# Adaptive Node Clustering Technique for Smart Ocean under Water Sensor Network (SOSNET)
## 4 SOSNET-Proposed Methodology
SOSNET works efficiently, not only in the presence of dense traffic, but it also performs equally well in large network sizes and in any transmission range, which makes it an ideal choice for a routing algorithm that can be used in UWSNs. \[\text{Delta}_{\text{Diff}}=\text{ABS}\;(\text{Ideal}_{\text{Degree}}-\text{ Node}_{\text{Degree}}). \tag{2}\]1 3D grid, transmission range 50 m to 200 m, grid sizes from 500 m to 2000 m for 40 and 80 nodes. ## 7 Conclusions

In the current research work, we have proposed an efficient algorithm SOSNET for UWSN. SOSNET is a scalable and efficient protocol that utilizes the MFO technique to work in a search space and selects the optimal number of clusters for routing. SOSNET is an evolutionary algorithm that works iteratively in a given search space. As the total number of clusters required are decreased, the routing cost of the packets is also reduced. It provides a near optimal number of clusters required

\[\text{Delta}_{\text{Diff}}=\text{ABS}\;(\text{Ideal}_{\text{Degree}}-\text{ Node}_{\text{Degree}}). \tag{2}\]2 Load balancing factor for 1000 m \(\times\) 1000 m grid and transmission ranges 25 m to 200 m.

for routing, and this ultimately results in decreasing the routing cost as well as the conservation of energy in the nodes. To check the effectiveness of the proposed algorithm, different simulations were executed. The algorithm was tested with various levels of node density; similarly, the algorithm was tested by varying the transmission range of sensor nodes. The results prove that SOSNET is an ideal solution that can be adopted for routing in the said networks. SOSNET is compared with other well-known evolutionary algorithms, i.e., GWO, CLPSO, and ACO, and the results show the superiority of the SOSNET. Conceptualization, M.Y.D. and F.A. ; Methodology, R.T. and F.A; Validation, R.T., M.M. and Y.N. ; Formal Analysis, F.A. ; Investigation, R.T. and K.M. ; Data Curation, R.T.; Writing-Original Draft Preparation, M.Y.D. and F.A. ; Writing-Review and Editing, F.A. and M.M. ; Visualization, F.A. and K.M. ; Supervision, Y.N. Project Administration, K.M. ; Funding Acquisition, Y.N. This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2018-2014-1-00720) supervised by the IITP (Institute for Information & communications Technology Promotion) and the Soonchunhyang University Research Fund. The authors declare no conflict of interest. ## References

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