Energy Efficient HVAC System with Distributed Sensing and Control

Cheng Li1, 5, Zhenjiang Li1, 5, Mo Li1, 5, Forrest Meggers2, 5

Arno Schlueter3, 5, Lim Hock Beng4, 5

1School of Computer Engineering, Nanyang Technological University2School of Architecture, Princeton University3Department of Architecture, ETH Zurich

4IntelliSys Laboratory, Nanyang Technological University5Future Cities Laboratory, Singapore ETH Center, ETH Zurich

Abstract—This paper presents our implementation experi-ence in building an energy efficient HVAC system for coolingand air conditioning. The system exercises the ”low exergy”theory and leverages high temperature water (18◦C) coolingfor better energy efficiency. In order to achieve this, thesystem decomposes the cooling and dehumidification function-alities, and employs decentralized air control for on-demanddehumidification and ventilation. The system comprises twocontrol modules, namely, radiant cooling module and dis-tributed ventilation module, cooperating with each other toprovide the HVAC control. Abundant sensors and embeddedcontrol devices are customized and instrumented, and wedevelop a wireless sensor network to support control dataexchange among those devices. Our experimental evaluationdemonstrates that the system achieves accurate control targetsand promptly responses to environment dynamics. The wirelesssensor network effectively supports the system needs with longsystem lifespan. Compared with traditional HVAC systems, oursystem is of much higher energy efficiency, as measured by thestandard COP metric.

I. INTRODUCTION

Heating, ventilation, and air conditioning (HVAC) systemsprovide occupants thermal comfort (cooling or heating), airdryness (dehumidification), and good air quality (ventila-tion). Towards green buildings, energy efficiency of HVACsystems continuously draws people’s attention. Until 2012,buildings solely contribute up to half of the world electricityconsumption and 47% of the building energy is attributed toHVAC systems [28], while it was recently reported by U.S.Energy Information Administration (EIA) that at least 30%of the HVAC energy usage is wasted [27].

This paper presents our implementation experience ofBubbleZERO, an energy efficient HVAC system, deployedin a tropical country, Singapore, for cooling and air con-ditioning. BubbleZERO exercises the “low exergy” theoryin building designs and leverages high temperature water(18◦C) cooling to reduce the temperature gradient in theheat circulation process, and thus achieves higher energyefficiency.

The novelties of BubbleZERO are two-folds. First, Bub-bleZERO features a distributed HVAC framework that sepa-rates the cooling and ventilation functionalities in two con-trol modules. Control decomposition allows each individual

module to operate with the lowest exergy and achieve thebest energy efficiency in the heat exchange. The controlmanagement is, however, challenged in such a framework.For instance, due to the control decomposition, condensationoccurs when the air temperature and humidity mismatch.The control management needs to ensure not only the properexecution of individual modules but also their collaboration.We develop and deploy abundant sensing and control devicesand build a wireless network to address the challenges.Second, the unique nature of BubbleZERO and the appli-cation requirements ask for tailored techniques for efficientwireless communication. In particular, we differently treatAC and battery powered devices so as to enable efficientmessage exchange and prolong the network lifespan. Weduty cycle the battery powered devices and control theirtransmission periods according to sensory data dynamics. Byso doing, we reduce the data volume to be transmitted forsaving energy of batter powered devices yet still meet controlneeds. In addition, we let the AC powered devices adapt theirtransmission schedules to alleviate channel contentions. Inlight of this, we reduce the packet loss and delay, and furtherimprove the energy efficiency of battery powered devices.

We implement and comprehensively exercise a prototypesystem to evaluate the performance of BubbleZERO. Ourexperimental study demonstrates that the system accuratelyachieves control targets and promptly responses to environ-ment dynamics. With the outdoor temperature of 28.9◦Cand dew point of 27.4◦C, the system approaches the targetcondition, 25◦C temperature and 18◦C dew point, in 30minutes and maintains on the equilibrium. The distributedcontrol in BubbleZERO well accommodates the disturbancefrom human activities and reacts in low convergence delay.Compared with traditional HVAC systems, BubbleZEROincreases the energy efficiency by up to 45.5%, measured bythe standard COP metric. The wireless network effectivelysupports the control message exchange in BubbleZERO.Our proposed adaptive transmission schedule satisfies thecontrol timeliness requirement and also enables low energyconsumption for battery powered devices, ensuring a longsystem lifespan.

The rest of this paper is organized as follows. §II in-troduces the background of low exergy HVAC. The HVAC

Figure 1. The experimental laboratory of BubbleZERO.

logic and wireless networking in BubbleZERO are detailedin §III and §IV, respectively. In §V we evaluate Bub-bleZERO. §VI is the literature review and §VII concludesthis work.

II. BACKGROUND

Low exergy HVAC. Modern HVAC systems are designedbased on the first law of thermodynamics, i.e., the conser-vation of energy. To approach a desired/target indoor tem-perature, some amount of heat must be absorbed (supplied)for cooling (heating), while the same amount of heat has tobe exhausted to (taken from) the environment. The secondlaw of thermodynamics indicates that an isolated systemspontaneously evolves towards thermodynamic equilibrium.Extra power thus has to be consumed to stimulate thethermodynamic cycle for heat movement, as Carnot andKelvin have proven in [16]. This part of energy consump-tion is directly related to the temperature gradient in thethermodynamic cycle, which is quantified by the conceptof “exergy” in building designs [23]. The exergy, Ex, of aheat flux, Q, moved from a room at reference temperature,T0, compared to its working temperature, T , is defined asEx = Q(1 − T/T0). It has been shown that lower exergyleads to less energy consumption to move the same amountof heat [23]. The temperature gradient determines the exergy,i.e., a higher difference between T0 and T will lead to thedramatically increased energy consumption to accomplishthe heat exchange.

To exercise a low exergy design, we develop the HVACsystem using the 18◦C water as the thermal media tocool the room. Benefiting from the high heat capacity ofwater, we can achieve as much cooling power as the lowtemperature air used in traditional HVAC systems, but witha much lower exergy because of its relatively higher workingtemperature. Due to the humid weather in the tropical area,we need strong dehumidification capacity in addition toprevent condensation, which in our case, however, requiresbelow 10◦C dry air supply and has to be decomposedfrom the cooling module. Our system thus comprises twomodules of separate functionalities, namely radiation coolingmodule (cooling) and distributed ventilation module (de-humidification & ventilation), each of which with its owncontrol logic. The decomposition allows each module to bestreduce its own temperature gradient for the lowest exergyoperation. Compared with traditional systems that use as

1

2

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Figure 2. Overview of BubbleZERO HVAC modules.

low as 8◦C air for both cooling and dehumidification, wecan separately use 18◦C water for radiation cooling and 8◦Cair for dehumidification.

The need for wireless sensors. However, the decomposi-tion of HVAC functional modules requires distributed con-trol decision making, which relies on extensive interactionsand communications among sensing and control devices overthe space. Connecting devices by wire is straightforwardwhereas undesirable as it would add tens or even hundredsof endpoints and miles of cables to the system. The cabledeployment is constrained by existing indoor layout, whichsignificantly challenges endpoints’ installment. Moreover, awired deployment is relatively fixed. If additional hardwareneeds to be integrated for new control demands or existingdevices need to be repaired, it will lead to high systemupgrade and maintenance cost and complexity.

For those reasons, wireless networks serve as the feasibleoption. In addition, networks with IEEE 802.15.4 radios aremore attractive than Wi-Fi radios or bluetooth. 802.15.4network has a simpler protocol stack and devices onlyneed low-end MCUs, e.g., MSP430, which reduces thedevelopment and implementation complexity. On the otherhand, 802.15.4 network stack is completely open-source,which facilitates customizing drivers or interfaces to enable802.15.4 based devices to communicate with commercialHVAC units adopted. Finally, the combination of low-power802.15.4 radio and low-power MCU leads to lower overallpower consumption of the system. The need for low-powerenergy consumption is apparent as HVAC systems requiresustainable deployment.

Deploying an efficient 802.15.4 network for the dis-tributed HVAC system is, however, non-trivial. First, avail-able interfaces of 802.15.4 devices are limited usually suchthat devices may have no common interfaces with com-mercial HVAC units. The network design needs extensivehardware and driver development efforts for interfacing allnetworking endpoints. Second, the data rate of 802.15.4radio is limited, i.e., with the 250 Kbps peak rate while theeffective rate is much lower due to the MAC-layer overhead.It is non-trivial to achieve an effective message exchangefor satisfying control requirements and promptly adapting

2

Figure 3. Radiant cooling module. Cold water tank supplies to the paneland the final water to each ceiling panel is the mixture of supplied coldwater and returned warm water. Flow ¬ and temperature ­ sensors areembedded in the pipes to monitor the water conditions. Two pumps ®control the flow speeds of supplied and returned water.

to environmental dynamics. Effective message exchange isfurther challenged for the high device density in the space.Wireless contention unnecessarily becomes severe and re-sults in high MAC-layer overhead to reduce the channelutilization.

BubbleZERO laboratory. This paper is conceived as partof an interdisciplinary effort in zero emission research inSingapore. As shown in Figure 1, the research laboratoryis built with two standard shipping containers. We removeone metal side of each container and combine them sideby side to mimic an office environment. The indoor volumeis 60m3 = 6m × 5m × 2m. Some novel technologies areapplied to mitigate the thermal difference between metalcontainer and concrete buildings, including special plasticcover filled up with flowing air that wraps the containers toreduce heat exchange, double glazing that selectively allowsmost visible daylight but rejects most heat from infrared, andinsulated high performance facades that have high thermalresistance. With the configuration made by building engi-neering experts, the laboratory has similar thermal attributesand heat exchange performance as normal buildings.

III. DISTRIBUTED HVAC SYSTEM OF BUBBLEZEROA. BubbleZERO HVAC overview

Figure 2 depicts the architecture of BubbleZERO, whichcomprises radiant cooling (blue) and distributed ventilation(yellow and red) two modules. The indoor space is orga-nized into four equal subspaces labelled from subspace-1 tosubspace-4 for ease of performance evaluation in Section V.

Radiant cooling module: radiant cooling module includesone water tank, one chiller, and two metal ceiling panels.Each ceiling panel is associated with two pumps that supplycold water from the tank and cools down the room to apreferred temperature through thermal radiation. The majordesign challenge is to avoid condensation on the ceilingpanel surface in the cooling. Condensation happens if thetemperature T and the humidity H of the air beneath ceilingpanels mismatch, i.e., given H , condensation happens whenT drops below a threshold. It can happen when peopleopen the door or window, the outdoor humid air entersthe room. Hence, this module provides sufficient coolingpower while strictly satisfies the humidity constraint to avoidcondensation.

Figure 4. Deployed radiant cooling module. (a) The pipes mixing supplyand returned water for feedback control;(b) Temperature and humiditysensors deployed on the ceiling panel.

Distributed ventilation module: the distributed ventilationmodule controls the indoor humidity to the target level con-figured by the user. In addition, it ensures a proper humiditythreshold such that the radiant cooling module can providesufficient cooling power without causing condensation. Thismodule also controls the indoor air freshness based on theindoor CO2 concentration.

Above two modules work together to achieve the HVACcontrol objective. A total number of 38 sensors of differenttypes (wired and wireless, general and customized, AC andbattery powered ones) are deployed to coordinate and controlthe two modules. In the rest of this section, we elaborate thedetailed design and implementation of each module.

B. Radiant cooling module

1) Radiant cooling control logic: A chiller is installed tocool down the water in a tank and the cold water is suppliedto ceiling panels via supply pumps. The temperature of thesupplied water Tsupp is 18◦C, which is calculated basedon the cooling need of BubbleZERO. Two radiant panelsare deployed on the ceiling and controlled separately. Forthe ease of description, we depict one ceiling panel and theassociated hydraulic loop in Figure 3. Through the thermalradiation and heat exchange, panels absorb heat from theroom to lower the indoor temperature.

Directly supplying cold water to ceiling panels may leadto condensation on the surface and water drops. Condensa-tion occurs when the air temperature is below a threshold,called dew point. Given the air temperature T and humidityH , the dew point (Tdew) is calculated by:

Tdew(T,H) = a · ln(H/100) + (b · T )/(a+ T )

b− ln(H/100)− (b · T )/(a+ T ),

where a = 243.12 and b = 17.62. The radiant coolingmodule avoids condensation by a feedback design as shownin Figure 3. With a recycle pipe bridging the supply pipeand the return pipe, the module can redirect certain warmwater from the return pipe and mix it with the cold watersupplied from the tank. By controlling the speeds of the twopumps, we can adjust the percentage of the cold and warmwater in the final mixture and thus control its temperature.Figure 4(a) depicts the connections of those pipes.

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Figure 5. Customized boards for radiant cooling module: (a) Control-C-1for temperature sensor control; (b) Control-C-2 for flow sensor and pumpcontrol.

In Figure 3, Tmix and Fmix represent the temperature andthe flow rate of the mixed water, respectively. They are thecontrol parameters that together determine the final coolingperformance. A lower Tmix and a larger Fmix providehigher cooling capacity. Tmix should be sufficiently low toguarantee adequate cooling capability; Meanwhile, it shouldalso be sufficiently high (above the ceiling panel surface dewpoint T c

dew) to prevent condensation. In our design, Tmix iscontrolled to achieve the target T t

mix = max{Tsupp, T cdew}.

T cdew is calculated based on the readings collected from

6 temperature and humidity sensors deployed below theceiling panel as shown in Figure 4(b).

• If Tsupp > T cdew, the cold water from tank can be sup-

plied to the ceiling panel directly, i.e., Tmix = Tsupp.• If Tsupp < T c

dew, the recycle pump is enabled toincrease Tmix to T c

dew for preventing condensation.Given Tmix, we further control Fmix to provide suitable

cooling capacity according to the preferred temperatureTpref (set by the occupant) and the current room temperatureTroom. We use the temperature difference ∆T = Troom −Tpref to control Fmix. Troom is computed by averagingtemperature readings from a set of sensors deployed in theroom. If ∆T is positive, Fmix is increased to provide morecooling power; Otherwise, Fmix is decreased. To achieve arapid and robust control of Fmix, we adopt the Proportional-Integral-Derivative (PID) algorithm in the control. The PIDcontroller takes the preferred temperature Tpref as the input,transforms it to a flow rate value F t

mix as output. F tmix acts

as the control target of Fmix. F tmix is adapted by properly

controlling voltages Vsupp and Vrcyc of the supply andreturn pumps. The adaptation of Fmix impacts the indoortemperature, which deductively feedbacks to refine the F t

mix.The PID controller finally outputs a stable flow rate valuewhen ∆T approaches to zero.

2) Sensor development and deployment: We embed 8ADT7410 digital temperature sensors (“temperature sensor”in Figure 5) in the water pipes to measure Tmix, Tsupp, Trcycfor both ceiling panels. The sensing accuracy is ±0.5◦C. Wedevelop an interface board, named Control-C-1, as shownin Figure 5(a), which embeds an ARM micro-controllerLPC1114 to connect ADT7410 temperature sensors via I2Cinterfaces. The board is then integrated with a TelosB motefor computation and communication. We calculate T t

mix on

8

Figure 6. Overview of the distributed ventilation module.

this board.We embed 6 VISION-2000 flow sensors (“flow sensor”

in Figure 5) in the water pipes to measure Fmix, Fsupp,Frcyc. A flow sensor outputs a series of pulses and thepulse frequency is proportional to its measured flow rate. Weuse two DC pumps (“water pump” in Figure 5) to circulatewater in the cooling system, which takes a voltage signalranging from 0V to 5V as the input to control its speed. Wedesign another control board, named Control-C-2, as shownin Figure 5 to collect the flow rate data and control theDC pumps. The attached TelosB mote receives temperaturemeasurement from Control-C-1 as well. The PID controllerfor F t

mix is implemented on this board. Based on the PIDoutput, the micro-controller LPC1114 instructs the DAC togenerates appropriate voltage values to drive the two DCpumps. Both boards are deployed behind the water pipesand wired to the pipe sensors (see Figure 4(a)).

C. Distributed ventilation module1) Distributed ventilation control logic: Distributed ven-

tilation module controls indoor humidity and CO2 concen-tration levels. It contains a tank supplying cold water (8◦C),and four pairs of airbox and CO2flap, dividing BubbleZEROinto four equal subspaces. Each subspace contains oneairbox and CO2flap pair as shown in Figure 6. Airboxesand CO2flaps cooperate to ventilate the room and four pairsare controlled separately. An airbox consists of four DC fans(inhale air), one damper (prevent the air leakage when fansare not working), one filter (remove dusts), and 3 copperpipes (dehumidify) circulated with cold water. A CO2flapis integrated with an exhaust channel. By driving the flap,CO2flap exhausts the indoor air to the outside.

The ventilation module blows dry and fresh air into theroom to neutralize both the indoor humidity and air CO2

concentration to the target levels. As the outdoor air isusually humid, it needs to be dehumidified before blowninto the room. The airbox dehumidifies the outdoor airby utilizing the copper pipe array flowing with cold water(8◦C). As the air temperature decreases, the contained watervapor becomes saturated and condensates to drops. Due tothe separation of water vapors from the air, the air humiditydecreases. Two parameters are controlled to achieve effec-tive ventilation, i.e., the dryness of the airbox output air

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Figure 7. The deployed distributed ventilation module. (a) The deployedairbox; (b) The boards and sensor to control Airbox; (c) The CO2flap; (d)The CO2flap installed on the ceiling.

(measured by its dew point T adew) and the ventilation speed

Fvent.We first introduce how to decide the dew point of the

output air from airboxes T adew. We denote its control target

as T a,tdew, which is determined by other three values T p

dew,Tsupp, and T r

dew. T pdew is the dew point calculated by the

preferred temperature Tpref and humidity Hpref set bythe occupant. Tsupp is the water temperature in the tankfor radiant cooling. T r

dew is the current room dew pointcalculated using the average room temperature Troom andhumidity Hroom that can be obtained from temperature andhumidity sensors deployed in the room. We set the targetdew point of the room air T r,t

dew to min{T pdew, Tsupp}. To

ensure the humidity requirement from the occupant andavoid condensation, T a,t

dew is determined by:

• If T r,tdew < T r

dew, T a,tdew is set to T r,t

dew − 2◦C to quicklypull down the room air dew point to the preferred level.

• If T r,tdew > T r

dew, T a,tdew is set to T r,t

dew to maintain theroom air dew point.

The flow rate of the circulated water inside the copper arrayin airboxes is linearly proportional to the dew point of the air,i.e., a higher flow rate leads to a lower output air dew point.This property suggests to control the water pump speed forapproaching the target output dew point T a,t

dew. We design asimilar PID controller as that in the radiant cooling moduleto ensure the control accuracy and convergence speed.

DC fan speeds determine the ventilation volume. Theexact amount of air needed individually for dehumidi-fication Vhumd and CO2 concentration deduction VCO2

can be calculated based on the volume of the room, thedifference between the current and target humidity levels,and the difference between the current and target CO2

concentration levels. To promptly approach to the controltargets in T seconds (e.g., 60 seconds), the final ventilationspeed Fvent is calculated by max{Fhumd, FCO2

}, whereFhumd = Vhumd/T and FCO2

= VCO2/T . According to

the airbox hardware specification, we can lookup the bestmatched DC fan speed for the given Fvent. When DC fansare working, CO2flaps are open, driven by a stepper motor,for exhaust.

2) Sensor development and deployment: We embed oneDC pump and one VISION-2000 flow sensor in the supplypipe (for each airbox) from the tank. All sensors and pumps(of four airboxes) are connected to another control boardthe same as Control-C-2 depicted in Figure 5(b), namedControl-V-1. Control-V-1 locally calculates T p

dew. It alsocommunicates with Control-C-1 to obtain Tsupp, and collectsreadings from temperature and humidity sensors deployedin the room to calculate T r

dew. T a,tdew can be thereafter

computed. We further install one SHT75 sensor for eachairbox to measure the temperature and the humidity of theoutput air to calculate T a

dew. Based on the difference betweenthe measured T a

dew and its target, the PID controller isimplemented on Control-V-1 to adjust the flow rate.

The commercial airbox fans used in BubbleZERO onlyhave RS232 ports for interfacing. To control their speeds,we integrate with a TelosB mote through an RS232 adapterand collect readings from Control-V-1 (for T a

dew and Hroom)and CO2 sensors deployed in the room. The TelosB driverthus drives the speeds of DC fans with RS232 read andwrite. We denote the TelosB based driver as Control-V-2. In addition, SHT75 sensors are also connected to thecustomized TelosB motes through the extended pins on themotes. Such temperature and humidity information is neededby Control-V-1 for the T a

dew calculation. Figure 7(b) depictsour developed RS232 adapter and Control-V-2.

Similar to airboxes, we integrate the CO2flap with TelosBvia the RS232 adapter. We upgrade the TelosB-based driverto control the stepper motor and also communicate withthe CO2 sensor of CO2flaps, as shown in Figure 7(c). Wedenote the TelosB based control board as Control-V-3. EachCO2flap is integrated with one Control-V-3 board which isattached to the ceiling panel as shown by Figure 7(d).

IV. WIRELESS NETWORKING

As the previous section introduces, the BubbleZEROsystem involves rich control and sensing devices distributedover the space. Each of the control boards and specialpurpose sensors is integrated with a TelosB mote such thatall devices can compute and communicate through TelosBmotes. Figure 8 depicts the data supply and consumptionrelationship among those devices. Each arrow in the figureindicates one pair of supplier and consumer. Sensors serveas data suppliers, while most control boards serve as dataconsumers and some of them also provide processed datato others. One data packet is usually needed by multipledestinations. On the other hand, the system also featuresheterogeneous power supplies. Some devices get AC power(e.g., ac-device), while others only get batteries due to prac-tical limits (e.g., bt-device). This section describes our effortin developing a suitable wireless networking mechanism thataddress these challenges.

A. Message exchangeUnlike most traditional sensor network solutions that first

aggregate the sensor data at some “sink” node [14], we letthe data suppliers directly feed the data to the consumersin a peer-to-peer style, which complies with the distributed

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Figure 8. Data supply and consumption relationship among differentdevices.

decision making nature of our system. As Figure 8 shows,the same supplied data may be consumed by multiple controlmodules, so we do not explicitly address the receivers of thedata. Instead, we let the suppliers categorize and address itsdata messages to certain “types”, e.g., temperature, humidity,CO2 concentration, etc, and broadcast data to the wirelesschannel. All potential consumers fetch data messages fromthe wireless channel and filter out messages with undesiredtypes. Since TelosB motes can reliably communicate up to50m in the indoor environment, all potential consumers canreceive messages with high success rates in BubbleZERO.Devices pick up the useful ones for calculation and controlaccording to their application needs, which makes the bestuse of the wireless broadcast effect and thus saves unnec-essary transmissions. When multi-hop communication mustbe concerned in large-scale environments, we can poten-tially extend our design by forming “type” based multicastgroups and routing messages with existing ad-hoc multicastapproaches (e.g. [22]). We leave it as an important futurework of this paper.

B. Adaptive sensory data transmission by bt-devices

A half of devices in BubbleZERO are powered by batter-ies. It is prohibitive to configure bt-devices in an always-onmode; Otherwise, batteries last less than one week. In fact,controllers can preserve control accuracy as long as the gran-ularity of the input data captures environmental dynamics.To prolong the network lifespan meanwhile meet controlneeds, we thus duty cycle data sampling and transmittingfor bt-devices.

Sampling period. The period length of data sampling canbe empirically investigated. We first analyze sensory datastability in stable indoor environments. We further triggerdifferent events in BubbleZERO, e.g., opening door, openingwindow, occupant density varying, occupant transition be-tween different rooms, which influence the indoor environ-ments, e.g., temperature, humidity, CO2 concentration, and

varmin=0 varmax=10C1 C2 C3 C4 C5j

U1=5

U2=10

U3=3

U4=7

U5=5

Figure 9. Illustrative example of histogram based approximation mecha-nism.

analyze the resulting sensory reading dynamics. Throughexperiments, we observe that indoor environmental factorsdemonstrate high stability while external events could causereadings to diverge from the stable state for several minutes.To ensure a high sampling granularity, we set the datasampling rate two orders higher than the dynamic duration.In particular, the sampling period Tspl for temperature,humidity, CO2 concentration sensors in BubbleZERO is setto be 3s, 2s, and 4s, respectively.

Sending period. As packets in BubbleZERO are addressedby data types, e.g., temperature, humidity, CO2 concentra-tion, different types of sensor data are transmitted usingseparate packets with individual transmission periods. Aftertransmitting a data packet, a bt-device sets a timer for thenext packet of the same data type using the correspondingtransmission period Tsnd. When timer becomes expired, itsends out the next packet and resets the timer using Tsnd(the value of Tsnd may change which will be detailed soon).Due to the fact that the power consumption of readingon-board sensors is much lower than transmitting packets,e.g., 0.3mW for sampling and 54mW for transmitting, weduty cycle sampling and transmitting with different periods.When sensory data are stable, a long Tsnd is used tosave bt-devices’ energy. Tsnd will adjust to be short if theenvironmental dynamics occur. In particular, Tsnd is set to bew times of the sampling period Tspl, i.e., Tsnd = w× Tspl,where w is a positive integer. Parameter w is adaptivelydetermined based on the data variance within a sliding timewindow.

With m samples in the time window, X ={x1, x2, ..., xm}, the data variance is calculated asvar(X) = E[(X −E(X))2] = E(X2)− (E(X))2. Slidingwindow advances whenever a new sample enters the windowand the new variance is calculated. If the variance is greaterthan a threshold λ, the sensory reading is considered notstable, i.e., in a transition state. The device adjusts Tsndthe same as Tspl and immediately resets the timer usingthe updated Tsnd (= Tspl) to provide a prompt and fine-grained sensory data updating. If the variance is smaller thanλ, the device regards the reading to be stable and remains tofollow the current transmission period Tsnd, whereas Tsndis doubled if the variance does not exceed the threshold after10 successive Tspls, until an upper bound is reached. We setthe maximum w to be 32 in our current implementation.

Threshold λ. Threshold λ aims to divide historical vari-

6

ances into two clusters. The cluster with smaller variancesrepresents the stable state and another cluster represents thetransition state. How Tsnd is adapted is determined by whichcluster the new variance value falls into. We note that afixed λ configuration limits the feasibility and applicabilityof the system as the environment dynamics levels are highlydiverse in different time and areas. Threshold λ shouldbe determined automatically as the system executes. Theoptimal threshold is the one that minimizes the total intra-cluster distance, i.e., summation of the distances from eachvariance value to its cluster center, which can be learnedfrom historical variance values. However, it is not practicalto locally store all specific variance values as it requiresincreasing amount of memory space and computation com-plexity in the clustering. To address this issue, we pro-pose a histogram based mechanism to approximately recordvariances and compute λ, trading certain classification ac-curacy for constant memory occupancy and computationcomplexity. In our method, devices record the maximumand minimum variances, varmax and varmin, observed sofar, and divide its difference into N slots. Each slot is of∆var = (varmax−varmin)/N step length and any slot i isrepresented by its slot center ci = varmin+(i−0.5) ·∆var,where i = 1, 2, · · · , N . Instead of storing all historicalvariance values, devices round each variance value to theclosest slot center and maintain a counter Ui to count thenumber of variance values falling into each slot i. Devicesuse such a histogram to approximate all variance valueswithin each slot. In Figure 9 for cluster 1, U1 = 5 meansfive variance values falling into slot 1. For any slot i, devicesonly maintain a counter Ui. The memory occupancy of ourmethod is thus constant for any given N .

Given varmax, varmin, N , and Ui, where i =1, 2, · · · , N , we numerate all possible N − 1 clusters, i.e.,slots 1 to j form the first cluster, denoted as fcls, andslots j + 1 to N form the second cluster denoted as scls,where j = 1, 2, · · · , N − 1. Centers of clusters fcls andscls are cc1 = 1

j

∑jk=1 varmin + (k − 0.5) · ∆var and

cc2 = 1N−j

∑Nk=j+1 varmin+(k−0.5) ·∆var, respectively.

We find the index j? which minimizes the total intra-clusterdistance as the optimal classification position and set thethreshold λ = varmin+j? ·∆var. The threshold λ selectionis formalized in Algorithm 1. In principle, the optimal λcan be calculated whenever a new variance is obtained. Tosave energy for bt-devices in practice, the updating of λis periodical, which is empirically set to be 20 minutes inour current implementation, i.e., before the next updating, λremains the value obtained in the latest updating.

In Figure 9, we illustrate the intra-cluster distance cal-culation. In this example, varmax = 10, varmin = 0,N = 5, and j = 3. Each Ui is plotted in the figure, wherei = 1, 2, · · · , 5. cc1 = 1

3

∑3k=1 0 + (k − 0.5) · 2 = 3 and

cc2 = 12

∑5k=4 0 + (k − 0.5) · 2 = 8. The total intra-cluster

distances Sum1 and Sum2 in fcls and scls equal to 16(=

∑3k=1 Uk×|ck−cck| = 5×|3−1|+10×|3−3|+3×|5−3|)

and 12 (= 7×|8− 7|+ 5×|9− 7|), respectively. Therefore,the total intra-cluster distance is 28 (= 16+12) when j = 3.

Algorithm 1 Threshold λ selectionSummin = +∞for (j = 1; j < N ; j + +) do

Sum1 = sum of intra− cluster distance in fclsSum2 = sum of intra− cluster distance in sclsif Sum1 + Sum2 < Summin then

Summin = Sum1 + Sum2

λ = varmin + j ·∆V arend if

end forReturn λ

It is clear that the computation complexity of our method isalso a constant for any given N .

During the system execution, if either varmax or varmin

is changed, histogram values will be rounded to N new slotcenters. In Section V, we evaluate the impact of the varmax

and varmin dynamics on the clustering performance. Weobserve that after observing certain events, varmax andvarmin become stable and our histogram based methodachieves comparable performance with the method usingeach original variance value. On the other hand, after Al-gorithm 1 runs for a long time, e.g., one week, each Ui

can be reset to be zero to eliminate approximation errorscumulated in the past week.

V. PERFORMANCE EVALUATION

The full BubbleZERO system has been implemented andstably operated in several real experimental trials. We installTelosB based sniffer nodes to collect all network packetsand log all control data with time stamps, based on whichwe conduct full analysis on the system performance. Toexamine the energy efficiency, we also install power metersat major energy consuming devices, including chillers andpumps. The power consumption of remaining devices canbe calculated based on their working statuses (e.g., voltageand workload) and durations from the data logs.

A. HVAC performanceThis section evaluates the HVAC performance of Bub-

bleZERO. For a fine-grained demonstration, we look atthe four equal subspaces, each containing one airbox andCO2flap pair, and examine the performance for each sub-space. Figure 10 summarizes the experimental results fromour latest trial.

Overall performance: Figure 10 depicts the result loggedfrom the experiment conducted from 13:00 to 14:45 in oneafternoon. The experiment includes two phases. Initially,the indoor condition is similar as the outdoor, with 28.9◦Ctemperature, 27.4◦C dew point of humidity. The controltargets of temperature and humidity dew point are set to25◦C and 18◦C respectively. In the first 60 minutes, thesystem boots up, approaches, and maintains at the targetstate. From Figure 10(a), we see that the system cools downall four subspaces from 28.9◦C to 25◦C in the first 30minutes. Afterwards, the room gradually approaches andmaintains at the target temperature value. The humidity

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13:00 13:30 14:05 14:25

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)

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Figure 10. Overall HVAC performance in Bub-bleZERO from 13:00 to 14:45 in one afternoon.

AirCon Bubble−C Bubble−V BubbleZERO0

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Figure 11. Energy efficiency comparison viastandard COP metric measuring from power me-ters.

0 10 20 30 40 50 60 70

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Figure 12. Parameter N selection in histogrambased method for adaptive sensory data transmis-sions.

reduction has a similar trend. Dew points of four subspacesdrop from 27.4◦C to 18◦C in 30 min and stay around thisvalue as depicted in Figure 10(b).

We introduce disturbance in phase two to examine thecontrol stability. The second phase starts from 14:00 andlasts 45 minutes. At 14:05, we open the door for 15seconds, but do not enter the room, during which the hotand humid outdoor air enters the room. As the door isin subspace-1 and close to subspace-2, the humidities ofthe two subspaces immediately increase but with a slightamount (0.6◦C at its dew point). The airboxes in subspace-1 and subspace-2 promptly react and the humidity changeis quickly controlled. Figure 10(b) shows such a process(between 14:05 and 14:15). Meanwhile, the temperaturesof subspace-1 and subspace-2 also slightly increase but arequickly pulled down to the target value. At time 14:25,we leave the door open for a relatively long period of 2minutes. Both the temperatures and the humidities of allfour subspaces are significantly increased. As Figure 10(a)and (b) show, the system reacts and adapts back to the targettemperature in 15 minutes.

In summary, Figure 10 demonstrates the effectiveness ofBubbleZERO in accurately achieving the control targets andpromptly responding to environment dynamics.

B. Energy efficiency

We use the standard Coefficient of Performance (COP)metric to examine the energy efficiency of BubbleZERO. Fora cooling system, COP = Removed heat from the room

Consumed power . Alarger COP value indicates higher energy efficiency. We in-stall energy meters to measure the Consumed power of thechillers. We calculate the Removed heat from the roomof the cooling and ventilation systems based on the watersupply measurements: with the suppling water of supplytemperature Tsupp, return temperature Tretn, and flow rateF , the removed heat is Premove = c · F · (Tretn − Tsupp),where c is a constant related to the water thermal capacityand density.

We compare the energy efficiency of BubbleZERO withprevalent Aircon HVAC systems in Figure 11. Traditional airconditioning systems (AirCon) achieve the COP of about 2.8[23][26]. For BubbleZERO, the power meter results showthat the radiant cooling module absorbs 964.8W of heat

from room, while its chiller consumes 213.4W of electricalpower. Ventilation module absorbs 213.2W of heat frominhaled air and consumes 75.6W . We thus can calculatethe COP of BubbleZERO according to the measurementresults. In particular, the COPs of the radiant cooling module(Bubble-C) and the ventilation module (Bubble-V) are 4.52(= 964.8/213.4) and 2.82 (213.2/75.6), respectively, whichtranslate to an overall COP value of 4.07 (= 964.8+213.2

213.45+75.6 ),denoted by BubbleZERO in Figure 11. Compared with“AirCon”, BubbleZERO improves the energy efficiency byup to 45.5%.

C. Networking performance

Overall BubbleZERO meets the control requirements withhigh energy efficiency. For a fine-grained understanding, wedetail the performance of our adaptive transmission methodsfor both bt-devices and ac-devices in this subsection. To thisend, we re-launch BubbleZERO for 5 hours in one afternoonand trigger external events, e.g., door opening and windowopening, about every 30 minutes. In addition, we install flashmemory to each device and record the ground truth of allreceived and generated data by the device with time stampsfor the performance analysis.

Choosing the right N . We first investigate parameter N inthe histogram based mechanism, which divides the variancedifference varmax−varmin into N slots to cluster the newlyobserved variance and decide how to adapt transmissionperiod Tsnd. In general, a larger N leads to higher clusteringaccuracy while incurs more computation overhead and bufferoccupancy. By referring to the data log, we can get theadaptation decisions made by each bt-device. As we alsologged the ground truth of all variance values, we canfurther use exact variance values to conduct clustering andobtain the optimal adaptation decisions. For each bt-device,we define accuracy as the ratio between the number ofadaptation decisions made by our histogram mechanismwhich are the same as the corresponding optimal decisions,and the number of adaptation decisions made in total. Theaccuracy depicted in Figure 12(a) is the average accuracyamong all bt-devices. Figure 12(a) shows that when N islarge enough, inconsistent adaptations due to the histogramapproximation are minimal and the accuracy reaches around98%. However, a large histogram size could incur extensive

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0 3600 7200 10800 14400

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Figure 13. Adaptation accuracy as time elapses.

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Figure 14. Tsnd adaptation due to externalevents.

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Figure 15. CDF of Tsnd.

computation and storage overhead to bt-devices, e.g., whenN = 60, it takes 130 bytes (out of 10K bytes RAM) to storethe entire histogram (Figure 12(b)) and 1600ms to completeclustering (Figure 12(c)). To balance accuracy, storage, andcomputation, we select N = 40 as the default setting.

Accuracy as time elapses. Figure 13 further depicts the av-erage adaptation accuracy of bt-devices as time elapses givenN = 40. Initially, the accuracy is relatively low. It is becausevarmax and varmin are not stable before sufficient externalevents are encountered. When either varmax or varmin

varies, the histogram is reformed and the approximationerror might lead to clustering errors. Improper adaptationsmay either unnecessarily lead to a short transmitting periodto waste energy or harm the timeliness of the updating. Fromthe experiment results, we find that the latency for varmax

and varmin becoming stable is short in practice. In partic-ular, varmax stabilizes after 1.5 hour and varmin stabilizesafter 140 seconds. Afterwards, the accuracy remains to behigh, ranging between 97% to 99%.

The direct consequence of the transmission adaptationmethod is that Tsnd of bt-devices varies to save energy forbt-devices. In Figure 14, we plot a snapshot covering fivedoor opening events for one bt-device. When the indoorenvironments are stable (measured by the indoor dew point),Tsnd for temperature data equals to 64 seconds which isthe multiply of the sampling period (2 seconds) and themaximum w (= 32). After the door is open, the indoordew point increases rapidly, and Tsnd adjusts to 2 secondspromptly for a fine-grained updating against such dynamics.However, due to clustering errors, Tsnd adaptation might bedelayed as shown by the zoomed-in sub-figure in Figure 14.According to the statistics, the delay is slightly, where themaximum delay in this experiment trail is 4s and the averagedelay is 2.7s.

We further investigate the Tsnd distribution in Figure 15.Compared with the Fixed scheme which conservatively setsTsnd to be the same as Tspl (= 2 seconds), our BT-ADPT scheme adaptively adjusts Tsnd from 2 seconds till64 seconds, and achieves an average transmission period of48 seconds. According to the energy profile of bt-devices,if external events on average occur every 30 minutes, thebattery powered nodes can sustain longer than 3.2 years with2 common AA batteries benefitting from our transmissionadaptation method. On the contrary, if the transmission

period is fixed, the lifespan of bt-devices is reduced to 0.7years merely.

VI. RELATED WORK

HVAC acts as a basic component in today’s buildings.Modern HVAC systems, however, still suffer from verylow efficiency, wasting tremendous energy [3]. BubbleZEROboosts the efficiency of the HVAC system for cooling and airconditioning by leveraging high temperature water at 18◦Cas the cooling medium. Similar ideas of the water-basedradiation and distributed ventilation have been previouslyexplored in [23] and [5]. Water based radiation has beenexplored for heating purpose [23]. Google also employssea water for cooling the hot servers at its data centers[1]. Those two applications, however, work at relativelyhigh temperature or with dry air, and never face the con-densation challenge as BubbleZERO does. BubbleZEROemploys distributed and wireless cooperated control modulesto overcome such challenges.

Many sensing based approaches have been explored tofacilitate the HVAC control. The Smart Thermostat [21]instruments buildings with passive infrared sensors to learnthe occupant behavior patterns and adaptively control theprogrammable thermostat to save energy. [2] further in-vestigates the deployment of cheap occupancy sensors inbuildings for aggressively duty-cycling the HVAC systemwithout compromising users’ comfort. Thermvote [10] lever-ages participatory sensing and takes human feeling of com-fort for efficient HVAC operation to achieve both humancomfort and energy saving. There are abundant other worksthat study optimal HVAC control strategy in response tooccupancy activities [11], [9], [24], [8], [25]. A most recentstudy looks at the central HVAC system of a building andprovides a unified way of building service abstraction andHVAC control [7]. Sentinel [4] further leverages existingWi-Fi infrastructure to save HVAC energy in commercialbuildings. None of existing works, however, aim at deeplyinstrumenting and coordinating the HVAC system itself toimprove its energy efficiency as BubbleZERO does.

Many efforts have been put into wireless sensor basednetworking and monitoring. Gu et al. [15] propose a dy-namic switch based forwarding scheme DSF to optimize datadelivery ratio, delay, and energy consumption. Ganti et al. in[13] design a datalink streaming transmission paradigm for

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high network throughput. [6] introduces a utility-based asyn-chronous flow control algorithm for congestion avoidance.Wireless bus [12] and Chaos [18] leverage the concurrenttransmissions for higher throughput. A reliability-orientedtransmission service for sensor networks is proposed in[20]. On the other hand, Zhu et al. [30] introduce aprobabilistic approach to provisioning guaranteed QoS fordistributed event detection. Lin et al. [19] introduce anadaptive transmission power control scheme called ATPCfor wireless sensor networks. TriopusNet is proposed in [17]for the pipeline monitoring using wireless sensor networks.However, those works are not directly related to HVACcontrol and do not take control timeliness and power het-erogeneity into consideration as targeted in this paper.

VII. CONCLUSION

This paper presents our implementation and experiment-ing experience of BubbleZERO, an energy efficient HVACsystem with distributed cooling, dehumidification and ven-tilation. Leveraging a network of abundant sensors and cus-tomized control devices, we fully instrument the HVAC loopand provide coordinated control in a decentralized way. Ourexperiment results demonstrate that BubbleZERO meets thestringent control requirements with higher energy efficiencycompared to traditional HVAC systems. In our future work,we will mainly focus on improving the scalability of Bub-bleZERO, including the extension to multihop networkingconditions, improvement on its real time reactions, as wellas optimized aggregation of sensing and control information,so as to support building level deployment and integration.

VIII. ACKNOWLEDGMENT

This work was established at the Singapore-ETH Cen-tre for Global Environmental Sustainability (SEC), co-funded by the Singapore National Research Foundation(NRF) and ETH Zurich. We acknowledge the support fromSingapore MOE AcRF Tier 2 grant MOE2012-T2-1-070,and NTU Nanyang Assistant Professorship (NAP) grantM4080738.020.

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