# Deduplication-Aware Healthcare Data Distribution in IoMT
## 1 Introduction
Deduplication is considered a subcategory of data compression in which long, identical data patterns are changed with small data values. In the case of cyclic and redundant healthcare data distributions, a sensing bottleneck can be faced, which needs to be fixed by utilizing a caching approach [10]. Deduplication can be categorized as (a) post-processing and (b) in-line. In the former case, the latest data are saved on a device and later examined for duplicated values. This confirms that the efficiency of storage actions is not reduced; instead, it saves identical values for only a short time. In the latter case, it involves hash calculations, performed in real time, to remove redundant blocks. This requires minimal storage space as the data are not replicated. However, the disadvantage is that the calculations involved in hash and lookup operations need extensive time for processing [11]. This means that the data saving on the device can be time-consuming, consequently affecting the backup. The main problem is that sensor devices produce a huge volume of data constantly, so the aggregator node is faced with identical data. From the aggregator device, the collected information is then sent to the main server. The transmission of large volumes of information utilizes a large amount of energy from the battery and decreases the network's lifespan. Furthermore, the storage of identical data recurrently on the server results in the wastage of storage. The limited power and dynamic nature of sensors additionally exacerbate the issue of effective data dissemination. To decrease congestion in the network, as well as reduce data latency, it is vital to eliminate similar information values via a data deduplication method. The huge volumes of similar data result in per-bit energy losses on the server side, as well as upsurges in energy consumption. This study includes an extensive examination of various data collection techniques that facilitate the sending of patient data to servers located in hospitals. The deployed sensors collect huge amounts of information, which utilizes high energy due to the frequent transmission of identical data. By recognizing the challenges associated with energy-efficient schemes, the present research contributes in the following ways. 1. We examined schemes that underscore the significance of data deduplication to overcome the duplication of the collected data for WBAN cases;
2. Data deduplication of level 1 was performed at the collector node before transmission to the sink. Next, a second level of deduplication was performed after receiving data from different collectors at the sink or fog server. The study further identified redundancies to improve deduplication. This helps to decrease the storage cost and transmission cost correspondingly;
3. Next, we presented a new Delimiter-Based Incremental Chunking Algorithm (DICA), which considers breakpoint identification among two windows. The main target is to obtain bigger chunks to improve the rate of deduplication. 4. We performed extensive simulations to compare DICA with base schemes for data deduplication. The results indicate the supremacy of the DICA over its counterparts. The subsequent sections of the manuscript are structured as follows. The existing studies are examined in Section 2. Section 3 presents the system model and the research problem. Section 4 elaborates on the proposed DICA scheme used for chunking. The results and analysis are presented in Section 5. In Section 6, the conclusion of the work is stated.