Energy-Aware WiFi Network Selection via Forecasting Energy Consumption

Covering a wide area by large number of WiFi networks is anticipated to become very popular with Internet-of-things (IoT) and initiatives such as smart cities. Such network configuration normally realized through deploying access points (APs) overlapped coverage. However, the imbalanced traffic load distribution among different APs affects energy consumption device if it associated loaded AP. This research work aims at predicting communication-related that shall be consumed transferred some amount data certain selected In this paper, forecast proposed obtained using an algorithm supported mathematical model. Consequently, can automatically select best (best AP) connect in order minimize consumption. The experimentally validated realistic lab setting. observed performance indicates provide accurate transceiver sending via specific