Abstract
In the year 2015, the United Nation has adopted 17 Sustainable Development Goals (SDG) to ending poverty, saving the planet and bringing prosperity for all by the year 2030. Universal broadband connectivity is considered as one significant contributing factor to achieving these goals. There is a close correlation between the national Gross Domestic Product (GDP) and broadband availability. Broadband access has great potential in opening up work opportunities and boosting income for poverty-stricken people in the remote and underdeveloped countries. It is estimated that there are still about 1.2 billion people who are still not connected to the Internet. Broadband requirements from this segment along with rising broadband demand from urban consumerism have put pressure on the available frequency spectrum. The optical fiber communication has an abundance bandwidth. The Internet Service Provider (ISP) cannot provide the optical network in remote areas, due to cost constraints, climate, weather, and high investment costs. Hence its wireless counterpart WiMAX has short set up time and low deployment cost. Hence universal broadband connectivity can be achieved by Hybrid Optical WiMAX networks. Further, the abovementioned remote areas suffer from low infrastructure and unreliable power supply. In this paper, we have used alternative sources of energy to mitigate the problem of unreliable electricity supply, particularly in the areas. The proposed machine learning-based renewable energy prediction depends on the geographical location of the network node. The predicted renewable energy can be used as a source for serving traffic demands. The traffic aggregation methods were used to minimize network resource consumption. The unpredictability in harnessing renewable energy is mitigated by using backup nonrenewable energy. The simulation results show that the proposed algorithm reduces nonrenewable energy consumption.
Supported by organization National Institute of Technology, Durgapur.
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Naik, D., Sireesha, P., De, T. (2021). Machine Learning-Based Green and Energy Efficient Traffic Grooming Architecture for Next Generation Cellular Networks. In: Suma, V., Bouhmala, N., Wang, H. (eds) Evolutionary Computing and Mobile Sustainable Networks. Lecture Notes on Data Engineering and Communications Technologies, vol 53. Springer, Singapore. https://doi.org/10.1007/978-981-15-5258-8_26
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DOI: https://doi.org/10.1007/978-981-15-5258-8_26
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