# Deduplication-Aware Healthcare Data Distribution in IoMT
## 4 Proposed Solution  
We propose a novel Delimiter-Based Incremental Chunking Algorithm (DICA) technique for intelligent healthcare IoT, including a fog server that is deployed at the network's edge. It reduces the transmission delays for healthcare data sent through the fog server to cloud repositories. We introduced an adaptive chunking algorithm at the data sink for secure message transmission from the sensor nodes to the CDs. It forwards the information to the fog for processing, storage, and analysis. Wearable health sensors such as sensor-equipped wristwatches are common in daily life. The identification of lethal viruses by sensor devices, such as the Chikungunya virus, in a timely manner has the potential to save human lives. These serious viruses tend to damage multiple organs. In areas with scarce medical facilities, the risk of disease outbreaks is high. The proposed model, as in [37], uses a three-layer fog and cloud system. The CD conducts the initial deduplication of the aggregated data prior to sending them to the fog server. The fog entity, after executing the deduplication process on the basis of CDC, forwards the data to the cloud server. This chunking mechanism eliminates redundant data, optimizing the storage space at the cloud server. It not only reduces the required bandwidth but also minimizes delays. The cloud server includes data repositories, ensuring that the data are securely stored and remain available on demand. Duplicated values must be identified before transmission to reduce the communication cost. It is also necessary to identify significant changes in readings based on health parameter thresholds. This scheme is applicable for collaboration between relevant medical staff for the analysis of patients' histories. Furthermore, sensing devices can continuously transmit health reports to the CD. This functionality ensures that practitioners are aware of a patient's current condition and enables them to take immediate action if an emergency occurs. The local storage on the fog server-side functions similarly to cache memory, as  
Figure 1: System model. it keeps recent data available. This setup assures that critical data readings are accessible, enabling fast decisions as per the current condition of a patient. The symbols used in the algorithm are provided in Table 1.  
The collector device is essential for deduplication, gathering data from sensors, and transferring them to SN or fog servers. The CDs must implement data deduplication at the primary level. For this purpose, replicated data values are replaced with a Boolean digit. The Boolean representation of data means that identical readings exist in the data stream that was transferred previously. Until any significant change is found in the readings, the data will remain in 1 bit Boolean digit format. The CDs perform encryption on the obtained data by utilizing the secret key, which is shared with the sink node. Next, the sink performs deduplication on large volumes of data using the DICA. This method assists in identifying data chunks of large sizes as the extensive data are presented for sharing with the cloud. These data are stored in a repository to be used later for analysis. The transmission of information by the sink to the cloud server is protected via encryption to avoid security threats. The CDs furthermore obtain the hash values of the healthcare information to protect it from bit tampering attacks, as well as to ensure data integrity. Timestamps are also included to avoid replay attacks. These checks do not merely minimize communication costs and energy consumption. Besides this, they lessen communication delays between the sensor nodes and cloud storage. In the present work, a comprehensive description of the data security measures was not given as it was beyond its scope, and the main emphasis was on data deduplication. Healthcare sensor readings are gathered and stored in repositories.