Skip to main content

Advertisement

SpringerLink
Log in

A reliable tree-based data aggregation method in wireless sensor networks

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

Aggregating the sensed data by nodes is a natural way to increase the network lifetime and reduce the number of bits sent and received by each sensor node. This paper presents a reliable tree-based data aggregation method. In the proposed method, sensor nodes are organized in the form of a binary tree. Then, aggregation requests are authenticated by a shared key, and if the request is acknowledged, the aggregation process begins. In the proposed method, using dynamic generator polynomial-size for cycle error detection code (CRC), the error generated along the path is detected hop by hop. In case of an error, the request for retransmission will be sent to the previous hop. Also, intermediate nodes apply certain aggregating functions like summation or averaging on the received packages, which cause a reduction in the amount of data transmission on the network. This method reduces the number of sent packets and the amount of energy consumed, so the network has a longer lifetime. Also, this method significantly increased reliability using the CRC code. The proposed method is compared with EESSDA, SDAACA, and LAG methods. The simulation results show that the proposed method has greater superiority over its comparative techniques, particularly on aspects such as energy consumption, network lifetime, and reliability.

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
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Cao N, Liu P, Li G, Zhang C, Cao S, Cao G, Yan M, Gupta BB (2018) Evaluation models for the nearest closer routing protocol in wireless sensor networks. IEEE Access 6:77043–77054

    Article  Google Scholar 

  2. Krishnamachari L, Estrin D, Wicker S (2002) The impact of data aggregation in wireless sensor networks. In: 22nd international conference on distributed computing systems workshops, 2002. Proceedings. IEEE, pp 575–578

  3. Kumar M, Dutta K (2016) Ldat: Lftm based data aggregation and transmission protocol for wireless sensor networks. Journal of Trust Management 3(1):2

    Article  MathSciNet  Google Scholar 

  4. Merzoug MA, Boukerche A, Mostefaoui A (2018) Efficient information gathering from large wireless sensor networks. Comput Commun 132:84–95

    Article  Google Scholar 

  5. Ardakani SP, Padget J, De Vos M (2017) A mobile agent routing protocol for data aggregation in wireless sensor networks. Int J Wireless Inf Networks 24(1):27–41

    Article  Google Scholar 

  6. Sharma V, Kumar R, Kumar N (2018) Dptr: Distributed priority tree-based routing protocol for fanets. Comput Commun 122:129–151

    Article  Google Scholar 

  7. Jose J, Jose J, Princy M (2014) A survey on privacy preserving data aggregation protocols forwireless sensor networks. J Comput Inform Technol 22(1):1–20

    Article  Google Scholar 

  8. Mohanty P, Kabat MR (2016) Energy efficient structure-free data aggregation and delivery in wsn. Egyptian Informatics Journal 17(3):273–284

    Article  Google Scholar 

  9. Wang T, Qin X, Liu L (2013) An energy-efficient and scalable secure data aggregation for wireless sensor networks. International Journal of Distributed Sensor Networks 9(12):843485

    Article  Google Scholar 

  10. Yadav S, Yadav RS (2016) A review on energy efficient protocols in wireless sensor networks. Wirel Netw 22(1):335–350

    Article  Google Scholar 

  11. Bagaa M, Derhab A, Lasla N, Ouadjaout A, Badache N (2012) Semi-structured and unstructured data aggregation scheduling in wireless sensor networks. In: 2012 Proceedings IEEE INFOCOM. IEEE, pp 2671–2675

  12. Moon S-H, Park S, Han S-J (2017) Energy efficient data collection in sink-centric wireless sensor networks: A cluster-ring approach. Comput Commun 101:12–25

    Article  Google Scholar 

  13. Duggan JR, Earnshaw AM, Creasy TJ (2016) Forward error correction decoding avoidance based on predicted code block reliability, US Patent App. 15/144,583

  14. Ozdemir S, Xiao Y (2009) Secure data aggregation in wireless sensor networks: A comprehensive overview. Comput Netw 53(12):2022–2037

    Article  Google Scholar 

  15. Prathima E, Prakash TS, Venugopal K, Iyengar S, Patnaik L (2016) Sdamq: Secure data aggregation for multiple queries in wireless sensor networks. Procedia Computer Science 89:283–292

    Article  Google Scholar 

  16. Pourghebleh B, Navimipour NJ (2017) Data aggregation mechanisms in the internet of things: a systematic review of the literature and recommendations for future research. J Netw Comput Appl 97:23–34

    Article  Google Scholar 

  17. Arumugam GS, Ponnuchamy T (2015) Ee-leach: Development of energy-efficient leach protocol for data gathering in wsn. EURASIP J Wirel Commun Netw 2015(1):76

    Article  Google Scholar 

  18. Gherbi C, Aliouat Z, Benmohammed M (2019) Comparative analysis of hierarchical cluster protocols for wireless sensor networks. International Journal of High Performance Computing and Networking 13 (4):366–377

    Article  Google Scholar 

  19. Amodu OA, Mahmood RAR (2018) Impact of the energy-based and location-based leach secondary cluster aggregation on wsn lifetime. Wirel Netw 24(5):1379–1402

    Article  Google Scholar 

  20. Lindsey S, Raghavendra CS (2002) Pegasis: Power-efficient gathering in sensor information systems. In: Proceedings, IEEE aerospace conference, vol 3. IEEE, pp 3–3

  21. Wang N-C, Chiang Y-K, Hsieh C-H (2018) An efficient grid-based data aggregation scheme for wireless sensor networks. Journal of Internet Technology 19(7):2197–2205

    Google Scholar 

  22. Rajathi N, Jayashree L (2016) Energy efficient grid clustering based data aggregation in wireless sensor networks. In: 2016 IEEE region 10 conference (TENCON). IEEE, pp 488–492

  23. Jaspal S, Kumar B, et al (2017) Energy saving using memorization: A novel energy efficient and fault tolerant cluster tree algorithm for wsn. In: Intelligent decision support systems for sustainable computing. Springer, pp 179–206

  24. Razaque A, Rizvi SS (2017) Secure data aggregation using access control and authentication for wireless sensor networks. Computers & Security 70:532–545

    Article  Google Scholar 

  25. Asemani M, Esnaashari M (2015) Learning automata based energy efficient data aggregation in wireless sensor networks. Wirel Netw 21(6):2035–2053

    Article  Google Scholar 

  26. Gopikrishnan S, Priakanth P (2016) Hybrid tree construction for sustainable delay aware data aggregation in wireless sensor networks. Wirel Pers Commun 90(2):923–945

    Article  Google Scholar 

  27. Gopikrishnan S, Priakanth P (2016) Hsda: Hybrid communication for secure data aggregation in wireless sensor network. Wirel Netw 22(3):1061–1078

    Article  Google Scholar 

  28. Metan J, Murthy KN (2017) Rask: Request authentication using shared keys for secured data aggregation in sensor network. In: Computer science on-line conference. Springer, pp 23–35

  29. Srouji MS, Wang Z, Henkel J (2011) Rdts: A reliable erasure-coding based data transfer scheme for wireless sensor networks. In: 2011 IEEE 17th international conference on parallel and distributed systems (ICPADS). IEEE, pp 481–488

  30. Othman SB, Bahattab AA, Trad A, Youssef H (2015) Confidentiality and integrity for data aggregation in wsn using homomorphic encryption. Wirel Pers Commun 80(2):867–889

    Article  Google Scholar 

  31. Stallings W (2006) Cryptography and network security, 4/E Pearson Education India

  32. Naghibi M, Barati H (2020) Egrpm: Energy efficient geographic routing protocol based on mobile sink in wireless sensor networks. Sustainable Computing: Informatics and Systems 25:100377

    Google Scholar 

  33. Mosavifard A, Barati H (2020) An energy-aware clustering and two-level routing method in wireless sensor networks. Computing

  34. Djukic P, Valaee S (2005) Maximum network lifetime in fault tolerant sensor networks. In: GLOBECOM’05. IEEE Global telecommunications conference, 2005, vol 5. IEEE, pp 5–pp

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran

    Elham Hasheminejad & Hamid Barati

Authors
  1. Elham Hasheminejad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hamid Barati
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hamid Barati.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hasheminejad, E., Barati, H. A reliable tree-based data aggregation method in wireless sensor networks. Peer-to-Peer Netw. Appl. 14, 873–887 (2021). https://doi.org/10.1007/s12083-020-01025-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-020-01025-x

Keywords

Search

Navigation