## 5 STCDRR Protocol
### Eliminating Temporal Data Redundancy at Source Level
Data latency at the cluster head is high for the STCDRR protocol as it undergoes approximately 30 more rounds of execution as compared to KAB and ED. For energy efficiency, the STCDRR protocol can be chosen as an efficient protocol as it preserves up to 90% of total energy. ## 8 Conclusions and Future Work

In Wireless Sensor Networks, eliminating the duplicate data measurements is a very limiting task to transmitting the required information as it absorbs most of the battery-driven energy. So, it is very important to conserve the finite amount of energy to enhance the overall network lifetime. In order to increase the network lifetime, it is necessary to reduce the data redundancy to eliminate the duplicate data. In this work, we have proposed the STCDRR protocol which works in two levels namely the source level and the sink level. Data aggregation is carried out by utilizing the JACCARD similarity function in the source level and Pearson's Correlation Coefficient technique in the sink level or aggregator. Our approach uses a real data set and it is implemented in the MATLAB simulator for data accuracy, energy consumption, and aggregation ratio. The graphical results show our protocol outperforms the existing techniques, KAB and ED. For future work, we will utilize this protocol for a multi-attribute data set with a greater number of key parameters and performance measures to minimize energy utilization for an enhanced network lifetime. In this work, we have used only one data set and only one attribute (temperature) of the data set. It is a limitation of this work. The potential result that our proposed protocol will give for two datas ets or two attributes of one data set is yet to be explored. In the future, we will try to experiment with our protocol, STCDRR, using more than one data set. Conceptualization, L.D., B.K.P., S.K.M., K.S.S., N.Z.J., M.B. and M.M. ; Data curation, L.D., B.K.P., S.K.M., K.S.S., N.Z.J., M.B. and M.M. ; Formal analysis, L.D., B.K.P., S.K.M., K.S.S., N.Z.J., M.B. and M.M. ; Funding acquisition, L.D., B.K.P., S.K.M., K.S.S., M.B. and M.M. ; Investigation, L.D., B.K.P., S.K.M., K.S.S., N.Z.J., M.B. and M.M. ; Methodology, L.D., B.K.P., S.K.M., K.S.S., N.Z.J., M.B. and M.M. ; Supervision, N.Z.J. ; Validation, L.D. and K.S.S. ; Visualization, L.D., B.K.P., S.K.M., N.Z.J., M.B. and M.M. ; Writing--original draft, L.D., B.K.P., S.K.M. and K.S.S. ; Writing--review & editing, L.D., B.K.P., S.K.M., K.S.S., N.Z.J., M.B. and M.M. All authors have read and agreed to the published version of the manuscript. Authors would like to give thanks for the support of Taif University Researchers Supporting Project number (TURSP-2020/239), Taif University, Taif, Saudi Arabia. Data Availability Statement: Data will be available on request. Figure 1:0 Execution time of STCDRR, KAB, ED in seconds for data measurements 50, 100, and 200. **Acknowledgments:** Authors would like to give thanks for the support of Taif University Researchers Supporting Project number (TURSP-2020/239), Taif University, Taif, Saudi Arabia. **Conflicts of Interest:** The authors declare no conflict of interest. ## References

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