Skip to main content

Advertisement

Log in

An approach by adopting multi-objective clustering and data collection along with node sleep scheduling for energy efficient and delay aware WSN

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

Abstract

Wireless sensor network (WSN) is composed of high density spatially dispersed and dedicated autonomous sensors for monitoring or recording the physical conditions of the environment. Still, the sensor nodes are battery-powered with limited energy supply; moreover, in many of the applications, sensor nodes are deployed in harsh natural environment or vast space so that the continuous energy supplement is impossible. Many authors focused to achieve trade-offs in terms of energy, and delay for such data collection tasks; few of them are concerned with sleep scheduling. In this paper, we propose energy efficient and delay aware routing using multi-objective clustering and a sleep schedules scheme for WSN (E2DR-MCS). The first contribution of this paper is to propose a multi-objective wolf optimization algorithm for clustering. Additionally, a sleep scheduling scheme is utilized to save power and increase the lifetime of the entire network. The second contribution is that the efficient data collection is achieved by a selective track search algorithm, which provides a suitable path between the source-destination pairs. The simulation results show that E2DR-MCS approach greatly contributes to minimizing energy consumption, delay, and overhead; maximize the network lifetime and delivery ratio.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

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
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Pu F, Wang Z, Chen N, Zhang W, Du C (2016) Semantic integration of wireless sensor networks into open geospatial consortium sensor observation service to access and share environmental monitoring systems. IET Softw 10(2):45–53. https://doi.org/10.1049/iet-sen.2014.0141

    Article  Google Scholar 

  2. More A, Wagh S, Joshi K (2015) A test-bed for habitat monitoring system using Wi-Fi in Wireless Sensor Networks. 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). https://doi.org/10.1109/iccic.2015.7435758

  3. Wan L, Han G, Shu L, Feng N, Zhu C, Lloret J (2015) Distributed parameter estimation for Mobile wireless sensor network based on cloud computing in battlefield surveillance system. IEEE Access 3:1729–1739. https://doi.org/10.1109/access.2015.2482981

    Article  Google Scholar 

  4. Chen J, Klein J, Wu Y et al (2016) A thermoelectric energy harvesting system for powering wireless sensors in nuclear power plants. IEEE Trans Nucl Sci 63(5):2738–2746. https://doi.org/10.1109/tns.2016.2606090

    Article  Google Scholar 

  5. Mendis C, Skvortsov A, Gunatilaka A, Karunasekera S (2012) Performance of wireless chemical sensor network with dynamic collaboration. IEEE Sensors J 12(8):2630–2637. https://doi.org/10.1109/jsen.2012.2198349

    Article  Google Scholar 

  6. Hao W, Yuqing Z (2014) AraTRM: attack resistible ant-based trust and reputation model. 2014 IEEE Int Conf Comput Inform Technol. https://doi.org/10.1109/cit.2014.78

  7. Dilhac J, Bafleur M (2014) Energy harvesting in aeronautics for battery-free wireless sensor networks. IEEE Aerosp Electron Syst Mag 29(8):18–22. https://doi.org/10.1109/maes.2014.130002

    Article  Google Scholar 

  8. Huang J, Wang H, Qian Y, Wang C (2013) Priority-based traffic scheduling and utility optimization for cognitive radio communication infrastructure-based smart grid. IEEE Trans Smart Grid 4(1):78–86. https://doi.org/10.1109/tsg.2012.2227282

    Article  Google Scholar 

  9. Balapuwaduge I, Jiao L, Pla V, Li F (2014) Channel assembling with priority-based queues in cognitive radio networks: strategies and performance evaluation. IEEE Trans Wirel Commun 13(2):630–645. https://doi.org/10.1109/twc.2013.120713.121948

    Article  Google Scholar 

  10. Xiong Z, Wang B, Wang Z (2015) Priority-based greedy scheduling for confident information coverage in energy harvesting wireless sensor networks. 2015 11th International Conference on Mobile Ad-hoc and Sensor Networks (MSN). doi:https://doi.org/10.1109/msn.2015.20

  11. Darbha S, Agrawal D (1994) A task duplication based optimal scheduling algorithm for variable execution time tasks. 1994 Int Conf Parallel Process (ICPP'94). https://doi.org/10.1109/icpp.1994.47

  12. Mei J, Li K (2012) Multi-copy deleting approach for duplication based scheduling on heterogeneous computing systems. 2012 41st Int Conf Parallel Process Workshops. https://doi.org/10.1109/icppw.2012.93

  13. Peng M, Xiao Y, Wang P (2009) Error analysis and kernel density approach of scheduling sleeping nodes in cluster-based wireless sensor networks. IEEE Trans Veh Technol 58(9):5105–5114. https://doi.org/10.1109/tvt.2009.2027908

    Article  Google Scholar 

  14. Hsieh M (2011) Data aggregation model using energy-efficient delay scheduling in multi-hop hierarchical wireless sensor networks. IET Commun 5(18):2703–2711. https://doi.org/10.1049/iet-com.2011.0140

    Article  MathSciNet  MATH  Google Scholar 

  15. Chae S, Kang K, Cho Y (2013) A scalable joint routing and scheduling scheme for large-scale wireless sensor networks. Ad Hoc Netw 11(1):427–441. https://doi.org/10.1016/j.adhoc.2012.07.004

    Article  Google Scholar 

  16. WU X, LI Y, JI H (2014) Clustering and scheduling methods based on SLNR in downlink CoMP system. J Chin Univ Posts Telecommun 21(1):74–103. https://doi.org/10.1016/s1005-8885(14)60271-5

    Article  Google Scholar 

  17. Sun Q, Zhuge Q, Hu J, Yi J, Sha E (2014) Efficient grouping-based mapping and scheduling on heterogeneous cluster architectures. Comput Electr Eng 40(5):1604–1620. https://doi.org/10.1016/j.compeleceng.2014.03.009

    Article  Google Scholar 

  18. Wang L, Wei R, Tian Z (2011) Cluster based node scheduling method for wireless sensor networks. Scientia Sinica Informationis: 1013–1023

  19. Indra Gandhi K, Narayanasamy P (2010) A cluster-based quad-tree partitioning for scheduling the mobile element in wireless sensor networks. Int J Wireless Inf Networks 18(1):50–55. https://doi.org/10.1007/s10776-010-0119-8

    Article  Google Scholar 

  20. Severino R, Pereira N, Tovar E (2014) Dynamic cluster scheduling for cluster-tree WSNs. SpringerPlus. 3(1):493. https://doi.org/10.1186/2193-1801-3-493

    Article  Google Scholar 

  21. Wu B, Feng Y, Zheng H, Chen X (2016) Dynamic cluster members scheduling for target tracking in sensor networks. IEEE Sensors J 16(19):7242–7249. https://doi.org/10.1109/jsen.2016.2597544

    Article  Google Scholar 

  22. Lersteau C, Rossi A, Sevaux M (2016) Robust scheduling of wireless sensor networks for target tracking under uncertainty. Eur J Oper Res 252(2):407–417. https://doi.org/10.1016/j.ejor.2016.01.018

    Article  MathSciNet  MATH  Google Scholar 

  23. Izadi D, Ghanavati S, Abawajy J, Herawan T (2016) An alternative data collection scheduling scheme in wireless sensor networks. Computing. 98(12):1287–1304. https://doi.org/10.1007/s00607-016-0484-y

    Article  MathSciNet  Google Scholar 

  24. Gentz R, Scaglione A, Ferrari L, Hong Y (2016) PulseSS: a pulse-coupled synchronization and scheduling protocol for clustered wireless sensor networks. IEEE Internet Things J 3(6):1222–1234. https://doi.org/10.1109/jiot.2016.2576923

    Article  Google Scholar 

  25. Yang C, Lu J, Yang W, Shi H (2017) Sensor scheduling for lifetime maximization in centralized state estimation. Neurocomputing. 270:43–53. https://doi.org/10.1016/j.neucom.2016.12.107

    Article  Google Scholar 

  26. Palaniappan S, Periasamy P (2017) Proposed energy efficient multi attribute time slot scheduling algorithm for quality of Service in Wireless Sensor Network. Wirel Pers Commun 97(4):5951–5968. https://doi.org/10.1007/s11277-017-4821-z

    Article  Google Scholar 

  27. Li J, Peng M, Cheng A, Yu Y, Wang C (2017) Resource allocation optimization for delay-sensitive traffic in fronthaul constrained cloud radio access networks. IEEE Syst J 11(4):2267–2278. https://doi.org/10.1109/JSYST.2014.2364252

    Article  Google Scholar 

  28. Peng M, Yu Y, Xiang H, Poor HV (2016) Energy-efficient resource allocation optimization for multimedia heterogeneous cloud radio access networks. IEEE Trans Multimed 18(5):879–892. https://doi.org/10.1109/TMM.2016.2535722

    Article  Google Scholar 

  29. Li J, Peng M, Yu Y, Ding Z (2016) Energy-efficient joint congestion control and resource optimization in heterogeneous cloud radio access networks. IEEE Trans Veh Technol 65(12):9873–9887. https://doi.org/10.1109/TVT.2016.2531184

    Article  Google Scholar 

  30. Peng M, Zhang K, Jiang J, Wang J, Wang W (2015) Energy-efficient resource assignment and power allocation in heterogeneous cloud radio access networks. IEEE Trans Veh Technol 64(11):5275–5287. https://doi.org/10.1109/TVT.2014.2379922

    Article  Google Scholar 

  31. Li J, Wu J, Peng M, Zhang P (2016) Queue-aware energy-efficient joint remote radio head activation and beamforming in cloud radio access networks. IEEE Trans Wirel Commun 15(6):3880–3894. https://doi.org/10.1109/TWC.2016.2530711

    Article  Google Scholar 

  32. Chen H, Lou W, Wang Z, Xia F (2018) On achieving asynchronous energy-efficient neighbour discovery for mobile sensor networks. IEEE Trans Emerg Top Comput 6(5):553–565. https://doi.org/10.1109/TETC.2016.2586192

    Article  Google Scholar 

  33. Caleffi M, Trianni V, Cacciapuoti AS (2018) Self-organizing strategy design for heterogeneous coexistence in the sub-6 GHZ. IEEE Trans Wirel Commun 17(11):7128–7143. https://doi.org/10.1109/TWC.2018.2864734

    Article  Google Scholar 

  34. Ashrafi S (2018) Inventor; NxGen partners IP LLC, assignee. Sdn-based channel estimation for multiplexing between los mmwaves, nlos sub-6 ghz and fso. United States patent application US 15/689,782

  35. Naqvi SA, Pervaiz H, Hassan SA, Musavian L, Ni Q, Imran MA, Ge X, Tafazolli R (2018) Energy-aware radio resource management in D2D-enabled multi-tier hetnets. IEEE Access 6:16610–16622. https://doi.org/10.1109/ACCESS.2018.2817189

    Article  Google Scholar 

  36. Neamatollahi P, Naghibzadeh M, Abrishami S, Yaghmaee M (2017) Distributed clustering-task scheduling for wireless sensor networks using dynamic hyper round policy. IEEE Trans Mob Comput :1–1. doi:https://doi.org/10.1109/tmc.2017.2710050

    Article  Google Scholar 

  37. Mirjalili S, Mirjalili S, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  38. Bai G, Tian Z, Zuo M (2016) An improved algorithm for finding all minimal paths in a network. Reliab Eng Syst Saf 150:1–10. https://doi.org/10.1016/j.ress.2016.01.011

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to B. Guruprakash.

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

Guruprakash, B., Balasubramanian, C. & Sukumar, R. An approach by adopting multi-objective clustering and data collection along with node sleep scheduling for energy efficient and delay aware WSN. Peer-to-Peer Netw. Appl. 13, 304–319 (2020). https://doi.org/10.1007/s12083-019-00779-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-019-00779-3

Keywords

Navigation