• Indira Umarji
• Soumya S Katageri
• Amulya U Naik
• Rashmi S S
• Bhuvan B

In the realm of wireless network management, the selection of appropriate algorithms plays a crucial role in enhancing performance metrics such as throughput, and security. This research project delves into a comprehensive study of various network algorithms, including K-means clustering, Markov Models, Time Series Analysis, Random Forest, and Fourier Series, Hidden Markov Model, Random Walk to evaluate their efficiency in optimizing network performance across varying numbers of nodes. The methodology involves implementing each algorithm meticulously and even evaluating its performance under diverse network configurations. Scrutinizing algorithm outputs throughput and drop ratio reveals trends, strengths, and weaknesses. Results are synthesized graphically, enabling a comparative assessment of algorithm performance across different network scenarios. This study provides valuable insights into algorithm selection for wireless network optimization. After the execution of algorithms with multiple iterations the average drop ratio is 9.55 percent and average throughput is 90.055 percent. By elucidating algorithm capabilities and limitations, this research contributes to improved real-world network management strategies.

Network algorithms, throughput, drop ratio, performance evaluation.

" Enhancing the Performance of a Wireless Network through Advanced Traffic Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.l466-l471, April-2024, Available :http://www.jetir.org/papers/JETIR2404B63.pdf

" Enhancing the Performance of a Wireless Network through Advanced Traffic Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppl466-l471, April-2024, Available at : http://www.jetir.org/papers/JETIR2404B63.pdf