Abstract

Being cheap and easy to deploy, dense WLANs are becoming the most popular solution to providing Internet access in locations where the population of users is large, such as on campuses, large enterprises, etc. The large density of access points (APs) comes from the need to have enough capacity to carry the traffic generated at peak hours although, in these scenarios, traffic varies a lot on a daily, weekly, or seasonal basis. During low or no traffic periods, APs are underutilized, even if they are consuming energy almost in the same amount as if they were fully loaded. Promising solutions to reducing this form of energy waste consist of activating only the number of APs that is strictly needed to carry the actual traffic; in other words, to make capacity dynamically adaptive through resource-on-demand (RoD) strategies. In this paper, we investigate the case of a portion of the dense WLAN on our campus. Through real trace analysis, we investigate users’ behavior in accessing the WLAN and formulate a stochastic characterization of it. We propose a simple model that describes RoD strategies and use it to study the system performance that is evaluated in terms of AP activity and inactivity periods, AP switching frequency, and energy saving. Finally, we present some results obtained by experimenting with RoD strategies in a portion of the WLAN. Our results show that RoD strategies for dense WLANs are feasible and effective in trading-off the opposite needs to save some energy and to guarantee a smooth network operation and high quality of service.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.