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Pervasive sensing technologies for facility management: A critical review
This is the peer-reviewed post-print version of the paper:
Xu, J., Chen, K., Zetkulic, A. E., Xue, F., Lu, W., & Niu, Y. (2020). Pervasive sensing technologies
for facility management: A critical review. Facilities. 38(1/2): 161-180. Doi: 10.1108/F-02-2019-0024
This copy is shared through CC BY-NC 4.0 under the permission by Emerald. Extra agreement must be
obtained from Emerald before any commercial or other uses of this copy.
The final version of this paper can be found at: http://doi.org/10.1108/F-02-2019-0024.

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Abstract

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Purpose: The practice of Facility management (FM) has been evolving with the rapid

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development of pervasive sensing technologies (PSTs) such as sensors, Auto-ID, laser scanning,

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and photogrammetry. Despite the proliferation of research on the use of PSTs for FM, a

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comprehensive review of such research is missing from the literature. This research aims to

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cover the knowledge void by examining the status quo and challenges of the selected PSTs

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with a focus on FM.

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Design/methodology/approach: This paper reviewed 204 journal papers recounting cases of

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using PSTs for FM. The reviewed papers were extracted from Elsevier Scopus database using

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the advanced search.

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Findings: Findings of this study revealed that PSTs and FM applications form a many-to-many

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mapping, i.e., one PST could facilitate many FM applications, and one application can also be

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supported by various PSTs. It is also found that energy modeling and management is the most

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referred purpose for FM to adopt PSTs, while space management, albeit important, received

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the least attention. Three challenges are identified, which include high investment on PSTs,

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data storage problem, absence of proper data exchange protocols for data interoperability, a

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lack of mature data processing methods for data utilization, and privacy of users.

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Originality/Value: This paper paints a full picture of PSTs adoption for FM. It pinpoints the

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promising explorations for tackling the key challenges to future development.

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Keywords: facility management; laser scanning; photogrammetry, videogrammetry; sensor;

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Auto-ID

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Paper type: Literature review

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1 Introduction

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A ‘facility’ refers to an asset that is built, installed or established to serve social and economic
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activities (Kaplan et al., 2004). Facility management (FM) is the “organizational function

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which integrates people, place, and process within the built environment with the purpose of

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improving the quality of life of people and the productivity of the core business” (ISO, 2016).

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In a building or infrastructure’s ‘lifecycle’, which spans from inception, design, construction,

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operation through to demolition (Xu and Lu, 2018), FM mainly transpires during the operation

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stage and normally lasts thirty to fifty years if not longer (Atkin and Brooks, 2015). With

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facilities accounting for nearly forty percent of global energy consumption (Bilgen, 2014), the

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effectiveness and efficiency of FM have become a pressing issue. Moreover, in the era of

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automation and intelligence, FM is the key scene of many smart and humanized services for

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users. Data collection of facilities and users becomes increasingly important for facility owners

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and managers to enhance smart services concerning individual comfort, quick response to

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requirements, cost-effectiveness of physical assets, as well as sustainable and environmental

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performance of facilities (Peng et al., 2017).

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Over the last three decades, the rapid development of pervasive sensing technologies (PSTs)

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has brought new opportunities for the longstanding FM profession. This paper uses the term

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‘PST’ as a common nomenclature for advanced data collection technologies, such as Auto-ID

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(Automatic identification), sensor, photogrammetry/videogrammetry, and laser scanning,

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which can collect data concerning the condition, operation, and users of a constructed facility.

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Photogrammetry and videogrammetry are the same processing on different data sources, i.e.,

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images versus video clips (Rashidi et al., 2017). Data is the key to understanding users’

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requirements and their behaviors, which can be incorporated by designers in a facility’s design,

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construction and renovation stages. Data is also the key to understand the ongoing conditions

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and users’ behavior during the FM stage. In comparison with manual methods, data collection

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by using PSTs are generally more precise, more cost effective, and less tedious.

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can make major contributions to FM because they can help designers, engineers, and facility

In short, PSTs

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managers understand what users need, prefer, and what is best for them.

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Several reviews have been conducted by focusing on specific types of PSTs in the existing

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literature. For example, Taneja et al. (2011) reviewed sensing and field data capture

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technologies, such as radio frequency identification (RFID) and Global Positioning System

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(GPS), for construction and facility operations. However, the operations were different for FM,

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which in theory features incremental changes over a relatively long period of time in contrast

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with construction’s moderately short lifespan. Wong et al. (2018) conducted one of the most

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comprehensive reviews on digital technologies, e.g., BIM (Building Information Modeling)

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and IoTs (Internet of Things), in FM research. Insightful though these studies were, the

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adoption of PSTs for FM applications has yet to be satisfactorily reviewed or discussed for the

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capture of data, the foundation of any smart FM applications and services. Moreover, a

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guidance for facility managers to choose appropriate PSTs to capture desired data for facility

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upgrade and smarter services is still missing.

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This study seeks to conduct a critical review of PSTs for FM in order to understand the status

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quo, provide guidance for stakeholders, and point out the challenges ahead for researchers. It

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should be noted that the review here only focuses on data sensing technologies rather than other

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technologies which may also be applied to FM. More importantly, it aims to yield a

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formalization of engineering knowledge to support future studies and applications. The paper

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is organized as follows. Section 2 elaborates on the research methods and general statistical

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analysis. Sections 3 provides brief descriptions and comparisons of the four types of PSTs, i.e.,

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Auto-ID, sensor, photogrammetry/videogrammetry, and laser scanning. Section 4 analyzes data

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extracted from the collected literature by mapping the PSTs and FM applications. Challenges

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in implementing PSTs for FM in actual practices are summarized in Section 5. Section 6

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concludes this study.

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2 Research methods

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This study follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-

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Analyses), a standard methodology for systematic review, to collect the literature data (Moher

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et al., 2009). First, various FM applications are grouped into eight categories based on authors’

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experience and relevant studies, e.g., Wong et al., 2018. Category A1 refers to conservation of

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historical buildings and maintenance of facilities; A2 investigates the analysis and management

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of facilities’ energy consumption; A3, the health and safety of users and security of facilities;

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A4, the quality and ambient comfort of indoor environment, e.g., air quality; A5, the assessment

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of infrastructures like pavements, highways, bridges, and property assets like furniture; A6, the

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access and visualization of facilities’ information through 2D or 3D models; A7, the utilization

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of interior space; and A8, the monitoring of structural health. The scope of PSTs in this review

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include sensors, laser scanning, photogrammetry and videogrammetry, and Auto-ID, as they

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are the most representative PSTs for FM (Fathi et al., 2015; Wong et al., 2018).

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A systematic literature search was then conducted on 6 August 2018 using Elsevier Scopus,

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one of the biggest and most comprehensive research publication databases with access to over

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12,000 journals that cover the majority of high-profile scientific publications (Falagas et al.,

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2008). The literature search used titles and keywords as matching conditions in order to focus

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on the exact research topics. Only journal articles were considered as they tend to be more

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original, mature, and precise as compared to other types of literature (Jesson et al., 2011). Each

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search query consisted of one type of PST, i.e., Auto-ID, sensor, photogrammetry and

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videogrammetry, or laser scanning, and keywords closely related to FM (Wong et al., 2018).

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For example, the query for searching literature that used laser scanning for FM is

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(TITLE ( ("laser scan*") AND ("facilit* manag*" OR "building service*" OR "HVAC" OR

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"maintenance" OR "post-occupancy" OR "refurbish*" OR "building assess*" OR "building

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energy" OR "built infrastructure")) OR KEY ( ("laser scan*") AND ("facilit* manag*" OR

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"building service*" OR "HVAC" OR "maintenance" OR "post-occupancy" OR "refurbish*" OR

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"building assess*" OR "building energy" OR "built infrastructure") ) AND ( LIMIT-

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TO ( DOCTYPE , "ar" )), where “ * ” denoted a fuzzy search. In addition, the date range of

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the search was set to before 2018 and the language restricted to English.

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The search initially returned 639 total hits. The authors then screened the abstracts of all the

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returned papers to ensure their applications fit within the eight categories. After screening, 204

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articles were selected for further analysis. Table 1 shows the distribution of these articles

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according to their source journals and publication years. Chronologically, the overall

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publication number notably climbed, despite some fluctuation in between, before peaking in

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2016 with thirty-two articles. Vertically, the 204 articles came from ninety-two journals. The

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top five journals were Automation in Construction, Advanced Engineering Informatics,

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Building and Environment, Energy and Buildings, and Facilities. Nineteen journals featured

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between two to ten such articles. Grouped into “Others” are the remaining journals, which

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published only one relevant article each after screening.

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Afterward, the authors thoroughly read each of the 204 articles to extract information

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concerning the adopted PST(s) and target FM application(s). The results are summarized in

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Table 2. The footer row in Table 2 indicates that Auto-ID and laser scanning are less utilized

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and somewhat equally so, while sensors and photogrammetry/videogrammetry prove relatively

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more popular. However, no PST appears to dominate, i.e., no one technique meets all the

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requirements in any FM category. Auto-ID was especially popular for “infrastructure and

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property asset management” (13) and the only mentioned option for space management.

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Sensors

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Photogrammetry/videogrammetry mostly pertains to “health, safety and security” (20), while

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laser scanning is largely used in “structural health monitoring” (12). The last column in Table

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2 shows the numbers of applications in the categories. It should be noted that the total number

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of applications may not be equal to the sum of all four PSTs, because one application may

are

more

adopted

in

“energy

modeling

and

management”

(39).

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involve two or more PSTs. In addition, the total number of the application in the last column is

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slightly greater than 204, as two or more applications sometimes occur in a single article. More

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than one-fourth of the research (54) goes after energy modeling and management. Information

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access and visualization (40) comes second followed by infrastructure and property asset

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management (34). Among all the applications, space management (3) receives the least

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attention. The reason why energy modelling and management receives most attention can be

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twofold: on the one hand energy related study is more direct to benefit the owners/users; on the

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other hand, it is easier to collect energy-related data with existing devices and PSTs.

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Table 1 Chronological distribution of the selected articles
Journal

pre-2004

2004

2005

2006

2007

2008

Automation in Construction

2009

Advanced Engineering Informatics

1

Building and Environment

1

2

2012

2013

2014

2015

2016

2017

Total

1

4

2

4

4

3

2

21

3

2
2

5

13

2

2

11

3

3

1
3

Journal of Computing in Civil Engineering
Journal of Performance of Constructed Facilities

2011

1

Energy and Buildings
Facilities

2010

1

1

2

1

1

1

3

1

1

1

1

1

Sensors

1

Computer-Aided Civil and Infrastructure
Engineering

1

1

1

4

1

1

1

2

11

1

2

3

1

11

1

2

2

4

10

1

1

1

Earthquake Spectra
IEEE Transactions on Geoscience and Remote
Sensing
Journal of Facility Management

4

1
2
1

1

1

3

1

1

3

1

3
3

1
1

1

1

Remote Sensing
Advances in Engineering Software

5

2

1

Construction and Building Materials

14

1

1

3

1

2
1

Disaster Advances
Electronic Journal of Structural Engineering

1

IEEE Sensors Journal

1

1

2

1

2

International Journal of Distributed Sensor Networks

1
1

International Journal of Remote Sensing

1

2

1

2

1

Journal of Building Performance Simulation

2

2

Journal of Information Technology in Construction

1

2

1

Journal of Infrastructure Systems

2

1

Structure and Infrastructure Engineering

1

1

2

1

2

2

Tsinghua Science and Technology

2

Others

0

0

1

2

2

1

4

2

3

4

9

10

11

11

7

67

Total

4

1

2

5

8

6

14

8

12

10

23

22

30

32

27

204
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Table 2 Mapping PSTs and FM applications
PSTs

Auto-ID Sensor

Applications
A1 Conservation and maintenance
7
A2 Energy modeling and management
2
A3 Health, safety and security
1
A4 Indoor environment management
A5 Infrastructure and property asset management
13
A6 Information access and visualization
10
A7 Space management
3
A8 Structural health monitoring
1
Total
34
*: Inequality due to possibly multiple PSTs in one application

5
39
10
13
14
7
3
83

Photo-/Videogrammetry

Laser
scanning

Total
*

3
11
20
12
15
10
63

6
6
1
1
1
11
12
36

18
54
32
14
34
40
3
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3 Pervasive sensing technologies
3.1 Auto-ID
Auto-ID describes a group of data identification technologies, including but not limited to
RFID, barcode, and QR code. Auto-ID generally has three parts, i.e., tag, reader, and backend
systems (Lu et al., 2011). The tag stores a unique identification number and other relevant data
concerning its attached objects. The reader can read the tag and collect data from multiple
signals. Back systems consist of a database for data storage and a communication system for
data exchange. Among the reviewed literature, twenty-nine papers adopted RFID and four
adopted barcode. RFID allows non-optical proximity communication, higher capacity, better
security, long-distance reading, multiple readings at one time, durability and usability in harsh
conditions (Xue et al., 2018), which help explain RFID’s dominant use.
3.2 Sensor
A sensor is a physical device, which receives an external signal, transforms that signal into an
analog or digital voltage, and then sends the voltage to electronic processors (Maser, 1988).
There are various types of sensors in line with the signals they receive. The signal, to which a
sensor is sensitive, can come from light, motion, temperature, humidity, moisture, sound,
chemicals, as well as magnetic and electrical fields. Sensors can be wired or wireless. The latter
is increasingly adopted due to its flexibility and convenience. A group of wireless sensors can
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form into networks, i.e., wireless sensor networks (WSNs), to detect multiple signals in
complex conditions. WSNs have been widely employed in buildings, utilities, transportation
systems to enable in-time collection of sensory data, fundamental to developing a smart
environment for FM.
3.3 Photogrammetry/videogrammetry
Photogrammetry and videogrammetry share the same working mechanism, obtaining reliable
measurements of target objects via images and video clips respectively (Klein et al., 2012). The
fundamental mechanism of photogrammetry is Collinearity Equations that effectively and
analytically transforms the image coordinate system (x, y) in the camera into an object
coordinate system (x, y, and z) in the global space (Dai and Lu, 2010).
Based on interior orientation, i.e., the principle point and principle distance of the camera in
the image coordinate system, and exterior orientation, i.e., the location coordinates and Euler
orientation angles of the camera in the global space, the coordinates of a point in the image
coordinate system and in the object coordinate system in the global space can be determined
(Dai

and

Lu,

2010).

Embedded

with

scale

and

location

information,

photogrammetry/videogrammetry offers a non-invasive and cost-effective means of
reconstructing complex 3D scenarios for FM (Rodriguez-Gonzalvez et al., 2014).
3.4 Laser scanning
A laser scanning system generally comprises a laser ranging unit, an opto-mechanical scanner,
a GPS receiver, and an inertial measurement unit (IMU). The operating principle of laser
scanning is based on the reflection of laser pulses on the surface of objects (Yang et al., 2014).
There are three major types of laser scanner, namely, airborne laser scanner (ALS), terrestrial
laser scanner (TLS), and mobile laser scanner (MLS). They capture large surface areas of earth,
local fine levels of structures, and mobile mapping respectively. The laser scanner can generate
computable point cloud data, which is widely used in applications measuring 3D surfaces and

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reconstructing 3D models.
3.5 Comparison of the four pervasive sensing technologies
Table 3 shows the comparison of characteristics of the four PSTs. Auto-ID, as its name suggests,
provides automatic identification of target objects. The identification contains the name,
location, and several other entities preserved. It is easy and relatively cheap to use in object
tracking. With a contactless scan of the tag or code, rapid information access and feedback is
achieved. However, when encountering disturbance and damage, Auto-ID can prove vulnerable
and undependable, especially for barcodes and QR codes. Furthermore, their stored
information is easy to access and thus susceptible to changes made by unauthorized parties,
e.g., information can be rewritten for multiple read-write RFID tags, causing reliability and
privacy difficulties. Sensor is generally unique to one type of signal, but a network of sensors
can collect a series of indicators from complex scenarios such as the various and dispersed
environmental conditions and activity data in a shopping mall. However, given the relatively
high price of a single sensor today, the customization of a WSN can cost a substantial amount
of money. Photogrammetry/videogrammetry and laser scanning share a similar function, the
remote sensing of objects’ shape. They can both be applied to 3D model reconstruction.
Compared to photogrammetry/videogrammetry, laser scanning generally enjoys higher
accuracy and fidelity, as it directly acquires a larger number of point clouds, but requires more
time for data processing and its associated equipment is far more expensive.
Table 3 Comparison between the four PSTs (Piramuthu, 2008; Prasad, 2015)
PST

FM-related functions

Strengths

Weaknesses

Auto-ID

Detection; Identification;
Tracking

Easy to use; Cheap; Fast;
Wireless; Timely
feedback

Vulnerability;
Reliability; Privacy

Sensor

Sensing measured properties;
Supporting IoT

Small in size; Feasible for
complex system

Cost; Customization

Photogrammetry/
videogrammetry

Position recovery;
Measurement; 3D model
reconstruction; Quality control

Cheap; High positional
accessibility

Indirect data
acquisition
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Laser scanning

Material processing;
Measurement; 3D model
reconstruction; Quality control

Direct data acquisition;
Accuracy; Fidelity

Cost; Low positional
accessibility; Time
consuming

4 Pervasive sensing technologies for FM applications
Table 4 highlights typical cases of FM applications reported in the reviewed papers. Notably,
while some cases rely on different PSTs for separate functions, there are cases demonstrating
the deployment of an integration of PSTs. How these PSTs are selected and used to support
these FM applications and cases will be further elaborated and discussed in the following
subsections.
4.1 Conservation and maintenance
Conservation concerns the preservation of historical buildings or facilities of special cultural
or heritage value. The purpose of maintenance is to fix functionally or structurally damaged
facilities or equipment. These objects require careful treatment and repair. PSTs can provide
help by generating a digital model, monitoring performance, documentation, and so on. In the
reviewed literature, a number of studies looked at detecting the state of conservation pertaining
to heritage buildings with photogrammetry (e.g., Gonçalves et al., 2009) or laser scanning (e.g.,
Quagliarini et al., 2017). These two remote sensing technologies can capture the shape of the
historical buildings and facilities, generate a 3D model, and report conditions. In some
conservation cases, laser scanning and photogrammetry are preferred when identifying the
materials (Fortes et al., 2007) and evaluating the performance of critical components (Gul et
al., 2013) are necessary, but direct contact and sampling are restricted. Sensors are preferred
for awareness of the situation. For instance, the WSN was employed for ambient and pest
monitoring of wooden masterpieces and structures in heritage buildings (Capella et al., 2011),
phasor measurement sensors were utilized to enhance real-time situation awareness of smart
grids maintenance for large geographical area measurement system (Ghosh et al., 2017). RFID
can also support conservation and maintenance by storing maintenance records in a database
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Table 4 A summary table of PSTs for FM application cases
Applications

Objects

Cases
State of conservation

A1 Conservation and
maintenance

Heritage/historical
buildings or sites,
facilities or
equipment need
maintenance

Chemical characteristics of materials
Performance of critical components
Detection of pests
Situation awareness
Usage and maintenance data/record
Data transfer between workers and system
Indoor localization
Post-occupancy evaluation
Real-time occupancy

A2 Energy modeling
and management

Occupant behavior pattern detection
Geometry modelling
Buildings

3D spatio-thermal model
Energy consumption
Building monitoring
Device control
Cooling effect
Solar potential analysis
Post-disaster damage assessment

A3 Health, safety
and security

Buildings, urban
facilities

PST
Laser scanning

Reference
Quagliarini et al., 2017

Photogrammetry

Gonçalves et al., 2009
Fortes et al., 2007

Laser scanning
Photogrammetry
Sensors
Sensors
RFID
RFID
RFID, WSN, IMU
Sensors
Sensors
Sensors, image
Passive RFID and IR sensor
Sensors
Photogrammetry
Photogrammetry
Laser scanning
Sensors
WSN
WSN
Sensors, laser scanning
Laser scanning
Photogrammetry, laser scanning

Gul et al., 2013
Capella et al., 2011
Ghosh et al., 2017
Ko et al., 2013
Ergen et al., 2007
Taneja et al., 2011
Azar and Menassa, 2016
Pedersen et al., 2017
Petersen et al., 2016
Manzoor et al., 2012
Dong and Lam, 2011
Cao et al., 2017
Ham and Golparvar-Fard, 2013
Demisse et al., 2015
Ploennigs et al., 2011
Wang et al. 2016
Gao et al., 2013
Yin et al., 2017
Jochem et al., 2011
Zhou et al., 2016

Disaster prevention

RFID
Sensor network

Aziz et al., 2009
Cheng et al., 2017

Health performance of buildings

Sensors

Tolman and Parkkila, 2009
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A4 Indoor
environment
management

Building

Air protection
Thermal comfort
Visual comfort
Hygiene
Localization and tracking

A5 Infrastructure
and property asset
management

Bridge, road,
highway, parking
facility, pipelines,
airport, equipment

Proactive monitoring and inspection
Performance assessment
Effective management

A6 Information
access and
visualization

Infrastructure,
buildings,
structures,
components,
environment

A7 Space
management

Indoor space

A8 Structural health
monitoring

Bridges, tunnels,
(high-rise)
buildings, beams

Information storage and access
Information visualization
Space usage
Structural condition assessment

Structural faults detection

Photogrammetry

Wu et al., 2014

Sensor network
WSN
Sensors
Sensor
RFID
Wireless sensors
RFID
Sensor
Laser scanning
Photogrammetry
Sensors
Sensors
RFID
QR code
Photogrammetry
Laser scanning
Videogrammetry

Caron et al., 2016
Marzouk and Abdelaty, 2014
Shen et al., 2014
Jones et al., 2016
Motamedi et al., 2013
Gikas et al., 2016
Kim et al., 2015
Kwon et al., 2014

RFID

Lindkvist and Elmualim, 2009

RFID
Laser scanning
Photogrammetry
Laser scanning
Photogrammetry
WSN

Zhang and Bai, 2015
Riveiro et al., 2016
Koch et al., 2014
Cabaleiro et al., 2017
Liu et al., 2014
Mascareñas et al., 2009

González-Jorge et al., 2016
Gul et al., 2013
Ryu et al., 2017
Aziz et al., 2017
Motamedi et al., 2016
Lavikka, 2017
Fathi et al., 2015
Jung et al., 2014
Brilakis et al., 2010

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(Ko et al., 2013) and transferring the stored records between maintenance workers and a webbased FM system (Ergen et al., 2007). Although different PSTs have overcome the constraints
of data availability, the accuracy of PST-enabled conservation assessments still needs
improvement, possibly through the integration of different types of data.
4.2 Energy modeling and management
The target of energy modeling and management in FM is mostly at the building-level. Accurate
measurement of the built environment and its energy consumption is the premise (Sait, 2013).
The former relies on the precise assembly of 3D model, reconstructed from images or point
cloud data (Demisse et al., 2015; Cao et al., 2017), while the latter requires accurate utility
meters or sensors (Ploennigs et al., 2011).
To optimize energy management, unnecessary energy consumption must be eliminated. It is
believed that appliances running while a facility is unoccupied constitutes a chief cause of
energy waste (Masoso and Grobler, 2010), thus a plethora of research, i.e., one-third of the
reviewed energy-related studies, starts from detecting occupancy. The most commonly used
sensor for occupancy detection is the passive infrared (PIR) sensor, which captures occupant
noise and motion. Other types of sensors detect occupancy by measuring the concentration of
CO2, CO, and PM2.5, temperature, and relative humidity. Occupancy information not only
helps control heating, ventilation, and air conditioning (HVAC) and lighting systems for energy
efficiency (Gao et al., 2013), but also enhances wellbeing in the indoor environment (Ploennigs
et al., 2011; Marzouk and Abdelaty, 2014). However, current occupancy detection methods via
sensors can only fully detect presence (Zikos et al., 2016) and duration (Dong and Lam, 2011).
They remain limited in providing comprehensive occupancy information including location,
activity, identity, and track (Labeodan et al., 2015).
Apart from the use of different sensors, some studies have explored the applications of RFID
to control lighting (Manzoor, et al., 2012). Additionally, by enabling the assessment of energy

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performance, 3D spatio-thermal models generated by thermographic images and point clouds
have proved popular in energy modeling and simulation (Ham and Golparvar-Fard, 2013;
Demisse et al., 2015). Laser scanning was also used to assess solar potential based on roof
plane detection (Jochem et al., 2009) and vertical walls extraction (Jochem et al., 2011), as well
as cooling effect of direct green façade (Yin et al., 2017). Further studies are expected to fuse
the data obtained from both indoor and outdoor environments in order to achieve more
comprehensive energy modeling and management.
4.3 Health, safety and security
Several PSTs can support health, safety and security in FM by preventing disaster, evaluating
disaster damage, and assessing the health performance of infrastructure and buildings. To
prevent disaster damage, Aziz et al. (2009) developed an emergency response and recovery
management system that used RFID to record the preliminary damage assessment of buildings.
Similarly, Cheng et al. (2017) invented a smart monitoring system to prevent fire. Their system
used WSN to perceive a building’s indoor environment information and occupants’ locations.
The collected data were integrated and visualized into a BIM model. In addition,
photogrammetry is widely employed in the post-damage assessment of buildings and
infrastructure, and has proved effective at estimating damage after disasters (Zhou et al., 2016).
To ensure health, safety and security of the building and its occupants, monitoring and
evaluating the health status of buildings is inevitable. Tolman and Parkkila (2009) adopted
sensors to collect humidity and temperature data, critical for the healthy performance of
buildings, to improve the occupants’ wellbeing. Similarly, Caron et al. (2016) evaluated the
performance of electronic gas sensors in investigating air pollutant. Meanwhile, Wu et al. (2014)
evaluated the seismic vulnerability of buildings for formulating pre-disaster mitigation
strategies based on a compendium of high-resolution images of building exteriors. More
exploration is expected to develop proactive mechanisms for health, safety and security

15

management.
4.4 Indoor environment management
Personalized thermal comfort, where personal preferences are taken into consideration, is
emerging as popular topic in indoor environment management. Personalized thermal comfort
requires data related to indoor thermal indicators and personal preference data, which can be
suitably obtained by WSN. For example, Marzouk and Abdelaty (2014) employed WSN to
measure air temperature and humidity at different spaces within the subway to enhance its
thermal comfort for passengers.
A few other studies investigate the visual comfort of an indoor environment based on lighting
system controls. Some also applied sensors to gauge the luminosity of indoor spaces and then
control the lighting and shading systems accordingly. For instance, in Shen et al. (2014),
occupancy sensors and photosensors were adopted to detect occupancy and light level
respectively. Apart from thermal and visual comfort, hygiene of the indoor environment is
another key aspect to consider. Indoor air quality can greatly impact people’s health, especially
that of the elderly and infirmed, and sensors have been adopted to measure the indicators of
indoor air quality (Sumanasekara and Jayasinghe, 2018). An example can be found in Jones et
al. (2016), which developed an aerosol sensor to assess the dust concentration in a building.

However, sensors seem to be the only PST utilized in indoor environment management studies.
Given their inability to proactively detect the real-time condition of an environment, using
Auto-ID, photogrammetry/videogrammetry, or laser scanning alone is difficult to support the
application of indoor environment management (Petersen et al., 2016).
4.5 Infrastructure and property asset management
The target objects of infrastructure and property asset management include bridges, roads,
highways, parking facilities, airports, and property equipment. To manage these facilities,
proactive monitoring and inspection, performance assessment, and enhancement of

16

management efficiency are three major research topics. Some of the equipment and properties
also need tracking. RFID has been widely adopted in automatic equipment localization
(Motamedi et al., 2013) and real-time resource localization (Costin and Teizer, 2015). Sensors
are deployed in elevators (Kwon et al., 2014), parking facilities (Gikas et al., 2016), joint
concrete pavements (Ryu et al., 2017), and highways (Aziz et al., 2017) by abetting the
collection of a diverse set of data for various functions. The spatial and geometric data
measured from images or videos is also a major source for reconstructing digital models of inuse facilities to assess their performance. Therefore, photogrammetry/videogrammetry has
been commonly adopted for inspection of building façades (Paulo et al, 2013), automatic
reconstruction of infrastructure (Fathi et al., 2015), and automatic analysis and classification
of roof surfaces (López-Fernández et al., 2015). The captured as-built data, in the form of point
clouds, has also been used to generate 3D models of exterior facilities (Brilakis et al., 2010)
and indoor environments (Nguyen et al., 2016). However, most of the available data processing
methods require more or less manual operation or can only be applied to specific FM objects.
Therefore, the automatic reconstruction of 3D models for FM remains to be addressed.
4.6 Information access and visualization
Many PSTs have been employed to improve information access and visualization for FM.
Among them, Auto-ID was mainly used to help users gain access to FM-related information
via tag scanning. Once a tag is scanned, the corresponding information, either recorded in the
tag or the backend system, can be presented to the users, who can make informed decision and
take action (Motamedi et al., 2016). In addition, photogrammetry/videogrammetry and laser
scanning are mainly applied for information visualization. With the availability of image/video
and point cloud data, 3D digital model of the facilities can be generated based on manual
modeling or different automatic or semi-automatic methods (Brilakis et al., 2010; Fathi et al.,
2015). The 3D digital model provides a vivid visualization of the facilities. However,

17

visualization alone will fail to achieve the value of the models if there is no further application
referring to the effectiveness and efficiency of FM.
4.7 Space management
Surprisingly, PSTs showed to be less studied in connection with space management,
supposedly one of the more important aspects of FM. Only RFID was adopted to assist in space
management. Specifically, an RFID tracking system was developed to collect occupancy data
from each room of a household in order to study how each space was used (Gillott et al., 2006).
Another example is the use of RFID to track customers in stores for retailing space management
(Uotila and Skogster, 2007). For flexible workspace management in Lindkvist and Elmualim
(2009), active RFID tags were placed into the ID cards of a large financial institution’s workers,
visitors, and contractors. Readers at value-adding points, e.g., entrances and secure areas, can
then pinpoint the direction where individuals are going and identify the areas being used. In
this case, with the help of RFID-tracked data, facility managers can manage the space in a more
effective manner, hereafter providing spare space for newcomers and supplying services.
4.8 Structural health monitoring
The structural health of bridges, tunnels, (high-rise) buildings, and other load bearing structures
is of great significance to FM. Existing research focuses on two major aspects of structure
health, i.e., structural condition assessment and structural faults detection. Though they are
very similar to one another, the former is more a routine inspection while the latter aims to
detect deformation and then repair. A passive wireless breakage-triggered strain sensor was
developed for rapid identification of spots surpassing pre-set strain thresholds. When the brittle
component of the sensor fractures, an RFID tag turns active and the stored data can be inquired
by a reader with antenna (Zhang and Bai, 2015). A WSN has also been explored for rapid
structural health monitoring wirelessly in the absence of electrical power and applied to the
under-structure

of

a

bridge

(Mascareñas

et

al.,

2009).

Additionally,

18

photogrammetry/videogrammetry have been used to automate crack detection in structures
such as pavements (Radopoulou and Brilakis, 2016) and bridges (Gul et al., 2013). This was
generally performed by identifying abnormalities or deteriorations found in images or videos
of these property and infrastructure assets or by comparing pictures or videos taken over time.
Laser scanning is also suitable for structural health monitoring, including the detection of
structural faults and damage (Cabaleiro et al., 2017; Jung and Jeong, 2019) and concrete cracks
(Liu et al., 2014). However, fewer studies were found to explore the use of PSTs to achieve
automatic, predictive maintenance.
4.9 Implications and guidance
After discussing and analyzing PSTs for different FM applications, some implications and
guidance for facility owners, managers, and researchers can be made: conservation and
maintenance is mainly targeted at historical buildings, photogrammetry and laser scanning are
the two major PSTs commonly adopted; energy modelling and management pays more
attention to buildings currently by relying on sensors and in some cases supported by
photogrammetry and point clouds; health, safety and security looks at the health of
infrastructure and buildings mainly powered by photogrammetry, as well as safety and security
for users with sensors; indoor environment management of residential and commercial
buildings is fueled by sensors and even WSN; asset management of infrastructure and
properties usually adopts Auto-ID to locate assets, and also photogrammetry/videogrammetry
recently to reconstruct digital models for infrastructures; photogrammetry/videogrammetry and
emerging laser scanning are most utilized for information access and visualization by
reconstructing digital models for buildings and infrastructure, meanwhile, Auto-ID and lately
sensors are also adopted to access non-geometric information; space management of buildings
only takes use of Auto-ID as found in the research cases, though videogrammetry and sensors
also have the potential for this purpose, which requires further exploration; structural health

19

monitoring of infrastructure and buildings is mostly driven by sensors, some cases also found
laser scanning applicable to detect structural faults, cracks, and damages; Auto-ID is a cheap
data source of object location; various sensors and WSN are promising in collecting physical
attributes of devices, components, and users; photogrammetry/videogrammetry are relatively
cheap and accurate data source of geometric information, while laser scanning provides
accurate and computable geometric data; one PST can be utilized to multiple applications for
different facilities, an FM application can make use of several PSTs.
5 Challenges
Although Auto-ID, sensors, photogrammetry and videogrammetry, and laser scanning are
promising PSTs for FM, their applications encounter many challenges requiring examination
from different perspectives. Five major challenges stand out given the analysis of existing
research on PST for FM, and deserve future research attention.
A primary challenge is the high investment adopting PSTs for FM demands. Although some
PSTs for FM have gradually become more affordable in recent years, higher-performance PSTs
generally still come with higher price. For example, 3D laser scanning can capture more
accurate geometric data than photogrammetry (Klein et al., 2012), while its device, i.e., the
laser scanner, is generally more expensive than the camera and involves additional operational
costs (Xiao et al., 2017). Likewise, when choosing RFID or barcode for tracking objects in FM
(Lu et al., 2011), the price of RFID readers and RFID tags are considerably higher than that of
the actual barcode. Such high investments might not be significant for small-scale FM
scenarios, but will become a serious issue when scaled up for real-life FM applications. It is
expected that continuous technology advancement can help reduce the cost of PSTs for FM.
Explorations to investigate cheaper technologies for higher-performance are also largely
needed and welcomed. It should also be noted that the high-invest is just a short-term pay, but
the benefits of adopting PSTs for FM, such as helping saving energy and operation and

20

maintenance cost, providing smart and humanized services, easier access to data, and better
visualization of information, will come in a long-run. The investments will surely be paid off.
Even PSTs are invested to collect data, the storage of data, which requires safety, reliability and
expandability, is still a second tricky challenge. To avoid loss, attack, leakage and ensure safe,
updated, cost-effective data storage and maintenance remains an area requires improvement
and research. Currently, there are two major data storage strategy, distributed and centric, one
put eggs in different baskets while the other put them together. The former is more independent
and reliable with the cost from complexity, security, and maintenance difficulty, vice versa for
centric data storage. Facility owners and managers can establish the combined data base
according to the nature and importance of different data, for general operation and maintenance
data updated from time to time, to keep them in distributed database is fine, for the important
information and sensitive data, a secured centric database is better. For data maintenance, with
enough funding from owners, required data structure from managers, operators should keep
the data up-to-date. For data ownership problem, leaving it to contracts can be a feasible
method.
The format, structure, unit, update frequency of captured data from different PSTs and even
different devices of the same PST kind can be very different, how to integrate data with
different protocols remain a third tough challenge. Without a unified data exchange protocol,
value of the precious data collected by PSTs cannot be exploited. A typical example is the
fusion of sensor data and BIM models: data collected by various types of PSTs ordinarily exists
in dissimilar formats, which have not been comprehensively wrapped in open standards like
IFC (Industry Foundation Classes) and COBie (Construction Operation Building Information
Exchange), where manual input is necessary to supplement the missing information in BIM
models. Therefore, When fusing various types of data from different sensing sources (Aziz et
al., 2017), stakeholders (Motamedi et al., 2016; Pärn and Edwards, 2017), as well as stages and

21

standards on a single platform, it is essential to first ensure the information interoperability by
determining the proper protocols for information exchange (Aziz et al., 2017). Although the
standardization of protocol can be extremely troublesome, but without which, considerable
information could be lost during the exchange. Research and industry efforts for unified
international standard of data interoperability is largely required.
Having data is one thing, how to make use of the golden mine is another. However, the lack of
mature data processing methods to support more advanced FM applications is a fourth
challenge. Practical applications require professional tools for processing various types of
captured data, such as a commercial device for automatically processing point clouds into
semantically-rich as-built models (Dimitrov and Golparvar-Fard, 2015). Indoor environment
management, thermal comfort, visual comfort, and indoor air quality were studied separately
by using different sensors respectively. These different types of indoor environment data could
be better visualized if fused with 3D thermal digital model generated by thermal photographs
for an integrated simulation. However, existing data processing methods are limited. The lack
of a suitable data processing technique, capable of extracting the decision-support information
from the raw captured data, may explain why some FM applications, e.g., space management,
have not been sufficiently studied in the reviewed literature. For instance, surveillance videos
are suitable sources for studying space usage rates, function division rationality, and activity
patterns in a large facility like a shopping mall, but the reviewed studies did not offer any
operable solutions since extracting space utilization features from videos remains challenging.
It should be emphasized that the collecting and utilizing of data must consider the privacy
concern of users, a fifth but also crucial challenge to be concerned. The collection of data from
users must be subject to the privacy policies of the facility location. With permission choice
and clearly stated terms in contracts, pop-up choice at APPs and websites, portal to notify
possible personal data leakage, the separation of personal data and usage preference, privacy

22

issue can be largely mitigated. When necessary, such as the facility owner or manager changes,
users will be notified and have the right to delete their personal data. In a word, it should be
guaranteed that for any FM application, the collection of data must strictly respect the privacy
of users with authorized permission.
6 Conclusion
Efficient and effective facility management (FM) demands real-time, accurate data, and
seamless coordination of people, assets, and spaces. New opportunities for FM have become
possible as pervasive sensing technologies (PSTs) have advanced. However, it is hard to find
appropriate technologies to capture desired data for targeted purpose, especially for facility
owners, managers, and some researchers who lacked the expertise or paid scant attention to
technologies. This study contributes to the body of knowledge by articulating the status quo
and challenges in PSTs for FM applications. Several key conclusions can be drawn from this
review: energy modeling and management, together with information access and visualization
via PSTs received more attention than others, while space management is the least referred;
one PST could facilitate many FM application categories and each PST has its own advantages
for specific applications; one single application sometimes can be achieved by different PSTs
using different data processing methods. These findings can both act as guidance for
practitioners to choose appropriate PST to captured desired data and add a new angle for FM
researchers who aims to update the theory for facility information management.
Five challenges associated with PSTs for FM were identified, which include high investment
on PSTs, data storage problem, absence of proper data exchange protocols for data
interoperability, a lack of mature data processing methods for data utilization, and privacy of
users. To address these challenges, future studies are recommended: to develop technologies
with higher cost-effectiveness for data sensing, to develop a unified protocol as a standard to
ensure compatibility of heterogeneous data for further fusion, to develop

automatic data

23

processing algorithms, to develop better way to collect and store different types of data in a
secured, reliable, and sustainable way.
Acknowledgements
This study was supported by grants from the Research Grants Council of the Hong Kong
SAR (No. 17201717).
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