Intelligent Large-Scale AP Control with Remarkable Energy Saving in Campus WiFi System

Full WiFi coverage is more and more prevalent in many places such as university, enterprise, big mall, etc. To achieve full WiFi coverage in a wide area is very costly. Not only extensive AP deployments are expensive, to operate and maintain such large-scale APs every day can also cost much, e.g., the huge power consumption. In this paper, we collect large-scale AP status data in our campus WiFi system, which contains over 8,000 APs and serves about 40,000 active end-users in the area of 3.0925 km². After conducting empirical studies on AP loads, we find Idle Phenomenon prevails throughout the trace. A large portion of APs are running without any user association, which will inevitably lead to unnecessary energy consumption. Inspired by this, we propose an intelligent large-scale AP control scheme, named as ACE (i.e., AP Control with Energy saving), to dynamically control large-scale APs (On or Off for energy saving meanwhile without loss of WiFi coverage. In ACE, the load of each AP is predicted first by the random forest algorithm, and those APs whose idle durations last for more than the length of the pre-defined sliding window will be turned off. We conduct extensive trace-driven simulations to demonstrate the efficiency of the ACE scheme; specifically, more than 70% of power energy can be saved with over 92 % of user WiFi coverage guaranteed in average.