Skip to main content

Advertisement

SpringerLink
Log in

Automata Theory-based Energy Efficient Area Algorithm for an Optimal Solution in Wireless Sensor Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The linked governance set has presently been introduced as an attractive scheme to the region analysis in wireless sensor networks (WSNs). Therefore the key issue bothering the behavior of the prevailing Minimal sized Governing Set (MGS) based analysis standards is that they are focused on increasing the count of sleep nodes to safeguard the energy. It makes the lively sensors to feel immense loads for handling voluminous adjacencies. The quicker depletion of the lively sensors might detach the network standard and discards the region explored. Hence for offering an improved transmission of the network association analysis and lifespan, a precise count of sensors must be triggered. The intention is based on the angle restricted minimal load allowance of the MGS issues termed as Angle Restricted minimal load MGS (ARGS) to design the region analysis in WSNs. The precise decision of the angle restriction of ARGS equalizes the load within the network on the lively sensors enhances the network analysis and lifespan. The knowledge automation-based heuristics termed as Automata Theory-based Energy Efficient Area algorithm (ATEEA) is designed for locating a close optimal solution to the substitution identical ARGS issues in WSN. The analysis difficulties of the designed scheme to locate the optimal solution of the region analysis issue are estimated. Diverse experiments are performed to depict the supremacy of the designed analysis standards over the prevailing MGS-based schemes in terms of rate of coverage region, remaining energy, number of lively nodes and the lifetime of the networks.

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

Similar content being viewed by others

References

  1. Karthikeyan, A., Arunachalam, V. P., & Karthik, S. (2019). Performing data assessment in terms of sensor node positioning over three dimensional wireless. Mobile Networks and Applications. https://doi.org/10.1007/s11036-019-01386-5

    Article  Google Scholar 

  2. Zeng, Y., Sreenan, C. J., Xiong, N., Yang, L. T., & Park, J. H. (2010). Connectivity and coverage maintenance in wireless sensor networks. Journal of Supercomputing, 52(1), 23–46

    Article  Google Scholar 

  3. Sengupta, S., Das, S., Nasir, M. D., & Panigrahi, B. K. (2012). Multi-objective node deployment in WSNs: In search of an optimal trade-off among coverage, lifetime, energy consumption, and connectivity. Engineering Applications of Artificial Intelligence, 26(01), 405–416

    Article  Google Scholar 

  4. Zhu, C., Zheng, C., Shu, L., & Han, G. (2012). A survey on coverage and connectivity issues in wireless sensor networks. Journal of Network and Computer Applications, 35(2), 619–632

    Article  Google Scholar 

  5. Rizvi, S., Qureshi, H. K., Khayam, S. A., Rakocevic, V., & Rajarajan, M. (2012). A1: An energy efficient topology control algorithm for connected area coverage in wireless sensor networks. Journal of Network and Computer Applications, 35(2), 597–605

    Article  Google Scholar 

  6. Karthikeyan, A., Arunachalam, V. P., & Karthik, S. (2019). Attempting to model a fresh three dimensional coverage scheme for wireless sensor networks. Wireless Personal Communications. https://doi.org/10.1007/s11277-019-06759-3

    Article  Google Scholar 

  7. Guvensan, M. A., & Yavuz, A. G. (2011). On coverage issues in directional sensor networks: A survey. Ad Hoc Networks, 9(7), 1238–1255

    Article  Google Scholar 

  8. Wightman, P. M., & Labrador, M. A. (2011). A family of simple distributed minimum connected dominating set-based topology construction algorithms. Journal of Network and Computer Applications, 34(6), 1997–2010

    Article  Google Scholar 

  9. Ammari, H. M., & Das, S. K. (2012). Centralized and clustered k-coverage protocols for wireless sensor networks. IEEE Transactions on Computers, 61(1), 118–133

    Article  MathSciNet  Google Scholar 

  10. Hefeeda, M., & Ahmadi, H. (2010). Energy-efficient protocol for deterministic and probabilistic coverage in sensor networks. IEEE Transactions on Parallel and Distributed Systems, 21(5), 579–593

    Article  Google Scholar 

  11. Tang, L., Lu, Z., & Fan, B. (2020). Energy efficient and reliable routing algorithm for wireless sensors networks. Applied Sciences, 10, 1885. https://doi.org/10.3390/app10051885

    Article  Google Scholar 

  12. Misra, S., Kumar, M. P., & Obaidat, M. S. (2011). Connectivity preserving localized coverage algorithm for area monitoring using wireless sensor networks. Computer Communications, 34(12), 1484–1496

    Article  Google Scholar 

  13. Lee, D. T., & Lin, A. K. (1986). Computational complexity of art gallery problems. IEEE Transactions on Information Theory, 32(2), 276–282

    Article  MathSciNet  Google Scholar 

  14. Gregg, W. W., Esaias, W. E., Feldman, G. C., Frouin, R., Hooker, S. B., McClain, C. R., & Woodward, R. H. (1998). Coverage opportunities for global ocean color in a multimission era. IEEE Transactions on Geoscience and Remote Sensing, 36(5), 1620–1627

    Article  Google Scholar 

  15. Huang, C. F., & Tseng, Y. C. (2005). A survey of solutions to the coverage problems in wireless sensor networks. Journal of Internet Technology, 6(1), 1–8

    Google Scholar 

  16. Cardei, M., & Wu, J. (2006). Energy-efficient coverage problems in wireless ad hoc sensor networks. Computer Communications, 29(4), 413–420

    Article  Google Scholar 

  17. Watfa, M. K., & Commuri, S. (2007). Boundary coverage and coverage boundary problems in wireless sensor networks. International Journal of Sensor Networks, 2(3), 273–283

    Article  Google Scholar 

  18. Ram, S., Majunath, D., Iyer, S., & Yogeshwaran, D. (2007). On the path coverage properties of random sensor networks. IEEE Transactions on Mobile Computing, 6(5), 494–506

    Article  Google Scholar 

  19. Cheng, X., Du, D. Z., Wang, L., & Xu, B. (2008). Relay sensor placement in wireless sensor networks. Wireless Networks, 14(3), 347–355

    Article  Google Scholar 

  20. Fang, Z., & Wang, J. (2008). Convex combination approximation for the mincost WSN point coverage problem. In Proceedings of the third international conference on wireless algorithms, systems, and applications, Dallas, Texas (pp. 188–199).

  21. Wang, B., Lim, H. B., & Ma, D. (2009). A survey of movement strategies for improving network coverage in wireless sensor networks. Computer Communications, 32(13–14), 1427–1436

    Article  Google Scholar 

  22. Ghosh, A., & Das, S. K. (2008). Coverage and connectivity issues in wireless sensor networks: A survey. Pervasive and Mobile Computing, 4(3), 303–334

    Article  Google Scholar 

  23. Rajaram, P., & Prakasam, P. (2016). Shape and area based coverage connectivity using robots in wireless sensor networks. Journal of Signal Processing and Wireless Networks, 01(01), 29–34

    Google Scholar 

  24. Li, Y., Thai, M. T., Wang, F., Yi, C. W., Wang, P. J., & Du, D. Z. (2005). On greedy construction of connected dominating sets in wireless networks. Wireless Communications and Mobile Computing, 5, 927–932

    Article  Google Scholar 

  25. Alzoubi, K. M., Li, X. Y., Wang, Y., Wan, P. J., & Frieder, O. (2003). Geometric spanners for wireless ad hoc network. IEEE Transactions on Parallel and Distributed Systems, 14(4), 408–421

    Article  Google Scholar 

  26. Dai, F., & Wu, J. (2004). An extended localized algorithm for connected dominating set formation in ad hoc wireless networks. IEEE Transactions on Parallel and Distributed Systems, 15(10), 908–920

    Article  Google Scholar 

  27. Butenko, S., Cheng, X., Oliveira, C., & Pardalos, P. M. (2004) A new heuristic for the minimum connected dominating set problem on ad hoc wireless networks. In Recent developments in cooperative control and optimization, Springer book series (pp. 61–73).

  28. Wightman, P., & Labrador, M. (2008) A3: A topology construction algorithm for wireless sensor networks. In Proceedings of IEEE global communications conference (GLOBECOM), New Orleans, USA.

  29. Biabani, M., Fotouhi, H., & Yazdani, N. (2020). An energy-efficient evolutionary clustering technique for disaster management in IoT networks. Sensors, 20(9), 2647. https://doi.org/10.3390/s20092647

    Article  Google Scholar 

  30. Sathyaprakash, P., & Prakasam, P. (2017). Proposed energy efficient multi attribute time slot scheduling algorithm for quality of service in wireless sensor network. Wireless Personal Communications, 97(4), 5951–5968

    Article  Google Scholar 

  31. Alghamdi, T. A. (2020). Energy efficient protocol in wireless sensor network: optimized cluster head selection model. Telecommunication Systems, 74, 331–345

    Article  Google Scholar 

  32. Narendran, M., & Prakasam, P. (2017). An energy aware competition based clustering for cluster head selection in wireless sensor network with mobility. Cluster Computing, 22(s5), 11019–11028

    Article  Google Scholar 

  33. Mittal, N. (2020). An energy efficient stable clustering approach using fuzzy type-2 bat flower pollinator for wireless sensor networks. Wireless Personal Communications, 112, 1137–1163

    Article  Google Scholar 

  34. Maheshwari, P., Sharma, A. K., & Verma, K. (2021). Energy efficient cluster based routing protocol for WSN using butterfly optimization algorithm and ant colony optimization. Ad Hoc Networks, 110. https://doi.org/10.1016/j.adhoc.2020.102317.

  35. Narendra, K. S., & Thathachar, M. A. L. (1989). Learning automata: An introduction. Prentice-Hall.

    Google Scholar 

  36. Thathachar, M. A. L., & Harita, B. R. (1987). Learning automata with changing number of actions. IEEE Transactions on Systems, Man, and Cybernetics, 17(6), 1095–1100

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, SNS College of Technology, Coimbatore, India

    A. Karthikeyan

  2. School of Electronics Engineering, Vellore Institute of Technology, Vellore, India

    P. Prakasam

  3. Department of Computer Science and Engineering, SNS College of Technology, Coimbatore, India

    S. Karthik

  4. Department of Electronics and Communication Engineering, SR University, Warangal, India

    J. Ajayan

  5. Wireless Telecommunication Systems and IoT, Institut Supérieur D’électronique de Paris, Grand école, Paris, France

    S. Sai Gokul

Authors
  1. A. Karthikeyan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. Prakasam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. Karthik
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. J. Ajayan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. S. Sai Gokul
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P. Prakasam.

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

Karthikeyan, A., Prakasam, P., Karthik, S. et al. Automata Theory-based Energy Efficient Area Algorithm for an Optimal Solution in Wireless Sensor Networks. Wireless Pers Commun 120, 1125–1143 (2021). https://doi.org/10.1007/s11277-021-08507-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-08507-y

Keywords

Search

Navigation