# Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT
###### Abstract  
The rapid development of the Internet of Things (IoT) has opened the way for transformative advances in numerous fields, including healthcare. IoT-based healthcare systems provide unprecedented opportunities to gather patients' real-time data and make appropriate decisions at the right time. Yet, the deployed sensors generate normal readings most of the time, which are transmitted to Cluster Heads (CHs). Handling these voluminous duplicated data is quite challenging. The existing techniques have high energy consumption, storage costs, and communication costs. To overcome these problems, in this paper, an innovative Energy-Efficient Fuzzy Data Aggregation System (EE-FDAS) has been presented. In it, at the first level, it is checked that sensors either generate normal or critical readings. In the first case, readings are converted to Boolean digit 0. This reduced data size takes only 1 digit which considerably reduces energy consumption. In the second scenario, sensors generating irregular readings are transmitted in their original 16 or 32-bit form. Then, data are aggregated and transmitted to respective CHs. Afterwards, these data are further transmitted to Fog servers, from where doctors have access. Lastly, for later usage, data are stored in the cloud server. For checking the proficiency of the proposed EE-FDAS scheme, extensive simulations are performed using NS-2.35. The results showed that EE-FDAS has performed well in terms of aggregation factor, energy consumption, packet drop rate, communication, and storage cost. eurm  
produce data in different formats [8]. Based on network size, different data aggregation approaches are considered. Centralized data aggregation (CDA) and in-network data aggregation (IDA) are the most commonly used techniques. The CDA is most commonly used for small networks, in which there is only one aggregator that gathers data from all nodes of the network. The IDA approach eliminates the drawbacks of the former technique by providing multiple AN nodes distributed in the network to collect data from nodes and send it to the Base Station (BS) [9]. De-duplication is a mechanism to identify and remove similar copies of data and serves as a central approach to cope with the growing volumes of data efficiently [10]. The de-duplication process is employed by using two main strategies. The inline de-duplication approach occurs in real-time when data are checked for duplicates before storing it in the storage system. Background de-duplication works as a post-process once data has been primarily written to the storage system. It includes periodically scanning the present data to detect and remove duplicates [11]. In the healthcare sector, the vast collection of medical records, diagnostic reports, and patient histories can lead to data redundancy, which ultimately utilizes high storage space and increases energy consumption. Energy consumption is directly proportional to the network lifetime and unnecessary repeated data wastes the sensor device resources [12]. The necessity of developing a new technique in healthcare is because of the critical need for continuous improvement and innovation in medical monitoring systems. Existing healthcare schemes suffer from the challenges of ineffective data aggregation, inappropriate redundant data handling, energy consumption, and storage costs in heterogeneous environments [13]. In the healthcare sector, sensor devices collect data in detail and frequently transmit it to BS. This unnecessary replication of data drains the limited resources of sensor nodes [14]. These resources need to be used wisely, especially in the context of healthcare where precise information is required for better treatment. The development of the proposed technique becomes imperative to minimize these challenges comprehensively. The research question for this work is as follows: How can we improve the de-duplication process of healthcare data to achieve better aggregation and packet delivery ratio while reducing the energy consumption and communication cost?