Skip to main content
SpringerLink
Log in

Node utilization index-based data routing and aggregation protocol for energy-efficient wireless sensor networks

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Data aggregation using the shortest path is a challenging issue in wireless sensor networks. Data aggregation slows the communication process and consumes many resources available to sensor nodes. So, while transmitting the aggregated data, it is crucial to identify the shortest and most reliable route for energy-efficient data aggregation from the source to the sink node. However, the nodes closer to the sink node are heavily utilized in multi-hop transmission and succumb to early death, which causes energy imbalance and an energy hole problem in the network. To overcome this problem, we propose an innovative node utilization index-based data routing and aggregation (NUIDRA) protocol. The NUIDRA protocol is designed and implemented in two phases. The first phase is to determine the shortest path from the source node to the sink node based on the amount of bandwidth utilized by each sensor node, minimum hop count, node’s residual energy, and data aggregation factor. In the second phase, the selected shortest path is used for data transmission and aggregation using the dynamic selection of the aggregator node. The choice of aggregator node is based on the node’s utilization index (UI) and adjacent node count from which it receives the data. The NUIDRA protocol is compared and analysed with I-LEACH and QADA protocols. The extensive simulation results show that in the proposed NUIDRA protocol, there is an increase in the average throughput of 70%, packet delivery ratio by 41.93%, and the average latency is reduced by 58.15% as compared to the I-LEACH protocol. Further, there is an increase in the average throughput of 24%, packet delivery ratio by 7.31%, and the average latency is reduced by 53.23% as compared to the QADA protocol for a data packet size of 512 bytes.

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

Data availability

We have not used any online database. Data are generated by the NS2 simulator tool. If required, we can provide .xls or .csv file.

References

  1. Lin Y, Xie X, Wei M, Zeng T, Chen X, Wu X, Mechali O (2021) An Energy-Efficient and Redundancy-Reduced Protocol of wsn Under Non-uniform Deployment. In: 2021 IEEE International Conference on Mechatronics and Automation (ICMA), pp 802–807. IEEE

  2. Banerjee A, Ghosh S (2019) Weight-based energy-efficient multicasting (weem) in mobile ad hoc networks. Proc Comput Sci 152:291–300

    Article  Google Scholar 

  3. More A, Raisinghani V (2017) A node failure and battery-aware coverage protocol for wireless sensor networks. Comput Electr Eng 64:200–219

    Article  Google Scholar 

  4. Heinzelman WR, Chandrakasan A, Balakrishnan H (2000) Energy-Efficient Communication Protocol for Wireless Microsensor Networks. In: Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, pp 10. IEEE

  5. Venkatesan TP, Rajakumar P, Pitchaikkannu A (2014) Overview of Proactive Routing Protocols in Manet. In: 2014 Fourth International Conference on Communication Systems and Network Technologies, pp 173–177. IEEE

  6. Pottie GJ, Kaiser WJ (2000) Wireless integrated network sensors. Commun ACM 43(5):51–58

    Article  Google Scholar 

  7. Liu A, Huang M, Zhao M, Wang T (2018) A smart high-speed backbone path construction approach for energy and delay optimization in wsns. IEEE Access 6:13836–13854

    Article  Google Scholar 

  8. Wu X, Chen G, Das SK (2006) On the Energy Hole Problem of Nonuniform Node Distribution in Wireless Sensor Networks. In: 2006 IEEE International Conference on Mobile Ad Hoc and Sensor Systems, pp 180–187. IEEE

  9. Li J, Mohapatra P (2007) Analytical modeling and mitigation techniques for the energy hole problem in sensor networks. Pervasive Mob Comput 3(3):233–254

    Article  Google Scholar 

  10. Sawant T, Sirsikar S (2018) A comparative study of various routing technique for wireless sensor network with sink and node mobility. Intell Commun Comput Technol 19:227–236

    Google Scholar 

  11. Marappan P, Rodrigues P (2016) An energy efficient routing protocol for correlated data using cl-leach in wsn. Wirel Netw 22(4):1415–1423

    Article  Google Scholar 

  12. Wang XZ (2018) The comparison of three algorithms in shortest path issue. J Phys: Conf Ser 1087:011–022

    Google Scholar 

  13. Kurian S, Ramasamy L (2021) Novel aodv based service discovery protocol for manets. Wirel Netw 27(4):2497–2508

    Article  Google Scholar 

  14. Maivizhi R, Yogesh P (2020) Spatial Correlation Based Data Redundancy Elimination for Data Aggregation in Wireless Sensor Networks. In: 2020 International Conference on Innovative Trends in Information Technology (ICITIIT), pp 1–5. IEEE

  15. Dash L, Pattanayak BK, Mishra SK, Sahoo KS, Jhanjhi NZ, Baz M, Masud M (2022) A data aggregation approach exploiting spatial and temporal correlation among sensor data in wireless sensor networks. Electronics 11(7):989

    Article  Google Scholar 

  16. Sekar K, Suganya Devi K, Srinivasan P (2021) Energy efficient data gathering using spatio-temporal compressive sensing for wsns. Wirel Pers Commun 117(2):1279–1295

    Article  Google Scholar 

  17. Agarkhed J, Kadrolli V, Patil SR (2022) Efficient bandwidth-aware routing protocol in wireless sensor networks (ebarp). Int J Inform Technol 14:1967–1979

    Google Scholar 

  18. Zhang J, Sun Z (2016) Assessing Multi-Hop Performance of Reactive Routing Protocols in Wireless Sensor Networks. In: 2016 8th IEEE International Conference on Communication Software and Networks (ICCSN), pp 444–449. IEEE

  19. Kothandaraman D, Chellappan C (2019) Energy efficient node rank-based routing algorithm in mobile ad-hoc networks. Int J Comput Netw Commun 11(1):45–61

    Article  Google Scholar 

  20. Ghori MR, Wan T-C, Sodhy GC, Rizwan A (2021) Optimization of the aodv-based packet forwarding mechanism for ble mesh networks. Electronics 10(18):2274

    Article  Google Scholar 

  21. Lee T, Kim DS, Choo H, Kim M (2013) A delay-Aware Scheduling for Data Aggregation in Duty-Cycled Wireless Sensor Networks. In: 2013 IEEE 9th International Conference on Mobile Ad-hoc and Sensor Networks, pp 254–261. IEEE

  22. Pourroostaei Ardakani S, Padget J, De Vos M (2017) A mobile agent routing protocol for data aggregation in wireless sensor networks. Int J Wirel Inf Netw 24(1):27–41

    Article  Google Scholar 

  23. Yu B, Li J-Z (2011) Minimum-time aggregation scheduling in duty-cycled wireless sensor networks. J Comput Sci Technol 26(6):962–970

    Article  Google Scholar 

  24. Tang J, Jiao X, Xiao W (2013) Minimum-Latency Data Aggregation in Duty-Cycled Wireless Sensor Networks Under Physical Interference Model. In: 2013 22nd Wireless and Optical Communication Conference, pp 309–314. IEEE

  25. Jiao X, Lou W, Wang X, Cao J, Xu M, Zhou X et al (2012) Data aggregation scheduling in uncoordinated duty-cycled wireless sensor networks under protocol interference model. Ad Hoc Sens Wirel Netw 15(2–4):315–338

    Google Scholar 

  26. Yun W-K, Yoo S-J (2021) Q-learning-based data-aggregation-aware energy-efficient routing protocol for wireless sensor networks. IEEE Access 9:10737–10750

    Article  Google Scholar 

  27. Abbasian Dehkordi S, Farajzadeh K, Rezazadeh J, Farahbakhsh R, Sandrasegaran K, Abbasian Dehkordi M (2020) A survey on data aggregation techniques in iot sensor networks. Wirel Netw 26(2):1243–1263

    Article  Google Scholar 

  28. Bomnale A, Malgaonkar S (2018) Power Optimization in Wireless Sensor Networks. In: 2018 International Conference on Communication Information and Computing Technology (ICCICT), pp 1–6. IEEE

  29. Li J, Cheng S, Cai Z, Yu J, Wang C, Li Y (2017) Approximate holistic aggregation in wireless sensor networks. ACM Transn Sensor Netw (TOSN) 13(2):1–24

    Article  Google Scholar 

  30. Cheng S, Cai Z, Li J, Gao H (2016) Extracting kernel dataset from big sensory data in wireless sensor networks. IEEE Trans Knowl Data Eng 29(4):813–827

    Article  Google Scholar 

  31. Yan M, Ji S, Han M, Li Y, Cai Z (2014) Data Aggregation Scheduling in Wireless Networks with Cognitive Radio Capability. In: 2014 Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), pp 513–521. IEEE

  32. Sivasankar P, Chellappan C, Balaji S (2008) Performance Evaluation of Energy Efficient on-Demand Routing Algorithms for Manet. In: 2008 IEEE Region 10 and the Third International Conference on Industrial and Information Systems, pp. 1–5. IEEE

  33. Jan SR, Khan R, Khan F, Jan MA, Alshehri MD, Balasubramaniam V, Sehdev PS (2021) Marginal and average weight-enabled data aggregation mechanism for the resource-constrained networks. Comput Commun 174:101–108

    Article  Google Scholar 

  34. Fang W, Zhang W, Yang W, Li Z, Gao W, Yang Y (2021) Trust management-based and energy efficient hierarchical routing protocol in wireless sensor networks. Digit Commun Netw 7(4):470–478

    Article  Google Scholar 

  35. Behera TM, Samal UC, Mohapatra SK (2018) Energy-efficient modified leach protocol for iot application. IET Wirel Sensor Syst 8(5):223–228

    Article  Google Scholar 

  36. Joshi P, Raghuvanshi AS, Kumar S (2022) An intelligent delay efficient data aggregation scheduling for distributed sensor networks. Microprocess Microsyst. https://doi.org/10.1016/j.micpro.2022.104608

    Article  Google Scholar 

  37. Nguyen T-D, Le D-T, Vo V-V, Kim M, Choo H (2020) Fast sensory data aggregation in iot networks: collision-resistant dynamic approach. IEEE Internet Things J 8(2):766–777

    Article  Google Scholar 

  38. Bagaa M, Younis M, Djenouri D, Derhab A, Badache N (2015) Distributed low-latency data aggregation scheduling in wireless sensor networks. ACM Trans Sensor Netw (TOSN) 11(3):1–36

    Article  Google Scholar 

  39. Rahman H, Ahmed N, Hussain MI (2018) A qos-aware hybrid data aggregation scheme for internet of things. Ann Telecommun 73:475–486

    Article  Google Scholar 

  40. Shah SG, Ahmed A, Ullah I, Noor W (2019) A novel data aggregation scheme for wireless sensor networks. Int J Adv Comput Sci Appl 10(2):585–590

    Google Scholar 

  41. Yu B, Li J, Li Y (2009) Distributed data aggregation scheduling in wireless sensor networks. In: IEEE INFOCOM 2009, pp 2159–2167. IEEE

  42. Chen X, Hu X, Zhu J (2005) Minimum Data Aggregation Time Problem in Wireless Sensor Networks. In: Mobile Ad-hoc and Sensor Networks: First International Conference, MSN 2005, Wuhan, China, Dec 13-15, 2005. Proceedings 1, pp 133–142. Springer

  43. Huang S-H, Wan P-J, Vu CT, Li Y, Yao F (2007) Nearly Constant Approximation for Data Aggregation Scheduling in Wireless Sensor Networks. In: IEEE INFOCOM 2007-26th IEEE International Conference on Computer Communications, pp 366–372. IEEE

Download references

Funding

Not Applicable.

Author information

Authors and Affiliations

  1. Department of Electronics and Telecommunication Engineering, MPSTME, NMIMS University, Mumbai, 400056, India

    Archana Bomnale & Avinash More

Authors
  1. Archana Bomnale
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Avinash More
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

AB involved in conceptualization, methodology, software programming, writing, reviewing, and editing. AM involved in investigation, supervision, reviewing, and editing.

Corresponding author

Correspondence to Archana Bomnale.

Ethics declarations

Conflict of interest

The authors declare they have no financial interests.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bomnale, A., More, A. Node utilization index-based data routing and aggregation protocol for energy-efficient wireless sensor networks. J Supercomput 80, 9220–9252 (2024). https://doi.org/10.1007/s11227-023-05800-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-023-05800-4

Keywords

Search

Navigation