# Deduplication-Aware Healthcare Data Distribution in IoMT
## 6 Conclusions  
The proposed adaptive chunking scheme for smart healthcare IoT offers a robust solution to lessen energy consumption and enhance data deduplication efficiency in the healthcare sector. The sink node obtains healthcare parameters through concatenated strings transmitted by collector devices. These data strings make use of delimiters to differentiate the data of patients or individuals equipped with wearable sensors. Duplication is controlled in the proposed technique at two levels. At the collector device (CD) level, data deduplication is ensured by replacing identical data values with Boolean digits. At the second level, the sink node further refines the process using the Delimiter-Based Incremental Chunking Algorithm (DICA), employing delimiters and variable-sized windows for ideal chunking. The precise transmission of healthcare data readings permits doctors to make the best decisions at any time, especially in emergencies. To check the validity of the proposed scheme, an extensive simulation was performed. The NS-2.35 simulation tool was used to exploit different nodes and their functionalities using the C language and TCL script. The experimental results showed that the variable-sized window increased by up to 66.7%,68%, and 72.1% for the RAM, DSW, and DICA. Besides this, a better deduplication level is achieved while using minimal energy. The proposed DICA attains improved performance in terms of power consumption, static and dynamic data chunks, energy efficiency, and throughput.  
While the DICA demonstrates significant potential, its key limitations are as follows. The average chunk size is maintained to ensure efficient performance for data-level deduplication, but it is not suitable for file-level deduplication, which requires larger chunk sizes as per the available resources. The dynamic chunk size is kept larger than the fixed-size chunks, and the total number of chunks is also reduced to improve the computational costs and avoid possible delays in processing, but the proposed DICA may be improved further by incorporating reinforcement learning-based chunking. This would enable it to dynamically respond to a variety of situations with different types of data streams. The proposed DICA considers the healthcare scenario, and it is not fully applicable for deduplication in other scenarios, such as vehicular networks, flat wireless sensor networks, industrial data-sharing networks, and underwater sensor networks. Thus, the need for the dynamic adaptability of the deduplication scheme as per the nature of the data shared on the network is highlighted. Moreover, the sensitive information of patients must be protected against unauthorized access or breaches by utilizing security and privacy schemes for healthcare data.  
In future work, we will consider file-level deduplication scenarios where reinforcement learning-based chunking mechanisms for deduplication may be considered to analyze the performance. Moreover, the impact of deduplication and breakpoint recognition on the cloud will be examined to reduce the storage overhead for healthcare-related data. This will be helpful to resolve a few of the above-mentioned limitations.  
The author extends his appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA for funding this research work through the project number \"NBU-FPEJ-2024-2429-01\".  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The author declares no conflicts of interest.