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PROPOSED FUZZYBASED CLUSTERING AND DATA AGGREGATION (FCDR) PROTOCOLTable of Contents:
In this section, the working principle of the FCDR protocol has been presented. The basic architecture of clusteringbased IoTWSN model is depicted in Figure 4.2. The presented FCDR method operates on three major stages: node clustering, data collection, and data aggregation. Initially, the IoT sensor nodes perform FC process to select CHs and organize clusters. In the second level, the CMs observe the environment and forward the data to the CH. Finally, in the third level, the CHs perform data aggregation using EBLC technique. FuzzyBased Clustering ProcessFor reduced energy utilization, the cluster formation process plays an important part. It applies kmeans clustering technique to the cluster formed. The counts of datasets are FIGURE 4.2 Architecture of IoTWSN. separated into /сclusters that utilize these techniques. A value of к is estimated as given in equation (4.1): where n is the node count of sensor nodes, D is the network size, and is the average distance of every node for the BS. Utilizing the Euclidean distance, the distance among all of the sensors nodes for the entire clusters center is planned as given in equation (4.2): where X_{n2CC} indicates node’s distance from cluster center, Xj signifies the direct node j, and X_{cc} is the cluster center. The CH selection takes place using remaining energy, communication rate between a node and its neighboring node, link quality, restart value, and count of neighboring nodes, and node marginality, respectively. 4.3.1.1 Remaining Energy Level Energy is a significant resource in WSN. The CHs are the nodes that utilize and further energy than CMs if they contain aggregating, computing, and routing information. The remaining energy is calculated as given in equation (4.3): where E_{0} and E_{c} are the primary energy and the energy utilized with the node, respectively, and E,. is the remaining energy of a standard node. 4.3.1.2 Communication Rate The broadcasting message utilizes the energy that is simultaneously a square of the distance between the applicant and source nodes. A charge of communication rate is determined as given in equation (4.4): where d_{mg} signifies the average distance among the neighboring nodes and d_{0} is the transmitting range of the nodes. 4.3.1.3 Link Quality In WSN, the disappearing channel is usually arbitrary and timedifferent. Until a receiver does not examine the signal properly, a rebroadcast can happen and it needs further energy dissipation of the broadcaster. Thus, a link quality should be estimated for accomplishing energy competence. A link quality can be computed as given in equation (4.5): where Q_{max} and Q_{min} are the maximal and minimal count of rebroadcast from the neighborhood, respectively, and Q, indicates the entire rebroadcast number among the neighbors and the node. 4.3.1.4 Restart Value A node is essentially an embedding method. Occasionally, the methods affect a software or hardware fault. For solving these issues, the watchdog circuit is employed for restarting the PC scheme to ensure the node continues functioning. So, the frequent restart is utilizing any further energy. A value of restart value is estimated utilizing equation (4.6): where S_{max} and S_{mj}„ are the maximal and minimal restart values obtained from neighboring, nodes, respectively, and S_{0} indicates the entire restart value behind the WSN has been arranged. 4.3.1.5 Node Degree A principle is that the nearer the neighboring nodes, the more effective the node and the greater possibility of becoming a CH. The node degree can be calculated as given in equation (4.7): where D, indicates the count of neighboring nodes and D_{0} is the better count of neighboring nodes. 4.3.1.6 Node Marginality A division of coverage area will incorporate absence of nodes when a node is placed at the boundary of the observing region. A node simply encloses a limited area. Thus, the entire count of CHs in the network gets enhanced. Node marginality is determined as follows: where q indicates the quadrant number. In fuzzy surroundings, fuzzy analytic hierarchy process (AHP) is a valuable method under several conditions of decisionmaking. According to the function objectives and user preferences, the weights are assigned for all conditions in AHP. A fuzzy AHP technique depends upon the following stages: 1. Creation of pairwise comparative decision matrix A pairwise comparative matrices are provided as follows: where y„ = 1, y_{?} = 11 у 2. Normalization of decision matrix as computed in equation (4.10) 3. Weighted normalized decision matrix as provided in equation (4.11) where n is the criterion number. Data Aggregation ProcessThe EBLC method such as SZ performs highperformance computing (HPC) functions. These compression methods have been presented to manage the massive counts of data created in the implementation of HPC functions. The actual SZ reduces input data records, which are in binary formats and contain several data shapes as well as types. It is presented for adapting the SZ technique to IoT tools by considering the floating point data type and removing other types that create the code tiny in size and are simple for compiling on small tools. Besides, the technique was modified for taking a ID arrangement of float sensor data as input and replacing a byte range, which exists to be broadcasted for the edge node. After selecting, SZ for IoT functions as follows:
It is regarded that the information are broadcasted for the edge behind all periods P of time f. The gathered information is in the structure of M X N array, where M indicates the count of readings and N refers to the count of features. In the beginning, the 2D array is changed to the ID array. Thereafter, the flattened array compresses utilizing the lossy SZ method. Finally, the outcome binary array is broadcasted for the edge. It is noticeable that the adaption has been completed through removing the essential performances from the actual SZ for making it suitable on wearable and resourcelimited tools. An SZ compressing technique begins with reducing the ID array utilizing adaptive curvefitting methods. The optimal fit stage uses three forecast methods: preceding neighbor fitting (PNF), linear curvefitting (LCF), and quadratic curvefitting (QCF). A variation among the three methods is found in the count of precursor data points needed to suit the actual rate. An adapted method is the one that gives the nearer estimate. It is to be noticed that the suitable information is changed into integer quantization factors and encoding that utilize Huffman tree. If no forecast methods in the curvefitting stage ensure the error limit, the data point is clear as uncertain and the next encoding examines the IEEE 754 binary illustration. In the error bound, an absolute error bound (AEB) is utilized, implying that the compressing or decompressing faults are restricted to be in an AEB. For example, when the rate of data point is regarded to be X, an AEB of 10"^{1} implies, and thus the decompressed rate might be in the series [X10"^{1}, X + 10^{1}]. 
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