# Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT
## 2 Literature Review
### Clustering-Based Data Aggregation Schemes  
In clustering-based data aggregation techniques, sensor nodes are grouped into clusters. Each cluster comprises a Cluster Head (CH) which is selected according to some conditions. The CH collects data from Cluster Members (CMs) and transmits it to the base station. For clustering, the K-means algorithm is employed and the numbers of clusters are determined by using the elbow method as mentioned in Equation (1) as it informs about total clusters. To enhance security and resolve the cost issue of healthcare aggregated data, clusters are further divided into sub-clusters. \[SSE=\sum_{k=1}^{k}\sum_{xi\in\textit{k}\textit{k}}||xi-ck||x^{2} \tag{1}\]  
\(SSE\) represents the sum of squared errors, \(x\) denotes the total sensors in the cluster, and ck is the kth cluster. Data are aggregated at the sensor level by considering extreme points only where the duplicated data is not transmitted to CH. The Extrema Point (EP) mechanism is not very flexible to use as it may not apply to all kinds of data [13; 14]. The CH uses a Bayesian-fusion algorithm to calculate trust scores for CMs and transmits aggregated data to BS. In the same context, to enhance the security of healthcare data, an anonymity-based clustering method has been used. By employing the client-server model, anonymization is ensured before transmitting it to the aggregator node [15]. [PERSON] et al. [16] proposed that IoT devices gather data from devices and transmit it to BS using a fuzzy matrix. The collected data are then sent to the edge server. The cloud server validates the edge server and blockchain technology is utilized to avoid malicious attacks. For security, sensor nodes transmit encrypted data to Aggregator Nodes (Ans). To reduce energy consumption, communication, and storage costs, Ans further transmit compressed data to fog servers [17]. While [PERSON] et al. [18] performed clustering by the glow swarm optimization method. The proposed scheme has lower latency and is suitable for medical applications but multi-layer clustering may increase complexity [19]. [PERSON] et al. proposed a technique to enhance efficiency and security in WSNs while resolving energy consumption issues. The Conditional Tug of War Optimization is used to calculate node trust. Energy optimization was achieved by cluster-based data aggregation, yet the method only focused on node energy levels. Major factors, including node distance and degree, were neglected [20]. [PERSON] et al. have employed multi-clustering in which the aggregator node's energy level is checked from time to time. In case it is greater than the threshold factor, the Candidate Flag bit will be set as 1. In case energy is low, the Aggregator node is replaced with the nearest node. It has a better load-balancing strategy but if the nearest chosen aggregator has low energy, the whole network will be affected [21]. To overcome energy utilization in the healthcare sector, a fuzzy-based data aggregation scheme is introduced. By considering the heterogeneous environment, an appropriate parent node is selected for each node, afterwards when data is aggregated it is checked for duplicate values. If they exist, they are replaced with the Boolean digit 0 at Level 2. This significantly reduces data size, overcomes storage space, as well as increases the aggregation factor [22]. [PERSON] et al. [23] employed K-means clustering and fuzzy logic for aggregation. The aggregation rate, energy utilization, and data persistence are taken as fuzzy input, and network lifetime is obtained as output. The scheme has reduced the duplicate ratio. To monitor patient data in the healthcare system. [PERSON] et al. [24] have presented a centralized approach to reduce energy consumption and enhance efficiency. The selection of the CH is performed by BS and energy is preserved by switching idle sensors to a sleep state. The scheme has a better network lifetime but a high storage cost and some other parameters should also be considered for CH selection [24]. [PERSON] et al.