In underwater wireless sensor networks (UWSNs), the limited availability and non-rechargeability of sensor node batteries necessitated the advancement of energy optimization techniques. Optimal clustering is one such technique that reduces the energy consumption of the networks. In this letter, we propose optimal cluster compression technique jointly with energy harvesting. In optimal clustering compression, we perform optimal clustering of networks with singular value decomposition (SVD) as compression technique to reduce the redundant data generated at the cluster heads (CHs). Besides, adopting energy harvesting technique, node batteries are periodically recharged. The performance of the proposed model is evaluated in terms of network lifetime and throughput.
2. Sharma, A. K., S. Yadav, S. N. Dandu, V. Kumar, and J. Sengupta, "Magnetic induction-based non-conventional media communications: A review," IEEE Sensors Journal, Vol. 17, No. 4, 926-940, 2016.
3. Yadav, S., V. Kumar, S. B. Dhok, and D. N. K. Jayakody, "Energy-efficient design of MI communication-based 3-D non-conventional WSNs," IEEE Systems Journal, Vol. 14, No. 2, 2585-2588, 2019.
4. Kumar, V., S. Yadav, A. Sharma, A. Prakash, R. Tripathi, and D. N. K. Jayakody, "3D-multilayer magneto-inductive transceiver coil structure and optimal placement of relays for non-conventional media," Wireless Networks, Vol. 28, 2115-2129, Springer, 2022.
5. Liu, Y., S. Gong, Q. Liu, and M. Hou, "A mechanical transmitter for undersea magnetic induction communication," IEEE Transactions on Antennas and Propagation, Vol. 69, 6391-6400, 2022.
6. Kumar, V., R. Bhusari, S. B. Dhok, A. Prakash, R. Tripathi, and S. Tiwari, "Design of magnetic induction based energy-efficient WSNs for non-conventional media using multi-layer transmitter-enabled novel energy model," IEEE Systems Journal, Vol. 13, No. 2, 1285-1296, 2018.
7. Gulbahar, B. and O. B. Akan, "A communication theoretical modeling and analysis of underwater magneto-inductive wireless channels," IEEE Transactions on Wireless Communications, Vol. 11, No. 9, 3326-3334, 2012.
8. Sun, Z. and I. F. Akyildiz, "Magnetic induction communications for wireless underground sensor networks," IEEE Transactions on Antennas and Propagation, Vol. 58, No. 7, 2426-2435, 2010.
9. Paek, J. and J. Ko, "K-means clustering-based data compression scheme for wireless imaging sensor networks," IEEE Systems Journal, Vol. 11, No. 4, 2652-2662, 2015.
10. Wang, S., T. L. N. Nguyen, and Y. Shin, "Data collection strategy for magnetic induction based monitoring in underwater sensor networks," IEEE Access, Vol. 6, 43644-43653, 2018.
11. Ghoreyshi, S. M., A. Shahrabi, T. Boutaleb, and M. Khalily, "Mobile data gathering with hop-constrained clustering in underwater sensor networks," IEEE Access, Vol. 7, 21118-21132, 2019.
12. Rufai, A. M., G. Anbarjafari, and H. Demirel, "Lossy image compression using singular value decomposition and wavelet difference reduction," Digital Signal Processing, Vol. 24, 117-123, Elsevier, 2014.
13. Amini, N., A. Vahdatpour, W. Xu, and M. Gerla, "Cluster size optimization in sensor networks with decentralized cluster-based protocols," Computer Communications, Vol. 35, 207-220, Elsevier, 2012.
14. Vermaak, H. J., K. Kusakana, and S. P. Koko, "Status of micro-hydro-kinetic river technology in rural applications: A review of literature," Renewable and Sustainable Energy Reviews, Vol. 29, 625-633, Elsevier, 2014.
15. Pobering, S. and N. Schwesinger, "A novel hydropower harvesting device," 2004 International Conference on MEMS, NANO and Smart Systems (ICMENS'04), 480-485, IEEE, 2004.