Abstract
In this paper, we design a power-aware distributed access point scheduling algorithm, PowerNap, to enhance power conservation of co-existing multiple access points (APs) each having multiple clients in the same wireless vicinity. This consequently addresses low channel utilization, degraded throughput, and unfairness problems of Wi-Fi networks in an energy-efficient way. PowerNap schedules transmission periods of APs according to their traffic loads to ensure fair access of the medium from their respective clients’ perspective. It supports dynamic rescheduling of AP transmission periods to aid client mobility and traffic fluctuations. The scheduling also ensures that no two APs, in a shared environment, wake up their clients at exactly the same time, decreasing data packet collisions and thus increasing network throughput and energy-efficiency. PowerNap achieves decentralization by exploiting single-hop neighborhood information (e.g., traffic loads) only, and thus, it is scalable. Performance evaluations, carried out in ns-3, depict that the effectiveness of the proposed PowerNap algorithm surpasses the state-of-the-art approaches in terms of energy consumption, network throughput, and fairness.
Highlights
Wi-Fi-enabled smart devices run numerous applications which require added processing power, untethered Internet access and battery-life [1]
A PowerNap-enabled access points (APs) computes its weighted fair share with reference to the neighboring AP’s traffic loads to allocate transmission periods to the clients, and it avoids unfairness
When we look at the transmission ring produced by APE in Fig. 2b, we see that APA
Summary
Wi-Fi-enabled smart devices run numerous applications which require added processing power, untethered Internet access and battery-life [1]. SleepWell [12] proposes an algorithm to schedule multiple APs in a neighborhood so that they do not overlap It fails to detect hidden terminals which add unfairness and low channel utilization. A PowerNap-enabled AP computes its weighted fair share with reference to the neighboring (e.g., one hop neighborhood) AP’s traffic loads (e.g., weighted fair share is proportional to traffic load) to allocate transmission periods to the clients, and it avoids unfairness. The novelty of our proposed PowerNap algorithm stems from the following differences between SleepWell and PowerNap. Firstly, we give weighted fair share from the very beginning, i.e., we estimate traffic loads of all APs in a distributed manner and distribute the transmission intervals to the contending APs according to this weighted estimation. 4.1 Initialization Initially, every AP calculates its initial workload by exploiting neighborhood information and network characteristics, e.g., channel characteristics, AP’s own capacity [33], etc. and traffic demands of associated clients
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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 callMore From: EURASIP Journal on Wireless Communications and Networking
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.
