3G v.s WIFI Radio Energy with YouTube downloads

Energy in Mobile Phone Data Transfers In 3G, there are three states –Idle –DCH (Dedicated Channel), do not share the channel with any other device. Used for high data rate and low delay when downloading a large file. –FACH (Forward Access Channel), share the channel with other devices. Use when only a low data rate is needed Power –DCH uses the most power –FACH is half of what DCH uses –Idle uses about 10% of FACH State Transition –Transition from idle to DCH takes about 4 seconds. This is delay and wasted power. –Transition from FACH to DCH does not have this wasted energy. One issue is how long of idle activity until switching tom DCH to FACH and from FACH to idle (Note that idle after the download is not the same as idle before download) WIFI –WIFI requires much more energy to associate with an access point that the transition to DCH does. –However, WIFI has an efficient power save mode, like the FACH, but much lower power. (I don't have numbers. Perhaps this can be a project)

YouTube via 3G and Wifi Youtube uses progressive download, not streaming Three phases –Initial download: file is downloaded but not played. Instead, cache is filled –Download and play –Play only Most likely, the 3G includes transitioning to DCH. This can be observed in the rmap up to full power ∙ It appears that the WIFI was already associated.

Playback from Local Storage Three options –Playback from flash storage –Playback from memory –Playback from on-chip cache (replay) Note that some energy is needed to move data from memory to on-chip cache. Once the data is on the chip, the power usage is low. Playback vs download –Since the radios are off for this playback, the energy usage is lower than the "play-only" phase of the progressive download. –On the other hand, the energy usage is not that much lower, especially in the wifi case. This shows that in mobile phones, wifi radio on uses much less than the CPU, display, etc. Total energy from playback –From memory: J. –From Flash J –From cache (replay) J –In all cases, about 1/3 of download form 3G, 1/2 of download from wifi

Impact of Temperature Erratic variation in energy. However, there is little change in temperature. Perhaps other changes in temperature could cause larger impacts. Then again, how would temperature impact energy usage. It could impact battery capacity, but should not impact energy usage. So why the variation?

Download and Then Play There is little different between progressive download and two-phase The power during 3G play-back in the two-phase is less than the power used in play-back only phase. This is because the radio is in FACH state during much of the playback-only phase However, playing from local storage uses less energy than the second phase of the two-phase download. This does not make sense

Fun with 3G, WIFI and Bluetooth Wireless wakeups revisited: energy management for voip over wi-fi smartphones, Agarwal et al, –Proposed an architecture to use the GSM radio to wake up the WiFi radio upon an incoming VoIP call to leverage the better quality and energy-efficiency ofWiFi while keeping its scanning costs low. Efficient and transparent dynamic content updates for mobile clients, Armstrong et al, –Application level modifications to reduce energy consumption for updates to dynamic web content. –Use a proxy to 1.only push new content when the portion of the web document of interest to the user is updated, 2.batch updates to avoid the overhead of repeated polling, and 3.use SMS on GSM to signal the selection of WiFi or GSM based on the transfer size for energy-efficient data transfer. Reducing the power consumption of wireless mobile devices with multiple radio interfaces, Pering, –Strategies to intelligently switch between WiFi and Bluetooth. Context-for-wireless: context-sensitive energy-efficient wireless data transfer, Rahmati et al –Show that intelligently switching between WiFi and GSM reduces energy consumption substantially as WiFi consumes less transmission power. However, in order to avoid the cost of unnecessary scanning in the face of poor WiFi availability, the authors design an algorithm that predicts WiFi availability, and the device scans forWiFi access points only in areas whereWiFi is available with high probability. Energy Consumption in Mobile Phones: A Measurement Study and Implications for Network Applications, Balasubramanian et al, 2009, –Schedule download to be in bulk, and hence save on the start-up and FACH energy. Note that these energies can be relatively large when small files are downloaded Many papers on adjusting timers and trying to predict when radios should be turned off.