TOWARDS GREEN WIFI NETWORKS: AN ML AND AI-BASED FRAMEWORK FOR ENERGY EFFICIENCY OPTIMIZATION
Keywords:
Energy Efficiency Optimization, Machine Learning In WiFi Networks, Artificial Intelligence For Energy Management, Predictive Maintenance For WiFi Devices, Sustainability In Wireless NetworksAbstract
The increasing demand for wireless connectivity has led to a significant rise in energy consumption by WiFi devices, necessitating the development of efficient energy management strategies. This paper presents a novel framework that leverages machine learning (ML) and artificial intelligence (AI) techniques to optimize the energy efficiency of WiFi devices without compromising network performance. The proposed approach utilizes predictive models to analyze historical data on device usage and network activity, allowing for the identification of energy-saving opportunities. In addition, AI-driven algorithms are used to adapt to changing environmental conditions and user behaviors, enabling real-time optimization of energy consumption. The framework additionally integrates predictive maintenance techniques to proactively identify and address energy inefficiencies and hardware issues. Experimental results show significant improvements in energy efficiency, with energy consumption reduced by up to 30% compared to traditional approaches. The findings emphasize the potential of ML and AI in improving the sustainability and cost-effectiveness of WiFi networks, opening up opportunities for future research and development in this field.
References
Cisco. (2020). Cisco Annual Internet Report (2018–2023) White Paper.
Pei, L., Liu, J., Guinness, R., Chen, Y., Kroger, T., Chen, R., & Chen, L. (2012). The IEEE 802.11 power saving mechanism: An experimental study. 2012 IEEE Wireless Communications and Networking Conference (WCNC), 1362-1367.
Mahadevan, P., Shah, A., & Bash, C. (2010). Reducing lifecycle energy consumption of network switches. 2010 Proceedings of the International Symposium on Sustainable Systems and Technology, 1-6.
Li, R., Zhao, Z., Zhou, X., Ding, G., Chen, Y., Wang, Z., & Zhang, H. (2017). Intelligent 5G: When cellular networks meet artificial intelligence. IEEE Wireless Communications, 24(5), 175-183.
Zhou, X., Zhao, Z., Li, R., Zhou, Y., Chen, T., Niu, Z., & Zhu, X. (2019). Intelligent wireless communications enabled by cognitive radio and machine learning. China Communications, 16(12), 1-15.
Tsao, S.-L., & Huang, C.-H. (2011). A survey of energy efficient MAC protocols for IEEE 802.11 WLAN. Computer Communications, 34(1), 54-67.
IEEE. (2016). IEEE Standard for Information technology—Telecommunications and information exchange between systems - Local and metropolitan area networks—Specific requirements - Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications. IEEE Std 802.11-2016 (Revision of IEEE Std 802.11-2012), 1-3534.
Lee, J.-R., Kwon, S.-W., & Cho, D.-H. (2010). An energy efficient power saving mechanism for multi-hop wireless networks. 2010 IEEE 72nd Vehicular Technology Conference - Fall, 1-5.
Rantala, E., Mäkelä, H., & Hämäläinen, T. (2008). Power efficiency optimization in IEEE 802.11 WLAN access points. 2008 IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications, 1-5.
Jang, H., Lim, K., & Yoo, S.-J. (2015). Energy-efficient scheduling based on delay-constrained data aggregation in wireless sensor networks. International Journal of Distributed Sensor Networks, 11(8), 645243.
García-Teodoro, P., Díaz-Verdejo, J., Maciá-Fernández, G., & Vázquez, E. (2009). Anomaly-based network intrusion detection: Techniques, systems and challenges. Computers & Security, 28(1-2), 18-28.
Balmau, O., Malhi, K., Nikolaidis, I., & Heurtefeux, K. (2019). Context-aware power management for IoT devices. IEEE Access, 7, 62734-62747.
Agarwal, Y., Savage, S., & Gupta, R. (2010). SleepServer: A software-only approach for reducing the energy consumption of PCs within enterprise environments. Proceedings of the 2010 USENIX Conference on USENIX Annual Technical Conference, 22-22.
Yau, K.-L. A., Qadir, J., Khoo, C., Ling, M. H., & Komisarczuk, P. (2017). A survey on reinforcement learning models and algorithms for traffic signal control. ACM Computing Surveys (CSUR), 50(3), 1-38.
Lane, N. D., Bhattacharya, S., Georgiev, P., Forlivesi, C., Jiao, L., Qendro, L., & Kawsar, F. (2016). DeepX: A software accelerator for low-power deep learning inference on mobile devices. 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), 1-12.
Bukhari, S. H. R., Rehmani, M. H., & Siraj, S. (2016). A survey of channel bonding for wireless networks and guidelines of channel bonding for futuristic cognitive radio sensor networks. IEEE Communications Surveys & Tutorials, 18(2), 924-948.
Zhang, W., Guo, W., Liu, X., Liu, Y., Zhou, J., Li, B., Lu, Q., & Yang, S. (2018). LSTM-based analysis of industrial IoT equipment. IEEE Access, 6, 23551-23560.
Schulz, P., Matthe, M., Klessig, H., Simsek, M., Fettweis, G., Ansari, J., Ashraf, S. A., Almeroth, B., Voigt, J., Riedel, I., Puschmann, A., Mitschele-Thiel, A., Müller, M., Elste, T., & Windisch, M. (2017). Latency critical IoT applications in 5G: Perspective on the design of radio interface and network architecture. IEEE Communications Magazine, 55(2), 70-78.
Florwick, J., Whiteaker, J., Amrod, A. C., & Woodhams, J. (2013). Wireless LAN design guide for high density client environments in higher education. Cisco Systems, Inc.
Ku, M.-L., Li, W., Chen, Y., & Liu, K. J. R. (2016). Advances in energy harvesting communications: Past, present, and future challenges. IEEE Communications Surveys & Tutorials, 18(2), 1384-1412.
Halperin, D., Greenstein, B., Sheth, A., & Wetherall, D. (2010). Demystifying 802.11n power consumption. Proceedings of the 2010 International Conference on Power Aware Computing and Systems, 1-5.
Lee, J., Kim, H., Park, G.-L., & Kang, M. (2014). Energy-efficient base station operation using dynamic scheduling with optimization of cooling system. IEEE Access, 2, 1364-1374.
Zeng, Y., Zhang, R., & Lim, T. J. (2016). Wireless communications with unmanned aerial vehicles: Opportunities and challenges. IEEE Communications Magazine, 54(5), 36-42.
Li, S., Da Xu, L., & Zhao, S. (2018). 5G internet of things: A survey. Journal of Industrial Information Integration, 10, 1-9.
Bkassiny, M., Li, Y., & Jayaweera, S. K. (2013). A survey on machine-learning techniques in cognitive radios. IEEE Communications Surveys & Tutorials, 15(3), 1136-1159.
Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345-1359.
Wen, Z., Wang, Y., Gao, X., Wan, L., & Qiu, T. (2020). A survey of 5G energy efficiency optimization techniques. International Journal of Distributed Sensor Networks, 16(3), 1550147720913403.
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2), 1-19.
Ramírez-Gallego, S., García, S., Mouriño-Talín, H., Martínez-Rego, D., Bolón-Canedo, V., Alonso-Betanzos, A., Benítez, J. M., & Herrera, F. (2018). Data discretization: taxonomy and big data challenge. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(1), e1230.
Ding, N., Wagner, D., Chen, X., Pathak, A., Hu, Y. C., & Rice, A. (2013). Characterizing and modeling the impact of wireless signal strength on smartphone battery drain. ACM SIGMETRICS Performance Evaluation Review, 41(1), 29-40.
Zhang, R., Wang, M., Shen, X., & Xie, L. L. (2016). Probabilistic analysis on QoS provisioning for internet of things in LTE-A heterogeneous networks with partial spectrum usage. IEEE Internet of Things Journal, 3(3), 354-365.
Zhao, Y., Hu, L., Jia, Z., & Ding, X. (2019). Energy-efficient resource allocation for heterogeneous wireless network with multi-factor user association. IEEE Access, 7, 124387-124399.
Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., ... & Zhao, S. (2019). Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977.
