Abstract
Wireless Sensor Network (WSN) forms an essential part of IoT. It is embedded in the target environment to observe the physical parameters based on the type of application. Sensor nodes in WSN are constrained by different features such as memory, bandwidth, energy, and its processing capabilities. In WSN, data transmission process consumes the maximum amount of energy than sensing and processing of the sensors. So, diverse clustering and data aggregation techniques are designed to achieve excellent energy efficiency in WSN. In this view, the current research article presents a novel Type II Fuzzy Logic-based Cluster Head selection with Low Complexity Data Aggregation (T2FLCH-LCDA) technique for WSN. The presented model involves a two-stage process such as clustering and data aggregation. Initially, three input parameters such as residual energy, distance to Base Station (BS), and node centrality are used in T2FLCH technique for CH selection and cluster construction. Besides, the LCDA technique which follows Dictionary Based Encoding (DBE) process is used to perform the data aggregation at CHs. Finally, the aggregated data is transmitted to the BS where it achieves energy efficiency. The experimental validation of the T2FLCH-LCDA technique was executed under three different scenarios based on the position of BS. The experimental results revealed that the T2FLCH-LCDA technique achieved maximum energy efficiency, lifetime, Compression Ratio (CR), and power saving than the compared methods.
Highlights
Wireless Sensor Networks (WSN), a kind of wireless network, are composed of several isolated and small sensors placed in a target field
The experimental values inferred that the T2FLCHLCDA technique achieved maximum energy efficiency, lifetime, Compression Ratio (CR), and power saving over the compared methods
T2FLCH technique has three input parameters namely, Residual Energy (RE), distance to base station (DBS), and Node Centrality (NC) which are used in Cluster Heads (CH) selection and cluster construction processes
Summary
Wireless Sensor Networks (WSN), a kind of wireless network, are composed of several isolated and small sensors placed in a target field. The power assets in sensors are restricted to WSN due to which it ensures the proper functioning of the network It collects all the necessary data and transmits the same to BS. All the CHs aggregate the data from its sets and transmits the data to BS This method prevents every node from getting exhausted. The current research article presents a novel Type-II fuzzy Logic-Based Cluster Head selection with Low Complexity Data Aggregation (T2FLCH-LCDA) technique for WSN. Three input parameters namely, Residual Energy (RE), distance to base station (DBS), and node centrality (NC) are used for CH selection and cluster construction in T2FLCH technique. The LCDA technique encompasses a Dictionary Based Encoding (DBE) process to achieve data aggregation at the CHs. At last, the aggregated data is transmitted to BS where it achieves the energy efficiency. The proposed T2FLCH-LCDA technique was simulated under three dissimilar scenarios based on the position of BS and the results are present in upcoming sections
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