Support Vector Machines for Scheduled Harvesting of Wi-Fi Signals
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Institute of Electrical and Electronics Engineers Inc.
Abstract
This letter investigates the incorporation of support vector machines (SVMs) to predict the time and location of maximum Wi-Fi coverage for energy harvesting. Such incorporation of machine learning, along with radio frequency energy harvesting systems, improves the proposed rectenna efficiency especially when scavenging wireless routers and access points. Experiments showed that the proposed framework that uses SVM was able to predict the time and location of maximum Wi-Fi coverage. Such prediction constitutes the basis of a scheduling mechanism for directed harvesting of Wi-Fi signals. © 2002-2011 IEEE.
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Keywords
Internet of things (iot), Machine learning, Radio frequency (rf) energy harvesting, Rectenna, Sensors, Support vector machines, Forecasting, Internet of things, Learning systems, Radio waves, Rectennas, Routers, Wireless local area networks (wlan), Access points, Radio-frequency energy harvesting, Scheduling mechanism, Support vector machine (svms), Wi-fi signals, Wireless routers, Energy harvesting