# Deduplication-Aware Healthcare Data Distribution in IoMT
## 2 Literature Review
### Sub-Chunk Deduplication-Based Schemes  
The content-defined chunking algorithm faces criticism due to its calculation overhead, as explored by [25]. The CDC hashing algorithm utilizes extra processing time throughout the data deduplication mechanism. An anchor-based deduplication scheme [26] introduces dynamic sub-chunk data deduplication. This scheme makes use of a multi-step mechanism to calculate fingerprints for the data chunks. Afterward, these fingerprints are encoded using a feature model. Then, the data chunks are checked for identical data. Optimization approaches are employed to ensure a high rate of precision, along with checking the similarity to avoid duplication [27]. This scheme provides an effective technique for deduplication and the subsequent storage of the data on the cloud. In this method, deduplication is performed, which improves the storage capacity by holding only a singular instance of a file. For sharing health data, advanced deduplication mechanisms are involved to increase storage and maintain data confidentiality. At first, sensitive information is masked, and deduplication is performed in the cloud by creating unique tags for each encoded data block. When a new block is uploaded, the CS checks if the tag is already present in the system. If it does, then it is not uploaded again, thus preventing redundant storage. Additionally, the cloud server checks duplication ratios of diagnostic data, classifying data into high, intermediate, or low duplicate ratios. This method effectively manages efficient storage and retains low duplication [28]. ESDedup utilizes an efficient deduplication strategy for patients' data that also checks for duplication. The data are divided into chunks, and idle CPU resources are used to concurrently check all chunks. The Simiprim algorithm is used for similarity detection. Afterward, a rewriting algorithm is used to ensure that only unique data blocks are stored. The redundant copies and cost of storing data are minimized. To avoid illegal access to the data, a blockchain strategy is employed [29]. The FastDedup scheme comprises three main layers. Each layer performs the function of refining the data. The first layer serves the function of deduplication and managing both already stored and new data. This layer comprises various FastDedup nodes that provide data storage functionalities. These nodes perform separate deduplication operations on the data provided by the first layer. It allows merging operations, too, which provides the ability to update the files to attain global deduplication. This scheme provides a better deduplication ratio but has a high processing overhead [30]. The presented technique attains better bit error and data rates simultaneously, along with security, as compared to RF. [PERSON] et al. [31] introduced a technique that aims to overcome the needed power for communication; this approach involves using an energy-harvesting mechanism in conjunction with low-energy MAC protocols. Besides this, the application layer implements an improved selection and data broadcasting policy that complies with the application. Wireless channels assist in measuring the frequency among the hubs and four predominant nodes located on the upper body, posterior side, and upper arms. The simulation outcomes demonstrate that when the hub is positioned on the temple, it achieves better results, while the least favorable overall performance is attained when the hub is located on the waist. Another technique is offered with a static-size sliding window to recognize the local maximum data bytes. After a predefined interval, a unique pattern can be detected between two windows taken from the input string. However, this requires a longer processing time and increases the computation costs. The major drawback of this scheme is that it is not suitable for use in deduplication mechanisms for healthcare data, as the lives of patients rely on the effectiveness of the adopted technique [32].