Fuzzy-Based Efficient Healthcare Data Collection and Analysis Mechanism Using Edge Nodes in the IoMT

Abstract

The Internet of Things (IoT) is an advanced technology that comprises numerous devices with carrying sensors to collect, send, and receive data. Due to its vast popularity and efficiency, it is employed in collecting crucial data for the health sector. As the sensors generate huge amounts of data, it is better for the data to be aggregated before being transmitting the data further. These sensors generate redundant data frequently and transmit the same values again and again unless there is no variation in the data. The base scheme has no mechanism to comprehend duplicate data. This problem has a negative effect on the performance of heterogeneous networks.It increases energy consumption; and requires high control overhead, and additional transmission slots are required to send data. To address the above-mentioned challenges posed by duplicate data in the IoT-based health sector, this paper presents a fuzzy data aggregation system (FDAS) that aggregates data proficiently and reduces the same range of normal data sizes to increase network performance and decrease energy consumption. The appropriate parent node is selected by implementing fuzzy logic, considering important input parameters that are crucial from the parent node selection perspective and share Boolean digit 0 for the redundant values to store in a repository for future use. This increases the network lifespan by reducing the energy consumption of sensors in heterogeneous environments. Therefore, when the complexity of the environment surges, the efficiency of FDAS remains stable. The performance of the proposed scheme has been validated using the network simulator and compared with base schemes. According to the findings, the proposed technique (FDAS) dominates in terms of reducing energy consumption in both phases, achieves better aggregation, reduces control overhead, and requires the fewest transmission slots.

Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT

“…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]. Randhawa et al [23] employed K-means clustering and fuzzy logic for aggregation.…”

“…For such cases, all the values are sent in their original format. The detailed algorithm is explored in the base paper [22]. Practically, the likelihood of this case is much lower than the first two cases.…”

Parameter Prediction with Novel Enhanced Wagner Hagras Interval Type-3 Takagi–Sugeno–Kang Fuzzy System with Type-1 Non-Singleton Inputs

“…In the world of modern business, it is required to improve the performance and the quality of fuzzy systems when they are used to predict and control real-time nonlinear dynamical industrial processes. Among others, the processes of financial systems [1][2][3][4][5], industrial manufacturing processes [6][7][8], autonomous mobile robots [9][10][11][12][13], intelligent controllers [14][15][16][17][18][19][20][21][22][23][24][25][26], route selection [27,28], clustering systems [29,30], medical systems [31][32][33], vision and pattern recognition systems [34][35][36], granular computing and optimization [37,38], database and information systems [39,40], and plant monitoring and diagnostics [18,[41][42][43][44] are characterized by high uncertainty, nonlinearity, and time-varying behavior …”