SiFi: exploiting VoIP silence for WiFi energy savings insmart phones

Since one-third of a smart phone's battery energy is consumed by its WiFi interface, it is critical to switch the WiFi radio from its active or Constantly Awake Mode (CAM), which draws high power (726mW with screen off), to its sleep or Power Save Mode (PSM), which consumes little power (36mW). Applications like VoIP do not perform well under PSM mode however, due to their real-time nature, so the energy footprint is quite high. The challenge is to save energy while not affecting performance. In this paper we present SiFi: Silence prediction based WiFi energy adaptation. SiFi examines audio streams from phone calls and tracks when silence periods start and stop. This data is stored in a prediction model. Using this historical data, we predict the length of future silence periods and place the WiFi radio to sleep during these periods. We implement the design on an Android Smart phone and acheive 40% energy savings while maintaining high voice fidelity.

[1]  Ahmad Rahmati,et al.  Users and Batteries: Interactions and Adaptive Energy Management in Mobile Systems , 2007, UbiComp.

[2]  Robert G. Cole,et al.  Voice over IP performance monitoring , 2001, CCRV.

[3]  Rafael Estepa,et al.  A new approach for VoIP traffic characterization , 2004, IEEE Communications Letters.

[4]  Mark Handley,et al.  SIP: Session Initiation Protocol , 1999, RFC.

[5]  Arun Venkataramani,et al.  Interactive wifi connectivity for moving vehicles , 2008, SIGCOMM '08.

[6]  Lixin Gao,et al.  Towards energy efficient VoIP over wireless LANs , 2008, MobiHoc '08.

[7]  Lin Zhong,et al.  Micro power management of active 802.11 interfaces , 2008, MobiSys '08.

[8]  Ahmad Rahmati,et al.  Pervasive and Mobile Computing , 2009 .

[9]  Chris Chatfield,et al.  The Analysis of Time Series: An Introduction , 1981 .

[10]  Zhigang Liu,et al.  The Jigsaw continuous sensing engine for mobile phone applications , 2010, SenSys '10.

[11]  Chris Chatfield,et al.  The Analysis of Time Series : An Introduction, Sixth Edition , 2003 .

[12]  Yi Wang,et al.  A framework of energy efficient mobile sensing for automatic user state recognition , 2009, MobiSys '09.

[13]  Mirco Musolesi,et al.  Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application , 2008, SenSys '08.

[14]  Zhigang Liu,et al.  Darwin phones: the evolution of sensing and inference on mobile phones , 2010, MobiSys '10.

[15]  Aaron E. Rosenberg,et al.  An improved endpoint detector for isolated word recognition , 1981 .

[16]  Konstantina Papagiannaki,et al.  Catnap: exploiting high bandwidth wireless interfaces to save energy for mobile devices , 2010, MobiSys '10.

[17]  Songqing Chen,et al.  PSM-throttling: Minimizing Energy Consumption for Bulk Data Communications in WLANs , 2007, 2007 IEEE International Conference on Network Protocols.

[18]  Donald F. Towsley,et al.  Estimation and removal of clock skew from network delay measurements , 1999, IEEE INFOCOM '99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No.99CH36320).

[19]  Ahmad Rahmati,et al.  Context-for-wireless: context-sensitive energy-efficient wireless data transfer , 2007, MobiSys '07.

[20]  Rajesh K. Gupta,et al.  CoolSpots: reducing the power consumption of wireless mobile devices with multiple radio interfaces , 2006, MobiSys '06.

[21]  Jason Flinn,et al.  Self-Tuning Wireless Network Power Management , 2003, MobiCom '03.

[22]  Huaiyu Zhu.  On Information and Sufficiency , 1997 .

[23]  Romit Roy Choudhury,et al.  SurroundSense: mobile phone localization via ambience fingerprinting , 2009, MobiCom '09.

[24]  Ramachandran Ramjee,et al.  NAPman: network-assisted power management for wifi devices , 2010, MobiSys '10.

[25]  Hyun-Ho Choi,et al.  Hybrid Power Saving Mechanism for VoIP Services with Silence Suppression in IEEE 802.16e Systems , 2007, IEEE Communications Letters.

[26]  F. Vagliani,et al.  Digital Dynamic Speech Detectors , 1978, IEEE Trans. Commun..

[27]  Masakiyo Fujimoto,et al.  A voice activity detection based on the adaptive integration of multiple speech features and a signal decision scheme , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[28]  Hari Balakrishnan,et al.  Minimizing Energy for Wireless Web Access with Bounded Slowdown , 2002, MobiCom '02.