Optimal rendezvous points selection to reliably acquire data from wireless sensor networks using mobile sink

Abstract

In rendezvous points (RPs) based data collection, the mobile sink (MS) visits a set of sensor nodes known as RPs for data gathering from wireless sensor networks to minimize the trajectory of MS. Existing RPs based methods are suitable for the scenarios where sensor nodes have uniform data generation rates along with having limited buffer capacity to store the forwarded data. However, in some situations, the sensing rate increases due to the occurrence of unusual events in the surrounding, and the RPs receive more data packets than their capacity. This creates data loss due to buffer overflow. Therefore, the selection of optimal RPs for reliable data gathering, while minimizing the trajectory of MS, is a challenging task. This paper proposes a squirrel search algorithm-based rendezvous points selection (SSA-RPS) method that chooses a set of optimal RPs for reliable data collection. The objective of the SSA-RPS is to minimize the trajectory of MS while visiting a set of optimal RPs under non-uniform data generation and limited buffer capacity constraints of sensor nodes for reliable data acquisition. The SSA-RPS applies an efficient encoding scheme to generate variable dimension squirrels that represent each possible trajectory of MS, and the dimension of squirrel presents the number of RPs. The SSA-RPS also adopts the reselection of RPs to provide a fair energy share among sensor nodes. The simulation results demonstrate that the SSA-RPS outperforms the existing state-of-the-art methods in terms of the number of dropped packets, data gathering ratio, energy consumption, and network lifetime.

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References

  1. 1.

    Kumar S, Duttagupta S, Rangan VP, Ramesh MV (2019) Reliable network connectivity in wireless sensor networks for remote monitoring of landslides. Wirel Netw. https://doi.org/10.1007/s11276-019-02059-7

    Article  Google Scholar 

  2. 2.

    Ghosh K, Neogy S, Das PK, Mehta M (2018) Intrusion detection at international borders and large military barracks with multi-sink wireless sensor networks: an energy efficient solution. Wirel Pers Commun 98(1):1083–1101

    Article  Google Scholar 

  3. 3.

    Chen D, Liu Z, Wang L, Dou M, Chen J, Li H (2013) Natural disaster monitoring with wireless sensor networks: a case study of data-intensive applications upon low-cost scalable systems. Mobile Netw Appl 18(5):651–663

    Article  Google Scholar 

  4. 4.

    Nam WH, Kim T, Hong EM, Choi JY, Kim JT (2017) A wireless sensor network (WSN) application for irrigation facilities management based on information and communication technologies (ICTs). Comput Electron Agric 143:185–192

    Article  Google Scholar 

  5. 5.

    Marrero D, Macías E, Suárez Á, Santana JA, Mena V (2019) Energy saving in smart city wireless backbone network for environment sensors. Mobile Netw Appl 24(2):700–711

    Article  Google Scholar 

  6. 6.

    Wang J, Gu X, Liu W, Sangaiah AK, Kim HJ (2019a) An empower Hamilton loop based data collection algorithm with mobile agent for WSNs. Human Centric Comput Inf Sci 9(1):1–14

    Article  Google Scholar 

  7. 7.

    Wang J, Gao Y, Wang K, Sangaiah AK, Lim SJ (2019b) An affinity propagation-based self-adaptive clustering method for wireless sensor networks. Sensors 19(11):2579

    Article  Google Scholar 

  8. 8.

    Kulshrestha J, Mishra MK (2018) Energy balanced data gathering approaches in wireless sensor networks using mixed-hop communication. Computing 100(10):1033–1058

    MathSciNet  Article  Google Scholar 

  9. 9.

    Wang YC, Chen KC (2018) Efficient path planning for a mobile sink to reliably gather data from sensors with diverse sensing rates and limited buffers. IEEE Trans Mob Comput 18(7):1527–1540

    Article  Google Scholar 

  10. 10.

    Izadi D, Ghanavati S, Abawajy J, Herawan T (2016) An alternative data collection scheduling scheme in wireless sensor networks. Computing 98(12):1287–1304

    MathSciNet  Article  Google Scholar 

  11. 11.

    Wang J, Gao Y, Liu W, Wu W, Lim SJ (2019) An asynchronous clustering and mobile data gathering schema based on timer mechanism in wireless sensor networks. Comput Mater Contin 58(3):711–725

    Article  Google Scholar 

  12. 12.

    Zhu C, Wu S, Han G, Shu L, Wu H (2015) A tree-cluster-based data-gathering algorithm for industrial WSNs with a mobile sink. IEEE Access 3:381–396

    Article  Google Scholar 

  13. 13.

    Jain S, Pattanaik KK, Shukla A (2019) QWRP: query-driven virtual wheel based routing protocol for wireless sensor networks with mobile sink. J Netw Comput Appli. https://doi.org/10.1016/j.jnca.2019.102430

    Article  Google Scholar 

  14. 14.

    Wang J, Gao Y, Yin X, Li F (2018) Kim HJ (2018) An enhanced PEGASIS algorithm with mobile sink support for wireless sensor networks. Wirel Commun Mobile Comput 2018:1–9

  15. 15.

    Mathew M, Weng N, Vespa LJ (2012) Quality-of-information modeling and adapting for delay-sensitive sensor network applications. In: 2012 IEEE 31st international performance computing and communications conference (IPCCC). pp 471–477

  16. 16.

    Tilak S, Murphy A, Heinzelman W (2003) Non-uniform information dissemination for sensor networks. In: 11th IEEE international conference on network protocols, 2003. Proceedings. pp 295–304. https://doi.org/10.1109/ICNP.2003.1249779

  17. 17.

    Xing G, Wang T, Xie Z, Jia W (2008) Rendezvous planning in wireless sensor networks with mobile elements. IEEE Trans Mob Comput 7(12):1430–1443

    Article  Google Scholar 

  18. 18.

    Mehto A, Tapaswi S, Pattanaik KK (2019) A review on rendezvous based data acquisition methods in wireless sensor networks with mobile sink. Wireless Netw. https://doi.org/10.1007/s11276-019-02022-6

    Article  Google Scholar 

  19. 19.

    Wang J, Gao Y, Zhou C, Sherratt S, Wang L (2020) Optimal coverage multi-path scheduling scheme with multiple mobile sinks for WSNs. Comput Mater Contin 62(2):695–711

    Article  Google Scholar 

  20. 20.

    Kumar P, Amgoth T, Annavarapu CSR (2018) ACO-based mobile sink path determination for wireless sensor networks under non-uniform data constraints. Appl Soft Comput 69:528–540

    Article  Google Scholar 

  21. 21.

    Wang YC, Wei CT (2016) Lightweight, latency-aware routing for data compression in wireless sensor networks with heterogeneous traffics. Wirel Commun Mobile Comput 16(9):1035–1049

    Article  Google Scholar 

  22. 22.

    Durisic MP, Tafa Z, Dimic G, Milutinovic V (2012) A survey of military applications of wireless sensor networks. In: 2012 Mediterranean conference on embedded computing (MECO). pp 196–199

  23. 23.

    Mothku SK, Rout RR (2019) Markov decision process and network coding for reliable data transmission in wireless sensor and actor networks. Pervas Mobile Comput 56:29–44

    Article  Google Scholar 

  24. 24.

    Wang W, Shi H, Wu D, Huang P, Gao B, Wu F, Xu D, Chen X (2017) VD-PSO: an efficient mobile sink routing algorithm in wireless sensor networks. Peer Peer Netw Appl 10(3):537–546

    Article  Google Scholar 

  25. 25.

    Wang J, Cao J, Li B, Lee S, Sherratt RS (2015) Bio-inspired ant colony optimization based clustering algorithm with mobile sinks for applications in consumer home automation networks. IEEE Trans Consum Electron 61(4):438–444

    Article  Google Scholar 

  26. 26.

    Kaswan A, Singh V, Jana PK (2018) A multi-objective and PSO based energy efficient path design for mobile sink in wireless sensor networks. Pervas Mobile Comput 46:122–136

    Article  Google Scholar 

  27. 27.

    Podili P, Pattanaik K, Rana PS (2017) BAT and hybrid bat meta-heuristic for quality of service-based web service selection. J Intell Syst 26(1):123–137

    Article  Google Scholar 

  28. 28.

    Jain M, Singh V, Rani A (2019) A novel nature-inspired algorithm for optimization: Squirrel search algorithm. Swarm Evolut Comput 44:148–175

    Article  Google Scholar 

  29. 29.

    Wen W, Zhao S, Shang C, Chang CY (2018) EAPC: energy-aware path construction for data collection using mobile sink in wireless sensor networks. IEEE Sens J 18(2):890–901

    Article  Google Scholar 

  30. 30.

    Lu Y, Sun N, Pan X (2019) Mobile sink-based path optimization strategy in wireless sensor networks using artificial bee colony algorithm. IEEE Access 7:11668–11678

    Article  Google Scholar 

  31. 31.

    Ren G, Wu J, Versonnen F (2018) Bee-based reliable data collection for mobile wireless sensor network. Clust Comput. https://doi.org/10.1007/s10586-018-2116-0

    Article  Google Scholar 

  32. 32.

    Zhang H, Li Z, Shu W (2019) Chou J (2019) Ant colony optimization algorithm based on mobile sink data collection in industrial wireless sensor networks. EURASIP J Wirel Commun Netw 1:1–10. https://doi.org/10.1186/s13638-019-1472-7

    Article  Google Scholar 

  33. 33.

    Tasgetiren MF, Sevkli M, Liang YC, Gencyilmaz G (2004) Particle swarm optimization algorithm for single machine total weighted tardiness problem. In: Proceedings of the 2004 congress on evolutionary computation (IEEE), vol 2. pp 1412–1419

  34. 34.

    Zhang W (2000) Depth-first branch-and-bound versus local search: a case study. In: AAAI/IAAI. pp 930–935

  35. 35.

    Yetgin H, Cheung KTK, El-Hajjar M, Hanzo LH (2017) A survey of network lifetime maximization techniques in wireless sensor networks. IEEE Commun Surv Tutor 19(2):828–854

    Article  Google Scholar 

  36. 36.

    Soyturk M, Altilar DT (2008) Reliable real-time data acquisition for rapidly deployable mission-critical wireless sensor networks. IEEE INFOCOM Workshops 2008:1–6

    Google Scholar 

  37. 37.

    Winkler M, Tuchs KD, Hughes K, Barclay G (2008) Theoretical and practical aspects of military wireless sensor networks. J Telecommun Inf Technol 2:37–45

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Correspondence to Anjula Mehto.

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Mehto, A., Tapaswi, S. & Pattanaik, K.K. Optimal rendezvous points selection to reliably acquire data from wireless sensor networks using mobile sink. Computing (2021). https://doi.org/10.1007/s00607-021-00917-x

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Keywords

  • Wireless sensor network
  • Mobile sink
  • Rendezvous point
  • Squirrel search algorithm

Mathematics Subject Classification

  • 68M18