# Deduplication-Aware Healthcare Data Distribution in IoMT
## 2 Literature Review  
The existing literature was examined to identify mechanisms for the gathering of data on patients from various healthcare services. The emphasis of the study was on healthcare data collection, as well as the dissemination of such data to the data collection center (DCC), serving diverse uses. Within this context, we investigated the secure aggregation of data and examined the aspect of deduplication. The transmission of data collected using sensors can be facilitated through a cluster-based model [12]. This scheme incorporates an effective method for improved energy consumption and data transmission. The proposed scheme includes four clusters, each comprising Ad hoc Relay Stations (ARSs), Relay Nodes (RNs), and sensor nodes attached to the body for health checking. Layer 1 highlights consistent and effective relay-dependent routing, attaining a 99.9% PDR under puzzling situations. Layer 2 incorporates IoT-based smart homes to assist aging care, retaining relay routing for operative data transfer. Layer 3 uses BS, guaranteeing quality of service (QoS), energy efficiency, nominal end-to-end delay, and strong support for healthcare environments [13]. For better link quality for the calculated shortest path, a composite Route Cost Function along with integrating Link Reliability Factor is utilized [14]. This can prove useful for the efficient transmission of aggregated healthcare data among sensing devices and the BS, improving storage overhead and data reliability. The aggregation of healthcare-based data requires a broadband setup, including 3G and LTE. The sensed information is sent to cloud servers on the basis of 360 data packets per hour. Initially, Bluetooth is employed for data aggregation, after which the data are transmitted to the SN and subsequently to smartphones and personal digital assistants (PDAs) via the Internet. Finally, the cloud serves as the ultimate destination for the further analysis of the data. The AODV protocol is employed within a priority queue to distinguish the traffic. The results obtained in this way show the impact of both high-priority and low-priority queues. However, AODV falls short due to the vulnerabilities associated with the FIFO approach, particularly when addressing emergency cases. Pre-preemption and non-preemption cases are added to reduce the shortcomings associated with AODV [15]. To check healthcare-related data, different types of communication schemes are employed in [16]. A comparison between RF and HBC was conducted on the basis of usage. The WBAN hub facilitates communication between various devices, such as relays or bridges. The model comprises three main modules, namely the BAN, the node, and the organization of the data. The data and control channels are employed independently. Due to its distinctive features, the smart body area network (BAN) finds application in specialized scenarios or for the observation of unique processes. To minimize packet losses and maintain low transmission delays, AODV, which is a routing protocol, is introduced [17]. The data are categorized based on priority (urgent or routine), ensuring efficient handling. A three-tier architecture is employed, incorporating a diverse range of sensors, such as ECG, movement, acoustic, and BP devices. All devices are attached to the HN. In the third stage, the HN further sends the aggregated data to either a smartphone or a nursing station. Finally, the gathered information is forwarded to the central database to save these records. In an emergency, an alarm is activated. Each sensor is assigned a threshold value. The aggregated data are transferred and shared with the server. This scheme transmits the data when the value increases beyond the threshold. It forwards the data to the SN in a short time to reduce delays and maintains a good data packet transfer ratio along with high throughput. In [18], Elasticity-Aware Deduplication (EAD) is introduced.