A Framework for the Internet of Things that is Energy-Efficient and based on Swarm Intelligence

Authors

  • Tejraj, Manvi Chopra, Yogesh Kumar

DOI:

https://doi.org/10.17762/msea.v71i4.690

Abstract

In recent years, Internet of Things (IoT) technology has been created for use in a wide range of industries. The Internet of Things network is equipped with a great number of sensors that may collect information immediately from their surroundings. The sensing components of the network serve as sources by monitoring environmental events and transmitting vital data to the relevant data center. When the sensors pick up on the aforementioned occurrence, they transmit the data about the world to a central station. On the other hand, sensors have limited processing, energy, transmission, and memory capacity, which may have a negative impact on the system. These limitations may cause the system to malfunction. Our present research efforts are focused on finding ways to reduce the amount of energy that is used by sensors in the Internet of Things networks. The goal of this research is to identify the Internet of Things (IoT) network potential node that has the most potential to improve energy efficiency. Throughout this whole research, we present a fusion of techniques that combines the skills of PSO's exploitation with the capabilities of GWO's exploration. Specifically, this fusion would merge the two sets of capabilities into one. The essential idea is to combine the capabilities of the PSO to efficiently exploit prospective nodes with the capabilities of the Grey Wolf Optimizer to choose potential nodes in the most efficient way possible. On the basis of many performance measures, the suggested technique is contrasted with the conventional PSO, GWO, Hybrid WSO-SA, and HABC-MBOA algorithms.

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Published

2022-09-02

How to Cite

Tejraj, Manvi Chopra, Yogesh Kumar. (2022). A Framework for the Internet of Things that is Energy-Efficient and based on Swarm Intelligence. Mathematical Statistician and Engineering Applications, 71(4), 1664–1680. https://doi.org/10.17762/msea.v71i4.690

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Section

Articles