2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 12 , Issue 2 , PP: 178-186, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Neelima Gurrapu 1 * , Akhil Nair R. 2 , C. Laxmikanth Reddy 3 , V. V. J. Rama Krishnaiah 4 , S. Shiek Aalam 5 , Kancharla Suresh 6
Doi: https://doi.org/10.54216/JISIoT.120213
Rural communities struggle to connect to the internet, a phenomenon known as the "digital divide." Mesh networks, with improved access in rural regions, might help to tackle this problem. From a social, economic, and scientific standpoint, this study investigated whether mesh networks may improve rural connectivity. This project developed and implemented methodologies to assess community participation, cost, and network coverage. Five well-known methods were pitted against these ones. Locals are working on a mesh node placement project in a rural location with diverse topography. In terms of network coverage, the Network Coverage Assessment revealed that the proposed approach frequently outperformed the most recent approaches. Finding the ideal locations for mesh nodes helped to tackle challenges in rural regions. After putting the strategy into effect, the Cost-Effectiveness Analysis revealed a positive ROI. Many alternative options seemed unprofitable. On the Community Engagement Index, the recommended method performed better than others. Participating in network activities with individuals from the local community helps to foster ownership and shared accountability.
Economic viability , Engagement , Infrastructure , Mesh networks , Network coverage , Sustainability , Wireless technology , Network deployment
[1] M. Miller, H. Pérez-Rosés, and J. Ryan, “The maximum degree and diameter-bounded subgraph in the mesh,” Discrete Applied Mathematics, vol. 160, no. 12, pp. 1782–1790, 2012.
[2] R. Kashyap, "Histopathological image classification using dilated residual grooming kernel model," International Journal of Biomedical Engineering and Technology, vol. 41, no. 3, p. 272, 2023. [Online]. Available: https://doi.org/10.1504/ijbet.2023.129819
[3] M. Miller and J. Sirán, “Moore graphs and beyond: a survey of the degree/diameter problem,” The Electronic Journal of Combinatorics, vol. 1000, pp. 1–92, 2013.
[4] V. Roy and S. Shukla, "Mth Order FIR Filtering for EEG Denoising Using Adaptive Recursive Least Squares Algorithm," 2015 International Conference on Computational Intelligence and Communication Networks (CICN), 2015, pp. 401-404, doi: 10.1109/CICN.2015.85.
[5] M. S. Akhtar, “Degree diameter problem on oxide networks,” Journal of Computational and Applied Mathematics, vol. 7, pp. 1–7, 2018.
[6] Roy, V., Shukla, S. Effective EEG Motion Artifacts Elimination Based on Comparative Interpolation Analysis. Wireless Pers Commun 97, 6441–6451 (2017). https://doi.org/10.1007/s11277-017-4846-3.
[7] H.P. Sahu and R. Kashyap, "FINE_DENSEIGANET: Automatic medical image classification in chest CT scan using Hybrid Deep Learning Framework," International Journal of Image and Graphics, 2023. [Online]. Available: 10.1142/s0219467825500044
[8] P. Holub, M. Miller, H. Pérez-Rosés, and J. Ryan, “Degree diameter problem on honeycomb networks,” Discrete Applied Mathematics, vol. 179, pp. 139–151, 2014.
[9] V. Parashar et al., "Aggregation-Based Dynamic Channel Bonding to Maximise the Performance of Wireless Local Area Networks (WLAN)," Wireless Communications and Mobile Computing, vol. 2022, Article ID 4464447, pp. 1–11, 2022. [Online]. Available: https://doi.org/10.1155/2022/4464447
[10] P. Holub and J. Ryan, “Degree diameter problem on triangular networks,” The Australasian Journal of Combinatorics, vol. 63, no. 3, pp. 333–345, 2015.
[11] J. Kotwal, R. Kashyap, and S. Pathan, "Agricultural plant diseases identification: From traditional approach to deep learning," Materials Today: Proceedings, vol. 80, pp. 344–356, 2023. [Online]. Available: https://doi.org/10.1016/j.matpr.2023.02.370
[12] A. Dekker, H. Pérez-Rosés, G. Pineda-Villavicencio, and P. Watters, “The maximum degree and diameter-bounded subgraph and its applications,” Journal of Mathematical Modelling and Algorithms, vol. 11, no. 3, pp. 249–268, 2002.
[13] D. Bavkar, R. Kashyap, and V. Khairnar, "Deep hybrid model with trained weights for multimodal sarcasm detection," Lecture Notes in Networks and Systems, pp. 179–194, 2023. [Online]. Available: 10.1007/978-981-99-5166-6_13
[14] M. S. Akhtar, U. Ali, G. Abbas, and M. Batool, “On the game chromatic number of splitting graphs of path and cycle,” Theoretical Computer Science, vol. 795, pp. 50–56, 2019.
[15] Piyush Kumar Shukla, Vandana Roy, Prashant Kumar Shukla, Anoop Kumar Chaturvedi, Aumreesh Kumar Saxena, Manish Maheshwari, Parashu Ram Pal, An Advanced EEG Motion Artifacts Eradication Algorithm, The Computer Journal, 2021;, bxab170, https://doi.org/10.1093/comjnl/bxab170.
[16] J.-B. Liu, J. Zhao, and Z. X. Zhu, “On the number of spanning trees and normalized Laplacian of linear octagonal quadrilateral networks,” International Journal of Quantum Chemistry, vol. 119, p. 25971, 2019.
[17] V. Roy and S. Shukla, "Image Denoising by Data Adaptive and Non-Data Adaptive Transform Domain Denoising Method Using EEG Signal," in Proceedings of All India Seminar on Biomedical Engineering 2012 (AISOBE 2012), V. Kumar and M. Bhatele (eds.), Lecture Notes in Bioengineering. Springer, India, 2013. https://doi.org/10.1007/978-81-322-0970-6_2.
[18] J.-B. Liu, J. Zhao, J. Min, and J. D. Cao, “On the hosoya index of graphs formed by a fractal graph,” Fractals-Complex Geometry Patterns and Scaling in Nature and Society, vol. 27, no. 3, Article ID 1950135, 2019.
[19] P. Kumar, A. Baliyan, K.R. Prasad, N. Sreekanth, P. Jawarkar, V. Roy, E.T. Amoatey, "Machine Learning Enabled Techniques for Protecting Wireless Sensor Networks by Estimating Attack Prevalence and Device Deployment Strategy for 5G Networks," Wireless Communications and Mobile Computing, vol. 2022, Article ID 5713092, pp. 1-15, 2022. https://doi.org/10.1155/2022/5713092.
[20] H. Ngarianto, E. S. Purwanto, and H. Andrean, "Cultivation of Flowerhorn Species in Search of Superior Quality Seeds using IoT and Open CV," Int. J. Emerg. Technol. Adv. Eng., vol. 12, no. 12, pp. 75–83, 2022.
[21] R. Yap, E. D. Rosario, and R. M. F. Munchua, "An FPGA Library Based Design of Variable CNN Weight Compression using Resizable K-Means Clustering," Int. J. Emerg. Technol. Adv. Eng., vol. 12, no. 12, pp. 84–93, 2022.
[22] M. Bathre and P. K. Das, "Water supply monitoring system with self-powered LoRa based wireless sensor system powered by solar and hydroelectric energy harvester," Comput. Stand. Interfaces, vol. 82, Art. no. 103630, 2022.
[23] R. K. Bhujade and S. Asthana, "An Extensive Comparative Analysis on Various Efficient Techniques for Image Super-Resolution," Int. J. Emerg. Technol. Adv. Eng., vol. 12, no. 11, pp. 153–158, 2022.
[24] S. J. Mohammed, M. J. M-Ridha, K. M. Abed, and A. A. M. Elgharbawy, "Removal of levofloxacin and ciprofloxacin from aqueous solutions and an economic evaluation using the electrocoagulation process," Int. J. Environ. Anal. Chem., vol. 103, no. 16, pp. 3801-3819, 2023.
