METIS: Exploring mobile phone sensing offloading for efficiently supportingsocial sensing applications

Kiran K. Rachuri†, Christos Efstratiou†, Ilias Leontiadis†, Cecilia Mascolo†, Peter J. Rentfrow‡†Computer Laboratory, ‡Department of Psychology, University of Cambridge, UK

Abstract—Mobile phones play a pivotal role in supportingubiquitous and unobtrusive sensing of human activities. How-ever, maintaining a highly accurate record of a user’s behaviorthroughout the day imposes significant energy demands onthe phone’s battery. In this paper, we present the design,implementation, and evaluation of METIS: an adaptive mobilesensing platform that efficiently supports social sensing applica-tions. The platform implements a novel sensor task distributionscheme that dynamically decides whether to perform sensingon the phone or in the infrastructure, considering the energyconsumption, accuracy, and mobility patterns of the user.By comparing the sensing distribution scheme with sensingperformed solely on the phone or exclusively on the fixedremote sensors, we show, through benchmarks using realtraces, that the opportunistic sensing distribution achieves over60% and 40% energy savings, respectively. This is confirmedthrough a real world deployment in an office environmentfor over a month: we developed a social application overour frameworks, that is able to infer the collaborations andmeetings of the users. In this setting the system preserves over35% more battery life over pure phone sensing.

I. INTRODUCTION

The proliferation of smartphones with sensing capabilitieshave created an opportunity to design systems that capturevast amounts of information about people’s social behavior.By leveraging the device’s sensing and communication capa-bilities, applications can capture an accurate depiction of theuser’s social context, which enables the design of novel ap-plications that can enhance the user experience [2], improveproductivity [10], or facilitates new business opportunitiessuch as targeted advertisements [8]. It is expected that thenext generation of mobile applications will use continuoussensing of social context at an extremely fine granularity.

A major challenge in achieving accurate social sensingis the significant impact that continuous sensing has on thephone’s battery life. The detection of social context throughmobile phone sensing, typically leverages a wide rangeof sensing modalities. Systems such as CenceMe [2], orEmotionsense [12], utilize a combination of accelerometer,Bluetooth, location, and microphone, in order to characterizea particular social situation. The significant energy cost ofcollecting such information, reduces the phone’s battery life,and hinders the wider adoption of such applications. Existingefforts to minimize the energy impact rely primarily onadaptive sensing techniques, trading off energy for accuracywith the aim of reducing unnecessary sensor sampling onthe device [9], [12]. Although such techniques have shownimprovements in terms of energy consumption, there is stilla need for more efficient solutions before continuous sensingapplications can be widely accepted by everyday users.

In this work, we introduce a novel approach that can offersignificantly bigger reductions in energy cost without com-promising on the accuracy, by opportunistically offloadingsensing to fixed sensors embedded in the environment. Mostmodern buildings are instrumented with a variety of sensors,such as RFID access control systems, Passive Infraredsensors, light sensors, etc. Intuitively, if a mobile applicationcan take advantage of such sensing infrastructures, it couldat times suspend local sensing, by leveraging remote sensors.For example, relying on a building’s access control system,a mobile application can decide to suspend any localizationmechanisms on the phone while the user remains in thesame building or even room. The feasibility of this approachand the massive energy gains that can be achieved havesignificant implications both for the design of future mobileapplications, and the deployment of sensing infrastructureswithin smart-buildings. Indeed, such approach imposes astrong argument for the need of sensing infrastructures thatsupport open access by third party systems, enabling a widerange of mobile and pervasive applications.

In this work, we explore the feasibility of efficient sensingoffloading by considering the requirements of social sensingapplications operating within a smart-building environment.Through an experimental study within our research institu-tion we demonstrate that the benefits of sensing offloadingcan only be achieved by considering the potential gainof offloading a particular sensing task versus the potentialenergy cost incurred primarily due to network communica-tion. Estimating both metrics can depend on a number ofparameters and most significantly the behavior of the userand their peers. In this paper, we present the design, im-plementation, and evaluation of METIS, a sensing platformthat implements a novel adaptive sensing distribution schemethat automatically distributes the sensing tasks between thelocal phone and sensing infrastructure sensors in order tosupport accurate continuous sensing of social activities. Theproposed scheme considers various parameters such as themobility pattern of the user, duty cycling interval, cost ofsensing to determine whether sensing offload can result inenergy gain at any given situation. We show through bench-marks using real traces that the sensing distribution schemeachieves over 60% and 40% energy savings compared tostatic scenarios where only phone-based sensing is used, andonly remote sensors are used, respectively.

Finally, we evaluate the system through a real deploymentwith 11 users for a month in a working environment usingWorkSense, a social application that utilizes METIS andaims to raise awareness and improve visibility of social

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2013 IEEE International Conference on Pervasive Computing and Communications (PerCom), San Diego (18--22 March 2013)

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interactions at the workplace, by tracking formal and in-formal meetings during daily routines, and inferring howsocial interactions may impact the user’s performance. Thedeployment shows that the application is able to infer theeffect of various interaction and social patterns on the workof the users. Furthermore, we show that the system extendsbattery life by more than 35% compared to when no sensingoffloading to the infrastructure is used.

II. SAVING ENERGY THROUGH SENSING OFFLOADING

The idea of sensing offloading is built on the vision ofmobile phone users living in an environment instrumentedwith a range of sensors that can be accessed over the inter-net. Within such environment certain pieces of informationcan potentially be sensed through either the user’s mobiledevice or a sensor that is embedded in the environment.For example, detecting if a conversation is taking placein a room, can happen either through the mobile phone’smicrophone or a microphone in the room, both augmentedwith the necessary conversation detection software. Withinthis setting we attempt to explore if sensing offloading canbe used to reduce the energy consumption of continuoussensing on mobile phones.

In order to understand the requirements of sensing of-floading, and to help us frame our hypothesis, we conductedan exploratory deployment within our research institution.The primary objective of the deployment was to help usidentify the conditions under which offloading could reducethe energy cost on the mobile device, and those whereoffloading would lead to higher energy consumption thanlocal sensing. Furthermore, we tried to explore the feasibilityof designing a system that can adapt the sensing behavioron the mobile device, selecting the most appropriate actionat any given situation.

The focus of the study was to explore the support forsocial sensing applications. To that end we identified twokey sensing modalities: location / co-location sensing, andconversation detection. The experiment was conducted in aresearch institution involving 10 participants, and 10 officesinstrumented with sensors, and lasted one week. Duringthe deployment we collected traces about sensor data bothfrom the mobile devices carried by the participants, and thesensors deployed in the environment. No offloading was usedin this study. Using these traces we were able to re-constructthe behavior of the system when sensing is offloaded toremote sensors and compare the results with sensing takingplace solely on the phone.

A. Experimental Deployment

We deployed a logging system that was able to collectsensing traces from mobile phones carried by the partici-pants, and sensors deployed in the office environment ofour research institution. Both systems were able to detectco-location, location, and conversations of the users.

B. Mobile Phone Sensing

We designed an Android application that was able to per-form indoor localization, co-location detection (both usingBluetooth-based localization, utilizing Bluetooth anchors inthe environment), and conversation detection (using the mi-crophone). The application gathers data from accelerometer,Bluetooth, and microphone sensors. The conversation recog-nition module is based on that used in the EmotionSensesystem [12].

C. Sensing Infrastructure

In our deployment we aimed at exploiting the exist-ing sensing infrastructure in the environment. As part ofprevious sensing experiments, within our research institu-tion [4], two sensing infrastructures were already available:indoor localization, and room occupancy. In particular Nokia6210 Navigator phones were tasked to act as Bluetoothanchors and assist in the localization of mobile phones.The infrastructure includes 12 such Bluetooth anchor pointscovering a space of 10 offices. Furthermore, each Nokianode periodically scans for Bluetooth devices in proximityusing the lightblue module for Python for S60 (PyS60). Inorder to offer additional support for conversation detectionwe extended the sensing module on the Nokia deviceswith conversation sensing functionality (using the samescheme [12]). To capture accurate room occupancy data,a network of imote2 sensors had already been deployedaround the office spaces (13 nodes covering 10 offices). Thesensors are attached to desks and are able to detect whena particular desk is occupied. The desk occupancy statusis inferred by detecting vibration patterns using the 3-axisaccelerometer sensors embedded in the node. Each of thenodes periodically sends the current state (i.e., whether thedesk is occupied or not) to the root node that is connectedto a server. The sensing infrastructure is interfaced with theInternet through an sMap [3] type backend.

D. Benchmarks

In order to assess the effectiveness of sensing offloadingwe used the collected traces to investigate the performanceusing the following schemes.• Never Offload. This scheme resembles the typical

behavior of a mobile application where sensing reliessolely on the local phone sensors.

• Always Offload. In this scheme the mobile phoneoffloads sensing every time it is in an environmentwhere appropriate sensors are available.

The benchmarks consider two sensing modalities: indoorlocalization using Bluetooth scanning, and conversation de-tection using microphones. For the always offload scheme,desk occupancy sensors are used to suspend the Bluetoothscanning on the phone when the user is at his desk, andmicrophone sensors in the environment are used to offloadthe conversation detection.

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Figure 1. Energy per hour for varyingsensing cost.

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Figure 2. Energy per hour for loca-tion detection vs. sampling interval.

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Figure 3. CDF of time spentfor each location visit.

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Figure 4. Energy per hour for locationdetection with varying mobility patterns.

In our benchmarks we estimate the energy cost of per-forming a sensing task locally on the phone, and the costof communication between the phone and the infrastruc-ture when sensing is performed on the infrastructure. Theestimation of local sensing is based on the power con-sumption values reported by [5], [15]. The estimation ofnetwork traffic includes the baseline cost of keeping theWi-Fi interface on and the average cost of communicationper byte as it is estimated by [13]. We note that in arealistic setting, users typically enable Wi-Fi for their ownpurposes, which is further explored in Section IV. Thecommunication traffic between the infrastructure and themobile device depends on the number of events that aredetected by the deployed sensors, and therefore depends onthe actual behavior of the participants in the instrumentedspaces. Furthermore, in order to understand how these valuesaffect the performance of offloading, we used the same tracesto simulate scenarios by modifying certain parameters suchas sensing cost or sampling rate. The measurements reportthe average energy consumption across all the participantsfor the entire duration of the experiment.

E. Lessons LearntWhich Sensing Tasks to Offload. We identified two

key parameters in our scenarios that affect the energycost of sensing: the sampling rate, and the energy cost ofsensing and processing data from a particular sensor. In ourdeployment we had two modalities that can be consideredas mid-cost and high-cost respectively in terms of energy.Based on works such as [5], [15], a low-cost sensor suchas the accelerometer, for example, consumes about 30mW,the Bluetooth scanning consumes about 160mW, and anexpensive sensor such as GPS consumes around 430mW.In order to generalize from our traces, we simulated theperformance of the two schemes considering a range ofsensor sampling costs. Figure 1, as expected, shows that forlow cost sensing, offloading does not lead to energy gain,as the cost of network communication outweighs the smallbenefit of suspending local sensing.

The varying sampling rate has a similar effect. Highersampling rate incurs more energy cost on the device, how-ever, it may increase the number of events reported by theinfrastructure (fewer missed events). We analyzed the impact

of sampling rate by sub-sampling our original dataset. Asillustrated in Figure 2 the increase in sampling interval(decrease in sampling rate) reduces the sampling cost fasterthan the network cost for offloading. The figure demonstratesthe overall cost for Bluetooth scanning with increasingsampling interval, as shown, at low sampling rates, localsensing can in fact perform better than offloading.

The Impact of Mobility. Apart from the parameters af-fecting the cost of local sensing, efficient offloading dependson the varying cost of network communication that includesthe cost of “hand-over” (subscribe / unsubscribe to a sensor),and the cost of receiving events that are detected by theinfrastructure. Both these costs depend on the behavior ofthe users in the instrumented spaces. We explored the impactof the participants’ mobility patterns on the cost of sensingoffloading. In our traces, users spent 64% of their timeinside their own office and 21% of their time in other officeswith sensing capabilities. For those times, where offloadingopportunities were available, we analyzed the average timethat people spent when they visited a particular location.Figure 3 shows a CDF of the time each user spent in eachvisit. We observe a high number of short visits (50% ofthem last less than 20 minutes), which can be the causeof increased network traffic due to hand-overs, moreover,there is a possibility of sensing events to be either missedor wrong (reported by the wrong environment).

We investigated the impact of user mobility looking atscenarios with different average times that people spent ina given location. We used the original traces collected andmodified the amount of time that each user spent in eachroom. As illustrated in Figure 4, the results demonstratehow high mobility can significantly increase the cost ofsensing offloading. Essentially, for a given visit to a location,offloading can only deliver positive results if an offloadingscheme can predict the possible time that a user will spendin that location, and ensure that the duration is enough tooffer an energy gain.

The results clearly suggest that neither pure phone sensingnor exclusive remote sensing is an optimal solution for allthe scenarios, and a dynamic sensing offloading scheme thatconsiders the sensing parameters and the user’s mobility isrequired for achieving an optimal performance.

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Mobile Phone

METIS

Phone Sensors

Network Interface

Sensing Infrastructure

Access Point

Social Sensing Application

Remote SensingLocal Sensing

Sensing Task Distribution

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Figure 5. Architecture of the METIS system.

III. THE METIS SYSTEM

Motivated by the results of the exploratory deployment,we designed the METIS system. METIS is a mobile phoneservice that offers efficient continuous sensing for mobilesocial applications by leveraging both phone and infrastruc-ture sensing. It provides an abstraction over the sensingmodalities required by typical social sensing applications,such as location, co-location, and conversation detectionand offers a framework for the incorporation of additionalsensing modalities when needed (Figure 5). The operationof METIS includes the discovery of sensing devices thatare available in the immediate environment of the mobilephone user, the identification of devices that could be usedfor offloading, and the decision to perform such offloading inorder to maintain overall energy efficiency. If such offloadingis not considered beneficial METIS falls back to local sens-ing utilizing the resources of the mobile device. The primarychallenges and the main focus of this work are related tothe utilization of remote sensors in order to improve sensingperformance, which we address in the following subsections.

A. Interaction with Sensing Infrastructure

One of the key motivations for the design of METIS isthe emergence of a range of Web-based architectures thatallow interaction with the sensing infrastructure over IPnetworks. We decided to assume the presence of sensinginfrastructures that follow the same architectural principlesthat are adopted by such architectures. Systems such asSenseWeb [7] identify two key elements in their architecture:the presence of a rendezvous point, which allows a clientto query the infrastructure about available sensing resourcesand their capabilities, and the support for a resource commu-nication protocol that enables clients to interact with specificsensing resources. The operation of METIS imposes thefollowing two requirements on the sensing infrastructure:(i) the specification of the physical location of a sensingresource as reported by the rendezvous point, and (ii) thesupport for an asynchronous publish-subscribe interface forcommunication with a sensing resource. Both of theserequirements are supported by most common Web-based

<?xml version="1.0" encoding="UTF-8"?><mt:METIS xmlns:mt="http://our.domain.org/metisML"><!-- ............ --><mt:SensorNetwork><mt:sMAPURI>http://our.domain.org/</mt:sMAPURI><mt:SensorList><mt:SensorNode><mt:SensorId>BT_D11_R01</mt:SensorId><mt:SensorType>BluetoothScanner</mt:SensorType><mt:Location><mt:Label type="office">R01</mt:Label>

</mt:Location></mt:SensorNode><mt:SensorNode><mt:SensorId>ACC_D1_R01</mt:SensorId><mt:SensorType>DeskUseDetector</mt:SensorType><!--..........-->

</mt:SensorList></mt:SensorNetwork></mt:METIS>

Figure 6. Sample sensing infrastructure manifest obtained by the METISsystem from a service provider.

sensing architectures. In the design of METIS we adoptthe sMAP [3] communication protocol for interaction withspecific sensing resources.

The information that can be retrieved through the ren-dezvous point plays a key role in the operation of METIS.The expectation is that METIS can identify the physicallocation of sensor points. In the design of METIS we targetindoor environments with the aim of taking advantage ofcommon sensing technologies that can be found in smarthomes or office buildings. To that end, we define a minimalXML schema of the sensing infrastructure that incorporatesinformation about the physical location of sensor pointswithin a building (Figure 6). Although the format of theschema is designed to meet our needs, the same informationcan be easily extracted by standard-based schemata such asSensorML. METIS can be trivially extended with additionalschema parsers to support multiple infrastructure interfaces.

Sensor Mapping. One of the primary functions of METISis the association of social sensing modalities with possibleremote sensors that can be used for offloading. METISenables this association with the incorporation of sensor-specific drivers in the form of plug-ins. The Sensor Map-ping component acts as a repository of sensing plug-ins,triggering them on demand when a particular sensor deviceis within the proximity of the user. Each plug-in maps aspecific high-level social sensing task to a combination ofsubscriptions to certain sensor nodes in the environment.Plug-ins that have been implemented for the METIS systeminclude: 1) If real-time room occupancy information isavailable, subscribe to receive events about the current room,and switch off the location scanning when the number ofpeople in that room has not changed. 2) If desk occupancyinformation is available, subscribe to receive notificationsabout the current desk, and report current activity as “sitting”without using the accelerometer. 3) If noise level detection isavailable in the room, subscribe to receive notifications aboutchanging noise levels, and adjust conversation detection onthe phone when not needed.

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B. Sensing OffloadingBy analyzing the results of the exploratory study we were

in a position to identify the parameters that can affect theenergy trade-offs when deciding to perform sensing offload-ing to remote sensors. Specifically, the decision is based onthe estimation of the energy cost when sensing is performedon the phone, and the prediction of the network energy costwhen sensing is performed remotely. In estimating the latter,the user’s mobility pattern, the time spent in a particularlocation, and the rate at which events are reported by theremote sensor are factors that affect the energy cost. In thissubsection we describe an offloading scheme that achievesenergy efficiency by considering all these parameters.

Gain Threshold based Offloading. The Gain Thresh-old scheme operates by calculating the probability thatoffloading a particular sensing task would result in energygain when compared to the corresponding local sensingtask. If the probability of gain is greater than 0.5, i.e., ifoffloading has more than 50% chance of resulting in gainthen the offloading is performed. The probability of gain iscalculated by estimating the possible communication coststhat the phone may incur if offloading is performed. When asensing task is offloaded, there are two types of energy costsinvolved, fixed and variable costs. The fixed costs are thecosts involved in maintaining a network connection betweenthe phone and the infrastructure system, subscribing to aremote service for offloading, and canceling the subscriptionat the end. The variable costs are the communication costsincurred by updates received as part of the infrastructuresensor events that depend on the user behavior.

A decision to offload a sensing task results in energygain if the user stays in the location for long enough sothat the sum of the fixed and variable costs is less thanthe cost of local phone sensing for that period. We refer tothis minimum time period as gainTimeThreshold. We notethat fixed costs can be calculated based on offline estimatedvalues for data transfer over the network (for example, thiscould be measured on the Android phones using a powermeter [15] and on the Nokia phones using the Nokia EnergyProfiler), and variable costs (or event rate or sensor statechange rate) can be calculated based on the past historyof event traces as recorded by the sensing platform. ThegainTimeThreshold varies for each of the sensors as the costand event rate for these change.

1) Gain Time Threshold: In this subsection, we presentthe calculation of the gainTimeThreshold. Notation used:

Gs : gain threshold time of a sensor sCsl : cost per sample of local sensingSsl : sampling rate of the sensor sCso : fixed cost of offloading the sensing taskCsr : cost per update of remote sensingCnr : baseline cost for maintaining network connectionUsr : update rate of remote sensingN : number of sensing tasks that can be offloaded

According to the definition of gainTimeThreshold, for asensor s, the energy consumption of local phone sensing isequal to the sum of fixed and variable costs of remote sens-ing for gainTimeThreshold amount of time. In other words,if remote sensing is used for more than gainTimeThresholdamount of time, then it results in a positive energy gain, andif remote sensing is used for less than gainTimeThresholdamount of time, then it results in a negative energy gain,i.e., offloading is not beneficial in this case.

The local sensing cost for Gs amount of time for asensor s (Ls) = The cost of local sensing per sample (Csl)× Local sampling rate (Ssl) × Gs.

The remote sensing cost for Gs amount of time (Rs) =Fixed control traffic cost (Cso) + (Event update rate (Usr) ×Cost of network transfer per update (Csr) × Gs) + Baselinenetwork connection cost per sensor for Gs amount of time.

Per the definition, Gs is the amount of time for which,local cost = remote cost, i.e., Ls = Rs.

=⇒ Csl × Ssl ×Gs = Cso + Csr × Usr ×Gs +Cnr

N×Gs (1)

=⇒ Gs =Cso

Csl × Ssl − Csr × Usr −Cnr

N

(2)

Gs for a sensor s quantifies the minimum amount of timethe sensing task should suspend local sensing and use remotesensing to achieve energy cost benefit i.e., it is the minimumamount of time the user should stay in the current locationafter offloading the task to achieve energy gain.

2) Probability of Gain Estimation: In this section wepresent the estimation of the probability that offloading asensing task (s) will result in gain. This value is used tomake a decision on whether this offloading is beneficial.Let {vj1 , vj2 , . . . vjk} be the total visits of the user to alocation j, let {tvj1 , tvj2 . . . tvjk} be the total duration ofeach of the k visits, respectively. First, for each sensor s,we divide the total duration of lth visit to a room j into twoparts: favorable time (ftsjl) and unfavorable time (utsjl).Favorable time for a sensor is the time during which theoffloading of the sensing task results in a positive gain, andunfavorable time for a sensor is the time during which theoffloading results in a negative gain. Therefore, for a visit toa room, the favorable time is the total visit time subtractedby the Gs value (as we need at least Gs amount of time toachieve positive gain), and unfavorable time is Gs, i.e., forlth visit to a room j by the user, favorable and unfavorabletimes for a sensor s are calculated as:

ftsjl = tvjl −Gs (3)utsjl = Gs (4)

Then, the probability (ptsj) that a user stays at a location jfor more than the gainTimeThreshold (Gs) value for a sensors is calculated as: the total favorable time at location j forall visits divided by the total time at the location j.

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=⇒ ptsj =

∑kl=1 ftsjl∑k

i=1(ftsji + utsji)(5)

ptsj is the probability of gain that is used to make anoffloading decision for a sensing task s when the user isin room j. Finally, if this probability value is greater than0.5, it indicates a more than 50% chance of resulting inpositive gain, and in this case the sensing task is offloaded.A task that is offloaded to a remote sensor is cancelled(unsubscribed from remote sensing updates) when the usermoves away from the current location, as the remote sensormay not capture the user’s activities accurately, as it is notin proximity to the user. We present a detailed evaluation ofthis scheme through micro-benchmarks in the next section.

IV. BENCHMARKS

In this section we present the evaluation of the gainthreshold offloading scheme through micro-benchmark tests.We compared the energy performance of the scheme with thetwo schemes described in Section II. The dataset collectedfrom the initial study was used for these tests. In thisevaluation, the gain threshold technique works in an onlinefashion, i.e., as the traces are replayed the gain threshold(Eq. 2) and the probability values (Eq. 5) are continuallylearned and the decision on offloading is taken accordingly.

Sensing Cost, Sampling Interval, and Mobility. Weshowed in the results of the initial study that the two simplesensing schemes may not be suitable for all conditions.We now compare the performance of the proposed schemeconsidering varying cost of sensing, sampling interval, andmobility patterns. Figures 7, 8, and 9 show the total energyconsumption of the schemes with respect to these variables.We can observe that the threshold scheme tends to matchthe best performing scheme in all the cases as it considersthese parameters in its decision to offload.

Excluding Wi-Fi Baseline Cost. In the results presentedso far, we include in the network cost function the Wi-Fibaseline cost (cost of keeping Wi-Fi on). However, it istypical for many users to keep Wi-Fi on for other unrelatedpurposes. In order to analyze this situation, we benchmarkedthe behavior of the scheme excluding the Wi-Fi baselinecost from the calculation considering only the cost ofdata exchange. Figure 10 shows the local sensing, networkexchange, and total energy consumption for location andconversation detection. We now observe that the offloadingschemes result in considerable energy savings due to thefact that the Wi-Fi was already enabled. The thresholdscheme resembles the remote sensing scheme in this case,achieving high efficiency, saving around 60% of energywhen compared to pure phone sensing scheme.

As shown in the results so far, the threshold schemetends to follow the most optimum scheme in different

circumstances. However, the optimal strategy may not al-ways include one of the extreme offloading schemes. Todemonstrate this, we evaluate the schemes with respect tothe following two scenarios.

Adaptive Sampling. The use of adaptive sampling is atypical approach in mobile applications that use continuossensing [12]. In these cases the local phone sensor samplingrate changes in the face of changing behavior of the user.In this test, we used two adaptive sampling schemes: Inadaptive scheme 1, we used an exponential back-off and alinear advance of sampling interval, and in adaptive scheme2, we used a linear back-off and an exponential advanceof sampling interval. The sampling interval is increasedusing the back-off function when there is no change tothe user’s context and advance function is used when thereis a change to the user’s context. The context of the userfor this evaluation is defined as the co-located Bluetoothdevices. Figure 11 shows this result. We observe that, inour deployment, for the purely local sensing the exponentialback-off (scheme 1) saves more energy than the linear back-off (scheme 2). Also, the choice of adaptive scheme does nothave a significant impact on always offload scheme as it usesremote sensing most of the time (except in uninstrumentedareas). However, in both cases neither the local or alwaysschemes is optimal. This is because when using adaptivesampling, each of them will be optimal for a subset ofthe possible situations. The threshold scheme appears tooutperform the two others, by selecting the most optimalapproach in every case.

Multiple Sensors. In this case, we evaluate the schemesusing an inexpensive (30mW) and an expensive (1.2W)sensors. We assume that the inexpensive sensor generateslarge amount of data per sensor sampling (100KB persample, similar to the audio recording for 5 seconds usingthe PCM format in the conversation detection module).Figure 12 shows the result of this evaluation, where wecan observe that the energy consumption of the thresholdscheme is much lower than the other schemes, in particular,when the Wi-Fi baseline cost is considered, the thresholdschemes consumes 46%, 31% less energy than the alwaysoffload and always local schemes, respectively. When theWi-Fi baseline cost is not considered, the threshold schemeconsumes 57%, 63% less energy than the always offloadand always local schemes, respectively. The always localscheme consumes high energy because of local sensingof the expensive sensor and the always offload schemeconsumes high energy because of the communication costof the inexpensive sensor. However, the energy consumptionof the gain threshold based offloading scheme is muchlower than the other schemes, as it selects the most optimalconfiguration for each sensing modality.

The results presented clearly show that the proposedscheme dynamically offloads the sensing tasks by adaptingto the sensing parameters and the user’s mobility patterns.

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Figure 11. Energy consumption per hour fortwo adaptive sampling schemes.

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Figure 12. Energy consumption per hour foran inexpensive and an expensive sensors.

V. CASE STUDY

The ultimate evaluation of METIS was performed througha real deployment. The main goals of the deployment wereto evaluate the energy efficiency of METIS, and to showthat the applications can capture the behavioral patterns ofthe users utilizing the services of the METIS system.

A. The WorkSense Application

The design of the WorkSense application was motivatedby studies such as [10], in which, the authors showedhow we can significantly increase work performance byunderstanding the face-to-face interactions and the forma-tion of various social groups within the organization. Weexploited the METIS framework and a number of sensingmodalities that were available in our office environmentto design WorkSense: an application that aims to infer thecollaborations and meetings of users, and offers awarenesson the impact social interactions may have on their work.More specifically the application includes the followingfunctional components:Mobile Phone Application. The application was imple-mented on the Android platform and captures the mobilitypatterns of the users during working hours (visited offices,meeting rooms etc.) and interaction patterns. This informa-tion is used to infer social context, mining working patterns,and to offer awareness to the user. The WorkSense mobileapplication shows to users details about their collaborations,meetings, and the effect of meetings on their work activities.Meeting Detection. A meeting is considered a case wheretwo or more users are co-located for more than a pre-settime threshold (currently 15 minutes) and they are havinga conversation. In the definition, we did not specify a

prerequisite that the location should be a meeting room, aswe wanted to capture informal meetings that some times cantake place in a common room or in the corridor.Collaboration Detection. Although people in our researchinstitution are typically formed into groups that may collab-orate within the context of a particular research project, theWorkSense application attempts to capture a more objectivepicture of how collaborations are taking place within the re-search lab. Sometimes a user who is not officially assigned toa project can play an important role. Collaboration detectionis achieved by applying a community detection algorithmover the co-location data. However, as there are cases wheretwo or more colleagues may share the same office, a straightforward application of the community detection over the co-location data would result in communities that are heavilybiased towards people sharing offices, even if they do nottypically collaborate. In order to overcome this issue, wedefined an intended visit as the case where a person visitsanother person with the intention of having a conversation(deskEmpty(u1)∧colocated(u1, u2)∧conversation(u1)).If sensing infrastructure is unavailable, then we do not con-sider the desk sensor information. The result of an intendedvisit is a directional edge that links the two users where u1

is the initiator and u2 is the target. A community detectionalgorithm is then applied over the directed graph to detectcollaborations, which we describe in the next subsection.

From a design perspective, the operation of WorkSensedoes not require the presence of sensors embedded in theenvironment; however, when such infrastructure is available,the energy performance of the application can be dramati-cally improved and the accuracy of the gathered informationcan be increased as well.

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B. Deployment

We deployed the METIS system and the WorkSenseapplication in a working environment involving 11 usersfor about a month. During this period each user carried anAndroid phone (Samsung Galaxy S or HTC Desire). Themobile application was able to utilize existing sensing in-frastructure that was deployed in our institution (Figure 13).This included desk occupancy sensors, Bluetooth sensors,and conversation detection infrastructure (see Section II).Energy Impact Analysis. We evaluated the energy perfor-mance of METIS during the deployment. To compare thedifference in battery life with and without using the sensingoffloading, we used pairs of Samsung Galaxy S phones,carried by the same user. One of the phones was set tonever offload data (pure phone sensing with Wi-Fi switchedoff) while the other followed the threshold based offloadingscheme. This functionality was swapped between the twophones over consecutive measurements in order to reducethe impact that minor differences of the phone batteries mayhave on the results. The phones were allowed to dischargeonly during office hours, by switching them off overnight.During the experiment the phones were not used for anyother activities. We then also measured the battery life of thephone when using: local phone sensing with Wi-Fi switchedon, sensing disabled with Wi-Fi switched on, and sensingdisabled with Wi-Fi switched off. In all these cases, a batterymonitor was always running on the phone to measure thebattery discharge. The average consumption over multipleruns is shown in Figure 14. The results show that, whenusing the gain threshold scheme and exploiting the sensinginfrastructure, the battery lasts up to 45% longer comparedto the case of local phone sensing with Wi-Fi on, and 35%longer compared to the case with the Wi-Fi off. Furthermore,

the energy cost of using METIS is very close to a mobilephone with the Wi-Fi on and no sensing, and only 6% lesscompared to a phone that uses no sensing and no Wi-Fi.This is an indication that opportunistic sensing offloadingcan improve the support for the long-term deployment ofsensing applications, by significantly minimizing the impacton the phone’s battery life.Meeting Detection Analysis. One of the primary mo-tivations behind the design of WorkSense was the factthat social sensing has the potential to capture views overthe interactions and activities at the workplace. The mostexpected result of WorkSense was the detection of formaland informal meetings. The Meeting Detection service wasable to detect the exact times that meetings are taking placein the laboratory. Furthermore, by utilizing the calendarinformation such meetings were populated with appropriatemeta-data. Figure 15 shows a snapshot of a timeline wherethe calendar schedule is contrasted with the actual meetingsas they were detected by WorkSense. In this particularsnapshot we see how a person that has consecutive meetingscan slightly adapt the schedule based on the duration ofprevious meetings. In this case many meetings were movedearlier: this was not something updated in the calendar.Community Detection Analysis. Figure 16 shows the dif-ferent groups and project teams as they were reported bythe users. We used WorkSense to automatically detect suchcommunities. To do this, we first create a weighted graphof detected collaborations where a link between two usersrepresent the amount of time they spent in meetings anddiscussions. Afterwards, we apply the Louvain’s communitydetection algorithm [1] to detect groups and projects thatusers belonged to. The Louvain method is a hierarchicalgreedy algorithm and operates in two phases, repetitively:

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first, it searches for small communities, and then it combinesnodes of the same community and forms a new network. Themethod uncovers hierarchies of communities and allows tofurther identify sub-communities, sub-sub-communities etc.This algorithm generates team formations on varying levelsof granularity: at the lowest level (Level 1) it identifiessmaller communities and at higher levels (e.g., Level 2)it merges smaller communities to form larger clusters. Thethickness of edges in the graph represent the weight/strengthof the link, and the colour of a node represents its com-munity. Figure 17 shows the Level 2 communities whereWorkSense was able to identify the separation betweenpeople belonging to different departmental groups. Thespatial placement of nodes is generated based on a springmodel: the larger the spatial difference between two nodesthe lesser is the collaboration/communication between them.More interestingly though, the output of the communitydetection when operating at Level 1 (Figure 18), was a morefine grained break down of groups that were not related tothe information that the users offered but to the projects thatusers have been working on, even within the same group.Furthermore, the WorkSense application was able to identifycross team collaborations and pin point people acting asbridges extending collaboration links across teams.

VI. RELATED WORK

Although, energy efficiency has been one of the keydesign considerations in a number of mobile sensing sys-tems [2], [9], [12], most of them consider only the caseof pure phone sensing. A number of systems attempted toaddress energy consumption issues by using adaptive sensorsampling techniques [9] allowing them to improve energyefficiency by trading-off accuracy. As shown in Section IV,METIS can complement adaptive sensing systems offeringfurther improvements in energy utilization.

With respect to using sensors in the infrastructure: Fol-lowMe [6] lets mobile applications to exploit the sensors likecameras, microphones that are available in the environment,for richer context detection. ErdOS [14] is a mobile oper-ating system that extends the battery life of mobile hand-sets by managing resources proactively and by exploitingopportunistic access to resources in nearby devices usingsocial connections among users. Comparing with FollowMeand ErdOS, our system opportunistically offloads the sensingtasks to the infrastructure to maximize the energy gain whileconsidering the sensing parameters and the user’s mobility.None of these works that exploit the sensing infrastructureprovided a solution to the problem of when to offload phonesensing to infrastructure. In [11], the authors propose thatmobile phones can serve as data mules for sensor networksdue to their ubiquity and show that opportunistic mullingis suitable for office-based deployments. Even though thiswork involves interaction of mobile phones with sensornetworks, it addresses a very different problem than METIS.

VII. CONCLUSIONS

In this paper we presented METIS, a mobile sensing plat-form that supports long-term deployment of social sensingapplications by leveraging both phone sensors and sensorsin the environment. The system implements a novel sensingdistribution scheme that is able to switch between phone andremote sensors considering the various sensing parameters,and mobility patterns of the users. We showed throughseveral benchmark experiments on real traces that the systemis able to achieve significant energy savings compared topure phone sensing and remote sensing schemes. We alsoreported on a real deployment of METIS through a socialapplication. We showed that the application is able to inferthe collaborations and meetings of the users while achievinga battery lifetime that is only 6% shorter in duration than amobile phone that performs no sensing. We plan to extendthe system by supporting additional sensing modalities andalso enhance the application to recommend effective workbehaviors to people at the workplace.

ACKNOWLEDGMENTS

This work was supported through the Gates CambridgeTrust and EPSRC grant EP/G069557/1.

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