# Deduplication-Aware Healthcare Data Distribution in IoMT
## 5 Results and Analysis
### Average Chunk Size  
Throughout the chunking mechanism within the context of deduplication, it is essential to find an appropriate breakpoint. This has a substantial effect on the size of the chunks. If the breakpoint is close to a fixed window, this results in a smaller average chunk size. In the DICA, optimal-sized chunks are attained by setting a variable-sized window and using an appropriate threshold. If the size of the window is smaller than the defined size, the breakpoint is reset dynamically. This avoids the generation of numerous small-sized data chunks that degrade the performance of the scheme. Additionally, an optimal average chunk size allows the DICA to maintain low latency and reduces its memory utilization, thereby ensuring smooth operation in resource-constrained IoMT environments. The CWCA and WCA-BF + ASE schemes also attain optimum data chunks because the minimum value is set to prevent the generation of excessively small chunks. Similarly, the maximum value stops the production of very large-sized data chunks. The RAM attains considerable chunks of smaller size, and it starts creating smaller chunks increasingly in the event that the input data strings exhibit more fluctuations. DSW determines chunk boundaries based on content rather than fixed intervals. By using hash values and specific conditions to find breakpoints, the algorithm creates chunks that can vary in size but are often smaller due to the frequent changes in the data patterns. In Figure 3, the average chunk size is presented. The results demonstrate that the RAM attained 664, while CWCA and WCA-BF + ASE maintained an average chunk size of 670, DSW attained an average chunk size of 640 bytes, and the DICA achieved an average of 680 bytes.  
Figure 3: Average chunk size.  
Figure 2: Average number of chunks.  
Figure 4 highlights the difference between IC and fixed-sized windows. Mostly, the dynamic window is greater than the fixed-sized window. In the case of a fixed-sized window, all schemes maintained fixed windows of 250 bytes. In the DSW technique, the dynamic data blocks are smaller due to the dual-window mechanism, which frequently identifies breakpoints based on two scenarios. When the Rabin fingerprint in \(W1\) meets a specific residual condition, a breakpoint is established. When the hash value of \(W1\) equals the initial hash value of \(W2\), another breakpoint is established. This increased sensitivity to data variations results in more frequent chunking. The DICA has better IC due to its adaptive chunking approach, efficient use of delimiters, and effective data deduplication. The DICA dynamically adjusts the chunk sizes by identifying breakpoints using the \(R_{A}\) and \(R_{B}\) delimiters, which segment the data efficiently. In the context of the RAM, the chunk size can become very small because there is no explicit limit applied to the minimum window size. The absence of a limit means that the RAM will continue to create small chunks. CWCA and WCA-BF + ASE have better IC as they use the window size chunking mechanism to dynamically adjust the data chunks based on a predefined window size, ensuring efficient data processing. The proposed scheme attained 680 bytes, whereas CWCA, RAM, DSW, and WCA-BF + ASE maintained 670 bytes, 664 bytes, 640 bytes, and 670 bytes, respectively. The results show that IC increased by up to 66.7%, 68%, 62%, and 72.1% for the RAM, CWCA, DSW, and DICA schemes.