Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

An efficient data collection using wireless sensor networks and internet of things to monitor the wild animals in the reserved area

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

Abstract

This paper investigates the possibility of using the Wireless Sensor Networks (WSNs) with the Internet of Things (IoT) in which the sensor nodes are attached to the collar of the animals to track the movement pattern of wild animals, and identify the territorial behavior, population and hunting. The random movement of animals creates the network issues such as the energy hole, void problem, poor network lifetime, coverage, and link failure due to animal mobility. To overcome these issues, an efficient data collection mechanism called Location based Clustering and Opportunistic Geographic Routing (LCOGR) is introduced. In this work, a Location Point (LP) is applied to select the Cluster Head (CH) that confirms the uniform distribution of CHs and improves energy efficiency. Also a BYPASS beacon based geographic routing is designed to transmit data to the Base Station (BS) which in turn is connected to the cloud sever. LCOGR ensures stable connectivity and complete coverage of the sensing area. The findings of the simulation show that the suggested strategy considerably increases network efficiency compared to the well-known protocols such as CSDGP, VELCT and MBC.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

References

  1. Ullah F, Habib MA, Farhan M, Khalid S, Durrani MY, Jabbar S (2017) Semantic interoperability for big-data in heterogeneous IoT infrastructure for healthcare. Sustain Urban Areas 34:90–96

    Google Scholar 

  2. Borges LM, Velez FJ, Lebres AS (2014) Survey on the Characterization and Classification of Wireless Sensor Networks Applications. IEEE Commun Surv Tutorials 16:1860–1890

    Article  Google Scholar 

  3. Bhanumathi V, Kalaivanan K (2019) Application Specific Sensor-Cloud: Architectural Model, In: Mishra B, Dehuri S, Panigrahi B, Nayak A, Mishra B, Das H (eds) Computational Intelligence in Sensor Networks. Studies in Computational Intelligence, Springer, Berlin, Heidelberg 776:277–306

  4. Chen HM, Lee S, Rao RM, Slamani MA, Varshney PK (2005) Imaging for concealed weapon detection: a tutorial overview of development in imaging sensors and processing. IEEE Signal Process Mag 22:52–61

    Article  Google Scholar 

  5. Wang F, Liu J (2011) Networked Wireless Sensor Data Collection: Issues, Challenges, and Approaches. IEEE Commun Surv Tutorials 13:673–687

    Article  Google Scholar 

  6. Bhanumathi V, Kalaivanan K (2019) The role of geospatial technology with IoT for precision agriculture, In: Das H, Barik R, Dubey H, Roy D (eds) Cloud Computing for Geospatial Big Data Analytics. Studies in Big Data, Springer, Cham 49:225–250

  7. Ge M, Bangui H, Buhnova B (2018) Big Data for Internet of Things: A Survey. Futur Gener Comput Syst 87:601–614

    Article  Google Scholar 

  8. Xu X, Liu Q, Luo Y, Peng K, Zhang X, Shunmei Meng S, Qi L (2019) A computation offloading method over big data for IoT-enabled cloud-edge computing. Futur Gener Comput Syst 95:522–533

    Article  Google Scholar 

  9. Bapat V, Kale P, Shinde V, Deshpande N, Shaligram A (2017) WSN application for crop protection to divert animal intrusions in the agricultural land. Comput Electron Agric 133:88–96

    Article  Google Scholar 

  10. Kiani F (2018) Animal behavior management by energy-efficient wireless sensor networks. Comput Electron Agric 151:478–484

    Article  Google Scholar 

  11. Nadimi ES, Jorgensen RN, Blanes-Vidal V, Christensen S (2012) Monitoring and classifying animal behavior using ZigBee-based mobile ad hoc wireless sensor networks and artificial neural networks. Comput Electron Agric 82:44–54

    Article  Google Scholar 

  12. Nadimi ES, Sogaard HT, Bak T (2008) ZigBee-based wireless sensor networks for classifying the behaviour of a herd of animals using classification trees. Biosys Eng 100:167–176

    Article  Google Scholar 

  13. Anni JS, Sangaiah AK (2018) Wireless Integrated Sensor Network: Boundary Intellect system for elephant detection via cognitive theory and Fuzzy Cognitive Maps. Futur Gener Comput Syst 83:522–534

    Article  Google Scholar 

  14. Karim L, Nasser N (2012) Reliable location-aware routing protocol for mobile wireless sensor network. IET Commun 6:2149–2158

    Article  Google Scholar 

  15. Abo-Zahhad M, Ahmed SA, Sabor N, Sasaki S (2015) Mobile sink based adaptive immune energy-efficient clustering protocol for improving the lifetime and stability period of wireless sensor networks. IEEE Sens J 13:4576–4586

    Article  Google Scholar 

  16. Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1:660–670

    Article  Google Scholar 

  17. Leu JS, Chiang TH, Yu MC, Su KW (2015) Energy efficient clustering scheme for prolonging the lifetime of wireless sensor network with isolated nodes. IEEE Commun Lett 19:259–262

    Article  Google Scholar 

  18. Velmani R, Kaarthick B (2015) An efficient cluster-tree based data collection scheme for large mobile wireless sensor networks. IEEE Sens J 15(4):2377–2390

    Article  Google Scholar 

  19. Kalaivanan K, Bhanumathi V (2019) CSDGP: cluster switched data gathering protocol for mobile wireless sensor networks. IET Commun 13(18):2973–2985

    Article  Google Scholar 

  20. Kalaivanan K, Bhanumathi V (2019) Unmanned aerial vehicle based reliable and energy efficient data collection from red alerted area using wireless sensor networks with IoT. J Inf Sci Eng 35(3):521–536

    Google Scholar 

  21. Deng S, Li J, Shen L (2011) Mobility-based clustering protocol for wireless sensor networks with mobile nodes. IET Wireless Sens Syst 1:39–47

    Article  Google Scholar 

  22. Srivastava JR, Sudarshan TSB (2015) A genetic fuzzy system based optimized zone based energy efficient routing protocol for mobile sensor networks (OZEEP). Appl Soft Comput 37:863–886

    Article  Google Scholar 

  23. Sasirekha S, Swamynathan S (2017) Cluster-chain mobile agent routing algorithm for efficient data aggregation in wireless sensor network. J Commun Networks 19:392–401

    Article  Google Scholar 

  24. Tarhani M, Kavian YS, Siavoshi S (2014) SEECH: Scalable Energy Efficient Clustering Hierarchy Protocol in Wireless Sensor Networks. IEEE Sens J 14:3944–3954

    Article  Google Scholar 

  25. Dahane A, Loukil A, Kechar B, Berrached NE (2015) Energy efficient and safe weighted clustering algorithm for mobile wireless sensor networks. Mob Inf Syst 2015:1–18

    Google Scholar 

  26. Kuperman G, Modiano E (2017) Providing guaranteed protection in multi-hop wireless networks with interference constraints. IEEE Trans Mob Comput 16:3502–3512

    Article  Google Scholar 

  27. Nath T, Azharuddin M (2019) Application of wireless sensor networks for Rhino protection against poachers in Kaziranga National Park, International Journal of Electronics and Communications (AEU) 111:152882

  28. Behera TM, Mohapatra SK, Samal UC, Khan MS (2019) Hybrid heterogeneous routing scheme for improved network performance in WSNs for animal tracking. Internet of Things 6:10047

    Article  Google Scholar 

  29. Ramkumar K, Ananthi N, Denslin Brabin DR, Goswami P, Baskar M, Bhatia KK, Kumar H (2021) Efficient routing mechanism for neighbour selection using fuzzy logic in wireless sensor network. Comput Electr Eng 94:107365

    Article  Google Scholar 

  30. Liu T, Li Q, Liang P (2012) An energy-balancing clustering approach for gradient-based routing in wireless sensor networks. Comput Commun 35(17):2150–2161

    Article  Google Scholar 

  31. Kalaivanan K, Bhanumathi V (2018) Reliable location aware and Cluster-Tap Root based data collection protocol for large scale wireless sensor networks. J Netw Comput Appl 118:83–101

    Article  Google Scholar 

  32. Bhanumathi V, Kalaivanan K (2020) Energy efficient cluster and travelling salesman problem based data collection using WSNs for intelligent water irrigation and fertigation. Measurement 161:107835

    Article  Google Scholar 

  33. Hao J, Zhang B, Mouftah HT (2012) Routing protocols for duty cycled wireless sensor networks: A survey. IEEE Commun Mag 50(12):116–123

    Article  Google Scholar 

  34. Intanagonwiwat C, Govindan R, Estrin D, Heidemann J, Silva F (2003) Directed diffusion for wireless sensor networking. IEEE/ACM Trans Networking 11(1):2–16

    Article  Google Scholar 

  35. Kulik J, Heinzelman WR, Balakrishnan H (1999) Adaptive protocols for information dissemination in wireless sensor networks, in Proc. 5thAnnu. ACM/IEEE Int. Conf. Mobile Comput Netw (MobiCom), Seattle, WA, USA pp. 174–185

  36. Migabo ME, Djouani K, Olwal OL (2015) A stochastic energy consumption model for wireless sensor networks using GBR techniques. AFRICON- 2015:1–5

    Google Scholar 

  37. Sadagopan N, Krishnamachari B, Helmy A (2003) The ACQUIRE mechanism for efficient querying in sensor networks, in Proc 1st IEEE Int Workshop Sensor Netw Protocols Appl., Anchorage, AK, USA pp. 149-155

  38. Popescu AM, Salman N, Kemp AH (2014) Energy efficient geographic routing robust against location errors. IEEE Sens J 14:1944–1951

    Article  Google Scholar 

  39. Xiang X, Wang X, Zhou Z (2012) Self-adaptive on-demand geographic routing for mobile ad hoc networks. IEEE Trans Mob Comput 11:1572–1586

    Article  Google Scholar 

  40. Zhang H, Shen H (2010) Energy-efficient beaconless geographic routing in wireless sensor networks. IEEE Trans Parallel Distrib Syst 21:881–896

    Article  Google Scholar 

  41. Huang H, Yin H, Min G, Zhang J, Wu Y, Zhang X (2018) Energy-aware dual-path geographic routing to bypass routing holes in wireless sensor networks. IEEE Trans Mob Comput 17:1339–1352

    Article  Google Scholar 

  42. Huang P, Wang C, Xiao L (2012) Improving end-to-end routing performance of greedy forwarding in sensor networks. IEEE Trans Parallel Distrib Syst 23:556–563

    Article  Google Scholar 

  43. Karp B, Kung HT (2000) GPSR: Greedy Perimeter Stateless Routing for Wireless Networks Proc. ACM MobiCom pp. 243–254

  44. Chen Q, Kanhere SS, Hassan M (2013) Adaptive position update for geographic routing in mobile ad hoc networks. IEEE Trans Mob Comput 12:489–501

    Article  Google Scholar 

  45. Rango FD, Guerriero F, Fazio P (2012) Link-stability and energy aware routing protocol in distributed wireless networks. IEEE Trans Parallel Distrib Syst 23:713–726

    Article  Google Scholar 

  46. Petrioli C, Nati M, Casari P, Zorzi M, Basagni S (2014) ALBA-R: Load-balancing geographic routing around connectivity holes in wireless sensor networks. IEEE Trans Parallel Distrib Syst 25:529–539

    Article  Google Scholar 

  47. Cheng L, Niu J, Cao J, Das SS, Gu Y (2014) QoS aware geographic opportunistic routing in wireless sensor networks. IEEE Trans Parallel Distrib Syst 25:1864–1875

    Article  Google Scholar 

  48. Lee E, Park S, Yu F, Kim SH (2010) Data gathering mechanism with local sink in geographic routing for wireless sensor networks. IEEE Trans Consum Electron 56:1433–1441

    Article  Google Scholar 

  49. Nayak P, Swetha GK, Gupta S, Madhavi K (2021) Routing in wireless sensor networks using machine learning techniques: Challenges and opportunities. Measurement 178:108974

    Article  Google Scholar 

  50. Leonia J, Tanellia M, Stradaa SC, Berger-Wolfb T (2020) Ethogram-based automatic wild animal monitoring through inertial sensors and GPS data. Eco Inform 59:101112

    Article  Google Scholar 

  51. Badescu A, Cotofana L (2015) A wireless sensor network to monitor and protect tigers in the wild. Ecol Ind 57:447–451

    Article  Google Scholar 

  52. Garcia-Sanchez A, Garcia-Sanchez F, Losilla F, Kulakowski P, Garcia-Haro J, Rodríuez A, López-Bao J, Palomares F (2010) Wireless sensor network deployment for monitoring wildlife passages. Sensors 10:7236–7262

    Article  Google Scholar 

  53. Ren J, Zhang Y, Zhang K, Liu A, Chen J, Shen XS (2014) Lifetime and energy hole evolution analysis in data-gathering wireless sensor networks. IEEE Trans Industr Inf 12:788–800

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electronics Engineering, Vellore Institute of Technology, Chennai, Tamilnadu, 600127, India

    Kalaivanan Karunanithy

  2. Department of Electronics and Communication Engineering, Anna University Regional Campus Coimbatore, Coimbatore, Tamilnadu, 641046, India

    Bhanumathi Velusamy

Authors
  1. Kalaivanan Karunanithy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bhanumathi Velusamy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kalaivanan Karunanithy.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

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

Karunanithy, K., Velusamy, B. An efficient data collection using wireless sensor networks and internet of things to monitor the wild animals in the reserved area. Peer-to-Peer Netw. Appl. 15, 1105–1125 (2022). https://doi.org/10.1007/s12083-021-01289-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-021-01289-x

Keywords

Search

Navigation