University of Things: Opportunities and Challenges for a Smart Campus

Environment based on IoT Sensors and Business Processes

Mevludin Blazevic

and Dennis M. Riehle

Institute for IS Research, University of Koblenz, Universit

atsstraße 1, Koblenz, Germany

Keywords:

Internet of Things, Business Processes, Learning Environments, Smart Data, Data Lake.

Abstract:

The university of things is an academic institution full of sensors, data, and automated processes. The col-

lection of information and states about objects and things enables diverse research and studies in the ﬁeld

of information systems. This paper presents a research project, where we have set up a Smart Campus in-

frastructure based on Internet of Things (IoT) sensors and Long Range Wide Area Network (LoRaWAN)

communication technology. From our real-world deployment, as well as from academic literature, we have

identiﬁed 6 opportunities and 11 challenges for the integration and use of sensor data for business processes

at universities, which are shown in this paper.

1 INTRODUCTION

Over the years, the Internet of Things (IoT) concept

has gained a lot of academic and industrial interest

and attention. It drives technological innovations and

creates signiﬁcant challenges for governments, orga-

nizations, and societies. IoT is cross-cutting various

scientiﬁc disciplines, such as Computer Science and

and Information System (IS) research. In particular,

the IS discipline is multi-faceted, focusing on social,

business, and technical aspects of Information Tech-

nology (IT) (Baiyere et al., 2020; Baskerville et al.,

2018; Benbasat and Zmud, 2003; King and Lyytinen,

2006).

Given its overarching scale, the IoT can address

a wide range of societal, technological, and business

challenges and opportunities (Avital et al., 2019). IS

research might contribute meaningfully to the IoT

area from a variety of perspectives and to scholarly

work (Baiyere et al., 2020).

With the emergence of IoT, a large amount of in-

terconnected and smart devices arises that might en-

hance and improve business processes in organiza-

tions (Del Giudice, 2016; Meyer et al., 2013). In

this paper, we are focusing on opportunities and chal-

lenges for existing and novel business processes with

the use of IoT sensor data for teaching, research,

and administration in the university context, which

is referred to Smart Campus or University of Things

(UoT) in the scientiﬁc literature. During our research,

https://orcid.org/0000-0003-0347-7392

https://orcid.org/0000-0002-5071-2589

different types of IoT sensors are gradually distributed

and installed.

A Smart Campus can be considered a part of a

Smart City (Zhang et al., 2022). Both are sharing a

similar structure; a Smart Campus can be seen as a

small-scale Smart City (Fortes et al., 2019; Silva-da-

obrega et al., 2022; Vasileva et al., 2018).

In our real-world setup, IoT can simplify and im-

prove processes and workﬂows in research and ad-

ministration and at the same time, a teaching platform

is built for students to study the ﬁeld of IoT and its ap-

plications.

Although a full-scale installation of IoT sensors is

not yet completed (installation of all sensors across

the university campus), a lot of interesting data and

information were created for further investigation and

research. Our research questions for this work can be

described as follows:

RQ1: How can an IoT sensor-based Smart Campus

infrastructure be implemented to support existing

and new business processes and workﬂows at the

university from a bottom-up perspective?

RQ2: What are the opportunities and challenges for

using sensor data for existing and new business

processes and workﬂows from a top-down per-

spective?

Different types of IoT domains are deﬁned to classify

and structure the use of sensor data in different ﬁelds

of human endeavor (Garda

sevi

c et al., 2017; Ibarra-

Esquer et al., 2017). However, we found the context

of UoT and Smart Campus interesting and challeng-

ing, since there are many opportunities on our campus

Blazevic, M. and Riehle, D.

University of Things: Opportunities and Challenges for a Smart Campus Environment based on IoT Sensors and Business Processes.

DOI: 10.5220/0011761900003482

In Proceedings of the 8th International Conference on Inter net of Things, Big Data and Security (IoTBDS 2023), pages 105-114

ISBN: 978-989-758-643-9; ISSN: 2184-4976

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

105

to explore the topic of IoT in combination with the IS

discipline.

In the next chapter, a brief overview of the

concepts of IoT and Business Process Management

(BPM) is given as well al related work in the Smart

Campus/UoT ﬁeld. After that, the adapted Design

Science Research (DSR) approach of Peffers et al.

(2007) is provided. In the following chapters, the

usage of IoT sensors at the university and the setup

of the sensor infrastructure are presented. Opportuni-

ties and challenges for enhancing Business Processes

(BP) with IoT data are discussed afterward.

2 BACKGROUND

2.1 IoT, Smart Campus and BPM

A wide variety of deﬁnitions for IoT exists in scien-

tiﬁc and non-scientiﬁc literature (e.g. Atzori et al.,

2010, and Baiyere et al., 2020). Mostly, the deﬁni-

tions have the interconnection between physical ob-

jects and digital technologies in common.

According to Baiyere et al. (2020), IoT can be de-

ﬁned as “a system of interconnections between digi-

tal technologies and physical objects that enable such

(traditionally mundane) objects to exhibit comput-

ing properties and interact with one another with or

without human intervention” (Baiyere et al., 2020, p.

557). Additionally, different IoT domains were de-

ﬁned, such as Smart City, Smart Agriculture, Smart

Manufacturing and many more (Garda

sevi

c et al.,

2017; Ibarra-Esquer et al., 2017). A Smart Campus

can be seen as an indispensable part of a Smart City

(Zhang et al., 2022), which consists of various sub-

domains, e.g. Smart Education, Smart Assessment,

Smart Learning, Smart Teaching, etc. These sub-

domains are supported by different IT technologies

(Mart

ınez et al., 2021; Mircea et al., 2021).

The sharing of IoT sensor data for various plat-

forms and IS might contribute largely to BPM sys-

tems and concepts (cf. Janiesch et al. (2020)). Ac-

cording to Weske (2012), BPM includes concepts,

methods, and techniques to support the design, ad-

ministration, conﬁguration, enactment, and analysis

of business processes. The explicit representation of

business processes with their activities forms the basis

of BPM. Business processes that are deﬁned might be

the subject of analysis, improvement, and enactment.

In contrast, Becker and Kahn (2011) add the cus-

tomer as an additional entity. According to them,

the efﬁciency of the process is measured by the cus-

tomer himself or herself, rather than by the controllers

within the company. Newer deﬁnition approaches of

BPM are adding more dimensions and entities. Ham-

mer (2010) a Process Management Cycle, consisting

of a Process Compliance activity as well as measuring

process performance and continuous business process

improvement.

The BPM ﬁeld can largely beneﬁt from IoT tech-

nology and vice versa, as presented by Janiesch et al.

(2020). The authors underline the importance of the

development of application scenarios for IoT-driven

BPM in a general manner. With sensor data, BPM can

contribute to environmental challenges, as in Green

Information Systems (Brendel et al., 2022). In our re-

search work, we delve into the university context and

investigate challenges and opportunities for IoT and

BPM applications.

2.2 Related Work

Several studies have been conducted in the ﬁeld of

IoT applications in the UoT/Smart Campus context.

A Smart Campus based on IoT sensors enables a

teaching and research platform for students and re-

searchers, where IoT use cases are implemented and

evaluated.

Gao (2021) describes a Smart Campus setup based

on a ZigBee wireless sensor infrastructure. The data

of this sensor network is stored in a Postgre SQL en-

vironment, the main sensors are a gyroscope and an

electronic compass from mobile phones carried by

students. The teaching effect of the ZigBee network is

evaluated in the research work using Artiﬁcial Intelli-

gence (AI) and Big Data technology, concluding that

the ZigBee infrastructure has greatly improved the

teaching process. However, the author does not de-

scribe limitations and ethical issues in tracking the lo-

cation of students using the sensors of mobile phones.

Mart

ınez et al. (2021) introduce an IoT infras-

tructure based on Long Range Wide Area Network

(LoRaWAN) that focuses on measuring air quality

and energy consumption. The IoT infrastructure pre-

sented can help to achieve energy efﬁciency, cost sav-

ings, and low energy consumption by adjusting air

conditioning systems and prediction models of energy

consumption and air quality in university buildings.

The authors conclude that the approach presented in

their research work is extendable to smart buildings

and smart cities.

Cheong and Nyaupane (2022) provide an empir-

ical study of IoT ecosystems in universities by con-

ducting focus group interviews. From the authors’ re-

search results, requirements from the student’s point

of view for a Smart Campus system can be derived.

While this study focuses only on the survey of stu-

dents, however, the inclusion of other stakeholders of

IoTBDS 2023 - 8th International Conference on Internet of Things, Big Data and Security

106

Identify Problem

& Motivate

Define Objectives

of a Solution

Design &

Development

Demonstration Evaluation

Communication

Identification of

Use Cases

Selection of

Communication

Technologies and

Hardware

Implementation

of Prototype

Collection of Data

from Edge Devices

in Private Cloud

Identification of

Challenges and

Opportunities

Paper at Hand

Figure 1: Design Science Research Cycle (adapted from Peffers et al. (2007).

a university is mentioned as a limitation and future

research work.

Sneesl et al. (2022) conduct an Analytical Hier-

archical Process (AHP) on 25 factors derived from

scientiﬁc literature regarding IoT based Smart Cam-

pus systems. The research outcomes reveal that the

most signiﬁcant factors are governmental support,

privacy concerns, social inﬂuence, and service collab-

oration. As the research work of Cheong and Nyau-

pane (2022), requirements and processes such as sup-

port activities can be derived from the ﬁndings from

Sneesl et al. (2022).

Furthermore, data from IoT sensors and devices,

so-called IoT data, enables big data analytics. Such

data ha special, multi-scale and multi-level character-

istics and corresponding analytics have gained more

interest by organizations in the last decade (Williams

et al., 2019). Moreover, IoT data can be integrated

into a single storage, a Data Lake, around which an

analytical ecosystem ca be built. Such systems have

been named Analytics as a Service (Aaas) in previous

research (Riehle et al., 2020).

3 RESEARCH METHOD

The goal of our research is to build a UoT by set-

ting up a sensor-based Smart Campus. As such, we

aim at designing an IT artifact and, hence, we fol-

low the principle of DSR. Our work adapts the DSR

approach by Peffers et al. (2007), who suggest six

different steps to follow (cf. Fig. 1).

Following the ﬁrst step according to Peffers et al.

(2007), we have already contributed to the motiva-

tion in section 1, and a more detailed identiﬁcation of

use cases of sensors at Smart Campuses will follow in

the next section. Besides, in section 4, we deﬁne the

objectives of your solution by selecting an appropri-

ate stack of communication technology and hardware

to use. Section 5 holds the development and demon-

stration of our prototypical platform and, hence, con-

tributes to steps three and four of Peffer’s DSR cycle.

In section 6, we analyze and evaluate our prototype

by identifying and describing both challenges and op-

portunities that occurred in the previous steps. This

correlates to an evaluation phase according to the re-

search method. Lastly, this paper itself contributes to

the communication of your research results.

The DSR approach by Peffers et al. (2007) con-

sists of several feedback loops, which allow for multi-

ple iterations of the overall process. Our research has

been conducted between June 2021 and May 2022.

During this time, we improved the implementation

of our prototype continuously to reﬂect changing re-

quirements (i.e., due to Covid19). However, in this

paper, we only report the latest iteration for the sake

of simplicity and comprehensibility.

4 USAGE OF IoT SENSORS AT A

SMART CAMPUS

For the research of socio-technical and cyber-physical

systems at our university, a computer cluster with

a LoRaWAN infrastructure was procured in 2020,

which includes a variety of different sensors for ob-

taining objects/things data and information. In the

ﬁrst step, use cases for a broad deployment of sen-

sors on the campus were identiﬁed as shown in Ta-

ble 1. The use cases were identiﬁed through discus-

sions with relevant stakeholders at the university, for

example, facility management staff. Before that, var-

ious sensors and gateways from different manufactur-

ers were investigated in a test environment as well as

data transmission technologies. Finally, sensors and

gateways from two different manufacturers were se-

lected.

Based on that, the communication technology and

communication method were selected, where the en-

ergy consumption for transmitting the data from the

sensors distributed on campus is crucial. For exam-

ple, sensors for tracking inventory and items can be

ble connection. Therefore, LoRaWAN with a license-

free transmission frequency was selected and it was

tested on the campus in advance. IoT devices from

University of Things: Opportunities and Challenges for a Smart Campus Environment based on IoT Sensors and Business Processes

107

Table 1: Use cases and sensors for a smart campus.

Use Case Sensor type(s)

Tracking inventory, tools, and equipment used by

the university administration.

GPS Tracking sensors

Measuring the air quality of workrooms, seminar

rooms, and lecture halls

Air quality sensors with the following sensor measurements:

Humidity, Air Pressure, Temperature, Carbon Dioxide level,

Particulate matter/dust

Measure whether ﬁxed objects/things fall from a

height (May be sensors themselves)

Accelerometers

Person Counter Combination of various sensors, e.g.: Carbon Dioxide Sen-

sor, Light Sensor, Radio-Frequency Identiﬁcation Reader

Locating sensors (2D/3D geolocation methods us-

ing the LoRa gateways)

Technology or method to locate sensors in 2D or 3D, e.g.,

geolocation. Might also be GPS Tracking sensors

Pycom

were selected, which include sensors, micro-

controllers, and boards. Pycom’s devices are espe-

cially beginner-friendly for students, who can install

and try out the devices for study and practical work.

They can be programmed by students and are food for

studying the ﬁeld of IoT and sensors.

The Pycom “Expansion Board 3.0” is needed for

programming sensors and microcontrollers. Further-

more, Pycom offers different types of sensor devices,

which contains a combination of various sensors. For

example, the Pycom Pyscan device consists of an ac-

celerometer, light sensor and RFID-NFC. In contrast,

the Pysense 2.0X device contains an accelerometer,

air pressure, temperature, and humidity sensors. In

addition, carbon dioxide sensors were procured as

add-on modules from third-party manufacturers. The

Pycom devices can be expanded with such modules.

The accelerometer might be useful for detecting the

falling of a Pysense 2.0X device from its mounting

point. All in all, for the collection of sensor data,

the devices “Pyscan”, “Pysense 2.0X” and “Pytrack

2.0X” were selected and purchased. The sensors op-

erate with the LoPy4 microcontroller, which can be

programmed using the Expansion Board 3.0. Various

cases and lithium polymer batteries allow the deploy-

ment of devices across the whole campus.

LoRaWAN gateways from Kerlink

are chosen for

the data transmission of the sensors. The installation

process can be done quickly due to well-written doc-

umentation from the manufacturer. Furthermore, they

can be operated via Power-over-Ethernet. We use the

product Kerlink “iFemtoCell” for indoor use, as well

as the product Kerlink “iBTS” for outdoor use. In ad-

dition, the geolocation of sensors can be tested with

the outdoor gateways.

see https://pycom.io

see https://www.kerlink.com/

5 INFRASTRUCTURE SETUP

AND IMPLEMENTATION

After the selection of sensors and gateways for a

Smart Campus infrastructure, a concept for the tech-

nical and spatial infrastructure was created. Fig. 2

shows the spatial distribution of four Kerlink iBTS

outdoor gateways on the campus to ensure sufﬁcient

signal coverage of the LoRaWAN sensors and to test

the possibility of 2D and 3D geolocation. If the sig-

nal quality is not sufﬁcient for sensors located deeper

in the buildings, Kerlink’s indoor gateways are used

for this purpose. Subsequently, a concept for trans-

mitting the sensor data via the LoRaWAN gateways

to the Private Cloud infrastructure was created.

LoRa-

Gateway 1

LoRa-

Gateway 2

LoRa-Gateway 4

LoRa-

Gateway 3

Figure 2: Spatial distribution of the LoRa gateways on the

University Campus.

As shown in Fig. 3, the IoT architecture can

be structured into the concept of Edge, Fog and

Cloud Computing (Bittencourt et al., 2018). The

various Pycom sensors with the LoPy4 as a micro-

controller form the edge devices. Using the Lo-

IoTBDS 2023 - 8th International Conference on Internet of Things, Big Data and Security

108

RaWAN protocol, the sensor data is transmitted to

the Fog layer, which represents the LoRaWAN gate-

ways. Subsequently, the data packets are transmit-

ted via TCP/IP protocols to the “The Things Stack

Version 3” network server, which is an open-source-

based LoRaWAN network server.

Data providers/

Edge Layer

IoT gateways/Fog Layer

Iaas Private Cloud/Cloud Layer

LoRa Sensor

LoRa gateway 1

(iBTS)

LoRa gateway 2

(iBTS)

LoRa gateway 3

(iBTS)

LoRa

gateway 4

(iBTS)

IoT platform: The

Things Stack Version 3

Network Server

Application

Server

Application server

+ database (virtual

machine)

Private cloud (IaaS cloud)

at the university campus

MQTT

Figure 3: Rough representation of the IoT infrastructure at

the University campus.

According to the The Things Network Documen-

tation, the network server operates the LoRaWAN

network layer, which includes MAC commands, re-

gional parameters and Adaptive Data Rate (ADR)

and consists of a Gateway Server. It maintains con-

nections with LoRaWAN gateways that support User

Datagram Protocol (UDP), MQ Telemetry Transport

(MQTT), and more. It forwards the uplink trafﬁc to

the network server directly or indirectly.

The processing of sensor data forwarded by the

gateways is not a direct part of the Fog layer, but

rather an intermediate step between the fog and the

cloud layer. On the cloud layer, a private Infrastruc-

ture as a Service (IaaS) cloud based on Apache Cloud-

stack

and Kernel-based Virtual Machine (KVM) is

https://www.thethingsindustries.com/docs/

getting-started/what-is-tts/ (accessed 21

Feb. 2022)

https://docs.cloudstack.apache.org/en/4.16.1.0/

conceptsandterminology/concepts.html (accessed 15

being built as the cloud infrastructure for central sen-

sor data processing at the university.

By now, 31 sensors are currently in operation on

campus, and a virtual machine based on Ubuntu 22.04

was set up as a prototype for storing and represent-

ing IoT data. For storing the sensor data, a MySQL

database is used initially. Although other database

systems might be more suitable for the purpose, e.g.

time-series databases, the MySQL database is used

for testing the Smart Campus architecture. For a

graphical representation of the sensor data, Grafana is

used. To receive sensor data from The-Things-Stack,

an MQTT broker based on Node-RED is run on the

virtual machine. Node-RED is a ﬂow-based program-

ming tool originally developed by IBM and consists

of a Node.js runtime environment. The ﬂow editor

can be operated via a web browser. In our case, the

Node-RED application also runs on the virtual ma-

chine.

6 OPPORTUNITIES AND

CHALLENGES FOR

ENHANCING BUSINESS

PROCESSES WITH IoT DATA

Since the ﬁrst appearance of IoT, many IoT sys-

tems have been developed in different IoT domains.

Prominent examples include Smart Home, Smart

City, Smart Agriculture, and others. Currently, not

much research has been conducted regarding IS and

BPM applications in the UoT/Smart Campus domain.

Therefore, it is particularly interesting for us what

possibilities IoT sensors offer for the design of busi-

ness processes and IS within our university.

The Smart Campus infrastructure based on IoT

data is installed and implemented on the bottom-up

principle. In the ﬁrst step, use cases were identiﬁed

based on the opportunities provided by our procured

IoT hardware. The research work of Janiesch et al.

(2020) offers suggestions, guidance, and approaches

for our work.

Six opportunities and 11 challenges for building

a Smart Campus setup with IoT devices and business

processes were discovered during our research by de-

ploying the sensors on the university campus.

For linking information generated by IoT sensors

with business processes, a top-down approach is con-

ducted in the context of this research work. In the ﬁrst

step, the main task areas of the university are iden-

tiﬁed. Subsequently, some individual tasks are as-

signed to the respective higher-level task area. Fig. 4

Aug. 2022)

University of Things: Opportunities and Challenges for a Smart Campus Environment based on IoT Sensors and Business Processes

109

shows the main task areas of the university with a se-

lection of relevant business processes.

Teaching

Research

Administration

Processes:

• Research

planning

• Integration of

IoT devices in

field studies

• Conducting

surveys

• Climate

monitoring

• Usage of IoT

data for

empirical

research

• Resource

allocation on

high

performance

computing

clusters

Processes:

• Planning of

courses

• Course room

organization

• Exam

management

• Study

organization

• Searching for a

room for group

work

• Campus

navigation

• Event planning

• Mobility

Processes:

• Enterprise Resource

Planning

• (Management) Accounting

• Procurement

• Fire detection

• Theft detection

• Evacuation

• Inventory tracking

• Campus navigation

• Air conditioning of lecture

halls (sensor – actuator

micro process)

Figure 4: Responsibilities of the university and selection of

business processes (own illustration).

Many processes within the university cannot always

be assigned precisely to one task area; rather, the var-

ious task areas overlap, which in turn leads to numer-

ous different processes. Compared to other public in-

stitutions, students, researchers, and administrators in

particular interact with each other at universities. The

interaction and communication between these actors

vary a lot and result in different requirements. Con-

sequently, various information systems are used, such

as groupware systems and university information sys-

tems, which are used by various stakeholders and stu-

dents. In addition, there are other IS that are mainly

used by administrative staff and teaching staff, such

as an accounting system and an Enterprise Resource

Planning (ERP) system. The IS for teaching and ex-

amination management is used in particular by stu-

dents and teaching staff.

Marakas and O’Brien (2013) introduced the con-

cept of the roles of the business applications of IS,

which is shown in Fig. 5. The representation is mod-

iﬁed to the UoT/Smart Campus context.

The authors provide three fundamental roles for

all business applications in IT, which IS can fulﬁll for

a business enterprise (or organization). First, the sup-

port of strategies of the university (role “A”). The role

formulation was adapted for the university context.

Here, IS should primarily support strategies for im-

proving study and teaching conditions, and research

conditions, as well as strategies in the area of admin-

istration.

Second, IS should support Business Decision

Making (role “B”). In our context, IS is intended

to support decision-making for students (study plan-

ning), faculty (teaching and research planning), as

well as administrative leaders.

Support

Strategies of

the University

Support Business

Decision Making

Support

Business Processes and Operations

Figure 5: Roles of Business Applications in IS (cf. Marakas

and O’Brien (2013)).

Last, IS should support of business processes and

operations (role “C”). Many business processes of the

universities are already implemented in the existing

IS. Data from IoT sensors may contribute meaning-

fully to these processes. However, new processes

might be created with the use of sensor data.

The starting point for collecting opportunities,

challenges, and requirements is deﬁned by the data

of the Pycom sensors about different information of

the objects, which are: air quality, humidity, tempera-

ture, location of sensors and objects (GPS), values of

the accelerometers, and RFID scanners.

The opportunities and challenges for a Smart

Campus infrastructure consisting of sensors, data, and

automated processes are shown in Table 2 and Table

3, each of them mapped to one or more Business Ap-

plication roles in IS as shown in Fig. 5.

In our real-world application, we also see the pos-

sibility of triggering various micro-processes (Jani-

esch et al., 2020) through sensor data (O1). In the

search for a learning and working place, for exam-

ple, different sensors could indicate the availability

of a free working place, or provide suitable informa-

tion about it in a Workﬂow Management System. A

student or employee can mark the place as reserved.

As soon as a workstation has been occupied, sensors

would register the status and indicate the worksta-

tion as no longer available. Therefore, we assign this

opportunity (O1) to the Business Application role C

(Fig. 5).

In addition to the development of micro business

processes, sensors can provide real-time data about

the condition of a room (lecture or seminar room) to

facilitate decision-making (O2) for all members of the

university. For example, another room may be sought

for a meeting or class if thermal comfort is not given

IoTBDS 2023 - 8th International Conference on Internet of Things, Big Data and Security

110

Table 2: Opportunities for enhancing business processes with IoT data.

No. Description IS roles (Fig. 5)

O1 New (micro)-processes and workﬂow can be triggered by sensor data as event

inputs

O2 Objects and things at the campus, e.g., rooms, ofﬁces, and labs can provide

real-time data for improving decision making

O3 New methods of computer science (e.g. Big Data Analytics, Machine Learning

& AI) can be applied to analyze the enormous amount of sensor data collected

over time to discover new insights and knowledge that lies in the data

A and B

O4 From the strategic to the operational layer, sensor data can add value to informa-

tion systems, such as ERP systems and collaborations systems at the university

A, B, and C

O5 Since the IoT infrastructure on the university campus is mostly based on open

technologies (open source IaaS cloud, extensibility of Pycom sensors with

third-party devices, TTN as an open IoT platform, LoRaWAN as license-free

data transmission technology), the Smart Campus can be extended with addi-

tional sensors and applications fast and at low cost.

O6 Green IS: Reducing energy consumption for the university through sensor-

driven BP and workﬂows

due to heating failure or outside weather conditions.

Business Application role B is assigned to this oppor-

tunity.

Collecting sensor data over a long period gener-

ates an enormous amount of data (Big Data, O3). This

allows modern methods of processing large amounts

of data to be applied in order to gain further insights.

In our opinion, this opportunity corresponds to roles

A and B, as data analytic methods can be useful for

strategic alignment of IS, and at a lower level, they

can support decision-making.

Moreover, sensor data can enrich existing IS on

campus, e.g. procurement systems for inventory or

groupware systems used by employees, researchers,

and students (O4). For example, a room for group

working can be searched using a groupware system

or a web dashboard, and sensor data can provide real-

time information. This opportunity is assigned to all

Business Application roles. The current setup of the

IoT sensor network on campus is by no means com-

plete or perfect. Based on the LoRaWAN standard

and the sensors and actuators available on the market

from different manufacturers, the Smart Campus can

be constantly expanded and improved with new actu-

ators and sensors (O5). This opportunity corresponds

to the Application role C, as it is fundamentally es-

sential for integrating sensor data into business pro-

cesses.

Because sensors can provide real-time data about

the state of a building or thing, resource-saving (mi-

cro)processes can be delivered (O6). A possible use

case would be monitoring indoor temperature, carbon

dioxide content, and humidity in indoor spaces (lec-

ture hall, library) with a fresh air supply. By deter-

mining the demand for fresh air in the interior, this

can be regulated variably so that it remains as efﬁ-

cient as possible and only consumes as much energy

as necessary (cf. Mart

ınez et al., 2021). From our

point of view, this opportunity is to be assigned to

Application role A, since a possible adjustment of the

strategic orientation of an IS is required here.

In addition to the six opportunities, we encoun-

tered 11 challenges and problems during our research,

which we have summarized in Table 3. Currently,

few, if any, business processes at the university are

comprehensively documented and modeled (C1). For

us to identify the business processes where sensor

data can provide added value for improvement, a

complete set of processes must ﬁrst be modeled and

documented centrally. Therefore, we have assigned

this challenge to the Business Application role C.

Once the business processes are collected, modeled,

and documented, we need to identify which processes

may be suitable for the integration and use of sensor

data (C2). In our view, this challenge would also cor-

respond to the Business Application role C.

As mentioned earlier, existing processes may be

enhanced and improved with sensor data, but addi-

tionally, micro-processes can also be modeled using

information from the sensors as a starting event (C3).

Such micro processes may be modeled and realized

using Workﬂow Management Systems. Therefore,

we classify this challenge in the Business Application

role C.

University of Things: Opportunities and Challenges for a Smart Campus Environment based on IoT Sensors and Business Processes

111

Table 3: Challenges for enhancing business processes with IoT data.

No. Description IS roles (Fig. 5)

C1 Collecting and modeling existing BP at the university C

C2 Identiﬁcation of existing BP to be supported by sensor data, so that the number

of manual interactions to the process is reduced

C3 Identiﬁcation of micro-processes and “habits” from employees and students

which can be derived from sensor data

C4 In order for sensor data to be reliably collected and made available for critical

BP, the data quality (cf. Liu et al. (2020)) of the sensors must be ensured

A, B, and C

C5 Smart data: The collection and processing of data should be targeted and accu-

rately designed for the area of application

B and C

C6 Data Lake: A data storage platform should be developed to reliably store a large

amount of sensor data

C7 The distribution of sensors poses infrastructural requirements, such as energy

supply, mounting points

C8 Since a comprehensive sensor data infrastructure needs to be maintained and

monitored at all times, this poses new challenges in terms of ﬁnancial, time, and

human resources

C9 Collection of sensor data from objects and things must be in accordance with the

data protection rules of the EU

C10 Collection of sensor data for BP and workﬂow modeling must be done in accor-

dance with existing ethical guidelines

C11 Smart Campus applications, BP and workﬂows with sensor data poses safety and

security requirements

For our IoT Smart Campus infrastructure that

needs to provide real value and beneﬁt to members

of our university, an adequate sensor data quality (cf.

Liu et al., 2020) is essential (C4). Since we believe

this affects all levels of Business Application roles,

we assign this challenge to the roles A, B, and C.

The installation of sensors and the deﬁnition of sen-

sor data transmission should be designed thoughtfully

and purposefully, also with regard to the correspond-

ing business process or micro process, a data process-

ing concept is necessary (C5). Therefore, this chal-

lenge applies to role B and C.

At the application level, sensor data needs to be

stored in a sort of a data lake that can reliably and

efﬁciently store a large amount of sensor data in our

cloud (C6), which corresponds to the Business Ap-

plication role C. As we distributed and installed the

sensors based on some use cases in a bottom-up man-

ner, we encountered problems at the infrastructure

layer. For example, it is difﬁcult to securely install

the sensors in the rooms, the sensors need to be pro-

tected from thieves. Usually, there is no power supply

nearby, and sometimes, sensors fell off their mount-

ing points (C7). In addition, the sensors must always

be checked and maintained (C8), recharging the bat-

tery and detecting erroneous sensors. Powered with

2000 mAh lithium polymer batteries, the Pycom de-

vices need to be recharged every 12 weeks. In con-

trast, the carbon dioxide sensor which may be oper-

ated with the LoPy4 microcontroller has a recharge

interval of 2 weeks. These challenges require ﬁnan-

cial, human, and time resources. In our opinion, the

safe installation and maintenance of the Pycom sen-

sors is a basic requirement for a Smart Campus Sys-

tem, therefore, we assign C7 and C8 to the Business

Application role C.

Installing a Smart Campus infrastructure poses not

only technical and organizational challenges but also

legal ones. The collection of object data through sen-

sors should be in line with the European data protec-

tion regulations (C9). This challenge is assigned to

role A. In addition, any ethical hurdles to building the

Smart Campus infrastructure must be addressed. It is

imperative that all university members should be in-

volved in the installation process. At no time should

the collection of sensor data lead to a disadvantage

for the stakeholders. Information should be provided

at appropriate points in the process (C10). Since both

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112

of these problems (C9, C10) are strategic challenges

from our perspective, they take on the Business Ap-

plications role A.

Our Smart Campus infrastructure is built on open

IT and networking standards such as LoRaWAN and

TCP/IP. Therefore, further challenges and problems

arise regarding IT security requirements (C11). For

example, the integrity of the sensor data must be pro-

tected, and access rights need to be managed. Other

opportunities and challenges can be identiﬁed, but the

above provides a foundation for further research in

our Smart Campus setup.

7 DISCUSSION AND

CONCLUSION

In this work, the planned setup of a smart campus in-

frastructure using IoT sensor data, LoRaWAN tech-

nology, the IoT platform The Things Stack, and an

in-house operated IaaS cloud infrastructure were pre-

sented. In the ﬁrst research question, the selection of

IoT devices, communication technologies, and data

transmission were presented according to the bottom-

up scheme. In the ﬁrst research question, it was de-

termined which sensor data should be collected for

which use cases. The later use of the information of

the objects was not considered in this step. However,

the aim is about ﬁnding meaningful data from objects

and things at the university.

The Smart Campus setup we present includes an

in-house cloud infrastructure as a private cloud. Com-

pared to public cloud infrastructures, operation poses

additional challenges such as installation, commis-

sioning, and maintenance. This delays the develop-

ment of a Smart Campus infrastructure. However,

operating a private cloud has many advantages, e.g.,

sensitive data can be stored in compliance with EU

data protection law. In addition, there is no longer

any dependence on public cloud providers. Although

we have already installed dozens of sensors, opportu-

nities for improving the infrastructure in terms of en-

ergy supply are becoming apparent. We have found in

our setup that the lithium polymer batteries with 2000

mAh capacity each can power the Pycom sensors for

about 12 weeks. In contrast, the carbon dioxide sen-

sors can only be supplied with energy from the batter-

ies for two weeks. The power consumption of these

sensors is too high, so for carbon dioxide measure-

ment, we need to connect the sensors to a permanent

power supply. Above that, some cases of the sensors

we ordered are less suitable for outdoor use than oth-

ers.

Nevertheless, the setup and installation of send-

ing sensor data and processing were done quickly as

expected. The data is also sent and received at re-

liable intervals. The LoRaWAN transmission tech-

nology we use turns out to be sufﬁcient for sending

and receiving sensor data and provides a more energy-

efﬁcient way to operate Pycom devices than WiFi or

Bluetooth. In addition, the LoRaWAN network with

the sensors would relieve the university’s WiFi net-

work.

The collected sensor data can then be used for var-

ious purposes. In the second research question, 11

challenges and 6 opportunities were gathered for the

integration of sensor data in business processes. The

opportunities and challenges gathered in this research

work are not comprehensive, and in the ongoing re-

search, more opportunities and challenges may arise.

The development of a concept to ensure an ade-

quate quality of the sensor data, reliable and efﬁcient

storage, as well as the sensible processing of the data

for further use (smart data) are the subject of further

research. As in the ﬁrst research question, a bottom-

up approach should be chosen here, the quality of

sensor data and efﬁcient data storage open up a wide

range of uses. In addition, data protection and data

security must be guaranteed during development. For

the use of sensor data in business processes, as in the

second research question, the challenges and opportu-

nities presented should be tackled to explore the pos-

sibilities of sensor data integration into the business

processes of the university.

ACKNOWLEDGEMENTS

This research has been supported by the Deutsche

Forschungsgemeinschaft (DFG) under Research

Grant No. 432399058.

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