Skip to main content

Advertisement

SpringerLink
Log in

Construction of multi-level data aggregation trees for energy efficiency and delivery delay in machine-to-machine communications

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

Abstract

Machine-to-Machine (M2M) communications have gone forth as the newest technology for succeeding in communication generations. The M2M connections use the sensor nodes to capture an event into data packets and relayed through a network. The sensor nodes consume more energy whenever the increase in data packets transmitted from the sensor nodes in the system. To reduce the energy utilization applying the data aggregation is essential. We proposed a comprehensive model for calculating energy utilization and delay-tolerance by using Multi-Level Data Aggregation Trees (MLDAT). In the proposed scheme, the first stage is about the construction of Multi-Level Data Aggregation Tree, which aggregates the data originated from various wireless sensor nodes in the communication network. In the second stage, a delay-tolerant scheduling algorithm for controlling the delivery delay for user queries presented. Ultimately, the simulation results of the proposed scheme show that the suggested algorithms have better performance than the existing state-of-the-art approaches significantly.

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

Similar content being viewed by others

References

  1. Ye Z, Mohamadian H (2014) Adaptive clustering based dynamic routing of wireless sensor networks via generalized ant Colony optimization. IERI Procedia 10:2–10

    Article  Google Scholar 

  2. Zhou Z, Guo Y, He Y, Zhao X, Bazzi WM (2019) Access control and resource allocation for M2M communications in industrial automation. IEEE Trans Ind Inf 15(5):3093–3103

    Article  Google Scholar 

  3. Manap Z, Ali BM, Ng CK, Noordin NK, Sali A (2013) A review on hierarchical routing protocols for wireless sensor networks. Wirel Pers Commun 72(2):1077–1104

    Article  Google Scholar 

  4. Al-Kahtani MS (2015) Efficient cluster-based sleep scheduling for M2M communication network. Arab J Sci Eng 40(8):2361–2373

    Article  Google Scholar 

  5. Nguyen N-T, Liu BH, Pham VT, Liou TY (2018) An efficient minimum-latency collision-free scheduling algorithm for data aggregation in wireless sensor networks. IEEE Syst J 12(3):2214–2225

    Article  Google Scholar 

  6. Haque M, Ahmad T, Imran M (2018) Review of hierarchical routing protocols for wireless sensor networks, In Intell Commun Comput Tech, Springer. p. 237–246

  7. Chen Q, Gao H, Cai Z, Cheng L, Li J (2018) Energy-collision aware data aggregation scheduling for energy harvesting sensor networks. In IEEE INFOCOM 2018-IEEE Conference on Computer Communications, pp 117–125. IEEE

  8. Zheng J, Cai Y, Shen X, Zheng Z, Yang W (2015) Green energy optimization in energy harvesting wireless sensor networks. IEEE Commun Mag 53(11):150–157

    Article  Google Scholar 

  9. Lee DJ, Zhu Z, Toscas P (2015) Spatio-temporal functional data analysis for wireless sensor networks data. Environmetrics 26(5):354–362

    Article  MathSciNet  Google Scholar 

  10. Mann PS, Singh S (2017) Energy-efficient hierarchical routing for wireless sensor networks: a swarm intelligence approach. Wirel Pers Commun 92(2):785–805

    Article  Google Scholar 

  11. Beal J, Pianini D, Viroli M (2015) Aggregate programming for the internet of things. Computer 48(9):22–30

    Article  Google Scholar 

  12. Rahman H, Ahmed N, Hussain I (2016) Comparison of data aggregation techniques in Internet of Things (IoT). In 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pp 1296–1300. IEEE

  13. Tsai S-Y, Sou S-I, Tsai M-H (2014) Reducing energy consumption by data aggregation in M2M networks. Wirel Pers Commun 74(4):1231–1244

    Article  Google Scholar 

  14. Brummet R, et al (2018) A flexible retransmission policy for industrial wireless sensor actuator networks. In 2018 IEEE International Conference on Industrial Internet (ICII). IEEE

  15. Kliks A (2015) Application of the cognitive radio concept for M2M communications: practical considerations. Wirel Pers Commun 83(1):117–133

    Article  Google Scholar 

  16. Akan OB, Karli OB, Ergul O (2009) Cognitive radio sensor networks. IEEE Netw 23(4):34–40

    Article  Google Scholar 

  17. Toor AS, Jain A (2019) Energy aware cluster based multi-hop energy efficient routing protocol using multiple mobile nodes (MEACBM) in wireless sensor networks. AEU Int J Electron Commun 102:41–53

    Article  Google Scholar 

  18. Chandirika B, Sakthivel N (2018) Performance Analysis of Clustering-Based Routing Protocols for Wireless Sensor Networks, in Advances in Big Data and Cloud Computing, Springer. p. 269–276

  19. Haseeb K, Bakar KA, Abdullah AH, Darwish T (2017) Adaptive energy aware cluster-based routing protocol for wireless sensor networks. Wirel Netw 23(6):1953–1966

    Article  Google Scholar 

  20. Kumar R, Kumar D (2016) Multi-objective fractional artificial bee colony algorithm to energy aware routing protocol in wireless sensor network. Wirel Netw 22(5):1461–1474

    Article  Google Scholar 

  21. Ding M, Cheng X, Xue G (2003) Aggregation tree construction in sensor networks. In 2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No. 03CH37484). IEEE

  22. Padmanabh K, Vuppala SK (2014) Method and system for adaptive aggregation of data in a wireless sensor network, Google Patents

  23. Lu Y, Chen J, Comsa I, Kuonen P, Hirsbrunner B (2014) Construction of data aggregation tree for multi-objectives in wireless sensor networks through jump particle swarm optimization. Procedia Comput Sci 35:73–82

    Article  Google Scholar 

  24. Randhawa S, Jain S (2017) Data aggregation in wireless sensor networks: previous research, current status and future directions. Wirel Pers Commun 97(3):3355–3425

    Article  Google Scholar 

  25. Mohsenifard E, Ghaffari A (2016) Data aggregation tree structure in wireless sensor networks using cuckoo optimization algorithm. Inf Syst Telecommunication 4(3):182–190

    Google Scholar 

  26. John N, Jyotsna A (2018) A survey on energy efficient tree-based data aggregation techniques in wireless sensor networks. In 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), pp 461–465. IEEE

  27. Le DT, Lee T, Choo H (2018) Delay-aware tree construction and scheduling for data aggregation in duty-cycled wireless sensor networks. EURASIP J Wirel Commun Netw 2018(1):1–15

    Article  Google Scholar 

  28. Zhang Z, Li J, Yang X (2020) Data aggregation in heterogeneous wireless sensor networks by using local tree reconstruction algorithm. Complexity, 2020

  29. Lu Y, Zhang T, He E, Comşa IS (2018) Self-learning-based data aggregation scheduling policy in wireless sensor networks. J Sens 2018:1–12

    Google Scholar 

  30. Kale PA, Nene MJ (2019) Scheduling of data aggregation trees using local heuristics to enhance network lifetime in sensor networks. Comput Netw 160:51–64

    Article  Google Scholar 

  31. Sun Z, Wang H, Liu B, Li C, Pan X, Nie Y (2018) CS-FCDA: a compressed sensing-based on fault-tolerant data aggregation in sensor networks. Sensors 18(11):3749

    Article  Google Scholar 

  32. Mohanty JP, Mandal C (2017) Connected Dominating Set in Wireless Sensor Network, in Handbook of Research on Advanced Wireless Sensor Network Applications, Protocols, and Architectures, IGI Global p 62–85

  33. He J et al (2013) Constructing load-balanced data aggregation trees in probabilistic wireless sensor networks. IEEE Trans Parallel Distrib Syst 25(7):1681–1690

    Article  Google Scholar 

  34. Riker A et al (2015) A two-tier adaptive data aggregation approach for m2m group-communication. IEEE Sensors J 16(3):823–835

    Article  Google Scholar 

  35. Hussain S, Islam O (2007) An energy efficient spanning tree based multi-hop routing in wireless sensor networks. In 2007 IEEE Wireless Communications and Networking Conference, pp 4383–4388. IEEE

Download references

Author information

Authors and Affiliations

  1. Department of CSE, JNTUA, Anantapuramu, Andhra Pradesh, India

    Prasad Challa & B. Eswara Reddy

Authors
  1. Prasad Challa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. B. Eswara Reddy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Prasad Challa.

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

Challa, P., Reddy, B.E. Construction of multi-level data aggregation trees for energy efficiency and delivery delay in machine-to-machine communications. Peer-to-Peer Netw. Appl. 14, 585–598 (2021). https://doi.org/10.1007/s12083-020-01016-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-020-01016-y

Keywords

Search

Navigation