Self-adaptive Sensing IoT Platform for Conserving Historic Buildings

and Collections in Museums

Rita Tse

, Marcus Im

, Su-Kit Tang

, Luís Filipe Menezes

Alfredo Manuel Pereira Geraldes Dias

and Giovanni Pau

5,6

School of Applied Sciences, Macao Polytechnic Institute, Macao, China

Macao Polytechnic Institute, Macao, China

Department of Mechanical Engineering, University of Coimbra, Coimbra, Portugal

University of Coimbra, Coimbra, Portugal

Department of Computer Science and Engineering, University of Bologna, Bologna, Italy

Department of Computer Science, University of California, Los Angeles, U.S.A.

Keywords: Self-adaptive Sensing, Internet of Things, World Heritage Conservation.

Abstract: As historic buildings and collections in museums are normally of deteriorated structure or materials, any

sudden change of weather or environment, such as oxygen level, temperature, humidity, air quality, etc., may

cause damages to them and it may not be recoverable. Internet of Things (IoT) is common in solving problems

by collecting environmental data using sensors. The data is live and immediate for visualizing the environment,

which is suitable for conserving the buildings and collections. However, there is no one-for-all IoT solution

for this conservation problem. In this paper, we propose the design of the sensor device in the IoT platform

for conserving historic buildings and collections in museums. The sensor device is self-adaptive, running

continuously without any interruption causing by the instability of power and network connection. The

platform is currently implemented for the conservation project in the Science museum, University of Coimbra,

Portugal. It has been running over a year and the conservation work is going well.

1 INTRODUCTION

Internet of things (IoT) is an architecture that allows

intelligent systems to collect environmental data from

sites at real time and to make immediate decision to

achieve the goals. In a typical IoT system, there are a

number of pervasive presence of things or objects,

called sensor devices in this paper, that are of

communicating capability to connect to edge servers

or backend servers in an IoT network. Data collected

and generated by the sensor devices will be sent to the

servers through wireless or wired connection,

creating a big data for further processing.

There is no one-for-all IoT solution for conserving

historic buildings and the collections in museums.

Depending on their types of materials and kinds of

environmental data to be collected, the sensor device

is designed. There are a number of existing solutions

(Tse et al, 2018) (Maksimović & Ćosović, 2019)

(Neri et al, 2009) (D'Amato et al, 2012) (Tse & Pau,

2016) (Pereira et al, 2017) (Aguiari et al, 2018) that

are engineered to deal with this materials and data

issues in their IoT systems.

To accurately collect the data (e.g. temperature,

humidity, location, CO2, pm2.5, etc.), the sensor

devices must be enabled with appropriate sensing

capabilities using sensors. As the devices generate

data continuously, the amount of data is unlimited but

the storage is limited that may raise a storage issue.

Moreover, deploying IoT systems in historic

buildings is very challenging as the buildings may be

short of power and network connection from time to

time. Data loss is likely to happen, resulting in the

failure of conservation.

To tackle the power and network connection

issues, this paper will disclose the design

consideration of the sensor device in the IoT platform.

The Self-adaptive sensing method will be proposed

which enables the sensor device to operate

continuously even though the power and network

connection are not stable. By estimating the size of

data generated by sensors and the amount of energy

consumed by the sensor device when backup power

392

Tse, R., Im, M., Tang, S., Menezes, L., Dias, A. and Pau, G.

Self-adaptive Sensing IoT Platform for Conserving Historic Buildings and Collections in Museums.

DOI: 10.5220/0009470203920398

In Proceedings of the 5th International Conference on Internet of Things, Big Data and Security (IoTBDS 2020), pages 392-398

ISBN: 978-989-758-426-8

is used, the design of the sensor device can be

optimized to work longer time until the main power

and network connection are back. This project is the

on-going research project for the Science Museum of

the University of Coimbra in Portugal. The IoT

platform has been deployed in the Museum and has

been running over a year.

The remaining of this paper is organized as

follows. In Section 2, we review the threats to historic

building and collections in museums and the types of

sensor to prevent the threats. After that, the self-

adaptive sensing IoT platform will be discussed,

which includes the architecture of the platform in

Section 3, and the self-adaptive sensing method in

Section 4. In Section 5, the ongoing research project

will be introduced as it implements the IoT platform.

Finally, we conclude this work in Section 6.

2 BACKGROUND

To conserve the historic buildings and the collections

in museums, it is crucial to study their structure,

quality and condition. There are many different kinds

of threats to the buildings and collections. Common

threats are fire, air pollution and visitor crowd that

can cause different levels of damage to them,

depending on their condition and types of material

(wooden, papers, textiles, photos and leathers, etc.).

Thus, the sensor device equipped with the oxygen

sensor, nitrogen oxide sensor and humidity sensor, or

some others sensors that can help against the threats,

is highly recommended.

2.1 Oxygen Sensor

Most of the historic buildings are of wooden structure

and they are flammable. After years of erosion, their

structure becomes deteriorated if their maintenance is

not properly done. When there is a fire, serious

damages would be caused to them and the result

would be unrecoverable. Thus, the fire safety

guideline is always in place. It describes the

principles and practices of fire safety for historic

buildings and for those who operate, use, or visit them

(Macau Cultural Affairs Bureau, 2019, October 5)

(National Fire Protection Association, 2019, October

5).

In China, burning incense is a Chinese custom.

Some Chinese temples allow visitors to burn

incenses. Staff are required to pay attention to any

unattended fire combustion caused by incense

burning. Moreover, clearance of the entrance of

temple is required as streets are narrow in some old

areas. Fire truck can hardly reach.

Therefore, in case of fire, controlling the oxygen

level is one of the feasible solutions to tackle the fire

problem (Jensen & Holmberg, 2006). The study

revealed that the air supply is one of the crucial

factors that can feed a fire, as oxygen in the air can

develop fire combustion. By reducing the oxygen

level in the air from 21% (normal) to 15%, the fire

development can be limited, while sufficient oxygen

is provided for evacuating people.

To control the level of oxygen, we may use the

oxygen sensor for monitoring the level at a site. The

sensor would be installed at some particular corners

near the places that are likely to have fire combustion.

The reason is that the oxygen level over there is

normally lower than other places. The sensor will

report the level constantly. If there is a fire, the level

is supposed to drop unexpectedly. In this case, some

procedures can be applied to control the air supply in

the site.

2.2 Nitrogen Oxide Sensor

Air pollution is an environmental problem in

developing cities that is mainly caused by highly

congested traffic (Pau & Tse, 2012). Tons of vehicle

exhausts are generated from the traffic every day,

which will produce tons of poisonous gas, called

nitrogen oxide. There are a number of studies

(Tétreault, 2003) (Szczepanowska, 2013) (Thickett &

Lee, 2004) (Florian, 2006) (Lavédrine, 2003) (Peng,

2017) conducted about how nitrogen oxide causes

damages to the materials commonly found in

museum, which include papers, textiles, photos and

leathers.

Nitrogen oxide will not cause damage to the

materials immediately. However, it is very likely that,

after exposing the materials for a certain period of

time, degree of damages can be developed. Nitrogen

oxide can weaken papers, textiles and photos, causing

the colour fading in intensity. For leathers, it can

create cracks on their surface. Therefore, protection is

needed (National Fire Protection Association, 2019,

October 5).

As nitrogen oxide is one of the key factors in

maintaining historic buildings and collections, it is

suggested to monitor the level of nitrogen oxide.

Similar to the monitoring of oxygen level, the

nitrogen oxide sensor can be used. By setting up an

acceptance level of nitrogen oxide, some procedures

can be applied if it is out of the level.

Self-adaptive Sensing IoT Platform for Conserving Historic Buildings and Collections in Museums

393

2.3 Humidity Sensor

Historic buildings and museums in World Heritage

are favourite tourist spots that would attract crowd of

visitors (UNESCO, 2019, October 5). If the visitors

are not well managed in a site, the current state of the

buildings or the collections inside the buildings may

be adversely affected by the humidity generated by

visitors, especially for books or textiles. High

humidity causes both dust mite populations and mold

colonies to grow (Public Broadcasting Service,

2019). Thus, limiting the number of visitors at a time

slot in a site becomes crucial. In order to limit the

number of visitors, the manual head count method at

the entrance or at the ticket booth is commonly used.

2.4 Thermal Sensor

Recently, existing solutions using thermal camera can

give the thermal map of a site (Abdelrahman &

Schmidt 2018). A thermal map can be used to detect,

track, and recognize humans without creating the

privacy issue (Gade & Moeslund, 2014). If a number

of thermal cameras are installed at somewhere giving

an overview of the place, we can compute the total

number of visitors at any time in a site (Tse et al,

2018). The humidity generated by visitors can be

estimated. In addition, the thermal camera can be

used for survey purpose, generating a statistic about

the favourite of visitors by counting their stopping

time for the collections. This minimizes the work

involved in staffing.

As the environment of developing cities is

complicated, conducting the conservation of historic

buildings and collections in museums is likely to be

harder. Using the right sensors in monitoring or

collecting data would be necessary. Once the sensor

device is designed and put in place, unlimited amount

of data will be generated by sensor devices.

3 THE ARCHITECTURE OF IoT

PLATFORM

The Self-Adaptive Sensing IoT Platform consists of a

number of sensor devices and servers locally and

remotely. The sensor devices are distributed in sites

and operate continuously. Figure 1 depicts the

architecture of the IoT platform.

In Figure 1, the architecture consists of a number

of sensor devices installed in a number of sites.

Depending on the conservation requirement for a site,

the sensor device is equipped with some sensors, such

as oxygen sensors, nitrogen oxide sensors and

thermal cameras. Each sensor device is implemented

by an Arduino or Raspberry Pie device. In order to

enable the sensor devices to perform their role

efficiently, each sensor device is equipped with a

battery for backup power. The battery capacity is

determined by the power consumption of the sensors

in the device. Currently, the battery can support the

operation of the sensor device for three days, which

is good enough for the weekends.

Figure 1: The Architecture of the IoT Platform.

To be scalable and stable, the sensor devices are

loosely connected to the backend server using the

wireless network connection, mainly relying on the

Wi-Fi network. Depending on the situation, if reliable

Wi-Fi network is not provided or not stable, 4G

cellular network is then preferred. For contingency

and efficiency purpose, an edge server is used if

needed. The edge server pre-processes the data and

check if the network connection is ready. If so,

processed data is then sent to the backend server for

further processing. The data is manipulated into a

general form that allows analysis before storing.

There is a local storage equipped in the sensor device

and its size is big enough for 7 days of data. When a

network connection is available, data will be

forwarded to backend servers. In the meantime, huge

amount of data is generated every day. It would raise

a performance issue on data retrieval in the IoT

platform. There are many solutions to resolve this

issue and data snapshots are used in the platform. To

minimize the data to be generated and extend the

operation time of the sensor devices on backup

power, we propose the self-adaptive sensing method.

IoTBDS 2020 - 5th International Conference on Internet of Things, Big Data and Security

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4 SELF-ADAPTIVE SENSING

METHOD

The Self-adaptive sensing method in the IoT platform

enables the sensor device to operate continuously

even though the power and network connection are

not stable. Once the device is up and running,

unlimited amount of data will be generated. If a

network connection is available, the device may

forward the data to the edge server, if any, for

preliminary processing or to the backend server for

processing. If not, data will be stored in local storage.

As local storage size is limited, it may get full quickly

depending on the size of data generated by sensors

every time. As mentioned previously, sensors in the

IoT platform will be strategically selected against

threats. One of the selection criterions for sensors is

the data size in one generation.

4.1 Data Size

The data size in one generation for a sensor is fixed.

If a sensor generates b bits of data, for a number of

data generations, t, in one day, the total amount of

data, d, to be generated can be expressed as below.







(1)

If a sensor device is equipped with X = {x

, x

, …,

} sensors, the total amount of data, D, to be

generated in bits per day will be expressed as below.







(2)

It is noted that the local storage, S, in a sensor

device is suggested to be bigger than D in order to

maintain data integrity for a certain number of days,

c, such that



(3)

Since D is determined by the fixed outputs B =

, b

, …, b

} from X = {x

, x

, …, x

} in one day,

for each sensor in X, t becomes a critical valuable for

D, affecting the determination of S, if c is constant.

To determine the value for t, it is necessary to

consider the wakeup time (another criterion) which is

about the power required by a sensor when it

operates.

4.2 Power Consumption

Another selection criterion for sensors is its power

consumption as it is directly relating to the capacity

required for backup power. In mathematical equation,

the amount of power consumption, P, depends on the

sensors selected to work in the sensor device, which

is calculated from the amount of energy, E = {e

, e

…, e

}, in kilowatt (kW), required by each sensor in

X = {x

, x

, …, x

} for t and the amount of energy, G,

required by the system of a sensor device, such that







(4)

To ensure the backup power is sufficient to

support the operation of the sensor device for a certain

number of days, c, the capacity required for backup

power, BP, in milliamp per hour (mAh) for the

raspberry pi at 5 volts is expressed below.

 

  1000

(5)

Nevertheless, the capacity of backup power is

limited. To minimize the energy consumption and

extend the operation time in offline, the running cycle

for a sensor in next section will be considered.

4.3 Running Cycle

In the Self-adaptive sensing IoT platform, there is a

running cycle for each sensor in the sensor device. In

the running cycle, there are five phases which are 1)

Data collection, 2) Data generation, 3) Data storing,

4) Sleep and 5) Wake up. Figure 2 shows the five

phases in the running cycle.

In phase 1, the sensor tries to collect

environmental data. Once, data is collected, the

sensor generates data in phase 2. In phase 3, the data

is then forwarded to servers if the network connection

is available. If not, the data will be stored at the local

storage (See Equation (3)) in the sensor device. If the

network connection becomes available in the next

cycle, all local data generated in previous cycles, will

be flushed and forwarded to the servers (edge server

or backend server). In phase 4, the sensor will be put

to sleep. When it is time out, a wake-up call will be

sent to the sensor in phase 5. One cycle is finished and

another cycle will begin.

Self-adaptive Sensing IoT Platform for Conserving Historic Buildings and Collections in Museums

395

Figure 2: The Running Cycle for a Sensor.

In each phase, it takes a certain amount of time to

complete its process when the power connection is

stable. If the power and network connection is not

stable in historic buildings, limited backup power is

then needed for the sensor device. In case of power

cut after hours, the backup power would operate its

sensor device for data transfer when the network

connection is available, and for data storing locally

when the connection is not available. To extend the

operation time of a sensor device, the device is better

put to sleep when the data variance is stable. It

minimizes the use of local storage, S, and saves the

backup power, BP.

To put sensors to sleep, an issue on the wakeup

time for a sensor will be raised. Another selection

criterion for sensors is the wakeup time it takes after

they sleep. Some sensors may take only a few milli-

seconds to wake up and some may take a minute. This

may create a time synchronization problem on the

data. To maintain the integrity and accuracy of data,

the amount of sleep time may be reduced to allow

more time for a wakeup call. Therefore, when

selecting a sensor, a balance between the sleep time

and the wakeup time in the overall operation is

needed. Figure 3 gives an overview of the operation

time in a running cycle.

In Figure 3, there are three sensors connected in a

sensor device, which are Sensor A, Sensor B and

Sensor C. Their running cycles are of the same length,

even though the portion of time for phases in a cycle

varies. In Sensor A, it requires 20% of the time to

wake up and 60% of the time to sleep. In Sensor C, it

takes 60% of time to finish a wakeup call, which is 3

times more than its sleep time. In this case, reducing

the sleep time to compensate the time taken for a

wakeup call can uniform the cycle length. It avoids

the data integrity and accuracy problems, and saves

energy consumption.

Figure 3: Operation Time in a Running Cycle for 3 Sensors.

5 ONGOING RESEARCH

PROJECT

This paper is based on the ongoing research project

for the Science Museum of the University of Coimbra

in Portugal, which is a collaboration between Macao

Polytechnic Institute, Macao SAR, China and

University of Coimbra, Portugal. The project is

designed for the conservation of the collections inside

the Museum using this self-adaptive sensing IoT

platform. Preliminarily the platform connects to a

number of sensor devices, monitoring the

temperature, humidity and indoor air quality (AIQ)

inside the Museum, as required by the Decree Law n.

78/2006 at April 4 in Portugal. As can be seen in

Figure 4, the sensor device (in green colour) was

being installed in the Museum in 2018.

The Science Museum is open to the public during

weekdays. When it is open, the power and network

connection are available for the Museum operation.

When it is closed, the power will be turned off and

network connection will be cut. The IoT platform

runs in the Museum and data is continuously

collected and generated by the sensor device. As the

platform is self-adaptive, the platform will switch to

run on the backup power when the power is down.

IoTBDS 2020 - 5th International Conference on Internet of Things, Big Data and Security

396

Figure 4: Installation of the Sensor Device in the Science

Museum of the University of Coimbra, Portugal.

The backup power for the sensor device is

sufficient to support its operation only for three days

over the weekend. As the running cycle suggested,

data is collected, generated and stored. When there is

no network connection, data is then stored in the

device locally. Afterwards, the device checks for the

network connectivity. If it is available, data in local

storage will be immediately flushed and forwarded to

servers.

Processed data in backend server is then

downloaded by an application in a mobile phone for

reference use. As can be seen in the mobile

application in Figure 5, the data, such as the current

PM figures, temperature and humidity, collected by

the sensor device inside the museum is shown. The

data is also illustrated in the line chart for a particular

period of time. It enables the management to have an

in-depth look of the environment in the Museum,

assisting them to design policies to meet the

conservation objectives.

6 CONCLUSIONS

The historic buildings and collections in museums

need immediate conservation as there are many

threats that can cause damages to them. The self-

adaptive sensing IoT platform provides a solution to

the conservation work. The platform implements the

self-adaptive sensing method which enables its sensor

devices to adapt itself to the environment with

unstable power and network connection. This is a

break-through in deploying IoT technologies in such

an environment without causing any interruption to

the conservation work. As the IoT platform has been

deployed in the Science Museum, University

of Coimbra, Portugal since 2018, it is shown that the

Figure 5: Monitoring Result of the Sensor Device.

conservation work is running well. This work is still

in-progress and the development of sensor device is

on-going as scheduled.

ACKNOWLEDGEMENTS

This work was supported in part by the Macao

Polytechnic Institute - Environmental Monitoring of

UNESCO Coimbra Science Museum (RP/ESAP-

01/2019). We would also like to thank Prof. Adélio

Manuel Rodrigues Gaspar and Prof. José Joaquim da

Costa for their support of the Coimbra Science

Museum.

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