Workshop held as part of ACM SIGCOMM 2009We present SenseLess, a system that leverages the different energy consumption characteristics of sensors to maximise battery life in mobile-sensing applications. We use the less expensive sensors more often, thereby enabling us to use the more expensive sensors less frequently. In the context of location-aware services, experimental results indicate that for a typical indoor and outdoor walk, compared to a simple GPS-based system, our SenseLess system can reduce energy consumption by more than 58% when determining a user's location, while maintaining the fidelity of the sensed data. This extends the battery life of a typical handheld device from 9 hours to 22 hours.Postprin

Ben Abdesslem, Fehmi

Henderson, Tristan

Phillips, Andrew

English

St Andrews Research Repository

Less is More:Energy-Efficient Mobile Sensing with SenseLessFehmi Ben AbdesslemSchool of Computer ScienceUniversity of St AndrewsSt Andrews, Fife, UKfehmi@cs.st-andrews.ac.ukAndrew PhillipsSchool of Computer ScienceUniversity of St AndrewsSt Andrews, Fife, UKap267@st-andrews.ac.ukTristan HendersonSchool of Computer ScienceUniversity of St AndrewsSt Andrews, Fife, UKtristan@cs.st-andrews.ac.ukABSTRACTWe present SenseLess, a system that leverages the different energyconsumption characteristics of sensors to maximise battery life inmobile-sensing applications. We use the less expensive sensorsmore often, thereby enabling us to use the more expensive sensorsless frequently. In the context of location-aware services, experi-mental results indicate that for a typical indoor and outdoor walk,compared to a simple GPS-based system, our SenseLess systemcan reduce energy consumption by more than 58% when determin-ing a user’s location, while maintaining the fidelity of the senseddata. This extends the battery life of a typical handheld device from9 hours to 22 hours.1. INTRODUCTIONMobile phones now increasingly contain a number of sensors,which can be leveraged to create interesting new applications. Forinstance, accelerometers can be used to determine movement or ori-entation, which aids functionality of games or photography. GlobalPositioning Service (GPS) receivers can be used to enable location-aware services.The use of these sensors, however, can have a severe impacton battery life. To demonstrate these effects, we conducted anexperiment using a typical smartphone, the Nokia N95 8GB. Weused firmware version v30.0.018, with all default settings and brandnew1 1200 mAh batteries (Nokia BL-6F Li-Ion). Simple Pythonscripts were run to trigger a particular sensor continuously; for in-stance, the 802.11 radio was set to scan continuously, while themicrophone was set to record audio continuously. Using the NokiaEnergy Profiler v1.1 software, we then measured how long the bat-tery took to deplete when the sensor was continously triggered (allother sensors and radios, including the GSM/UMTS cellular radiowere switched off). Table 1 indicates that applications which usesensors have the potential to run down a mobile device’s batteryvery quickly.From this very simple experiment, we propose the following hy-pothesis: by choosing to use more energy-efficient sensors whereappropriate, it is possible to decrease the energy consumption ofmobile sensing applications. In other words, we can sense more (inthat the device batteries will last longer) by doing less (using theless power-hungry sensors). We thus propose SenseLess, a system1A brand new battery was used for each experiment (first cycleafter full charge) to ensure that battery age was not a factor.Copyright is held by the author/owner(s).MobiHeld’09, August 17, 2009, Barcelona, Spain.ACM 978-1-60558-444-7/09/08.Table 1: Energy consumption of different sensors. Each sensorwas run continuously on a Nokia N95 8GB smartphone untilthe battery was depleted.Sensor Approximatebattery life (hrs)Average powerconsumption (mW)Video camera 3.5 1258IEEE 802.11 6.7 661GPS (outdoors) 7.1 623GPS (indoors) 11.6 383Microphone 13.6 329Bluetooth 21.0 211Accelerometer 45.9 96All sensors turned off 170.6 26for handheld devices that uses sensors in this manner to save en-ergy. Our intent is for this to be a general system, but for now wefocus on one particular application: obtaining a user’s location inan energy-efficient manner.2. THE SENSELESS SYSTEMTable 1 shows that the accelerometer is the cheapest sensor interms of energy consumption. For this reason, SenseLess makesextensive use of the accelerometer to sense movements. We ar-gue that sensing more movements can allow us to use high-energysensors less frequently, thus reducing the overall energy consump-tion. For example, in a location-aware application, using sensorsfor localisation is unnecessary when the user is not moving. Theaccelerometer can thus be used to detect when the user is not mov-ing and then stop sensing to save energy.We conducted a simple experiment where 3 different users wereasked to keep a Nokia N95 8GB in their pocket, and to sit down nor-mally for 20 minutes while continuing their current task (namelywatching TV, attending a talk or participating in a meeting). Wealso asked 3 volunteers to walk normally in the streets for at least20 minutes. During the experiments, the data returned by the ac-celerometer were logged every 10 seconds into a file, along withtimestamps. The data collected during our experiment show thatthe Euclidean distance computed by SenseLess, between the cur-rent coordinates returned by the accelerometer and the last one, isclearly greater when the subjects are walking than when they aresitting down. To differentiate the 2 groups of curves, we have ex-perimentally chosen a threshold Euclidean distance value of 50.A user, however, might also briefly move while sitting down, andbriefly stop while walking (e.g., before crossing a road). Hence,the threshold might be reached, incurring false positives when de-tecting movements or stops. False positives when detecting move-ments are rare enough to be ignored and allow the sensors to acti-vate, even if the user is not moving. The reason behind this designchoice is two-fold. First, occasional triggering of the sensors maynot consume a significant amount of energy. Second, SenseLesshas been designed to be responsive and avoid missing user’s move-ments, which would affect location accuracy. On the contrary, falsepositives when detecting stops must be avoided, as they can poten-tially alter the location accuracy. Hence, SenseLess does not switchoff sensors when only one value is under the threshold. Instead,switching off the sensors requires 3 consecutive values under thethreshold, which corresponds to 30 seconds of perceived inactivity.SenseLess uses both GPS and 802.11 localisation methods, switch-ing between the two methods to use the most energy-efficient sen-sor. By default, the GPS is used. When no GPS signal is available,SenseLess starts scanning for 802.11 access points. This is mostlyto overcome the lack of GPS signals indoors. At the same time,the GPS receiver keeps searching for signals, in case the user goesoutside or comes into GPS coverage. Keeping the GPS switched onindoors consumes less energy than outdoors, as shown in Table 1,because when no signal is received, the position is not computedand hence the CPU load is reduced. Once a GPS signal is avail-able, SenseLess stops scanning for 802.11 access points and usesthe available GPS position instead. When available, preference isalways given to the GPS for two reasons. Firstly, the GPS sensoruses slightly less energy than scanning for 802.11 access points,and secondly because it is more accurate in general, especially inareas with a low density of access points.3. EXPERIMENTAL EVALUATIONWe implemented SenseLess on Nokia N95 8GB smartphones,using the Python PyS60 API. To test the system, a user placedone device in each of his trouser front pockets. One device ranthe SenseLess system, whereas the other was set to poll the GPSreceiver every 10 seconds (hereafter referred to as “GPS-only”).Over the course of one hour, the user conducted several activities:a 150m outdoor walk, followed by a 20 minute period of sittingdown, followed by a 350m walk. The user then entered a build-ing, walked 100m inside the building, exited and entered anotherbuilding, walking for another 100m and sat down for 25 minutes.This experiment corresponds to a typical walk, both indoors andoutdoors, and including stationary periods.Table 2 shows that during this experiment, SenseLess consumedless than half the energy consumed by the GPS-only system (58.8%less). Whereas SenseLess uses more CPU because of frequent ac-celerometer usage, it consumes less power. If a user keeps the samebehavior in average (walking approximately 700m per hour, stay-ing half of the time outdoors), the estimated battery life would bearound 22 hours, against only 9 hours for a GPS-only system, thatdoes not give positions when indoors.Table 2: Resource consumption of SenseLess compared toGPS-only sensing.GPS-only SenseLessAverage power consumption (mW) 454 118Average CPU load (%) 1.5 2.6Electric charge (mAh) 131 54Estimated battery life (h) 9.2 22.2To calculate the fidelity of the sensed data, we calculate the aver-age distance between the positions sensed by both systems at each10 second interval. When the user was not moving, SenseLessreported the last sensed position, hence this position was used tocompute the distance with the GPS-only positions when the userwas sitting down. The period of time the user was indoors was notincluded to compute the difference between positions. We find thatthe overall average difference between the two systems is 8m, withvalues ranging from 0.4m to 41m, and a median of 3m. This aver-age distance between the two tracks is within the range of the GPSaccuracy of our N95 devices. Indeed, the GPS accuracy varies ac-cording to various factors. The best GPS accuracy is around 10m,but since the GPS receiver was in trouser pockets in our experi-ment, we expect the accuracy to be worse. Two GPS coordinatescomputed from the same position with different devices can both beat least 10m away from the real position, and hence up to 20m awayfrom each other. Thus, it appears that the fidelity of our sensed lo-cation data is maintained.4. RELATEDWORKThe notion of switching between radio interfaces to save energyhas been examined before. Ionut et al.[2] propose EnLoc, a sys-tem that uses habitual activity of individuals to switch between802.11 and GPS localisation methods. The Context-for-Wirelesssystem [4] aims to save energy by switching between 802.11 andEDGE cellular interfaces depending on network availability andconditions. Ananthanarayanan and Stoica [1] also look at selectingdifferent network interfaces to save energy — in this case 802.11and Bluetooth.5. CONCLUSIONSANDONGOINGWORKWe have described SenseLess, a system for saving energy con-sumption in sensing applications for mobile phones. We presentedpreliminary results from an early version of the system which demon-strate that we are able to save energy in a localisation application.In particular, our localisation runs show an increase in battery lifefrom 9.2 hours using GPS to 22.2 hours using a combination of ac-celerometer, GPS and 802.11, with little change in the fidelity ofthe location data.We are now interested in extending SenseLess to other servicesthan localisation and, as part of the PVNets project2, including oursystem in an experimental testbed for conducting context-aware ex-perience sampling method [3] studies in mobile social networks.6. REFERENCES[1] G. Ananthanarayanan and I. Stoica. Blue-Fi: Enhancing Wi-Fiperformance using Bluetooth signals. In Proceedings of ACMMobiSys 2009, Krakow, Poland, June 2009.[2] I. Constandache, S. Gaonkar, M. Sayler, R. R. Choudhury, andL. Cox. EnLoc: Energy-efficient localization for mobilephones. In Proceedings of INFOCOM Mini Conference, Apr.2009.[3] S. S. Intille, J. Rondoni, C. Kukla, I. Ancona, and L. Bao. Acontext-aware experience sampling tool. In CHI ’03 extendedabstracts on Human factors in computing systems, pp972–973, Ft. Lauderdale, FL, USA, Apr. 2003.[4] A. Rahmati and L. Zhong. Context-for-Wireless:context-sensitive energy-efficient wireless data transfer. InProceedings of ACM MobiSys 2007, pp 165–178, San Juan,Puerto Rico, June 2007.2http://www.pvnets.org/

A context-aware experience sampling tool.

Blue-Fi: Enhancing Wi-Fi performance using Bluetooth signals.

Context-for-Wireless: context-sensitive energy-efﬁcient wireless data transfer.

EnLoc: Energy-efﬁcient localization for mobile phones.

Less is more: energy-efficient mobile sensing with SenseLess

Less is more: energy-efficient mobile sensing with SenseLess

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