Skip to main content

Advertisement

SpringerLink
Log in

A novel multi-objective optimization strategy for enhancing quality of service in IoT-enabled WSN applications

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

Abstract

In today’s world, the Internet of Things (IoT) has become more relevant owing to the growth in smart grid, smart city and smart home applications. Network sustainability is considered as a significant characteristic for IoT based applications. Wireless Sensor Network (WSN) offers such network sustainability where WSN is acted as the subnets in the IoT model. However, the multi-objectives like coverage, connectivity and energy consumption are required to improve the quality of service in IoT based WSN (IWSN) model. An appropriate optimization strategy of these multi-objective facilitates the better development of IWSN. In this paper, the Adaptive Coverage and Connectivity (ACC) scheme is proposed to attain the efficient IWSN model. It employs two underlying methodologies in which the first method provides the optimal coverage to all target objects and its mathematical model guarantees the coverage rate. The second method deals with connectivity and energy consumption of the network. The experimental results manifest that, unlike existing schemes, the proposed ACC scheme can sustain the network for a prolonged time.

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

Similar content being viewed by others

References

  1. Ghasempour A (2019) Internet of things in smart grid: architecture, applications, services, key technologies, and challenges. Inventions 4:22

    Article  Google Scholar 

  2. Ullah A, Said G, Sher M, Ning H (2020) Fog-assisted secure healthcare data aggregation scheme in IoT-enabled WSN. Peer Peer Netw Appl 13:163–174

  3. Behera TM, Mohapatra SK, Samal UC, Khan MS, Daneshmand M, Gandomi AH (2019) Residual energy based cluster-head selection in WSNs for IoT application. IEEE Internet Things J 6:5132–5139

    Article  Google Scholar 

  4. Carreno R, Aguilar V, Pacheco D, Acevedo M (2019) An IoT expert system Shell in block-chain technology with ELM as inference engine. Int J Inf Technol Decis Mak 18:87–104

    Article  Google Scholar 

  5. He Y, Han G, Wang H (2019) A sector-based random routing scheme for protecting the source location privacy in WSNs for the internet of things. Futur Gener Comput Syst 96:438–448

    Article  Google Scholar 

  6. Zakariayi S, Babaie S (2019) DEHCIC: a distributed energy-aware hexagon based clustering algorithm to improve coverage in wireless sensor networks. Peer Peer Netw Appl 12(4):689–704

    Article  Google Scholar 

  7. Romero E, Blesa J, Araujo A (2019) An adaptive energy aware strategy based on game theory to add privacy in the physical layer for cognitive WSNs. Ad Hoc Netw 92:101800

    Article  Google Scholar 

  8. Wu J, Chen Z, Wu J (2020) An energy efficient data transmission approach for low-duty-cycle wireless sensor networks. Peer Peer Netw Appl 13:255–268

    Article  Google Scholar 

  9. Prasanth A, Pavalarajan S (2020) Implementation of efficient intra-and inter-zone routing for extending network consistency in wireless sensor networks. J Circuit Syst Comp 29:1–25

    Article  Google Scholar 

  10. Sharma G, Rajesh A, Babu L, Mohan E (2019) Three-dimensional localization in anisotropic wireless sensor networks using fuzzy logic system. Ad Hoc Sens Wirel Netw 45:29–57

    Google Scholar 

  11. Senouci MR, Mellouk A (2019) A robust uncertainty-aware cluster-based deployment approach for WSNs: coverage, connectivity, and lifespan. J Netw Comput Appl 146:102414

    Article  Google Scholar 

  12. Prasanth A, Pavalarajan S (2019) Zone-based sink mobility in wireless sensor networks. Sens Rev 39:874–880

    Article  Google Scholar 

  13. Farhat A, Guyeux C, Haddad M, Hakem M (2020) Energy-efficiency and coverage quality management for reliable diagnostics in wireless sensor networks. Int J Sens Netw 32:127–138

    Article  Google Scholar 

  14. Kavidha V, Ananthakumaran S (2019) Novel energy-efficient secure routing protocol for wireless sensor networks with Mobile sink. Peer Peer Netw Appl 12:881–892

    Article  Google Scholar 

  15. Boukerche A, Sun P (2018) Connectivity and coverage based protocols for wireless sensor networks. Ad Hoc Netw 80:54–69

    Article  Google Scholar 

  16. Zygowski C, Jaekel A (2020) Optimal path planning strategies for monitoring coverage holes in wireless sensor networks. Ad Hoc Netw 96:101990

    Article  Google Scholar 

  17. Chakraborty S, Goyala NK, Mahapatrac S, Sohb S (2020) A Monte-Carlo Markov chain approach for coverage-area reliability of mobile wireless sensor networks with multistate nodes. Reliab Eng Syst Saf 193:106662

    Article  Google Scholar 

  18. Binh H, Hanh N, Quan L, Nghia N, Dey N (2020) Metaheuristics for maximization of obstacles constrained area coverage in heterogeneous wireless sensor networks. Appl Soft Comput 86:105939

    Article  Google Scholar 

  19. Hajjej F, Hamdi M, Ejbali R, Zaied M (2020) A distributed coverage hole recovery approach based on reinforcement learning for wireless sensor networks. Ad Hoc Netw 101:102082

    Article  Google Scholar 

  20. Kabakulak B (2019) Sensor and sink placement, scheduling and routing algorithms for connected coverage of wireless sensor networks. Ad Hoc Netw 86:83–102

    Article  Google Scholar 

  21. Nguyen P, Hanh N, Khuong N (2019) Node placement for connected target coverage in wireless sensor networks with dynamic sinks. Pervasive Mob Comput 59:101070

    Article  Google Scholar 

  22. Etancelin J, Fabbri A, Guinand F, Rosalie M (2019) DACYCLEM: a decentralized algorithm for maximizing coverage and lifetime in a Mobile wireless sensor network. Ad Hoc Netw 87:174–187

    Article  Google Scholar 

  23. Elhoseny M, Tharwat A, Yuan X, Hassanien A (2018) Optimizing K-coverage of mobile WSNs. Expert Syst Appl 92:142–153

    Article  Google Scholar 

  24. Xu Y, Ding O, Qu R, Li K (2018) Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization. Appl Soft Comput 68:268–282

    Article  Google Scholar 

  25. Dahiya S, Singh PK (2018) Optimized Mobile sink based grid coverage-aware sensor deployment and link quality based routing in wireless sensor networks. Int J Electron Commun 89:191–196

    Article  Google Scholar 

  26. Mostafaei H, Montieri A, Persico V, Pescape A (2017) A sleep scheduling approach based on learning automata for WSN partial coverage. J Netw Comput Appl 80:67–78

    Article  Google Scholar 

  27. Guptaa SK, Kuilab P, Jana PK (2016) Genetic algorithm approach for k-coverage and m-connected node placement in target based wireless sensor networks. Comput Electr Eng 56:544–556

  28. Torkestani JA (2013) An adaptive energy-efficient area coverage algorithm for wireless sensor networks. Ad Hoc Netw 11:1655–1666

    Article  Google Scholar 

  29. Cardei M, Wu J (2006) Energy-efficient coverage problems in wireless adhoc sensor networks. Comput Commun 29:413–420

    Article  Google Scholar 

  30. Mostafaei H (2015) Stochastic barrier coverage in wireless sensor networks based on distributed learning automata. Comput Commun 55:51–61

    Article  Google Scholar 

  31. Kumar CS, Lai T (2010) Local barrier coverage in wireless sensor networks. IEEE Trans Mob Comput 9:491–504

    Article  Google Scholar 

  32. Yardibi T, Karasan E (2010) A distributed activity scheduling algorithm for wireless sensor networks with partial coverage. Wirel Netw 16:213–225

    Article  Google Scholar 

  33. Yetgin H, Cheung KK, El-Hajjar M, Hanzo L (2017) A survey of network lifetime maximization techniques in wireless sensor networks. IEEE Commun Surv Tutor 19:828–854

    Article  Google Scholar 

  34. Sun G, Liu Y, Li H, Wang A, Liang S (2018) A novel connectivity and coverage algorithm based on shortest path for wireless sensor networks. Comput Electr Eng 71:1025–1039

  35. Samie M, Dragffy G, Tyrrell AM (2013) Novel bio-inspired approach for fault-tolerant VLSI systems. IEEE Trans Very Large Scale Integr (VLSI) Syst 21:1878–1891

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, PSNA College of Engineering and Technology, Dindigul, India

    A. Prasanth

  2. Karpagam Academy of Higher Education, Coimbatore, India

    S. Jayachitra

Authors
  1. A. Prasanth
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Jayachitra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Prasanth.

Ethics declarations

Conflict of interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

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

Prasanth, A., Jayachitra, S. A novel multi-objective optimization strategy for enhancing quality of service in IoT-enabled WSN applications. Peer-to-Peer Netw. Appl. 13, 1905–1920 (2020). https://doi.org/10.1007/s12083-020-00945-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-020-00945-y

Keywords

Search

Navigation