# Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT
## 2 Literature Review
### Tree-Based Data Aggregation Schemes  
In this category, child sensor nodes transmit data to parent nodes and these parent nodes send aggregated data to the base station at the next level, creating a hierarchy. To efficiently aggregate data, a tree-based structure is employed. For fuzzification, min-max normalization is performed. A node with a lesser sum of weight and having a direct connection is elected as a parent. The scheme has considered the heterogeneous environment's complexity, but as attributes increase, no procedure is used to control energy utilization [33]. [PERSON] et al. [34] introduced a scheme in which sensor nodes are arranged in a grid-based structure and BS is the root node where aggregated data is transmitted. The child'sheads are gathered until all cell heads are added to the tree while using minimum energy. For energy utilization by cell head, Equations (2) and (3) are used. \[C_{ij}(k)=2*E_{elec}*k+E_{amp}*k*{d_{ij}}^{2} \tag{2}\]  
\[C_{iB}(k)=E_{elec}*k+E_{amp}*k*{d_{iB}}^{2} \tag{3}\]  
_C_ij_ is consumed energy for sending data packets from _i-th_ cell to the _j-th_ cell of the tree. _d_ij_ represents the distance between two cells, _C_iB_ shows the energy utilized for transmitting data packets from _i-th_ cell to the base station. The simulation proved that nodes have a higher lifetime. The shortcoming of the scheme is that when the child nodes are high it creates a longer depth, which increases the energy consumption of the parent node. To overcome the drawbacks of the previous scheme, the present scheme passes through phases of grid construction, construction of trees, and data transmission. The whole network is divided into a grid of MxN area. Every sensor possesses a Global Positioning System (GPS) to learn about its environmental location and calculate coordinates about the grid, in which the sensor lies. Limited child nodes are allowed to avoid uneven energy consumption and hotspot problems. The scheme has better data aggregation and efficiently manages network load but no fault tolerance policy is introduced in case node failure occurs [35]. The whole network area is distributed into grids, and an aggregating node is elected by using fuzzy logic. For it, CH distance, and link quality metrics like neighboring Overlap and Algebraic Connectivity are used, which in output informs the status of selecting an aggregating node. For relocation purposes, the Fruit Fly Optimization Algorithm (FOA) is used, which involves relocation conditions and the path to which the sink is switched. The scheme has a lower packet loss ratio but FOA is not very efficient for large networks [36]. This three-layered scheme comprises of Smart meters (SMs), Fog nodes, and cloud servers. During data transmission, SMs reduce data size and send it to local fog nodes. Then, fog nodes gather this data after checking it for integrity. Then, it is transmitted to the last layer of the cloud. The cloud extracts this data and computes its hash to check whether it is original. If data is not altered it is saved or otherwise removed [37]. After a detailed analysis of the existing schemes, the major identified gaps are outlined as follows. Various schemes primarily focus on energy optimization as it is a crucial parameter for prolonging the network lifetime and reducing overall energy consumption. As a result, the energy consumption did not decrease as much as it should have. Many techniques employ various strategies to efficiently aggregate data, but limited work has been undertaken to address redundant data specifically in the healthcare sector. There is a persistent need for more research to improve the effectiveness of handling redundancy, particularly in the healthcare domain. In comparison to existing data aggregation techniques, the proposed Energy-Efficient Fuzzy Data Aggregation System (EE-FDAS) showcases distinct strengths, particularly in addressing redundancy and energy consumption challenges within a healthcare environment.