ORIGINAL ARTICLE

Nathan Eagle Æ Alex (Sandy) Pentland

Reality mining: sensing complex social systems

Received: 5 October 2004 / Accepted: 15 April 2005 / Published online: 3 November 2005� Springer-Verlag London Limited 2005

Abstract We introduce a system for sensing complexsocial systems with data collected from 100 mobilephones over the course of 9 months. We demonstratethe ability to use standard Bluetooth-enabled mobiletelephones to measure information access and use indifferent contexts, recognize social patterns in daily useractivity, infer relationships, identify socially significantlocations, and model organizational rhythms.

Keywords Mobile phones Æ Bluetooth Æ Complex socialsystems Æ Wearable computing Æ User modeling

1 Introduction

The last 10 years could rightly be coined the decade ofthe mobile phone. In 2004, over 600 million handsetswere sold, dwarfing the number of personal computerssold that year [27]. The potential functionality of thisubiquitous infrastructure of mobile devices is dramati-cally increasing. In this paper we describe how datacollected from mobile phones can be used to uncoverregular rules and structure in the behavior of bothindividuals and organizations. In Sect. 2, we begin with adiscussion of the rationale for using phones as wearablesensors and the type of data they can collect. Subse-quently, Sect. 3 describes the benefits of modeling indi-vidual users by fusing information from cell towers withdiscovered Bluetooth IDs. Turning our attention awayfrom individuals and towards dyads, in Sect. 4 we ex-tract salient features indicative of the relationships be-tween subjects using proximity, time, and location data.Finally, with the nodes and edges of this social networkidentified, the concept of organizational rhythms is

introduced as a useful metric for quantifying organiza-tional behavior.

2 Mobile phones as wearable sensors

For over a century, social scientists have conductedsurveys to learn about human behavior. However, sur-veys are plagued with issues such as bias, sparsity ofdata, and lack of continuity between discrete question-naires. It is this absence of dense, continuous data thatalso hinders the machine learning and agent-basedmodeling communities from constructing more com-prehensive predictive models of human dynamics. Overthe last two decades there has been a significant amountof research attempting to address these issues by build-ing location-aware devices capable of collecting richbehavioral data [1, 6, 11, 16, 22, 24].

Although these projects were relatively successful, bydepending on a limited supply of custom hardware, theywere unsuitable for groups of a greater size. Whiledrawing extensively on previous work from the Ubiq-uitous Computing field, one of the contributions of thispaper is to show the potential for these ideas to scaleupwards. With the rapid technology adoption of mobilephones comes an opportunity to collect a much largerdataset on human behavior [10, 18]. The very nature ofmobile phones makes them an ideal vehicle to studyboth individuals and organizations: people habituallycarry their mobile phones with them and use them as amedium for much of their communication. In this paperwe capture all the information to which the phone hasaccess (with the exception of content from phone calls ortext messages) and describe how it can be used to pro-vide insight into both the individual and the collective.

2.1 Mobile phone proximity logs

One of the key ideas in this paper is to exploit the factthat modern phones use both a short-range RF network

N. Eagle (&) Æ A. (Sandy) PentlandMIT Media Laboratory, 20 Ames Street,Cambridge, MA, 02139 USAE-mail: nathan@media.mit.eduE-mail: sandy@media.mit.edu

Pers Ubiquit Comput (2006) 10: 255–268DOI 10.1007/s00779-005-0046-3

(e.g., Bluetooth) and a long-range RF network (e.g.,GSM), and that the two networks can augment eachother for location and activity inference. The idea oflogging cell tower ID to determine approximate locationwill be familiar to readers, but the idea of loggingBluetooth devices is relatively recent and provides dif-ferent types of information [15].

Bluetooth is a wireless protocol in the 2.40–2.48 GHzrange, developed by Ericsson in 1994 and released in1998 as a serial-cable replacement to connect differentdevices. Although market adoption has been initiallyslow, according to industry research estimates by 200690% of PDAs, 80% of laptops, and 75% of mobilephones will be shipped with Bluetooth [28]. EveryBluetooth device is capable of ‘‘device-discovery,’’ whichallows them to collect information on other Bluetoothdevices within 5–10 m. This information includes theBluetooth MAC address (BTID), device name, and de-vice type. The BTID is a 12-digit hex number unique tothe particular device. The device name can be set at theuser’s discretion; e.g., ‘‘Tony’s Nokia.’’ Finally, the de-vice type is a set of three integers that correspond to thedevice discovered; e.g., Nokia mobile phone or IBMlaptop.

To log BTIDs we designed a software application,BlueAware, that runs passively in the background onMIDP2-enabled mobile phones. Bluetooth was primar-ily designed to enable wireless headsets or laptops toconnect to phones, but as a by-product devices arebecoming aware of other Bluetooth devices carried bypeople nearby. Our application records and timestampsthe BTIDs encountered in a proximity log and makesthem available to other applications, similar to theJabberwocky project developed by Paulos et al. [19].BlueAware is automatically run in the background whenthe phone is turned on, making it essentially invisible tothe user.

A variation on BlueAware is Bluedar. Bluedar,shown in Fig. 1 (right), was developed to be placed in asocial setting and continuously scan for visible devices,wirelessly transmitting detected BTIDs to a server overan 802.11b network. The heart of the device is a Blue-tooth beacon designed by Mat Laibowitz, incorporating

a class 2 Bluetooth chipset that can be controlled by anXPort web server [14]. We integrated this beacon with an802.11b wireless bridge and packaged them in anunobtrusive box. An application was written to contin-uously telnet into multiple BlueDar systems, repeatedlyscan for Bluetooth devices, and transmit the discoveredproximate BTIDs to our server. Because the Bluetoothchipset is a class 2 device, it is able to detect any visibleBluetooth device within a working range of up to 25 m.We are currently using the system to prototype a prox-imity-based introduction service [9].

2.1.1 Refresh rate versus battery-life

Continually scanning and logging BTIDs can expend anolder mobile phone battery in about 18 h1. While con-tinuous scans provide a rich depiction of a user’s dy-namic environment, most individuals expect phones tohave standby times exceeding 48 h. Therefore Blue-Aware was modified to only scan the environment onceevery 5 min, providing at least 36 h of standby time.

2.2 Privacy implications

Mining the reality of our 100 users raises justifiableconcerns over privacy. However, the work in this paperis a social science experiment, conducted with humansubject approval and consent of the users. Outside thelab we envision a future where phones will have greatercomputational power and will be able to make relevantinferences using only data available to the user’s phone.In this future scenario, the inferences are done in real-time on the local device, making it unnecessary for pri-vate information to be taken off the handset. However,the computational models we are currently using cannotbe implemented on today’s phones. Thus, our resultsaim to show the potential of the information that can begleaned from the phone, rather than presenting a system

Fig. 1 Methods of detecting Bluetooth devices—BlueAware andBluedar. BlueAware (left) is running in the foreground on a Nokia3650. BlueAware is an application that runs on Symbian Series 60phones. It runs in the background and performs repeated

Bluetooth scans of the environment every 5 min. Bluedar (right)is comprised of a Bluetooth beacon coupled with a WiFi bridge. Italso performs cyclic Bluetooth scans and sends the resulting BTIDsover the 802.11b network to the Reality Mining server

1Using a 6-month old battery of a Nokia 6600 in a sparsely pop-ulated Bluetooth environment.

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that can be deployed today outside the realm ofresearch.

2.3 The dataset

Our study consists of 100 Nokia 6600 smart phones pre-installed with several pieces of software we have devel-oped as well as a version of the Context application fromthe University of Helsinki [20]. Seventy-five users areeither students or faculty in the MIT Media Laboratory,while the remaining twenty-five are incoming students atthe MIT Sloan business school adjacent to the Labora-tory. Of the 75 users at the Lab, 20 are incoming mas-ter’s students and 5 are incoming MIT freshman. Theinformation we are collecting includes call logs, Blue-tooth devices in proximity, cell tower IDs, applicationusage, and phone status (such as charging and idle), andcomes primarily from the Context application. Thestudy has generated data collected by 100 human sub-jects over the course of the academic year that representapproximately 450,000 h of information about users’location, communication and device usage behavior. Wereleased a public, anonymous version of the dataset onour website http://reality.media.mit.edu.

3 User modeling: identifying structure in routine

Although humans have the potential for relatively ran-dom patterns of behavior, there are easily identifiableroutines in every person’s life. These can be found on arange of timescales: from the daily routines of gettingout of bed, eating lunch, and driving home from work,to weekly patterns such as Saturday afternoon softballgames, to yearly patterns like seeing family during theholidays in December. While our ultimate goal is tocreate a predictive classifier that can perceive aspects of auser’s life more accurately than a human observer(including the actual user), we begin by building simplemechanisms that can recognize many of the commonstructures in the user’s routine. Learning the structure ofan individual’s routine has already been demonstratedusing other modalities, however we present this analysisas a foundation which will then be extended to demon-strate the learning of social structures.

We begin with a simple model of behavior in threestates: home, work, and elsewhere. The data are ob-tained from Bluetooth, cell tower, and temporal infor-mation collected from the phones. We then incorporateinformation from static Bluetooth devices (such asdesktop computers), using them as ‘cell towers’ toidentify significant locations and localize the user to aten-meter radius. We show that most users spend asignificant amount of time in the presence of staticBluetooth devices, particularly when they don’t have celltower reception (e.g., inside the office building). Thismakes them an ideal supplement to cell towers forlocation classification.

3.1 Location based on cell towers and Bluetooth

There has been a significant amount of research whichcorrelates cell tower ID with a user’s location [3, 4, 12].For example, Laasonen et al. [13] describe a method ofinferring significant locations from cell tower informa-tion through analysis of the adjacency matrix formed byproximate towers. They were able to show reasonableroute recognition rates, and most importantly, suc-ceeded in running their algorithms directly on the mobilephone.

Obtaining accurate location information from celltowers is complicated by the fact that phones can detectcell towers that are several miles away. Furthermore, inurban areas it is not uncommon to be within range ofseveral dozen different towers. The inclusion of infor-mation on all the current visible towers as well as theirrespective signal strengths would help solve the locationclassification problem, although multipath distortionmay still confound estimates.

We observe that relatively high location accuracymay also be achieved if the user spends enough time inone place to provide an estimate of the location’s celltower probability density function. A phone in somestatic location is associated with different cell towers atdifferent times. Thus, it is possible to generate the dis-tribution of time spent associated with a set of towers fora particular area. This distribution of detected towerscan vary substantially with even small changes in loca-tion. Figure 2 shows the distribution of cell towers seenfor a given area with a 10 m radius. Towers were onlyincluded in these distributions if the common area’sstatic Bluetooth desktop computer was also visible,ensuring the users’ location within 10 m (or less). Dis-crepancies in the distributions are attributed to the users’typical position within the 10 m radius. Users 4 and 5both share a window office and have virtually the samecell tower distribution, despite having a very differentdistribution of hours spent in the office (as verified bythe Bluetooth and cell tower logs). Users 1 and 2 bothspend the majority of their time in the common areaaway from the windows and see only half as manytowers as the others. User 3 is in a second office in thesame area, and has a distribution of cell towers that isintermediate between the two other sets of users.

Despite progress in mapping a cell tower to a loca-tion, the resolution simply cannot be as high as manylocation-based services require. GPS is an alternativeapproach that has been used for location detection andclassification [2, 17, 25] but the line-of-sight require-ments prohibit it from working well indoors. We havetherefore incorporated the use of static Bluetooth deviceID as an additional indicator of location, and shownthat it provides a significant improvement in userlocalization, especially within office environments. Thisfusion of data is particularly appropriate since areaswhere cellular signals are weak, such as in the middle oflarge buildings, often correspond to places where thereare many static Bluetooth devices, such as desktop

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computers. On average, the subjects in our study werewithout mobile phone reception for 6% of the time.When they did not have reception, however, they werewithin the range of a static Bluetooth device or anothermobile phone 21% and 29% of their time, respectively.We expect the coverage by Bluetooth devices to increasedramatically in the near future as they become morecommon in computers and electronic equipment.

If this trend continues, Bluetooth IDs may become asimportant as cell tower mapping for estimation of userlocation. Figure 3 shows the 10 most frequently detectedBluetooth devices for one subject averaged for themonth of January. This figure not only provides insightinto the times the user is in his office (from the fre-quencies of the ‘Desktop’), but, as mentioned in Sect. 4,also into the type of relationship with other subjects. Forexample, the figure suggests the user leaves his officeduring the hour of 14:00 and becomes increasingly

proximate to Subject 4. Judging from the strong cutoffsat 9:00 and 17:00, it is clear that this subject had veryregular hours during the month, and thus has fairlypredictable high-level behavior. This ‘‘low entropy’’behavior is also depicted in Fig. 4.

3.2 Models to identify location and activity

Human life is inherently imbued with routine across alltemporal scales, from minute-to-minute actions tomonthly or yearly patterns. Many of these patterns inbehavior are easy to recognize, however some are moresubtle. We attempt to quantify the amount of predictablestructure in an individual’s life using an information en-tropy metric. In information theory, the amount of ran-domness in a signal corresponds to its entropy, as definedin 1938 by Claude Shannon in the equation below.

Fig. 3 The top 10 Bluetooth devices encountered for Subject 9during the month of January. The subject is only regularlyproximate to other Bluetooth devices between 9:00 and 17:00,while at work—but never at any other times. This predictable

behavior will be defined in Chap. 4 as ‘low entropy.’ The subject’sdesktop computer is logged most frequently throughout the day,with the exception of the hour between 14:00 and 15:00. Duringthis time window, Subject 9 is most often proximate to Subject 4

Fig. 2 Cell tower probability density functions. The probability ofbeing associated with one of the 25 visible cell towers is plottedabove for five users who work on the third floor corner of the sameoffice building. Each tower is listed on the x-axis and theprobability of the phone logging it while the user is in his officeis shown on the y-axis. (Range was assured to 10 m by the presence

of a static Bluetooth device.) It can be seen that each user ‘sees’ adifferent distribution of cell towers depending on the location of hisoffice, with the exception of Users 4 and 5, who are officemates andhave the same distribution despite being in the office at differenttimes

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HðxÞ ¼ �Xn

i¼1pðiÞ log2 pðiÞ:

For a more concrete example, consider the problem ofimage compression (such as the jpeg standard) of anoverhead photo taken of just an empty checkerboard.This image (in theory) can be significantly compressedbecause it does not contain much ‘information’. Essen-tially the entire image could be recreated with the same,simple pattern. However, if the picture was taken duringthe middle of a match, the pieces on the board introducemore randomness into the image and therefore it willprove to be a larger file because it contains more infor-mation, or entropy.

Similarly, people who live entropic lives tend to bemore variable and harder to predict, while low-entropylives are characterized by strong patterns across all timescales. Figure 4 depicts the patterns in cell tower tran-sitions and the total number of Bluetooth devicesencountered at each hour during the month of Januaryfor Subject 9, a ‘low-entropy’ subject.

It is clear that the subject is typically at home duringthe evening and all night until 8:00, when he commutesin to work, and then stays at work until 17:00 when hereturns home. We can see that almost all of the Blue-tooth devices are detected during these regular officehours, Monday through Friday. This is certainly not thecase for many of the subjects. Figure 5 displays a dif-ferent set of behaviors for Subject 8. The subject hasmuch less regular patterns of location and in the eve-nings has other mobile devices in close proximity. Wewill use contextualized information about proximitywith other mobile devices to infer relationships, de-scribed in Sect. 4.

While calculating a life’s entropy be used as amethod of self-reflection on the routines (or ruts) inone’s life, it can also be used to compare the behaviorsof different demographics. Figure 6 shows the averageweekly entropy of each of the demographics in ourstudy, based on their location {work, home, no signal,elsewhere} each hour. Average weekly entropy wascalculated by drawing 100 samples of a 7-day period

Fig. 4 A ‘low-entropy’ (H = 30.9) subject’s daily distribution ofhome/work transitions and Bluetooth devices encounters duringthe month of January. The top figure shows the most likely locationof the subject: ‘‘Work, Home, Elsewhere, and No Signal.’’ While

the subject’s state sporadically jumps to ‘‘No Signal,’’ the otherstates occur with very regular frequency. This is confirmed by theBluetooth encounters plotted below representing the structuredworking schedule of the ‘low-entropy’ subject

Fig. 5 A ‘high entropy’(H = 48.5) subject’s dailydistribution of home/worktransitions and Bluetoothdevice encounters during themonth of January. In contrastto Fig. 4, the lack of readilyapparently routine andstructure makes this subject’sbehavior harder to model andpredict

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for each subject in the study. No surprise to most, theMedia Lab first-year undergraduates are the mostentropic of the group. The freshmen do not come intothe lab on a regular basis and have seemingly randombehavior with HðxÞ ¼ 47:3 (the entropy of a sequenceof 168 random numbers is approximately 60). Thegraduate students (Media Lab incoming, Media Labsenior, and Sloan incoming) are the next most entropicwith HðxÞ ¼ 44.5, 42.8, 37.6f g respectively. Finally,the Media Lab faculty and staff have most rigidity intheir schedules, reflected in their relatively low-averageentropy measures, HðxÞ ¼ 31.8, 29.1f g:

One similarity between the different demographicsshown above is the clear role time plays in determininguser behavior. To account for this, we have developed asimple Hidden Markov Model, shown in Fig. 7, con-ditioned on both the hour of day T 1 2 1; 2; 3:::; 24f g

� �

as well as on weekday or weekend T 2 2 1; 2f g� �

: Ini-tially observations in the model are simply the distri-

bution of cell towers Y 1 2 CT1;CT1; :::;CTn1f g� �

andBluetooth devices Y 2 2 BT1;BT1; :::;BTn2f g

� �: A

straightforward Expectation-Maximization inferenceengine was used to learn the parameters in the transi-tion model, P QtjQt�1ð Þ; and the observation modelP YtjQtð Þ; and performed clustering in which we definedthe dimensionality of the state space. The hidden stateis represented in terms of a single discrete randomvariable corresponding to three different situations,Q 2 home; work; otherf g: After training our modelwith one month of data from several subjects we wereable to provide a good separation of clusters, typicallywith greater than 95% accuracy. Examination of thedata shows that non-linear techniques will be requiredto obtain significantly higher accuracy. However, forthe purposes of this chapter, this accuracy has provensufficient. In future work we hope to leverage theinformation within LifeNet [23] to create more specificinferences about activity.

Fig. 6 Entropy, H(x), wascalculated from the {work,home, no signal, elsewhere} setof behaviors for 100 samples ofa 7-day period. The Media Labfreshmen have the leastpredictable schedules, whichmakes sense because they cometo the lab much less regularbasis. The staff and faculty havethe most least entropicschedules, typically adhering toa consistent work routine

Fig. 7 A Hidden MarkovModel conditioned on time forsituation identification. Themodel was designed to be ableto incorporate many additionalobservation vectors such asfriends nearby, traveling,sleeping and talking on thephone

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3.3 Mobile usage patterns in context

Capturing mobile phone usage patterns of 100 peoplefor an extended period of time can provide insight intoboth the users and the ease of use of the device itself. Forexample, 35% of our subjects use the clock applicationon a regular basis (primarily to set the alarm clock andthen subsequently to press snooze), yet it takes 10 key-strokes to open the application from the phone’s defaultsettings. Not surprisingly, specific applications, such asthe alarm clock, seem to be used much more oftenat home than at work. Figure 8 is a graph of theaggregate popularity of different applications when bothat home and at work. It is interesting to note that despitethe subjects being technically savvy, there was nota significant amount of usage in the sophisticated fea-tures of the phone—indeed the default game ‘‘Snake’’was used just as much as the elaborate Media Playerapplication.

While there is much to be gained from a contextualanalysis of application usage, perhaps the most impor-tant and still most popular use of the mobile phone is asa communication device. Figure 9 is a breakdown of thedifferent types of usage patterns from a selection of thesubjects. Approximately 81% of communication on thephone was completed by placing or receiving a voicecall. Data (primarily email) was at 13% of the commu-nication, while text messaging was 5%.

Learning a user’s application routines can enable thephone to place a well-used application in more promi-nent places, for example, as well as creating a bettermodel of the behavior of an individual [26]. As we shallsee in Sect. 4, these models can also be augmented withadditional information about a user’s social context.

3.4 Data characterization and validation

This section describes how errors may be introducedinto the data through data corruption, device detectionfailures, and most significantly, through human error(Fig. 10).

3.4.1 Data corruption

All the data from a phone are stored on a flash memorycard, which has a finite number of read–write cycles.Initial versions of our application wrote over the samecells of the memory card. This led to the failure of a newcard after about a month of data collection, resulting inthe complete loss of data. When the application waschanged to store the incremental logs in RAM andsubsequently write each complete log to the flashmemory, our data corruption issues virtually vanished.However, 10 cards were lost before this problem was

Fig. 9 Average communication mediums for 90 subjects (approx-imately 10 of the subjects did not use the phones as acommunication device and were excluded from this analysis). Thecolor bar on the right indicates the percentage each communication

medium (Voice, Text, and Data) is used. All subjects use voice asthe primary means of communication, while about 20% alsoactively use the data capabilities of the phone. Less than 10% of thesubjects send a significant number of text messages

Fig. 8 Average applicationusage in three locations (other,work, and home) for 100subjects. The x-axis displays thefraction of time eachapplication is used, as afunction of total applicationusage. For example, the usageat home of the clock applicationcomprises almost 3% of thetotal times the phone is used.The ‘phone’ application itselfcomprises more than 80% ofthe total usage and was notincluded in this figure

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identified, destroying portions of the data collectedduring the months of September and October for sixSloan students and four Media Lab students.

3.4.2 Bluetooth errors

One central intent of this research is to verify the accu-racy of automatically collected data from mobile phonesfor quantifying social networks. We are facing severaltechnical issues. The 10-m range of Bluetooth, alongwith the fact that it can penetrate some types of walls,means that people who are not physically proximatemay incorrectly be logged as such. By scanning onlyperiodically every 5 min, shorter proximity events mayalso be missed.

Additionally, there is a small probability (between 1and 3% depending on the phone) that a proximate,visible device will not be discovered during a scan.Typically this is due to either a low level Symbian crashof an application called the ‘‘BTServer,’’ or a lapse in thedevice discovery protocol. The BT server crashes andrestarts approximately once every three days (at a 5-minscanning interval) and accounts for a small fraction ofthe total error. However, to detect other subjects, we canleverage the redundancy implicit in the system. Becauseboth of the subjects’ phones are actually scanning, theprobability of a simultaneous crash or device discoveryerror is less than 1 in 1,000 scans.

In our tests at MIT, we have empirically found thatthese errors have little effect on the correlations betweeninteraction (survey data) and the 10 m Bluetooth prox-imity information. These problems therefore produce asmall amount of ‘background noise’ against which thetrue proximity relationships can be reasonably mea-sured. However, social interactions within an academicinstitution are not necessarily typical of a broader cross-

section of society, and the errors may be more severe ormore patterned. If testing in a more general populationshows that the level of background noise is unaccept-able, there are various technical remedies available. Forinstance, the temporal pattern of BTID logs allows us toidentify various anomalous situations. If someone is notinvolved in a specific group conversation but justwalking by, then they will often enter and leave the logat a different time than the members of the group.Similar geometric and temporal constraints can be usedto identify other anomalous logs.

3.4.3 Human-induced errors

The two primary types of human-induced errors in thisdataset result from the phone either being off, or sepa-rated from the user. The first error comes from thephone being either explicitly turned off by the user orexhausting the batteries. According to our collectedsurvey data, users report exhausting the batteriesapproximately 2.5-times each month. One-fifth of oursubjects manually turn the phone off on a regular basisduring specific contexts such as classes, movies, and(most frequently) when sleeping. Immediately before thephone powers down, the event is timestamped and themost recent log is closed. A new log is created when thephone is restarted and again a timestamp is associatedwith the event.

A more critical source of error occurs when the phoneis left on, but not carried by the user. From surveys, wehave found that 30% of our subjects claim to neverforget their phones, while 40% report forgetting it aboutonce each month, and the remaining 30% state that theyforget the phone approximately once each week. Iden-tifying the times where the phone is on, but left at homeor in the office presents a significant challenge when

Fig. 10 Movement andcommunication visualization ofthe Reality Mining subjects. Incollaboration with StephenGuerin of Redfish Inc, we havebuilt a Macromedia Shockwavevisualization of the movementand communication behavior ofour subjects. Location is basedon approximate location of celltowers, while the links betweensubjects are indicative of phonecommunication

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working with the dataset. To grapple with the problem,we have created a ‘forgotten phone’ classifier. Featuresincluded staying in the same location for an extendedperiod of time, charging, and remaining idle throughmissed phone calls, text messages, and alarms. Whenapplied to a subsection of the dataset which had corre-sponding diary text labels, the classifier was able toidentify the day where the phone was forgotten, but alsomislabeled a day when the user stayed home sick. Byignoring both days, we risk throwing out data on out-lying days, but have greater certainty that the phone isactually with the user. A significantly harder problem isto determine whether the user has temporarily movedbeyond 10 m of his or her office without taking thephone. Casual observation indicates that this appears tohappen with many subjects on a regular basis and thereare not enough unique features of the event to classify itaccurately. However, as discussed in the relationshipinference section, while frequency of proximity withinthe workplace can be useful, the most salient data comefrom detecting a proximity event outside MIT, wheretemporarily forgetting the phone is less likely torepeatedly occur.

3.4.4 Missing data

Because we know when each subject began the study, aswell as the dates that have been logged, we can knowexactly when we are missing data. This missing data isdue to two main errors discussed above: data corruptionand powered-off devices. On average we have logsaccounting for approximately 85.3% of the time sincethe phones have been deployed. Less than 5% of this isdue to data corruption, while the majority of the missing14.7% is due to almost one-fifth of the subjects turningoff their phones at night.

3.4.5 Surveys and diaries vs. phone data

In return for the use of the Nokia 6600 phones, stu-dents have been asked to fill out web-based surveysregarding their social activities and the people theyinteract with throughout the day. Comparison of thelogs with survey data has given us insight into ourdataset’s ability to map accurately social networkdynamics. Through surveys of approximately 40 seniorstudents, we have validated that the reported frequencyof (self-report) interaction is strongly correlated withthe number of logged BTIDs (R=0.78, p=0.003), andthat the dyadic self-report data has a similar correlationwith the dyadic proximity data (R=0.74, p<0.0001).Interestingly, the surveys were not significantly corre-lated with the proximity logs of the incoming students.Additionally, a subset of subjects kept detailed activitydiaries over several months. Comparisons revealed nosystematic errors with respect to proximity and loca-tion, except for omissions due to the phone beingturned off.

4 Community structure: complex social systems

In the previous section we showed that Bluetooth-en-abled mobile phones might be used to discover a greatdeal about the user’s patterns of activity. In this sectionwe will extend this base of user modeling to exploremodeling complex social systems.

By continually logging and time-stamping informa-tion about a user’s activity, location, and proximity toother users, the large-scale dynamics of collective humanbehavior can be analyzed. If deployed within a group ofpeople working closely together, correlations betweenthe phone log and proximity log could also be used toprovide insight behind the factors driving mobile phoneuse. Furthermore, a dataset providing the proximitypatterns and relationships within large groups of peoplehas implications within the computational epidemiologycommunities, and may help build more accurate modelsof airborne pathogen dissemination, as well as othermore innocuous contagions, such as the flow of infor-mation.

4.1 Human landmarks

As shown in Figs. 4 and 12, there are people who usersonly see in a specific context (in this instance, at work).If we know the user is at work, information about thetime of day, and optionally the location within thebuilding (using static Bluetooth devices) can be used tocalculate the probability of that user seeing a specificindividual, by the straightforward application of Bayes’rule.

In contrast to previous work that requires access tocalendar applications for automatic scheduling [21], wecan generate inferences about whether a person will beseen within the hour, given the user’s current context,with accuracies of up to 90% for ‘‘low entropy’’ subjects.These predictions can inform the user of the most likelytime and place to find specific colleagues or friends. Webelieve that the ability to reliably instigate casual meet-ings would be of significant value in the workplace. Wemust also remember, however, that the ability to predictpeople’s movements can be put to less savory uses.Careful consideration must be given to these possibilitiesbefore providing free access to such data.

4.2 Relationship inference

In Sect. 3 we discussed how information about locationand proximity can be used to infer a user’s context. Inmuch the same way, knowledge of the shared context oftwo users can provide insight into the nature of theirassociation. For example, being near someone at 3 pmby the coffee machines confers different meaning thanbeing near the person at 11 pm at a local bar. However,even simple proximity patterns provide an indication of

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the structure of the underlying friendship network asshown in Fig. 11. The clique on the top right of eachnetwork are the Sloan business students while the MediaLab senior students are at the center of the clique on thebottom left. The first year Media Lab students can befound on the periphery of both graphs.

We have trained a Gaussian mixture model [8] todetect patterns in proximity between users and correlatethem with the type of relationship. The labels for thismodel came from a survey taken by all the experimentalsubjects at the end of two months of data collection. Thesurvey asked who they spent time with, both in theworkplace and out of the workplace, and who theywould consider to be within their circle of friends. Wecompared these labels with estimated location (using celltower distribution and static Bluetooth device distribu-tion), proximity (measured from Bluetooth logs), andtime of day.

Workplace colleagues, outside friends, and peoplewithin a user’s circle of friends were identified with over90% accuracy, calculated over the 2,000 potential dyads.Initial examination of the errors indicates that theinclusion of communication logs combined with a more

powerful modeling technique, such as Support VectorMachine, will have considerably greater accuracy.

Some of the information that permits inference offriendship is illustrated in Fig. 12 and Table 1. Thisfigure shows that our sensing technique is picking up thecommon-sense phenomenon that office acquaintancesare frequently seen in the workplace, but rarely outsidethe workplace. Conversely, friends are often seen outsideof the workplace, even if they are co-workers. Deter-mining membership in the ‘circle of friends’ requirescross-referencing between friends: is this person amember of a cluster in the out-of-office proximity data?

4.3 Proximity networks of work groups

By continuously logging the people proximate to anindividual, we are able to quantify a variety of propertiesabout the individual’s work group. Although most workin networks assumes a static topology, proximity net-work data is extremely dynamic and sparse. We arecurrently building generative models to attempt toparameterize the underlying dynamics of these networks

Fig. 11 Friendship (left) and daily proximity (right) networks.Circles represent incoming Sloan business school students. Trian-gles, diamonds and squares represent senior students, incomingstudents, and faculty/staff/freshman at the Media Lab. While the

two networks share similar structure, inferring friendship fromproximity requires the additional information about the context(location and time) of the proximity

Fig. 12 Proximity frequencydata for a friend and aworkplace acquaintance. Thetop two plots are the times (timeof day and day of the week,respectively) when thisparticular subject encountersanother subject he has labeledas a ‘‘friend.’’ Similarly, thesubsequent two plots show thesame information for anotherindividual the subject haslabeled as ‘‘officeacquaintance.’’ It is clear thatwhile the office acquaintance isencountered more often, thedistribution is constrained toweekdays during typicalworking hours. In contrast, thesubject encounters his friendduring the workday, but also inthe evening and on weekends

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to gain insight into the functionality of the group itself.Additionally, we hope that by quantifying these prox-imity networks and contrasting the dynamics of the

different groups at the Media Lab, we will gain someinsight into the underlying characteristics of the researchgroups.

While each research group at the Media Lab is cen-tralized around a faculty director, the proximity net-works are not reflective of this static organizationalstructure. In many instances, the proximity network’sdegree distribution is indicative of a hub-and-spokeformation, however, the roles that are played within thisstructure are not static. Individuals that are hubs duringone period of time fluidly exchange places with otherteam members on the periphery of the proximity net-work. This type of dynamic may be characteristic of theunderlying nature of research groups at the Media Lab.As deadlines approach for specific individuals, they be-gin to spend more time in the Media Lab and increas-ingly rely on support from the rest of the group. Upon

Table 1 Statistics correlated (0.25<R<0.8, p<0.001) withfriendship generated from 60 subjects (comprising 75 friendships)who work together at the Media Lab

Friends Not friends

Avg Std Avg Std

Total proximity (min/day) 72 150 9.5 36Saturday night proximity (min/week) 7.3 18 .20 1.7Proximity with no signal (min/day) 12 20 2.9 20Total number of towers together 20 36 3.5 4.4Proximity at home (min/day) 3.7 8.4 .32 2.2Phone calls/day .11 .27 .001 .017

Fig. 13 Proximity network snapshots for a research group over thecourse of one day. In this example, if two of the group members areproximate during a 1-h window, an edge is drawn between them.The four plots represent four of these 1-h windows throughout the

day at 10:00, 13:00, 17:00, and 19:00. We have the ability togenerate these network snapshots at any granularity, with windowsranging from 5 min to three months

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Fig. 14 Proximity Network Degree distributions between twogroups. The left-most plot corresponds to the Human Dynamicsgroup’s degree distribution (i.e., the number of group memberseach person is proximate to over an aggregate of network

snapshots). The second left-most plot is simply zoomed-in on thetail of the previous plot’s distribution. Likewise, the two right-mostplots are of the Responsive Environments group’s degree distribu-tion

Fig. 15 Proximity Time-Seriesand Organizational Rhythms.The top plot is total number ofedges each hour in the MediaLab proximity network fromAugust 2004 to January 2005.When a discrete Fouriertransform is performed on thistime series, the bottom plotconfirms two most fundamentalfrequencies of the dynamicnetwork to be (not surprisingly)1 day and 7 days

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completion of a project, they resume their normal rou-tines and can provide similar support to others. As willbe discussed in the next section, this pattern of behaviorhas been shown to vanish when the entire group (ororganization) is working towards the same deadline.

4.4 Organizational rhythms and network dynamics

Organizations have been considered microcosms ofsociety, each with their own cultures and values. Similarto society, organizational behavior often shows recur-rent patterns despite being the sum of the idiosyncraticbehavior of individuals [5]. We are beginning to explorethe dynamics of behavior in organizations in response toboth external (stock market performance, a Red SoxWorld Series victory) and internal (deadlines, reorgani-zation) stimuli (Figs. 13, 14).

During October, the 75 Media Lab subjects had beenworking towards the annual visit of the Laboratory’ssponsors. Preparation for the upcoming events typicallyconsumes most people’s free time and schedules shiftdramatically to meet deadlines and project goals. It hasbeen observed that a significant fraction of the com-munity tends to spend much of the night in the Labfinishing up last-minute details just before the event. Weare beginning to uncover and model how the aggregatework cycles expand in reaction to these types of globaldeadlines. Figure 15 is a time series of the maximumnumber of links in the Media Lab proximity networkduring every 1-h window. It can be seen that the numberof links in the Media Lab proximity network remainedsignificantly greater than zero during the third week ofOctober and in early December, representing prepara-tion for a large Media Lab sponsor event and MIT’sfinal week. It is possible to convert this time series intothe frequency domain using a discrete Fourier Trans-form. The Fourier transform of this times series (Fig. 15,bottom) uncovers two fundamental frequencies, thestrongest being at 24 h (1 day), and the second being at168 h (7 days).

5 Conclusions

It is inevitable that the mobile devices of tomorrow willbecome both more powerful and more aware of theiruser and his or her context. We have distributed a fleetof one hundred context logging mobile phonesthroughout a laboratory and a business school at MIT.The data these devices have returned to us is unprece-dented in both magnitude and depth. The applicationswe have presented include ethnographic studies of deviceusage, relationship inference, individual behavior mod-eling, and group behavior analysis. However, there ismuch more to be done, and it is our hope that this newtype of data will inspire research in a variety of fieldsranging from qualitative social science to theoreticalartificial intelligence.

Acknowledgements The authors would like to express their grati-tude to Wen Dong, Stephen Guerin, Tony Pryor, and AaronClauset. We would also like to thank Hari Pennanen and Nokia fortheir support. Finally, Mika Raento deserves particular recognitionas the architect of Context and whose efforts were instrumental tothis research.

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