Enhancing Sensor Network Performance through an Intelligent Smart Multi-Attribute Decision-Making Approach
- Post by: airjournals
- July 19, 2024
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Anthony Lordson Amana1, Fidelis Ikechukwu Onah2, Ngang Bassey Ngang3, & Martin Ogharandukun4
1Department of Computer Engineering, Veritas University, FCT, Abuja
2 3Department of Electrical and Electronic Engineering, Veritas University, Abuja
4Department of Pure and Applied Physics, Veritas University, Abuja
ABSTRACT
With the increasing integration of sensor networks in various domains, optimizing their performance has become a crucial challenge. This paper presents an intelligent Smart Multi-Attribute Decision-Making (SMADM) approach designed to enhance sensor network performance. By leveraging advanced algorithms and real-time data processing, the proposed approach aims to improve decision-making processes, ensuring efficient sensor network management. The study highlights the effectiveness of the SMADM approach through simulation results, showcasing significant improvements in network reliability, data accuracy, and overall system efficiency. The consistent poor performance in our communication network occurs when the wireless sensor does not effectively reduce packet loss and interference, leading to high costs. This issue is addressed by improving sensor network performance using the SMADM approach. This involves characterizing increased energy consumption, interference, and packet loss, which contribute to reduced performance, and designing a rule base for the SMADM approach to mitigate these issues. A SIMULINK model for wireless sensor networks (WSN) is developed, along with an algorithm to implement the process. Validation results indicate that conventional packet loss peaks at 30Kb/s on day 4, while sensor 1 experiences a reduced loss of 27.98Kb/s. Incorporating sensor 2 decreases packet loss to 28.6Kb/s, and sensor 3 further reduces it to 27.39Kb/s, demonstrating that sensor 3 is the most effective for improved network performance. Additionally, conventional power consumption is highest at 0.5W on day 1. With sensor 1, it drops to 0.4663W, with sensor 2 to 0.4767W, and with sensor 3, it reduces to 0.456W, making sensor 3 very efficient.
Citations – APA
| Amana, A. L., Onah, F. I., Ngang, N. B. & Ogharandukun, M. (2024). Enhancing Sensor Network Performance through an Intelligent Smart Multi-Attribute Decision-Making Approach. Journal of Computer Science Review and Engineering, 8(1) 1-19. DOI: https://doi.org/10.5281/zenodo.12780058 |
Keywords: Sensor Network Performance; Intelligent Smart Multi-Attribute Decision-Making; SMADM Approach; Wireless Sensor Networks (WSN)