Distributed Sensor Network for Indirect OccupancyMeasurement in Smart Buildings

Colin Brennan∗†, Graham W. Taylor∗†, and Petros Spachos∗∗School of Engineering, University of Guelph, Guelph, ON, N1G 2W1, Canada

E-mail: cbrennan@mail.uoguelph.ca, {gwtaylor, petros}@uoguelph.ca†Vector Institute for Artificial Intelligence

Abstract—Many areas like smart buildings, crowd flow, actionrecognition, and assisted living rely on occupancy information.Although the use of smart cameras can alleviate the problem andprovide accurate occupancy information, at the same time it canbe cost prohibitive, invasive and not easy to scale or generalizeto different environments. An alternative solution should bringsimilar accuracy while minimizing the previous problems. Thiswork presents a candidate wireless sensor network for indirectoccupancy measurements in a smart building. A prototype wasbuilt, that consists of CO2, temperature and humidity sensors.The prototype was placed in a complex indoor environmentto collect data for a week. Then, the data was analyzed toexamine potential correlation between sensor data and occupancyinformation. According to experimental results, CO2 can be usedfor indirect occupancy measurements.

Index Terms—Room Occupancy, WSN, CO2.

I. INTRODUCTION

As the trend in technology continues to make computepower and integration among devices more accessible, thereis an increase in demand for intelligent, connected systems.Consumers seek systems that can anticipate their needs andand act accordingly to in some way make their lives easier.In order for any smart system to comply with this request itneeds to have enough information about what specific actionis required of them and about the state of the environment inwhich they may act [1]. This critical context must be observedand measured in some way to be integrated into the systemfor consideration.

Specific applications for these smart systems are alreadybeing addressed, including systems for smart homes andsmart offices, crowd behaviour analysis, and human actionrecognition. Common to each of these applications is thatthese systems require context on where the user or users arewithin the observable space. While some occupancy capablesystems do perform well for some of these applications, itis a challenge to produce a system that can generalize towork for any of these settings. The infrastructure availablefor a large office building seeking smart office control isquite different from that available in a smart home. SmartCamera systems may be well suited for such an office setting,but may be impractical for large crowds and may not bewelcome within a user’s home. In addition to generalizingacross applications, a suitable solution should thus includeconsideration for anonymous and non-invasive sensing asrequired.

Sensors which observe indoor environment state present aviable solution to this problem. These sensors track occupancyby identifying which changes in environment state are causedby changes in the number of persons present. When deployedas a Wireless Sensor Network (WSN), this capability can beextended to cover multiple environments such as the variousrooms in a smart home or office. Distributed occupancycapable sensing nodes may be deployed as a network to atarget area to serve as the context source for each individualspace, as well as for information between those spaces. Thescalability of the modular networks allows for generalizationto different size application domains.

In this work, a WSN prototype is presented which iscapable of interacting with its local environment to providethe necessary information to work with occupancy contextinformation. The prototype has three sensor units for CO2,temperature and humidity in order to provide the necessaryenvironment monitoring. All the data is forwarded to a serverin real time for further processing. The prototype was tested ina complex indoor environment and collected data for a week.Data analysis and correlation provide some useful insightsregarding the levels of CO2 and the number of the peoplein the monitoring area.

The major contributions of this work are listed below:• A wireless prototype consisting of three sensors was built

for experimentation.• Experiments were conducted for a week, with six pro-

totypes in two laboratories with different layouts. Eachprototype contributed an average of 54, 140 samples. Thesamples were labelled for further processing

• Data correlation between the sensor measurements andthe number of the occupants in the room was examined.According to experimental results, CO2 can be used forindirect occupancy measurements.

The rest of this paper is organized as follows: Section IIdiscusses the related work in this area. Section III provides abrief discussion on the proposed system implementation. Theexperimental results are presented in Section IV while SectionV concludes this work.

II. RELATED WORK

For applications that focus on the actions and behavioursof few or individual occupants, the state of occupancy canbe obtained as a consequence of tracking the users’ locations.

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Wearables such as a user’s smart-phone or other communica-tion sensor beacons can be used to this end. The accelerometerand coarse location sensing abilities of a standard smart phonehave been used for both the applications of human actionrecognition [2] and area departure prediction [3]. The work in[4] also showed that it is possible to obtain location accuraciesof five meters from machine learning models based on receivedsignal strength from statically placed beacons throughout atarget space. WSN systems based around the inclusion of awearable component can provide advantages in terms of spe-cific user location accuracy. However, the per person hardwarerequirements may limit the scale of the installation, and maynot abide by desire to provide an anonymous and non-invasivesolution.

Occupancy and location information have also previouslybeen obtained through sequences of activations of uniquesensors with known, fixed locations. In [5], a heterogeneoussensing network is used to identify and predict the activitiesof smart home residents. To identify the Activities of DailyLiving (ADLs) of interest, some of the sensors considered inthis type of network include: motion, light, door, contact, andtemperature. Many of the ADLs, such as sleeping, cooking,and personal hygiene, can be uniquely associated with a singleroom of the smart home and thus provide room occupancyinformation for the resident. Once the ADLs of a user areobtained, they can be used to provide services customized forthat user. Long-term trends of these ADLs have been shownto correlate with clinical mobility heath assessments for seniorresidents [6], allowing for remote assessment. In a similarmanner, the work in [7] has been used more generally foridentifying deviations from nominal resident activity in a targetsmart home. Sensor activation based location has also beenapplied to producing energy saving recommendations throughthe incorporation of appliance energy sensors [8].

For the action activation approach, location information isobtained from known relationships between the recognizedactions and the locations of the network sensors. It is oftenthe case that confidence in action recognition comes from asequence of multiple sensor events and from the proceedingand following actions. Depending on the complexity of theinstallation, this may impose restrictions on the delay beforeoccupancy information is known. This approach is suitableto maintain anonymity but the invasiveness depends on thesensors required to observe the desired ADLs.

In contrast to the user tracking approaches, area centricmethods obtain occupancy information through changes inenvironmental readings in a local proximity. Environmentalassessment WSNs, such as those for indoor air quality ap-plications [9]–[11], make use of sensors that can observevolatile organic compounds, temperature and humidity. Theseenvironmental readings can then be parsed for changes basedon known or learned relations to occupancy behaviours [12],[13]. As with the action activation method, knowledge ofthe sensor locations and their effective regions is required.Unlike the action activation method, nodes in this approachare not restricted in deployment location by a competing

Fig. 1. Prototype for the experiments. Top: Sensor configuration. Bottom:Processing boards.

sensing objective and thus can be adapted differently duringinstallation of the system. Environment sensing methods havean additional requirement of knowing or learning the rela-tionship between occupancy and the indirect measurementsthey conduct [14]. By not focusing on specific users, theenvironmental methods satisfy anonymity and the indirectnature avoids requiring user attention and interaction.

This work follows the area centric methods through a WSNwith CO2, temperature and humidity sensors for the desiredindirect occupancy measurements.

III. SYSTEM IMPLEMENTATION

This section provides a brief description on the prototypethat was built followed by the data collection procedure.This occupancy modelling network consists of a numberof hardware components for each node. These componentswere selected to facilitate the required sensing and distributedprocessing requirements of the system. The deployment andcoordination of nodes throughout the target space establishesthe sensing network.

A. Prototype

The developed prototype is shown in Fig. 1 and has thefollowing two main units:

i. Sensing Unit: To achieve the desired environmental ob-servation, two sensors are utilized, the IAQ-2000 indoorair quality sensor and the DHT22 Temperature and hu-midity sensor.The IAQ-2000 Volatile Organic Compound (VOC) sensoris used to measure the local concentration of CO2. Whilecapable of reading other compounds such as methane,carbon monoxide and alcohols, in the intended environ-ment CO2 from occupants will be the most prevalent.

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This sensor outputs continuous voltage values relatingto the parts-per-million concentration of the applicableVOCs. These reports are in the range of 450ppm to2000ppm [15] which encompasses the expected range foran office setting [16].The second sensor, the DHT22, provides readings oftemperature and humidity within the ranges of -40◦Cto 80◦C and 0-100% humidity [17] at a sensing periodof 2 seconds. These operation conditions are more thansufficient for the indoor setting. These sensors togetherpermit the observation of the complex relation betweenenvironmental readings and occupancy [12].

ii. Processing Unit: For each sensing node it is necessary tohave interfacing capabilities with the appropriate sensors,the capability to both store and communicate readingsto a centralized location, and the capacity to serve asa filtering point for the data. For these reasons, it wasdecided that the nodes shall include an Arduino UNOand a Raspberry Pi. Both of the selected sensors arewell supported on Arduino but the Arduino alone isinsufficient for the storage and communication capabilitiesrequired in the designed experiment. As such, the Arduinocode used in the prototype focuses on collecting andformatting the sensor data. Then the data are forwardedto the Raspberry Pi. The Raspberry Pi board satisfies theremaining requirements by enabling network connectivityand on-device data backups for the sensor nodes. Allnodes transmit data to a shared Sparkfun server.

The selected sensors are wired to the Arduino for bothpower and communication. The IAQ-2000 sensor runs fromthe available 5V source, drawing between 22mA and 30mAduring operation [15], and reads using an available analog I/Opin.

The DHT22 sensor, attached to the 3.3V source, uses2.5mA during a data request [17]. This request is communi-cated through the assigned digital I/O pin. The Arduino Unoboard receives power from a USB connection to the RaspberryPi which also permits serial communication between the twodevices. As the main processing unit, the Raspberry Pi ispowered through a wall adapter, and for this experiment, isprovided network connectivity through an ethernet connectionto minimize any collisions or interference with wireless com-munication during data collection process.

B. Data Collection

The experiment took place at the third floor of School ofEngineering, at the University of Guelph, Canada, shown inFig. 2. The duration of the experiment was one week.

1) Monitoring area: In total, six prototypes were placed intwo laboratories with different layouts and number of occu-pants. Four prototypes were placed in Laboratory 1, shownin Fig. 3(a), each with effective coverage of 4 desks. Twoprototypes were positioned to cover the doors to the lab. Theremaining two prototypes were placed in Laboratory 2, shownin Fig. 3(b).

Fig. 2. Experimental area: 3rd floor of School of Engineering, University ofGuelph.

(a) Laboratory 1 - larger room with four deployed sensing nodes.

(b) Laboratory 2 - smaller room with two deployed sensing nodes.

Fig. 3. Monitoring areas.

2) Filtering: Each prototype takes environmental readingsat a rate of 0.1Hz. Messages were omitted from transmissionto the central server if the sensor readings had not changed byat least 5 ppm of CO2, and if there was no observed changein temperature and humidity readings. The data was reducedto an effective rate of once per minute by taking the median

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of any readings occurring in the same minute, on a per nodebasis.

3) Data Labelling: Occupancy was labelled manuallythroughout each day at asynchronous intervals. This was doneto ensure a sufficient breadth of normal activity would becaptured for over the experiment period. Due to expectedrate of change of occupancy, each label was propagated to awindow of 10 minutes centred around the time of observation.This propagation was not applied if another label occurredwithin that time frame, in which case the label propagatedhalf way to the neighbouring sample.

IV. EXPERIMENTAL RESULTS

The experiment conducted serves to assess the ability ofthe proposed prototype to support sufficient environmentalobservation for occupancy based models.

A. Acquired Data

The experiment was conducted between the dates of April8, and April 15, 2017. In this time, each of the sensor nodescontributed an average of 54, 140 samples to the centralizedserver. Variation in specific contribution counts is attributedto sensor threshold policy for transmission omission underdiffering levels of activity. The combined samples amount toa total of 15.37MB.

As volatile organic compounds have been shown previouslyto be a strong environmental measure for occupancy [13],the acquired data is considered with respect to a commonbasis, being the observed CO2 concentration. In considerationof the correlations presented in works of indoor air quality,the concentration measurements are shown along with thetemperature readings in Fig. 4 as well as the humidity readingsin Fig. 5 for the complete test duration. The manually obtainedoccupancy labels are shown in Fig. 8. Together these figures al-low for inspection of independent and combined contributionsof each respective signal. These figures present the readingsof a subset of the sensor nodes for clarity. In each figure, CO2

concentration is shown as the lower data series.Throughout the span of the experiment, the sensor nodes

common to each laboratory setting show minimal deviation inthe measurements of both temperature and humidity. For thisindoor setting, nodes in the same space would be subject tothe same ventilation conditions and thus a well mixed thermalvolume is a reasonable model. Due to the scale and rate ofchanges in CO2 as a result of local area occupants, it is stillpossible to observe unique events in the shared air volume.This is seen in the events in the evening of April 9, and inthe afternoon of April 13 where each sensor samples unique,rapid changes in concentration.

Figure 6 shows the unique variations in concentration ob-served by the four sensors deployed in the first laboratory.With fewer deployed sensors, the contrast is immediatelyapparent in the sensors of the second laboratory setting, shownin Fig. 7. The second laboratory is a more compact spaceand the distance from a sensor node to each occupant isreduced compared to the distances in the larger laboratory.

Fig. 4. CO2 and Temperature for sensors one and two over the span of theexperiment.

Fig. 5. CO2 and Humidity for sensors one and two.

Fig. 6. CO2 Concentration for all sensors deployed in Laboratory 1.

A consequence of this proximity is seen in the sustained ele-vation in concentrations observed by sensor five. This compactplacement is also more likely to create a larger overlap in theobservation areas of the sensor nodes. Optimizing the union

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Fig. 7. CO2 Concentration for all sensors deployed in Laboratory 2.

Fig. 8. CO2 and Occupancy label for sensors one and two.

and intersection of coverage areas is not considered in thiswork.

Following a strong precipitation system on the morning ofApril 11, the outside windows in both laboratory settings wereopened at 13:00, remaining open until 9:00 on April 12. Thisperiod aligns with the sensor readings of above 22◦C andgreater than 30% humidity seen in Fig. 4 and Fig. 5. Themix of outside air following the storm is the cause of thepeaks in both temperature and humidity at 24.4◦C and 45.9%respectively, in the evening of April 11. During these elevatedenvironmental measurements, an increase in variance of CO2

concentration was observed.

The occupancy labels shown in Fig. 8 are those acquiredfrom the windowed neighbour propagation of the original,manual occupancy labels. Of particular interest is the narrowcrest in CO2 concentration occurring in the afternoon of April10 in the period of high label density. This region presents akey example of the complex relationship between environmen-tal measures and occupancy as this period is also subject tosignificant increases in both temperature and humidity. Thislimits the effectiveness of single variable separation methods.

TABLE ICORRELATION OF CO2 WITH HUMIDITY.

Occupants

Sensor ID 0 1 2 3 Any

1 0.5178 0.4350 0.4070 0.6130 0.47342 0.5038 0.4022 0.2294 - 0.33703 0.6339 0.5325 0.5560 0.2675 0.37614 0.0160 0.4160 0.4065 0.6793 0.4286

TABLE IICORRELATION OF CO2 WITH TEMPERATURE.

Occupants

Sensor ID 0 1 2 3 Any

1 0.0836 0.2916 0.1356 0.4977 0.26712 0.4071 0.2915 -0.2807 - 0.14253 0.6557 0.2903 -0.1629 -0.3784 0.13804 0.4120 0.0833 0.1282 0.4369 0.2644

B. Discussion

A number of outlier samples were observed in CO2 con-centration throughout the duration of the experiment. Theserapid changes in concentration were most noticeable in thereadings from sensors nodes 1 and 2 which were located nearthe doors of Laboratory 1. For this reason it is expected thatthese deviations are a consequence of transitions of occupantsnot local to the sensor’s area, resulting values beyond thoseexpected for static occupancy. For this experiment, the medianfiltering for minute scale resolution is the primary means ofnoise reduction for the model. Incorporating a moving averagefilter and further tuning the threshold policy for transmissionis expected to diminish the significance of these events.

Table I shows the correlation of CO2 and Humidity mea-surements at each sensor location in the varying states of occu-pancy. This relation of environmental measures is consideredin air quality applications [12] but not considered for previousoccupancy applications. Further investigation of the environ-mental state is shown in Table II which contains a similarcorrelation study targeting instead, the role of temperature.For the majority of cases, temperature was observed to havea less significant correlation to CO2 than that of humidityin the same cases. Infrequently observed situations, such asthree occupants in the area of sensor two which was observedat only three instances over the week, were omitted from thisstudy.

The experimental results have presented examples of thenon-linear and multivariate relationships between the environ-mental measurements and local area occupancy. In contrast tothe previous work, it is thus expected that correlation, a linearevaluation, will have limitations in effectively representing thecomplex relationship observed in standard user settings. Inconsumer applications, lab quality control of the temperatureand humidity in the environment cannot be guaranteed, thusthis complexity must be measured and considered to bestmodel the underlying CO2 occupancy model. Whereas airquality applications have investigated the relation between

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humidity and CO2 for static occupancy, and occupancy appli-cations consider primarily changes in CO2 with occupancy,this prototype permits the incorporation of both into a singlenetwork deployment.

Among sensor nodes in a shared space there is variationin the correlation for equivalent occupancy counts. This isattributed to the limitations in the sensor placements in thatnot all sensors could be placed exactly equidistant to the fourdesks in their local areas. As a result, the influence of a givenoccupant location is not guaranteed to be equal, leading tovariation in readings at for each occupancy count dependingon the observed permutation of seating.

Not all possible influences and correlations between theavailable signals where considered in this experiment. Whilenot considered as the basis for this work, temperature andhumidity were observed to have a minimum correlation of0.84 across all sensor nodes. For future applications of thisprototype, a combined heat index measurement may serve asa suitable replacement in order to reduce the dimensionality ofthe relational problem. For this prototype, it is better to havethe individual metrics available and to perform this operationon the centralized data so that the prototype network may befeasibly transferred to other applications.

V. CONCLUSION

In this work a prototype WSN for environmental monitoringin a smart building was presented. Through localized mea-surement of temperature, humidity and CO2 concentrationat each sensing node, the prototype provides the necessaryinformation to enable occupancy context for a smart system.In addition, the proposed prototype possesses the capability toattribute known environmental impacts on CO2 concentration,not regularly considered previous occupancy models. As ananonymous, non-invasive, and simple solution, refinement ofthe prototype would enable it to serve as the basis for a smartmanagement system.

To better investigate the specific complex relationshipsexisting between environmental measurements and occupancy,further tests of extended duration are required. Once sufficientdata is acquired, the prototype may be equipped with thenecessary functionality to learn and model these relationships,allowing for performance validation against any competingoccupancy methods. In this way, the proposed prototype maybe upgraded to serve as a test bed for learning algorithmswith the intent of improving the generalized deployment ofthe system.

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