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Original Article
A Comprehensive Survey of Prediction-Driven Communication Models in WSNs
Nitin Singh1
Renu2
1 P.G. Student, Department of CSE, Sat Kabir Institute of Technology and Management, Ladrawan, Haryana, India. 2 Assistant Professor, CSE, Sat Kabir Institute of Technology and Management, Ladrawan, Haryana, India.
Published Online: May-August 2026
Pages: 07-12
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1. Heinzelman, W.R., Chandrakasan, A. and Balakrishnan, H., 2000, January. Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd annual Hawaii International Conference on System Sciences (pp. 10-pp). IEEE.
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6. S. Bandyopadhyay and E. J. Coyle, “An energy efficient hierarchical clustering algorithm for wireless sensor networks ”, in Proc. INFOCOM, Mar. 2003, pp. 1713–1723.
I. Matta, G. Smaragdakis, and A. Bestavros, “SEP: a stable election protocol for clustered heterogeneous wireless sensor networks ”, in SANPA, Aug. 2004.
7. L. Qing, Q. Zhu, and M. Wang, “Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks ”, Computer Commun., vol. 29, pp. 2230–2237, 2006.
A. Bari, A. Jaekel, and S. Bandyopadhyay, “Clustering strategies for improving the lifetime of two-tiered sensor networks ”, Computer Commun., vol. 31, pp. 3451–3459, 2008.
8. M. Younis, M. Youssef, and K. Arisha, “Energy-aware management for cluster-based sensor networks ”, Computer Networks, vol. 43, pp. 649–668, 2003.
9. J. S. Liu and C. H. R. Lin, “Energy-efficiency clustering protocol in wireless sensor networks ”, Ad Hoc Networks, vol. 3, no. 3, pp. 371–388, May 2005.
10. S. Lee, J. Lee, H. Sin, S. Yoo, S. Lee, J. Lee, Y. Lee, and S. Kim, “An energy-efficient distributed unequal clustering protocol for wireless sensor networks ”, in Proc. PWASET, Dec. 2008, pp. 1274–1278.
11. M. Perillo, Z. Cheng, and W. Heinzelman, “An analysis of strategies for mitigating the sensor network hot spot problem ”, in Proc. Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, 2005, pp. 474–478.
12. Jain, A., Chang, E.Y. and Wang, Y.F., 2004, June. Adaptive stream resource management using kalman filters. In Proceedings of the 2004 ACM SIGMOD international conference on Management of data (pp. 11-22).
13. Manjeshwar, A. and Agrawal, D.P., 2002, April. APTEEN: A hybrid protocol for efficient routing and comprehensive information retrieval in wireless sensor networks. In Parallel and distributed processing symposium, international (Vol. 3, pp. 0195b-0195b). IEEE Computer Society.
14. Haykin, S., 2009. Neural networks and learning machines, 3/E. Pearson education india.
15. Deshpande, A., Guestrin, C., Madden, S.R., Hellerstein, J.M. and Hong, W., 2004, August. Model-driven data acquisition in sensor networks. In Proceedings of the Thirtieth international conference on Very large data bases-Volume 30 (pp. 588-599).
16. Vapnik, V.N., 1998. Statistical learning theory wiley-interscience. New York.
17. Hochreiter, S. and Schmidhuber, J., 1997. Long short-term memory. Neural computation, 9(8), pp.1735-1780.
18. Breiman, L., 2001. Random forests. Machine learning, 45(1), pp.5-32.
2. Lindsey, S. and Raghavendra, C.S., 2002, March. PEGASIS: Power-efficient gathering in sensor information systems. In Proceedings, IEEE aerospace conference (Vol. 3, pp. 3-3). IEEE.
3. Manjeshwar, A. and Agrawal, D.P., 2001, April. TEEN: ARouting Protocol for Enhanced Efficiency in Wireless Sensor Networks. In ipdps (Vol. 1, No. 2001, p. 189).
4. Kulik, J., Heinzelman, W. and Balakrishnan, H., 2002. Negotiation-based protocols for disseminating information in wireless sensor networks. Wireless networks, 8(2), pp.169-185.
5. Intanagonwiwat, C., Govindan, R. and Estrin, D., 2000, August. Directed diffusion: A scalable and robust communication paradigm for sensor networks. In Proceedings of the 6th annual international conference on Mobile computing and networking (pp. 56-67).
6. S. Bandyopadhyay and E. J. Coyle, “An energy efficient hierarchical clustering algorithm for wireless sensor networks ”, in Proc. INFOCOM, Mar. 2003, pp. 1713–1723.
I. Matta, G. Smaragdakis, and A. Bestavros, “SEP: a stable election protocol for clustered heterogeneous wireless sensor networks ”, in SANPA, Aug. 2004.
7. L. Qing, Q. Zhu, and M. Wang, “Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks ”, Computer Commun., vol. 29, pp. 2230–2237, 2006.
A. Bari, A. Jaekel, and S. Bandyopadhyay, “Clustering strategies for improving the lifetime of two-tiered sensor networks ”, Computer Commun., vol. 31, pp. 3451–3459, 2008.
8. M. Younis, M. Youssef, and K. Arisha, “Energy-aware management for cluster-based sensor networks ”, Computer Networks, vol. 43, pp. 649–668, 2003.
9. J. S. Liu and C. H. R. Lin, “Energy-efficiency clustering protocol in wireless sensor networks ”, Ad Hoc Networks, vol. 3, no. 3, pp. 371–388, May 2005.
10. S. Lee, J. Lee, H. Sin, S. Yoo, S. Lee, J. Lee, Y. Lee, and S. Kim, “An energy-efficient distributed unequal clustering protocol for wireless sensor networks ”, in Proc. PWASET, Dec. 2008, pp. 1274–1278.
11. M. Perillo, Z. Cheng, and W. Heinzelman, “An analysis of strategies for mitigating the sensor network hot spot problem ”, in Proc. Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, 2005, pp. 474–478.
12. Jain, A., Chang, E.Y. and Wang, Y.F., 2004, June. Adaptive stream resource management using kalman filters. In Proceedings of the 2004 ACM SIGMOD international conference on Management of data (pp. 11-22).
13. Manjeshwar, A. and Agrawal, D.P., 2002, April. APTEEN: A hybrid protocol for efficient routing and comprehensive information retrieval in wireless sensor networks. In Parallel and distributed processing symposium, international (Vol. 3, pp. 0195b-0195b). IEEE Computer Society.
14. Haykin, S., 2009. Neural networks and learning machines, 3/E. Pearson education india.
15. Deshpande, A., Guestrin, C., Madden, S.R., Hellerstein, J.M. and Hong, W., 2004, August. Model-driven data acquisition in sensor networks. In Proceedings of the Thirtieth international conference on Very large data bases-Volume 30 (pp. 588-599).
16. Vapnik, V.N., 1998. Statistical learning theory wiley-interscience. New York.
17. Hochreiter, S. and Schmidhuber, J., 1997. Long short-term memory. Neural computation, 9(8), pp.1735-1780.
18. Breiman, L., 2001. Random forests. Machine learning, 45(1), pp.5-32.
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