# Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT
## 6 Discussion  
As a comprehensive analysis, the proposed scheme EE-FDAS is compared with the existing robust schemes FDAS, EHDA, DICA, and DICA_EXTENSION. The proposed  
Figure 7: The Storage Cost by Number of packets per unit time. scheme significantly reduces duplicated data transmission, data volume, and minimizes the load on CH, resulting in high data aggregation in EE-FDAS, FDAS, and EHDA. This efficiency of transmitting only critical data significantly reduces data volume thereby enhancing the overall system's performance. In contrast, the data packet size increases for only the certain packets sent with full readings after a threshold. It has little affect in increasing the transmission cost per bit and hence, the energy consumption as well. In EHDA, ANs compresses data that reduces packet size and increases aggregation level, and FDAS selects suitable parents of the same attributes which enhances the aggregation and lessens energy consumption. On the contrary, DICA and DICA_EXTENSION have not implemented any such mechanism so data aggregation in both of them is quite low. Higher aggregation leads to lower SL and fewer transmission slots therefore EE-FDAS, FDAS, and EHDA have maintained lower values for these metrics as compared to DICA and DICA_EXTENSION. The SL analysis highlights EE-FDAS's ability to achieve a lower SL due to its importance on higher data aggregation. The data transmission time reduction improves overall effectiveness of the system. Considering the required transmission slots, EE-FDAS needed lower slots as a result of its efficient data aggregation strategy. This reduction not only minimizes the chances of collisions and retransmissions but also contributes to a more reliable and effective scheme in healthcare paradigm. Fewer transmission slots lead to lower control overhead because fewer messages are exchanged for slot selection as compared to base schemes. The lower control overhead contributes to decreased network congestion and, consequently, reduced likelihood of data loss. When data packet size is low, it retains a minimum packet loss ratio because in lower bandwidth data packets pass easily due to reduced size. The impact of the number of attributes on energy consumption further highlights the effectiveness of EE-FDAS. As the system complexity grows, EE-FDAS constantly exhibits lower energy consumption compared to its counterparts. By transmitting Boolean digit 0 for normal data readings, the scheme diminishes energy depletion, contributing to prolonged node lifetimes and persistent network efficiency. Communication costs and storage costs in EE-FDAS have economic advantages. The scheme's approach of showing normal data with Boolean digit 0 leads to smaller packet sizes, lessening both communication and storage costs. This economic efficiency is vital for the practical implementation of healthcare sensor networks, especially in scenarios where resources are limited. To further handle the scenarios where the full readings are required to analyze the differences of the out range values, the proposed scenario manages a threshold to send a packet with all readings. It helps to practically analyze and predict the health status based on the sensing values. In context of sampling theorem, certain values of the input variables cause impact on the resulting metrics. The number of attributes are varied from 2 to 16 as input and the results are shown for metrics including aggregation factor, energy consumption, schedule length, transmission slots, control overhead and PDR. It has been observed that the reducing the size of attributes achieves better results. The size of attributes is reduced by replacing in-range values with Boolean values for a certain threshold. By increasing the threshold value, the resultant average size of packet reduces which results in better performance. Overall, EE-FDAS has performed well for all metrics, and EHDA and FDAS have maintained good performance in most of the parameters.