Skip to main content

Area Coverage Strategy in IoT Networks Based on Redeployment, Descriptive Statistics, Correlation and Regression Parameters

Abstract

Both minimal set cover and maximum coverage are known to be NP-Hard when using homogeneous and heterogeneous wireless sensor networks due to the lack of knowledge of the minimum set that can cover the area of interest, limited power reserves, monitoring and communication ranges, and so...To avoid these issues, this paper proposes to measure the dispersion of sensor nodes in the large-scale area and construct a decision algorithm based on the descriptive statistics methods to maximize the coverage. The descriptive statistics methods (positional parameters as the arithmetic mean and dispersion parameters as variance or standard deviation) are necessaries to measure the dispersion of sensor nodes in a geographical area. A decision algorithm will be executed when we need redeployment stages of sensor nodes in area of low dispersion, especially on wide geographical areas where the large distances between the sensors nodes and the base station cause a rapid depletion of sensor nodes power. We hope by applying this approach to maintain: (a) area coverage, (b) connectivity, (c) lifetime and (d) ensuring a better quality of service (QoS) of a wide geographical areas. We evaluate the proposed approach by an mathematical proof, followed by a simulation step using Matlab simulator. We demonstrate through extensive simulations, correlation coefficient and regression function that the capability of the proposed approach guarantee full coverage with minimal energy consumption for a period as long as possible.

This is a preview of subscription content, access via your institution.

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

References

  1. Cardie MW. Critical sensor density. Chap 19. Handbook of sensor networks. Boca Raton: CRC Press; 2004.

    Google Scholar 

  2. Huang C-F, Tseng Y-C. A survey of solutions to the coverage problems in wireless sensor networks. J Internet Technol. 2005;6:1–8.

    Google Scholar 

  3. Ravelomanana V. Extremal properties of three-dimensional sensor networks with applications. IEEE Trans Mob Comput. 2004;3(3):246–57.

    Article  Google Scholar 

  4. Cheng G, Du D, Wang L, Xu B. Relay sensor placement in wireless sensor networks. IEEE Trans Comput. 2007;56(1):134–8.

    MathSciNet  Article  Google Scholar 

  5. Dhillon S, Chakrabarty K. Sensor placement for effective coverage and monitoring in distributed sensor networks. In: Proceedings of IEEE wireless communications and networking conference, New Orleans, LA; 2003.

  6. Efrat A, Har-Peled S, Mitchell J. Approximation algorithm for two problems optimal location in sensor networks. In: Proceedings of the 3rd international conference on broadband communications, networks and systems, Boston, Massachusetts; 2005. https://doi.org/10.1109/ICBN.2005.1589677.

  7. Grandham SR, Dawande RP, Venkatesan S. Energy Efficient Schemes for Wireless Sensor Networks with Multiple Mobile Base Stations. In: Proceedings of the IEEE Globecom, San Francisco, CA; 2003. https://doi.org/10.1109/GLOCOM.2003.1258265.

  8. Pan J, Cai L, Hou T, Shi Y, Shen SX. Optimal base-station locations in two-tiered wireless sensor networks. IEEE Trans Mob Comput. 2005;4(5):458–73.

    Article  Google Scholar 

  9. Pan J, Hou T, Cai L, Shi Y, Shen SX. Locating base stations for video sensor networks. In: Proceedings of the IEEE vehicular technology conference, Orlando, FL; 2003. p. 5.

  10. Holger KAW. Rotocols and architectures for wireless sensor networks. 1st ed. New York: Wiley; 2005.

    Google Scholar 

  11. Clouqueur T, Phipatanasuphorn P, Saluja K. Sensor deployment strategy for target detection. In: Proceedings of the th1 ACM international workshop on wireless sensor networks and applications (WSNA ’02); 2002.

  12. Younis M, Akkaya K. Dstrategies and techniques for node placement in wireless sensor networks: a survey. Ad Hoc Netw. 2008;6:621–55.

    Article  Google Scholar 

  13. O’Donovan T, Sreenan CJ. Deployment alternatives for performance debugging in wireless sensor networks, 4. IEEE; 2011.

  14. Ishizuka M, Aida M. Performance study of node placement in sensor networks. In: Proceedings of the 24th international conference on distributed computing systems workshops-W7, vol. 7: EC. (Icdesw’04); 2004.

  15. Carlos EO, Ivica K, Luis DO, Scott LM, Matthew W. A decision-making methodology for deployment of stochastic wireless sensor networks. In: th11 international conference on computer modeling and simulation, UKSim; 2009.

  16. Jme DB. Statistical analysis to extract parameters are effective overall energy consumption of wireless sensor network (WSN). In: 13th international conference on parallel and distributed computing, applications and technologies; 2012.

  17. Al-Turjman F. Cognitive-node architecture and a deployment strategy for the future sensor networks. Springer Mob Netw Appl. 2017;23(4):940–55. https://doi.org/10.1007/s11036-017-0891-0.

    Article  Google Scholar 

  18. Abdel-Mageid S, Ramadan R. Efficient deployment algorithms for mobile sensor networks. In: International conference on autonomous and intelligent systems (AIS); 2010. pp. 1–6. https://doi.org/10.1109/AIS.2010.5547017.

  19. Mahboubi H, Aghdam AG. Distributed deployment algorithms for coverage improvement in a network of wireless mobile sensors: relocation by virtual force. IEEE Trans Control Netw Syst. 2017;4(4):736–48. https://doi.org/10.1109/TCNS.2016.2547579.

    MathSciNet  Article  MATH  Google Scholar 

  20. Xie J, Wei D, Huang S, Bu X. Distributed deployment algorithms for coverage improvement in a network of wireless mobile sensors: relocation by virtual force. A sensor deployment approach using improved virtual force algorithm based on area intensity for multisensor networks. Math Probl Eng. 2017. https://doi.org/10.1155/2019/8015309.

    Article  Google Scholar 

  21. Aziz NABA, Mohemmed AW, Alias MY. A wireless sensor network coverage optimization algorithm based on particle swarm optimization and voronoi diagram. In: International congress on ultra modern telecommunications and control systems and workshops (ICUMT), Okayama, Japan; 2009. pp. 602–607. https://doi.org/10.1109/ICNSC.2009.4919346.

  22. Chang C-Y, Sheu J-P, Chen Y-C, Chang S. An obstacle-free and power-efficient deployment algorithm for wireless sensor networks. IEEE Trans Syst. 2009;39(4):795–806.

    Google Scholar 

  23. Habib MA, Sajal KD. Centralized and clustered k-coverage protocols for wireless sensor networks. IEEE Trans Comput. 2012;61(1):118–33. https://doi.org/10.1109/TC.2011.82.

    MathSciNet  Article  MATH  Google Scholar 

  24. Sun W, Tang M, Lijun Zhang ZH, Shu L. A survey of using swarm intelligence algorithms in iot. Sensors. 2020;20(1420):1–27. https://doi.org/10.3390/s20051420.

    Article  Google Scholar 

  25. Shu T, Dsouza KB, Bhargava V, de Silva C. Using geometric centroid of voronoi diagram for coverage and lifetime optimization in mobile wireless sensor networks. In: Conference: 2019 IEEE Canadian conference of electrical and computer engineering (CCECE); 2019. https://doi.org/10.1109/CCECE.2019.8861820.

  26. Nasri N, Mnasri S, Val T. 3d node deployment strategies prediction in wireless sensors network. Int J Electron. 2019;61(1):118–33. https://doi.org/10.1080/00207217.2019.1687759.

    Article  Google Scholar 

  27. Etancelin J-M, Fabbri A, Guinand F. MartinRosalie: Dacyclem: a decentralized algorithm for maximizingcoverage and lifetime in a mobile wireless sensor network. Ad HocNetw. 2018;87:174–87. https://doi.org/10.1016/j.adhoc.2018.12.008.hal-01969591.

    Article  Google Scholar 

  28. Tosun M, Cabuk UC, Vahid Khalilpour Akram OD. Using geometric centroid of voronoi diagram for coverage and on connectivity-aware distributed mobility models for area coverage in drone networks. In: International conference on artificial intelligence and applied mathematics in engineering (ICAIAME 2020), Antalya, Turkey; 2020.

  29. Yu Z, Tang R, Yuan K, Lin H, Qian X, Deng X, Liu S. Investigation of parameter effects on virtual-spring-force algorithm for wireless-sensor-network applications. Sensors. 2019;19(14):1–16. https://doi.org/10.3390/s19143082.

    Article  Google Scholar 

  30. Ogawa T, Shinjo T, Kitajima S, Hara T, Shio S. Node control methods to reduce power consumption using push-based broadcast for mobile sensor networks. J Mob Multimed. 2010;6(2):114–27.

    Google Scholar 

  31. Ji Luo DW, Zhang Q. Poster abstract: Double mobility: coverage of the sea surface with mobile sensor networks. Mob Comput Commun Rev. 2009;13(1):52–5.

    Article  Google Scholar 

  32. Mahjri I, Dhraief A, Belghith A, Drira K, Mathkour H. A gps-less framework for localization and coverage maintenance in wireless sensor networks. SII Trans Internet Inf Syst. 2016;10(1):96–116. https://doi.org/10.3837/tiis.2016.01.006.

    Article  Google Scholar 

  33. Kalantary M, Meybodi MR. Mobile sensor network deployment using cellular learning automata approach. In: International congress on ultra modern telecommunications and control systems and workshops (ICUMT), Moscow, Russia; 2010. pp. 976–980. https://doi.org/10.1109/ICUMT.2010.5676491.

  34. Wang Y, LedererJie S, Gao G. Connectivity-based sensor network localization with incremental delaunay refinement method. In: INFOCOM 2009. 28th IEEE international conference on computer communications, Rio de Janeiro, Brazil; 2009. pp. 2401–2409. https://doi.org/10.1109/INFCOM.2009.5062167.

  35. Elleuch M, Hasan O, Tahar S, Abid M. Formal probabilistic analysis of detection properties in wireless sensor networks. Form Asp Comput. 2015;27:79–102. https://doi.org/10.1007/s00165-014-0304-0.

    MathSciNet  Article  MATH  Google Scholar 

  36. Nasir HJA, Ku-Mahamud KR, Kamioka E. Ant colony optimization approaches in wireless sensor network: performance evaluation. IEEE Trans Mob Comput. 2017;13(6):153–64. https://doi.org/10.3844/jcssp.2017.153.164.

    Article  Google Scholar 

  37. Wuensch KL. International encyclopedia of statistical science. Technical report. Berlin: Springer; 2014. https://doi.org/10.1007/978-3-642-04898-2_173.

  38. Van VN, Nguyen TG, So-In C, Ha D-B. Secrecy performance analysis of energy harvesting wireless sensor networks with a friendly jammer. IEEE Access. 2017;1(1):1–12. https://doi.org/10.1109/ACCESS.2017.2768443.

    Article  Google Scholar 

  39. Boualem A. Stratégies d’amélioration de la couverture dans les réseaux de capteurs sans fil. Science, Ph.D. thesis, High National School of Computer Science; 2021.

  40. Boualem A, Dahmani Y, Maatoug A. Energetic sleep-scheduling via probabilistic interference K-barrier coverage with truth-table technique in sensor network. In: International conference on mathematics and information technology, Adrar, Algeria; 2017. pp. 255–262. https://doi.org/10.1109/MATHIT.2017.8259726.

  41. Boualem A, Ayaida M, Runz CD. Hybrid model approach for wireless sensor networks coverage improvement. In: IEEE, 8th international conference on wireless networks and mobile communications (WINCOM2020), Reims, France; 2020. pp. 1–6. https://doi.org/10.1109/WINCOM50532.2020.9272504.

  42. Boualem A, Ayaida M, Runz CD, Dahmani Y. An evidential approach for area coverage in mobile wireless sensor networks. Int J Fuzzy Syst Appl (IJFSA). 2021;10(3):30–54. https://doi.org/10.4018/IJFSA.2021070103.

    Article  Google Scholar 

  43. Boualem A, Dahmani Y, Maatoug A, Runz CD. Area coverage optimization in wireless sensor network by semi-random deployment. In: Proceedings of the th7 international conference on sensor networks (SENSORNETS 2018), Funchal, Madeira, Portugal; 2018. pp. 85–90. https://doi.org/10.5220/0006581900850090.

  44. Boualem A, Dahmani Y, Runz CD, Ayaida M. Spiderweb strategy: application for area coverage with mobile sensor nodes in 3d wireless sensor network. Int J Sens Netw (IJSNet). 2019;29(2):121–33. https://doi.org/10.1504/IJSNET.2019.097808.

    Article  Google Scholar 

  45. Boualem A, Ayaida M, Dahmani Y, Runz CD, Maatoug A. A new Dijkstra front-back algorithm for data routing-scheduling via efficient-energy area coverage in wireless sensor network. In: 15th international wireless communications & mobile computing conference (IWCMC), Tangier, Morocco; 2019. pp. 1971–1976. https://doi.org/10.1109/IWCMC.2019.8766593.

  46. Boualem A, Dahmani Y, Ayaida M, Runz CD. A new fuzzy/evidential approach to address the area coverage problem in mobile wireless sensor networks. In: The th34 ACM/SIGAPP symposium on applied computing (SAC’19), Limassol, Cyprus; 2019. pp. 2430–2433. https://doi.org/10.1145/3297280.3297635.

  47. STATCAN: http://www.statcan.gc.ca/edu/power-pouvoir/ch12/5214891-fra.htm/. Technical report, Statistics Cananda May 2016.

  48. Tian D. Node activity scheduling scheme in large-scale wireless sensor networks. Ph.D. thesis, SITE, University of Ottawa. Chap. 6; 2004.

  49. Zhang H, Hou J. Maintaining broad coverage and connectivity in sensor networks. International Workshop on Theoretical and Algorithmic Aspects of Sensor, Ad Hoc Sensor Wireless Networks; 2005.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Adda Boualem.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Sensor Networks” guest edited by César Benavente-Peces and Nirwan Ansari.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Boualem, A., De Runz, C. & Ayaida, M. Area Coverage Strategy in IoT Networks Based on Redeployment, Descriptive Statistics, Correlation and Regression Parameters. SN COMPUT. SCI. 3, 343 (2022). https://doi.org/10.1007/s42979-022-01235-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-022-01235-5

Keywords

  • Wireless sensor networks
  • Deployment coverage
  • Descriptive statistical parameters
  • Connectivity
  • Network lifetime