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MINIMUM POWER CONSUMPTION ROUTING USING HIERARCHICAL FUZZY LOGIC CLUSTERING FOR INTERNET OF THINGS.

Authors :
PRADEEPA, K.
PRAVEEN, M.
Source :
INFOCOMP: Journal of Computer Science; Jun2024, Vol. 23 Issue 1, p1-9, 9p
Publication Year :
2024

Abstract

The Internet of Things (IoT) is a very famous network because of its many applications. IoT network has an integration of large-scale IoT devices that generate data. These IoT devices are very low power computing devices due to which they have a low level of communication. These devices construct data and transmit the data to the base station via intermediate IoT devices. The base station gathers and integrates the data and sends it to the administrator for further processing. The data attains the base station using various routing algorithms with the goal of low power consumption. When discussing low power IoT devices, power efficiency is an important performance measurement when creating a routing algorithm. This paper proposes a Minimum Power Consumption Routing (MPCR) algorithm using Hierarchical Fuzzy Logic Clustering (HFLC) algorithm for IoT networks. The MPCR with HFLC algorithm is an energy-efficient algorithm because of its lower power consumption for the cluster by aggregating the data within the cluster head and decreasing the number of data transmissions to the base station. In this paper, the cluster formation and cluster-head selection are explained, and a simulation has been conducted. In addition, the proposed algorithm is compared with the existing algorithms based on different metrics such as throughput, packet delivery ratio, and energy consumption of the network. The experimental results show that the proposed MPCR with the HFLC algorithm provides high throughput and packet delivery ratio and reduces energy consumption more efficiently than other existing algorithms. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
18074545
Volume :
23
Issue :
1
Database :
Complementary Index
Journal :
INFOCOMP: Journal of Computer Science
Publication Type :
Academic Journal
Accession number :
178935604