SPIRI: Low Power IoT Solution for Monitoring Indoor Air Quality

John Esquiagola, Matheus Manini, Arthur Aikawa, Leopoldo Yoshioka and Marcelo Zuffo

Electronic Systems Department, Sao Paulo University, Sao Paulo, Brazil

Omni-Electronica Ltda, Sao Paulo, Brazil

Keywords:

Internet of Things, Smart Buildings, Indoor Air Quality.

Abstract:

Annually, millions of people worldwide die prematurely as a consequence of air pollution. Many of these

deaths occur in large cities, where exhaust from cars, factories, and power plants ﬁlls the air with hazardous

particles. However, the issue is not only in outdoor areas of the cities because most people spend more than

90% of their time in their houses, ofﬁces or cars. Indoor air pollution (IAP) affects human health, safety,

productivity, and comfort. There are some reports about attacking the indoor air quality (IAQ) problem by

utilizing IoT technology, but most solutions are driving the urban environmental problem. This paper presents

the SPIRI platform which proposes to measure IAP using an IoT network of connected sensors that gather and

send important information like temperature, relative humidity, volatile organic compounds (VOC), particulate

matter (PM), among others. Using this data, indoor environments can be mapped, track changes over time,

identify pollutions sources, and analyze potential interventions to reduce the IAP. Initial results of the current

development of our IoT platform to perform the realtime monitoring of the IAP are presented. Hardware

and software are also presented because our solution needs to be aware of the current IoT challenges such as

scalability, security and interoperability. Both 6LoWPAN and IEEE 802.15.4 standards were implemented to

establish the communication between the devices.

1 INTRODUCTION

Air pollution is a critical issue nowadays, millions

people worldwide die each year as a consequence of

air pollution. Many of these deaths occur in large

cities, where exhaust from cars, factories, and power

plants ﬁlls the air with hazardous particles (EPA,

2017b). Indoor concentrations of pollutants or indoor

air pollution (IAP) can be higher than outdoor con-

centrations up to ﬁve times, due to incorrect func-

tionality coming from Heating, ventilation, and air

conditioning (HVAC) system. People spend a con-

siderable amount of time indoors at home, ofﬁce or

transportation means. For instance, 4.3 million peo-

ple a year die prematurely due to IAP (WHO, 2017).

Indoor Air Quality (IAQ) can be affected by sev-

eral agents or parameters like temperature, humid-

ity, volatile organic compounds (VOC), particulate

matter (PM), aerosols, etc. There are many reports

that demonstrate insufﬁcient IAQ levels can generate

health problems to building occupants (Zhou et al.,

2017) (De Gennaro et al., 2013). The World Health

Organization (WHO) has published some guidelines

to protect people living in indoor environments. The

report presents the common substances that can be

found and the maximum concentrations to prevent

health risks (Penney D, 2010). The term ”Sick Build-

ing Syndrome” (SBS) has been reported many years

ago and it is used to describe health and comfort

problems related to time spent inside buildings (Joshi,

2008). These complaints can be found in speciﬁc ar-

eas or among the whole building. Popular symptoms

of SBS may include itchy, irritated, dry or watery

eyes, nasal congestion, throat soreness, itchy skin,

headache, lethargy, or difﬁculty concentrating. Some

causes of SBS can be high building temperature, poor

ventilation, high humidity, sealed windows, paints,

coatings, etc. (Guo et al., 2013) (EPA, 2017a). To

avoid such serious consequences of the SBS, an IAQ

monitoring system is utmost required. An IoT net-

work can be a wireless sensor network (WSN) with

several dedicated sensor nodes, which can sense and

monitor the physical parameters and transmit the col-

lected data to a central location using wireless com-

munication technologies. Then, we can take advan-

tage of the IoT technology to save lives and to detect,

analyze and improve indoor environments where the

pollution is a real problem.

In this paper, we present the SPIRI platform, our

IoT solution for monitoring indoor environments. We

focus on the main parameters that determine the pol-

lution inside buildings like ofﬁces, hospitals, homes,

schools, etc. We have developed our custom hard-

Esquiagola, J., Manini, M., Aikawa, A., Yoshioka, L. and Zuffo, M.

SPIRI: Low Power IoT Solution for Monitoring Indoor Air Quality.

DOI: 10.5220/0006783002850290

In Proceedings of the 3rd International Conference on Internet of Things, Big Data and Security (IoTBDS 2018), pages 285-290

ISBN: 978-989-758-296-7

285

Figure 1: IoT System Architecture.

ware and ﬁrmware focusing to drive current IoT chal-

lenges such as scalability and security. The remain-

der of this paper is organized as follows: Section II

presents the related work about IoT solutions for air

quality monitoring. Section III highlights the archi-

tecture overview of our IoT platform. Initial results

and analysis are presented in Section IV. Section V

concludes the paper with ﬁnal considerations and fu-

ture work.

2 RELATED WORK

In IoT systems, there are lots of applications being

developed by the academic and industry communi-

ties. The heterogeneity of the components of an IoT

solution implies the development of new test meth-

ods and architectures to ensure the performance of the

system and to meet user requirements. A system of

several distributed monitoring stations that communi-

cate wirelessly with a back-end server using machine-

to-machine communication was presented in (Kadri

et al., 2013). This solution collects urban information

from gaseous and meteorological sensors and send

through a wireless sensor network. Data is available

in a web portal and in a mobile application. A real

time wireless air pollution monitoring system is an ef-

fective solution for pollution monitoring using wire-

less sensor networks (WSN) (Prasad et al., 2011). The

solution uses commercially available discrete gas sen-

sors for sensing the concentration of gases like CO

, CO and O

. Zigbee technology was used to im-

plement the wireless sensor networks with multihop

data aggregation algorithm. A Smart global Air Qual-

ity Monitoring system is presented by (Mohieddine

et al., 2017). The system employs different technolo-

gies, such as gas sensing, WSN and Smart mobile. It

is a typical case of an IoT application where data is

collected and delivered to a local gateway and data

is displayed via a remote web server. (Lozano et al.,

2012) presents a sensor network for indoor air qual-

ity monitoring. The network consists of a base sta-

tion connected to the internet and several nodes sen-

sors to measure temperature, humidity, light and air

quality. The standard IEEE 802.15.4 (Zig-Bee pro-

tocol) using the XBee module was utilized to per-

form the communication between the nodes and the

host. State-of-the-art solutions are focused on ur-

ban pollution where pollutants are different from the

indoor pollutants. There are some reports covering

the IAP by using IoT networks, but most of these

solutions use Arduino-based hardware and common

IoT issues such as scalability, security and low power

characteristics are not being addressed (Raju et al.,

2013) (Pham et al., 2013).

3 PROPOSED ARCHITECTURE

The proposed IoT IAQ monitoring system was de-

signed and developed to obtain a ﬁne-grain record of

indoor environment conditions. The parameters mon-

itored were chosen, so that the following conditions

could be studied: hygrothermal comfort, IAP and sys-

tem operation optimization. The current sensor node

version is capable of simultaneously monitor temper-

ature, relative humidity, absolute pressure, luminos-

ity, PM, CO

and VOC. The architecture of the sys-

tem is shown in Figure 1. The system is composed of

a relatively simple infrastructure composed of sensor

IoTBDS 2018 - 3rd International Conference on Internet of Things, Big Data and Security

286

nodes, a border router and a gateway that pools all the

data acquired by each sensor node. The data is either

passed along the mesh network or sent directly to the

border router which communicates with the gateway.

The gateway then inserts new data points to the data

base which can be stored locally or remotely (cloud

system). The communication between all individuals

of the network is based on the IEEE 802.15.4 speci-

ﬁcation (IEEE, 2017). For a facilitated and acceler-

ated deployment, a development board was used to

implement the RF-Transceivers. The LAUNCHXL-

CC2650 SimpleLink

is a low-cost RF-Module with

integrated programmer and debugger.

3.1 Hardware

3.1.1 Gateway

The gateway is an important module of the system

because it performs the interface to Internet, store all

data in a local database and can execute some pre-

processing tasks. For the new IoT paradigm, board

size, power requirements and cost are also critic.

Raspberry Pi Zero W was selected because it pro-

vides reasonable functionalities to execute the gate-

way tasks (Raspberry, 2017), it is also very compact

and easy to conﬁgure and maintain. Raspbian linux

was installed and used as a operating system of the

platform. All these features are not available with

other embedded solutions. Finally, it offers a great

scalability factor, for the whole system can be booted

from a Micro SD card, which can be separately repro-

duced.

3.1.2 Border Router

The border router is the interface between the IoT net-

work and the gateway. It serves to maintain the com-

munication between the nodes in case the gateway is

not available. In our platform, the CC2650 System on

Chip (SoC) performs the border router functionality.

Communication is established by utilizing RPL pro-

tocol and a proprietary RF 802.15.4 protocol. Com-

munication to the gateway is accomplished through

an UART interface.

3.1.3 Sensor Nodes

The sensor nodes are designed to incorporate as many

sensors as possible without compromising the valid-

ity of each parameter. A special attention to Low

Power operation was taken to avoid critical tempera-

ture operation points and to evaluate the possibility of

a battery powered device in future works. Each sen-

sor node must have an RF communication interface

Table 1: SPIRI sensors.

Sensor Description

BME280 Temperature, Humidity , Pressure

CCS811 VOC and eqCO

PMS7003I Particulate Material Sensor

CDM7160 CO

sensor module

TSL4531 Luminosity sensor

SPH0645LM4H Sound Pressure sensor

Energy Energy meter sensor

PIR Pyroelectric InfraRed Sensor

to send out the acquired data. Thus, the sensor node

was designed as a shield PCB to the development plat-

form described in section 3. A small memory block

of the MCU is reserved to back up a few measure-

ments in case of a lost network signal. Figure 2 shows

a block diagram of the sensor node. The main difﬁ-

culties faced in this design was to aggregate as many

sensors as possible without compromising the opera-

tion of each one individually. The ﬁnal hardware is

shown in the ﬁgure 3. The sensors deployed in the

shield PCB are presented in Table 1.

Figure 2: Sensor Node Block Diagram.

3.2 Firmware

3.2.1 Contiki OS

To provide stability and the basic functionality to op-

erate the system, the sensor nodes were programmed

with the latest version of the Contiki Operating Sys-

tem (Contiki, 2017). The advantage of using Con-

tiki is the availability of timers, networking and many

other tools developed by the Contiki community. It

allows a quick and effective implementation of the ap-

plication needed. However, Contiki OS only provides

basic functionalities and hardware abstraction. To de-

velop a complete solution, an OSYS framework was

developed locally.

3.2.2 OSYS Framework

OSYS Framework is a hardware and operational sys-

tem abstraction that has only four functions to imple-

SPIRI: Low Power IoT Solution for Monitoring Indoor Air Quality

287

Figure 3: Final hardware.

ment when using in a new system: a start time, a stop

timer, a check if timer is expired, and a timestamp.

With only those functions OSYS builds a framework

of functionalities to help the development on any plat-

form, such as drivers timing, SNTP protocol, and

more. OSYS is the solution used by the developers for

fast programming and easy implementation of nec-

essary functionalities. Besides, interoperability is an

IoT challenge that can be driven by the OSYS frame-

work because it gives us a new abstraction layer be-

tween the OS and the hardware.

3.2.3 Protocols

As we described before, Contiki OS provides net-

working capabilities. Then, 6LowPAN protocol im-

plementation was chosen as a communication proto-

col. To implement the communication over 6LoW-

PAN with security and low footprint, Ripple (RPL)

was used as the routing protocol to map and con-

nect neighbor devices. As we use a low power and

low cost hardware platform, a new communication

protocol called CoEP was used. CoEP (Constrained

Extensible Protocol), is a protocol developed within

OSYS to be released as open-source. CoEP is capa-

ble in a single uniﬁed layer to provide full data se-

curity with public and symmetric key exchange and

management; authentication; single packet authenti-

cation; integrity and conﬁdentiality. Furthermore, it

can provide fragmentation; acknowledgment; mes-

sage concatenation; and modularity to implement cus-

tom users messages, handshakes (called connectors)

and protocols within it with a lower footprint than

conventional protocols such as CoAP. Figure 4 shows

the CoEP packet, where:

• V: Version of the protocol, currently 0x0.

• C: Type of cryptography used in the payload: 0x0

for none; 0x1 for public key (to be chosen and

implemented by the user, however its advised to

use the current implementation of elliptic curves

of type 256r1); 0x2 for point-to-point symmet-

ric key cryptography; 0x3 for network symmetric

key, where all nodes can read the message.

• F: Fragment number. For being designed for

6LoWPAN networks, the maximum payload is of

128 bytes, or 3 fragments maximum.

• A: Whether the packet needs an acknowledge-

ment reply.

• L: Last fragment identiﬁcation.

• Items marked with *: Parts of the message that

are encrypted.

3.2.4 Security Modes

CoEP protocol gives option to use asymmetric

and symmetric key exchange. The system was

implemented with micro-ECC, an cortex mthumb

optimized code that can handle varioyus eliptic

curves cryptography schemes. This application use

secp256r1 elliptic curves, using the Elliptic-curve

DifﬁeHellman (ECDH) key agreement protocol im-

plemented within CoEP as a connector. A custom

key derivation function (KDF) is used to generate the

symmetric key from the secret. Because the hand-

shake establishes the symmetric key and each CoEP

packet has its own authentication token, there is no

change in header length. However, because the sym-

metric cryptography is AES-128 CBC, packets pay-

load will always be multiples of 16bytes. CoEP limits

its payload to 48 bytes per packet in this application.

3.2.5 Power Saving

Low power consumption could be considered a crit-

ical feature in some IoT applications. To have low

power features we have chosen the CC2650 micro-

controller because it has speciﬁc low voltage/current

characteristics, for example: Normal operation volt-

age: 1.8 to 3.8 V, External regulator mode: 1.7 to

1.95V, Active-Mode RX: 5.9 mA, Active-Mode TX at

0 dBm: 6.1 mA and Active-Mode TX at +5 dBm:9.1

mA. Contiki OS takes advantage of the CC2650

power saving features. For example, when the de-

vice is sending data by using the RF interface, the

CPU will enter sleep mode and will resume after the

transmission process is complete. In case there are no

events in the Contiki event queue, the uC will enter in

low power mode that was previously conﬁgured with

the LPM (Low Power Mode) driver.

4 CASE OF STUDY

The object of the experimentation is a building with a

centralized HVAC System located in the Polytechnic

School of the University of S

ao Paulo. The build-

ing is called CITI-USP, it is a research center which

IoTBDS 2018 - 3rd International Conference on Internet of Things, Big Data and Security

288

1 2

5 6

11 12

15 16

21 22

25 26

30 31

V=0 C F A L

Token

Message ID 1

Payload for Message ID 1 *

Figure 4: CoEP packet.

can be considered an ofﬁce building. It is a multi-

disciplinary center where many people work in aca-

demic and industrial projects. Five sensor nodes, a

border router and a gateway were deployed in the sec-

ond ﬂoor of the building in speciﬁc locations where

people walk every day.

Figure 5: CITI-USP Building.

4.1 Initial Results and Analysis

The ﬁrst analysis to be performed are related to hy-

grothermal comfort. Figure 6 shows the tempera-

ture proﬁle of the ﬁrst ﬂoor of CITI-USP building on

November 11

of 2017 between 10am and 12pm. A

sudden decrease of the temperature during this time

frame can only occur as a consequence of a active

HVAC-System operating in cooling mode, which can

be veriﬁed by Figure 7 that shows clearly the cycling

operation of the HVAC-System with signiﬁcant drops

of up to 15% of relative humidity while it is on. This

is a common issue in mechanical ventilated environ-

ments, which can implicate in a aggravation of respi-

ratory conditions for sensitive people. It is also clear

to observe, how the Temperature signiﬁcantly drops

inside the meeting room of about 4

◦

C in approxi-

mately 45 minutes. This evidences a not well planned

system that could easily be retroﬁt to avoid such per-

formance and enhance people’s comfort throughout

the day.

The conjunction of varying Temperature and rel-

ative Humidity to increased IAP can further degrade

Figure 6: 2-hours temperature proﬁle of a ﬂoor in CITI-

USP.

Figure 7: 2-hours relative humidity proﬁle of a ﬂoor in

CITI-USP.

the environment for its occupants. These analysis will

be completed in the following weeks with a greater

dataset of almost 50 days. Our prototype has eight

sensors working simultaneously, so the estimated

power consumption of the hardware was 400mW with

some current peaks of 150mA. It can be considered a

low power prototype if compared with some industrial

solutions, for example CO2 sensor from Honeywell

(Honeywell, ).

SPIRI: Low Power IoT Solution for Monitoring Indoor Air Quality

289

5 CONCLUSION AND FUTURE

WORK

This work presents the initial results of our IoT plat-

form for monitoring indoor environments. We have

deﬁned the parameters to be monitored for some spe-

ciﬁc cases like houses, ofﬁces, schools, etc. Ad-

vanced sensor technologies were used to obtain ac-

curate results of the parameters which affects the in-

door air quality. We have deployed our system in a

research center of Sao Paulo University where data

collection was done each minute for each parameter

and preliminary results give us valuable information

about air quality behavior during the day.

For our future research, we plan to continue the real

time monitoring and try to emulate real situations

in an indoor environment like cooking, smoking and

painting and analyze the results. Another challenge is

to test the scalability of the system, so next phase is

to deploy 10, 30, 50 and 100 nodes simultaneously.

Another task is to implement a toolkit to view the live

air quality data of deployed regions. Other future re-

search is the implementation of some techniques to

improve the IAQ, for example the use of photocat-

alytic oxidation (PCO) to remove hazardous VOC el-

ements.

ACKNOWLEDGEMENTS

This work was supported by: grant #2015/22209-4

and #2016/15514-8, Sao Paulo Research Foundation

(FAPESP).

REFERENCES

Contiki (2017). Contiki operating system. URL:

http://www.contiki-os.org/. Accesed: 2017-11-20.

De Gennaro, G., Farella, G., Marzocca, A., Mazzone, A.,

and Tutino, M. (2013). Indoor and Outdoor Monitor-

ing of Volatile Organic Compounds in School Build-

ings: Indicators Based on Health Risk Assessment

to Single out Critical Issues. International Jour-

nal of Environmental Research and Public Health,

10(12):6273–6291.

EPA (2017a). Environment protection agency: Indoor

air quality. URL: https://www.epa.gov/indoor-air-

quality-iaq. Accesed: 2017-11-20.

EPA (2017b). The inside story: A guide to indoor air qual-

ity. URL: https://www.epa.gov/indoor-air-quality-

iaq/inside-story-guide-indoor-air-quality. Accesed:

2017-11-22.

Guo, P., Yokoyama, K., Piao, F., Sakai, K., Khalequzza-

man, M., Kamijima, M., Nakajima, T., and Kitamura,

F. (2013). Sick building syndrome by indoor air pol-

lution in dalian, china. International Journal of Envi-

ronmental Research and Public Health, 10(4):1489–

1504.

Honeywell. Co2 sensors. URL: https://goo.gl/3FWhNP.

Accesed: 2018-01-10.

IEEE (2017). 802.15.4 standard. URL:

https://goo.gl/DJRR7f. Accesed: 2017-11-20.

Joshi, S. M. (2008). The sick building syndrome. Indian J

Occup Environ Med, 12(2):61–64.

Kadri, A., Yaacoub, E., Mushtaha, M., and Abu-Dayya, A.

(2013). Wireless sensor network for real-time air pol-

lution monitoring. 2013 1st International Conference

on Communications, Signal Processing, and their Ap-

plications (ICCSPA), pages 1–5.

Lozano, J., Suarez, J. I., Arroyo, P., Ordiales, J. M., and Al-

varez, F. (2012). Wireless sensor network for indoor

air quality monitoring. Chemical Engineering Trans-

actions, 30:319324.

Mohieddine, B., Abderrazak, A., Ahmad, H. M. S., and

Farid, Touatiand Abdullah, K. (2017). Real-time in-

door air quality monitoring through wireless sensor

network. International Journal of Internet of Things

and Web Services, pages 7–13.

Penney D, Benignus V, K. S. (2010). Who guidelines for

indoor air quality: Selected pollutants.

Pham, D. T., Nguyen, D. C., Pham, V. V., Do, V. L., and

Tran, D. T. (2013). Development of a wireless sensor

network for indoor air quality monitoring. ICDV: the

2015 International Conference on Integrated Circuits,

Design, and Veriﬁcation, 4.

Prasad, R. V., Baig, M. Z., Mishra, R. K., P, R., Desai, U. B.,

and Merchant (2011). Real time wireless air pollution

monitoring system. 2011 ICTACT Journal on Com-

munication Technology, pages 370–375.

Raju, P., R.V.R.S.Aravind, and Kumar, S. (2013). Pollution

monitoring system using wireless sensor network in

visakhapatnam. International Journal of Engineering

Trends and Technology (IJETT), 4:591595.

Raspberry (2017). Raspberry pi foundation. URL:

https://www.raspberrypi.org/. Accesed: 2017-11-20.

WHO (2017). World health organization: Indoor air qual-

ity. URL: http://www.who.int/indoorair/en/. Accesed:

2017-11-20.

Zhou, C., Zhan, Y., Chen, S., Xia, M., Ronda, C., Sun,

M., Chen, H., and Shen, X. (2017). Combined ef-

fects of temperature and humidity on indoor VOCs

pollution: Intercity comparison. Building and Envi-

ronment, 121:26–34.

IoTBDS 2018 - 3rd International Conference on Internet of Things, Big Data and Security

290