CFP last date
22 April 2024
Reseach Article

An Improved Energy-Efficient Prediction-based Model for Animal Tracking in Wireless Sensor Networks

by Akinyemi Bodunde Odunola, Egunlayi Olutayo Cyril
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 177 - Number 37
Year of Publication: 2020
Authors: Akinyemi Bodunde Odunola, Egunlayi Olutayo Cyril
10.5120/ijca2020919860

Akinyemi Bodunde Odunola, Egunlayi Olutayo Cyril . An Improved Energy-Efficient Prediction-based Model for Animal Tracking in Wireless Sensor Networks. International Journal of Computer Applications. 177, 37 ( Feb 2020), 15-24. DOI=10.5120/ijca2020919860

@article{ 10.5120/ijca2020919860,
author = { Akinyemi Bodunde Odunola, Egunlayi Olutayo Cyril },
title = { An Improved Energy-Efficient Prediction-based Model for Animal Tracking in Wireless Sensor Networks },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2020 },
volume = { 177 },
number = { 37 },
month = { Feb },
year = { 2020 },
issn = { 0975-8887 },
pages = { 15-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume177/number37/31146-2020919860/ },
doi = { 10.5120/ijca2020919860 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:49:08.191615+05:30
%A Akinyemi Bodunde Odunola
%A Egunlayi Olutayo Cyril
%T An Improved Energy-Efficient Prediction-based Model for Animal Tracking in Wireless Sensor Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 177
%N 37
%P 15-24
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Most of the existing algorithms in Wireless Sensor Network (WSN) used to track the movement of animals consumes a lot of energy. These have led to discontinuation of tracking when the energy runs down. In this paper, an energy-efficient animal tracking model is proposed to improve the connection availability and duration of tracking by decreasing the energy consumption for sensing. An existing animal tracking model, which employed an energy-saving algorithm approach was selected. A simulation was carried to observe the energy consumption of the model using connection availability and connection duration as performance metrics. Then, an energy-efficient model was formulated by employing Prediction-based Variable Radius Sensor Activation algorithm (PRVARSA). A 15 minutes simulation was performed in a Wireless Sensor network consisting of 50, 100 and 200 sensor nodes randomly distributed in the network area. The performance of the formulated model was evaluated by benchmarking it with the existing model using the same metrics. The results showed that the average energy consumption of proposed and existing models are 3.93 J and 24.38 J respectively. It was observed that the proposed model consumed less energy for sensing and kept tracking the target after 13 minutes with an average energy consumption value below 20 J. Also, the proposed model provided higher connection availability of 115 compared to 0 for the existing model. The study concluded that the proposed model provides better energy-saving and thus extended the lifetime of the Wireless Sensor Network in a tracking system.

References
  1. Oluwaranti, A. I., Ayeni, S.: Monocular Vision Based Boundary Avoidance for Non Invasive Stray Control System for Cattle: A Conceptual Approach. Journal of Sensor Technology, 5:63-71, 2015.
  2. Eke, B., Egbono, F. Designing internet of things system for checking cattle rustling in Nigeria. International Journal of Computer Applications, 157(7):27–35, 2017.
  3. Awad, A. I.: From classical methods to animal biometrics: A review on cattle identification and tracking. Computers and Electronics in Agriculture. Volume 123, Pages 423-435, April 2016
  4. Crall, J.D., Gravish, N., Mountcastle, A. M., Combes, S. A.: BEEtag: a low-cost, image-based tracking system for the study of animal behavior and locomotion. Biorxiv, doi: https://doi.org/10.1101/020347, Jun. 3, 2015.
  5. Guo, Y., Corke, P., Poulton, G., Wark, T., Bishop-Hurley, G., Swain, D.: Animal behaviour understanding using wireless sensor networks. In Proceedings of 2006 31st IEEE Conference on Local Computer Networks, 607–614. Nov. 2006.
  6. Wark, T., Swain, D., Crossman, C., Valencia, P., Bishop-Hurley, G., Handcock, R.: Sensor and actuator networks: Protecting environmentally sensitive areas. IEEE Pervasive Computing, 8(1):30–36, 2009.
  7. Kwong, K., Wu, T., Goh, H., Sasloglou, K., Stephen, B., Glover, I., Shen, C., Du, W., Michie, C., Andonovic, I.: Implementation of herd management systems with wireless sensor networks. IET Wireless Sensor Systems, 1(2):55–65. 2011.
  8. Mittal, A., Jayaraman, S., Jagyasi, B. and Pande, A.: Mkrishi wireless sensor network platform for precision agriculture. IEEE Sensing Technology (ICST), 6:623–629, 2012.
  9. Chuang S. C.: Survey on Target Tracking in Wireless Sensor Networks. Dept. of Computer Science – National Tsing Hua University, (5/26/2005).
  10. Bhatti, S., Xu, J.: Survey of Target Tracking Protocols Using Wireless Sensor Network, in Proceedings of the Fifth International Conference on Wireless and Mobile Communications, 110-115, 2009
  11. You, C.W., Chea, Y. C., Chiang, J.R., Huang, P., Chua H.h.,, Lau, S.Y.: Sensor-Enhanced Mobility Prediction for Energy-Efficient Localization. In proceedings of the 2006 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks. 2006.
  12. Ramya, K., Kumar K. P., Srinivas R.S.: A survey on target tracking techniques in wireless sensor networks. International Journal of Computer Science and Engineering Survey, 3(4):93-108, 2012.
  13. Abbasi, A.A., Younis. M.: A Survey on Clustering Algorithms for Wireless Sensor Networks. Computer Communications, 30(14):2826-2841, 2007.
  14. Boyinbode,O., Le, H., Takizawa, M.: A survey on clustering algorithms for wireless sensor networks. International Journal of Space-Based and Situated Computing, (2):130-136, 2011.
  15. Xu, Y., Winter, J., Lee, W.C.: Prediction-based Strategies for Energy Saving in Object Tracking Sensor Networks, in Proceedings of the 2004 IEEE International Conference on Mobile Data Management (MDM’04), 2004a
  16. Xu, Y., Winter, J., Lee, W.C.: Dual Prediction-based Reporting for Object Tracking Sensor Networks, in Proceedings of the First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services (MobiQuitous’04), 2004b.
  17. Yang H., Sikdar, B.: A protocol for tracking mobile targets using sensor networks, in Proceedings of the First IEEE International Workshop on Sensor Network Protocols and Applications, 2003.
  18. Bhuiyan, M. Z. A., Wang G.-J., Zhang L., Peng Y.: Prediction-based energy-efficient target tracking protocol in wireless sensor networks, Journal of Central South University of Technology, vol. 17, no. 2, pp. 340–348, 2010.
  19. Deldar, F., Yaghmaee, M. H.: Designing a prediction-based clustering algorithm for target tracking in wireless sensor networks. In Proceedings of the International Symposium on Computer Networks and Distributed Systems (CNDS '11), pp. 199–203, IEEE, Tehran, Iran, February 2011.
  20. Hosseini, V., Haghighat, A., Esfahani, F.: Designing a clustering and prediction-based protocol for target tracking in wireless sensor networks (WSNs). Advances in Computer Science: an International Journal, 2(3):17–26, 2013.
  21. Shen, Y., Kim, K. T., Park, J. C., Youn H. Y.: Object tracking based on the prediction of trajectory in wireless sensor networks,” in Proceedings of the IEEE 10th International Conference on High Performance Computing and Communications & IEEE International Conference on Embedded and Ubiquitous Computing (HPCC-EUC '13), pp. 2317–2324, Zhangjiajie, China, November 2013.
  22. Shafiei, A. M. & Darehshoorzadeh, A. & Boukerche, A. VARSA: An Efficient VAriable Radius Sensor Activation Scheme for Target Tracking using Wireless Sensor Networks. In proceedings of the MobiWac '15 Proceedings of the 13th ACM International Symposium on Mobility Management and Wireless Access, 69-75, 2015.
  23. Wamuyu, P.: A conceptual framework for implementing a WSN based cattle recovery system in case of cattle rustling in kenya. Technologies, 5:1–13, 2017
  24. Zhao, K., Jurdak, R.: Understanding the spatiotemporal pattern of grazing cattle movement. Scientific Reports 6, 31967; doi 10.1038/srep31967, 2016.
  25. Aljumaily, M., AL-Suhail, G.: An Efficient-Energy Model for Mobile Target Tracking in Wireless Sensor Networks. International Journal of Computer Science Issues, Volume 15, Issue 2, 17–26 March 2018
Index Terms

Computer Science
Information Sciences

Keywords

Tracking Energy efficient Network lifetime Prediction-based Algorithms