczka, K. (2004). The impact of timing in data aggregation for sensor networks. In IEEE international conference on communications (ICC) (3640–3645).

  21. Solis, I., & Obraczka, K. (2006). In-network aggregation trade-offs for data collection in wireless sensor networks. International Journal of Sensor Networks, 1(3), 200–212.

    Article  Google Scholar 

  22. Ding, S. & Ishii, N. (2000). An online genetic algorithm for dynamic Steiner tree problem. In 26th annual conference of the IEEE industrial electronics society (IECON) (812–817).

Download references

Author information

Authors and Affiliations

  1. Department of Informatics, University of Fribourg, 1700, Fribourg, Switzerland

    Yao Lu & Beat Hirsbrunner

  2. Institute of Complex Systems, University of Applied Sciences of Western Switzerland, 1705, Fribourg, Switzerland

    Ioan-Sorin Comsa & Pierre Kuonen

Authors
  1. Yao Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ioan-Sorin Comsa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pierre Kuonen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Beat Hirsbrunner
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yao Lu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lu, Y., Comsa, IS., Kuonen, P. et al. Adaptive data aggregation with probabilistic routing in wireless sensor networks. Wireless Netw 22, 2485–2499 (2016). https://doi.org/10.1007/s11276-015-1108-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-015-1108-8

Keywords

Search

Navigation