Towards a Cloud-Based Smart Ofﬁce Solution for Shared Workplace

Individualization

Dominik Hasiwar

, Andreas Gruber

, Christian Dragschitz

and Igor Ivki

1,2 a

University of Applied Sciences Burgenland, Eisenstadt, Austria

Lancaster University, Lancaster, U.K.

Keywords:

Smart Ofﬁce, Shared Workplace Individualization, Workplace Environment Index.

Abstract:

In the evolving landscape of workplace dynamics, the shift towards hybrid working models has highlighted

inefﬁciencies in the use of traditional ofﬁce space and the need for an improved employee experience. In this

position paper we propose a Smart Ofﬁce solution that addresses these challenges by integrating a microser-

vice architecture with Internet of Things (IoT) technologies to provide a ﬂexible, personalized workspace

environment. The position paper focuses on the technical implementation of this solution, including the de-

sign of a Workplace Environment Index (WEI) to monitor and improve ofﬁce conditions. By using cloud

technology, IoT devices with sensors, and following a user-centred design, the proposed solution shows how

Shared Open Workspaces can be transformed into adaptive, efﬁcient environments that support the diverse

needs of the modern workforce. This position paper paves the way for future experimentation in real-world

ofﬁce environments to validate the effectiveness of the Smart Ofﬁce solution and provide insights into its po-

tential to redeﬁne the workplace for improved productivity and employee satisfaction.

1 INTRODUCTION

The COVID-19 pandemic initiated a critical and un-

foreseen shift in workplace dynamics, forcing com-

panies to quickly move from a traditional zero home

ofﬁce policy to a 100% home ofﬁce model. This dras-

tic change out of necessity has gradually evolved into

a hybrid work culture that includes a combination of

remote and in-ofﬁce work. In the post-pandemic land-

scape, this shift has brought ﬂexibility, improved em-

ployee satisfaction and signiﬁcant reductions in oper-

ational costs such as heating, electricity, and mainte-

nance. However, it has also led to a noticeable inefﬁ-

ciency in the use of ofﬁce space. The changing pres-

ence of staff, with some working remotely while oth-

ers are on site, often leaves ofﬁce desks unoccupied.

This represents a clear inefﬁciency in the current of-

ﬁce layout and highlights the need for more open and

adaptable environments to optimize the use of ofﬁce

space and reﬂect the changing nature of work in the

post-pandemic era (Elias, 2023).

Building on the idea of reconﬁgured ofﬁce spaces,

the concept of Shared Open Workspaces offers a

promising solution where, for example, employees

https://orcid.org/0000-0003-3037-7813

can reserve desks for daily use. This model increases

the efﬁciency of ofﬁce space use and ﬁts well with the

ﬂexible nature of the hybrid working model. How-

ever, there is a notable trade-off in terms of personal-

ization and comfort. In such environments, the lack

of customizable elements such as ergonomic adjust-

ments, personal memorabilia (e.g. family pictures)

and preferred desk locations can affect the sense of

belonging and comfort at work. In addition, the vary-

ing environmental conditions within the workspace,

such as uneven temperature, humidity, or noise lev-

els, can have a signiﬁcant impact on an employee’s

productivity and well-being. Some may ﬁnd them-

selves in less than ideal conditions, which can lead to

long-term discomfort or dissatisfaction.

Addressing these challenges requires an ap-

proach that improves the efﬁciency of Shared Open

Workspaces while allowing employees to personalize

their individual workspace. The overall aim of this

approach should be to reconcile the beneﬁts of ofﬁce

space optimization with the personal needs and com-

fort of each employee.

In response to the challenges identiﬁed, this posi-

tion paper presents a cloud-native solution based on a

microservice architecture, comprising three main ser-

vices to improve the experience within Shared Open

Hasiwar, D., Gruber, A., Dragschitz, C. and Ivki

c, I.

Towards a Cloud-Based Smart Ofﬁce Solution for Shared Workplace Individualization.

DOI: 10.5220/0012739000003711

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 14th International Conference on Cloud Computing and Services Science (CLOSER 2024), pages 367-374

ISBN: 978-989-758-701-6; ISSN: 2184-5042

367

Workspaces. The ﬁrst service (Workspace Manage-

ment Service) can be used to create and manage

workspaces (ofﬁces) and their workplaces (desks),

while the second service (Workplace Individualiza-

tion Service) enables workplace customization. This

includes personalizing of each workplace by enabling

ergonomic adjustments (e.g., desk height) and per-

sonalized memorabilia (e.g., digital frames displaying

private photographs). This service also collects envi-

ronmental data using sensors to monitor conditions

such as temperature, humidity, and noise levels. Fi-

nally, the third service (Workplace Booking Service)

offers employees the ﬂexibility to reserve and acti-

vate their workspaces, which adapt to their prefer-

ences when activated. Together, these services aim to

create a dynamic, user-friendly, and adaptable Shared

Open Workspace environment.

The remainder of this paper is organized as fol-

lows: Section 2 summarizes the related work in the

ﬁeld. Next, in Section 3, we present the architectural

building blocks of the proposed Smart Ofﬁce solu-

tion. Furthermore, we propose a methodological ap-

proach to calculate a Workplace Environment Index

(WEI) based on the collected environmental data. In

this context, we explain how the WEI can be used to

rank workplaces, or even to improve their environ-

mental conditions of them. Finally, Section 5 con-

cludes our work and outlines future work in the ﬁeld.

2 RELATED WORK

In the research area of smart ofﬁces, ofﬁce productiv-

ity, and smart workplace environments, a number of

studies have focused on the impact of various phys-

ical and technological factors on employee perfor-

mance and well-being. Mak and Lui (2011), and

Leaman (1995) evaluated the impact of environmen-

tal inﬂuences on productivity, highlighting the effects

of sound, temperature, and overall satisfaction with

workspace conditions.

Porras-Salazar et al. (2021) conducted a meta-

analysis on the effects of room temperature on work

performance in the ofﬁce and examined 35 studies to

assess the relationship between air temperature and

cognitive performance in ofﬁce environments. Lusa

et al. (2019) focused on multi-space ofﬁces, linking

workspace design elements like furniture and acous-

tics to employee well-being and satisfaction.

Ohrn et al. (2021), and Hanif and Saleem (2020)

examined the effects of ofﬁce design variations, in-

cluding Activity-Based Flex Ofﬁces and different uni-

versity library layouts, on productivity and employee

satisfaction. Pitchford et al. (2020) contributed an ex-

perimental perspective by comparing different ofﬁce

designs in a technology company, highlighting pref-

erences for zoned open-plan and team ofﬁce designs.

Robertson et al. (2013) focused on ergonomics,

demonstrating the beneﬁts of ergonomic training and

sit-stand workstations on worker performance and

discomfort. Wells’ (2000) study explored the role of

personalization in the ofﬁce, demonstrating its signif-

icant impact on employee well-being and job satisfac-

tion, with gender-based differences in personalization

practices.

In the research area of smart ofﬁce environments,

Zhang et al. (2022) reviewed Internet of Things (IoT)

and Artiﬁcial Intelligence (AI) applications for em-

ployee health promotion, while Choi et al. (2015)

presented a smart ofﬁce energy management system

using Bluetooth Low Energy (BLE) beacons and a

mobile app. Bhuyar and Ansari (2016) describe an in-

tegrated smart ofﬁce automation system, demonstrat-

ing the usefulness of IoT technologies in improving

ofﬁce environments.

Ryu et al. (2015) proposed an Integrated Se-

mantics Service Platform for smart ofﬁces, empha-

sizing semantic interoperability in IoT-based services.

Tuzcuo

glu et al. (2015) provided a user-centric ex-

ploration of smart ofﬁce environments, highlighting

the importance of understanding user expectations.

Finally, Uppal et al. (2021) introduced a cloud-

based IoT system for smart ofﬁces, focusing on de-

vice health monitoring and fault prediction to enhance

employee well-being.

Summarizing, the identiﬁed studies provide a

multifaceted view of how physical, ergonomic, and

technological elements interact to shape the modern

ofﬁce environment and inﬂuence employee produc-

tivity, satisfaction, and well-being. However, they

have not fully explored the use of IoT devices com-

bined with a management application to personalize

workspaces and monitor environmental factors in a

ﬂexible workplace culture. The proposed Smart Of-

ﬁce approach aims to ﬁll this gap by providing a com-

prehensive solution that combines workspace person-

alization with environmental monitoring and control,

leveraging IoT and cloud technology.

3 SMART OFFICE SOLUTION

In this section, we present the technical implemen-

tation of the proposed Smart Ofﬁce solution. First,

we provide an overview of the architectural building

blocks and explain the high-level architectural design.

Next, we describe the three core microservices of the

proposed solution and explain how they can be used

CLOSER 2024 - 14th International Conference on Cloud Computing and Services Science

368

to manage, individualize and book workplaces in a

Shared Open Workspace environment. Finally, we

present two mobile apps and explain how they can be

used by regular users and administrators to interact

with the Smart Ofﬁce solution.

3.1 Architectural Building Blocks

The Smart Ofﬁce is designed as a set of independent

microservices, each providing a distinct functionality,

yet seamlessly integrating to form a robust and ag-

ile application environment. The solution also relies

on a number of other components and managed ser-

vices that are used in a typical microservice architec-

ture. Figure 1 shows the individual components of the

Smart Ofﬁce application environment. This cloud-

native approach not only improves the maintainability

of the application, but also unlocks the full potential

of cloud capabilities (managed services, auto-scaling,

fault tolerance, and distributed processing).

At its core, the Smart Ofﬁce is orchestrated within

Kubernetes, an open-source platform that automates

the deployment, scaling, and management of con-

tainerized applications. This ensures that each mi-

croservice is efﬁciently managed and scaled to meet

the dynamic needs of the application requirements.

Kubernetes plays a central role in maintaining the

resilience and elasticity of the application, enabling

the seamless handling of variable workloads (Sharma,

2022).

Complementing the microservice architecture, the

Smart Ofﬁce uses the database-per-service pattern,

where each microservice has its own database. This

approach emphasizes the principle of loose coupling,

by ensuring that each service is completely indepen-

dent in terms of data management (Kumar and Shas-

try, 2017). These dedicated database instances are

provided by a managed database service, which ab-

stracts away the complexities of database manage-

ment such as provisioning, scaling, backup, and re-

covery. This ensures that the data processing aspect

of the Smart Ofﬁce is robust, secure, and performance

optimized.

In addition to the architectural components al-

ready mentioned, another important element of the

Smart Ofﬁce is the message broker. This application

unit acts as an intermediary between the individual

microservices and IoT devices, enabling the exchange

of information. Using a publish-subscribe model, the

microservices and IoT devices can publish messages

or subscribe to speciﬁc topics without having direct

knowledge of each other. This model increases the

scalability and ﬂexibility of the system, allowing ser-

vices and devices to be easily added or changed with-

out disrupting the entire application.

In addition to the Smart Ofﬁce microservices, the

individual IoT devices installed on site at each work-

place play an essential role in the overall functionality

of the application. These devices have two main func-

tions. The ﬁrst is to manage the customizable features

of a workplace, such as operating a digital picture

frame or controlling a height-adjustable desk. This

functionality allows for a personalized and adaptable

work environment, tailored to a user’s individual pref-

erences. The second function of the IoT devices is

to monitor environmental conditions. Through con-

nected sensors, they collect environmental data such

as temperature, CO

concentration, and humidity to

Figure 1: Architectural Building Blocks of the Smart Ofﬁce Solution.

Towards a Cloud-Based Smart Ofﬁce Solution for Shared Workplace Individualization

369

ensure a healthy and conducive working environment.

Although each of the components presented so far

plays an important role in the overall application ar-

chitecture, the Smart Ofﬁce microservices and mobile

applications are at the heart of the application. The

entire business logic of the solution is encapsulated

in these application elements. The following sections

provide a detailed introduction to their speciﬁc func-

tions and responsibilities.

3.2 Workspace Management Service

By mapping each individual workplace as a digital

object within the application, the Workspace Manage-

ment Service provides a detailed and dynamic view of

the ofﬁce space and facilitates the efﬁcient manage-

ment and allocation of workplaces. Beyond simple

mapping, the Workspace Management Service care-

fully monitors the allocation of available workplaces,

ensuring optimal utilization of the ofﬁce space while

also keeping track of the customizable attributes of

each workplace. These attributes, which currently

include features such as a digital picture frame or a

height-adjustable desk, greatly enhance the user ex-

perience by allowing the work environment to be tai-

lored to individual preferences. Importantly, the ar-

chitecture of the system is designed for future adapt-

ability, allowing additional features to be seamlessly

integrated as new technologies emerge.

3.3 Workplace Individualization Service

The Workplace Individualization Service specializes

in the management and aggregation of personalized

data speciﬁcally tailored to individual work environ-

ments. By focusing on personalized data collection,

this service plays an important role in optimizing the

work experience through a tailored approach that ad-

dresses the unique needs of each workplace. The

service’s responsibilities fall into three distinct cate-

gories:

• Ergonomic adaptability

• Personal memorabilia

• Environmental data collection

The ﬁrst category focuses on the management of the

data required to operate the height adjustable desks.

This aspect ensures that each workspace can be phys-

ically adapted to meet the ergonomic needs of its user,

promoting health and comfort in the ofﬁce workspace.

The second category relates to the management of

personal memorabilia. This involves managing users’

pictures, which are displayed in digital picture frames

and add a personal touch to any workplace. In this

way, the service helps to create a more familiar and

welcoming working environment, boosting morale

and a sense of belonging. The ﬁnal category relates to

the environmental data collected by the IoT devices,

such as temperature, CO

concentrations and humid-

ity. This information is then used to create the WEI

which gives administrators a quick overview of the

environmental status of each workplace.

3.4 Workplace Booking Service

The Workplace Booking Service is designed to allow

users to reserve a speciﬁc (open) workplace for a spe-

ciﬁc period of time. In addition, the service provides

a comprehensive view of workplace availability, en-

suring that users can easily ﬁnd and secure a space

that meets their needs, thereby increasing the ﬂexibil-

ity and efﬁciency of ofﬁce space utilization.

As well as managing reservations, the Workplace

Booking Service is also responsible for the actual ac-

tivation of a booking. Once a user has booked a

workspace, the service works with other components

of the Smart Ofﬁce application to personalize the

workspace according to the user’s preferences. This

personalization includes adjusting the height of the

desk and displaying user-speciﬁc images on a digital

picture frame. By integrating information from mul-

tiple sources, the service not only simpliﬁes the reser-

vation process but also ensures that each workspace

is tailored to provide a comfortable, personalized, and

productive environment for each user.

3.5 Mobile Applications

The Smart Ofﬁce microservice application has been

complemented by two specialized mobile applica-

tions, each designed to address different user require-

ments. The ﬁrst application is optimized for the regu-

lar users of the Smart Ofﬁce, offering a user-friendly

interface and a range of features designed to enhance

their daily ofﬁce experience. This includes the abil-

ity to make workplaces reservations, manage personal

preferences and customize individual workplace con-

ﬁgurations. Users can personalize their ofﬁce envi-

ronment, from conﬁguring their preferred images on

digital frames to adjusting the height of their desks.

The second application is designed for adminis-

trators of the Smart Ofﬁce solution, enabling them to

manage the complex aspects of ofﬁce space utilization

and maintenance. Through the application, adminis-

trators can oversee the allocation of workspaces, con-

solidate user bookings, and monitor the overall usage

of ofﬁce resources. In addition, the app provides ac-

cess to a wealth of environmental data collected from

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370

various IoT sensors, including the insightful WEI.

This feature enables administrators to make informed

decisions about workspace management, ensuring op-

timal use of ofﬁce space and maintaining a high stan-

dard of workplace environment.

4 WORKPLACE ENVIRONMENT

INDEX (WEI)

The WEI represents a signiﬁcant step forward in the

management and optimization of workplace condi-

tions, harnessing the power of IoT technology. The

index is calculated using data collected by an IoT de-

vice using various sensors installed in a workplace.

These sensors measure various environmental param-

eters such as temperature, humidity and CO

concen-

tration. By continuously collecting and analysing this

data, the WEI provides a comprehensive, real-time as-

sessment of a workplace’s environmental conditions.

The calculation of the WEI is based on the following

general equation:

W EI =

W EI

∗W

+W EI

∗W

+ ... +WEI

∗W

(1)

As shown in (1), the WEI is calculated by the sum

of weighted (W

, W

, ..., W

) workplace environment

measurements (W EI

, W EI

, ..., W EI

) using the IoT

device and its sensors divided by the total number of

WEI measurements (n). The resulting measurement

of each sensor represents a speciﬁc WEI (e.g., WEI

)

with a corresponding weight (e.g., W

) to be able to

emphasize one environmental measurement over an-

other one. This allows to prioritize certain WEI mea-

surements and give some values more weight com-

pared to others. For instance, a certain workplace en-

vironment measurement (e.g., temperature) might be

more important for a certain workplace compared to

another one (e.g., humidity). In this case the weight

of the ﬁrst measurement would be higher compared to

the second to show which one is more signiﬁcant in

this speciﬁc case.

The WEI in (1) enables to calculate the environ-

ment index of a workplace to evaluate its environmen-

tal conditions. This index can further be used when

analysing the booking pattern of certain workplaces to

identify the reasons why some are overbooked while

others are not favoured by the employees. In the fol-

lowing sections, the WEI calculation will be explored

in more detail.

4.1 WEI Calculation

Based on the general WEI equation from (1), the cal-

culation of the WEI follows a four-stage approach to

ensure accuracy and relevance.

4.1.1 Step 1: Deﬁne Range of Values

The ﬁrst step is to establish a range of values for the

environmental data to be measured by determining an

optimum, minimum and maximum value for each en-

vironmental parameter. This ﬁrst step is essential as it

lays the foundation for the subsequent normalization

process to ensure that the data accurately reﬂects the

quality of the workplace environment. The following

table shows an example range of values for the envi-

ronmental values of temperature, humidity and CO

concentration:

Table 1: Min-max Value Range of Environmental Data.

Minimum Maximum

Temperature 15

◦

C 30

◦

Humidity 10% 80%

Level 100 ppm 800 ppm

4.1.2 Step 2: Normalization

Once the value ranges are established, the second step

is to apply a linear min-max normalization, as pro-

posed by Vafaei et al. (2016) using the following

equation:

i j

− r

min

max

− r

min

(2)

The min-max normalization from (2) converts the

measured environmental values into a normalized

range from 0 to 1, where the value 0.5 represents

the previously deﬁned optimum or mean value for

each parameter. This normalized scale ensures that

the mean value accurately reﬂects the most desirable

environmental condition and allows a consistent and

fair assessment of different environmental parame-

ters. Based on this, the environmental measurements

of different metrics (with different units) can be ag-

gregated after the normalization step, allowing the

calculation of an overall WEI as shown in (1).

4.1.3 Step 3: Transformation

In the next step, the normalized values are converted

to the actual WEI value range of 0 to 100. This con-

version is based on the use of the following parabolic

function:

f (x) = a(x − h)

+ k (3)

Towards a Cloud-Based Smart Ofﬁce Solution for Shared Workplace Individualization

371

The idea is to have a parabola that peaks at 0.5

and intersects the x-axis at 0 and 1, corresponding to

the minimum and maximum of the normalized val-

ues. The variables h and k represent the vertex co-

ordinates of the parabola. As a result, the variable h

represents the previously established optimal normal-

ized value of 0.5, while k represents the upper limit

of the WEI scale, which is 100. Solving the equation

for the points of intersection of the parabola with the

x-axis gives the actual value of the variable a (Papula,

2018). Table 2 shows the conversion of the measured

example environmental data into the WEI using the

parabolic function:

Table 2: Normalized environmental data.

Measured Normalized WEI

Temperature 23.5

◦

C 0.57 98.04

Humidity 35% 0.36 92.16

Level 600 ppm 0.71 82.36

As shown in Table 2, the initial measured temper-

ature value of 23.5°C was normalized using the min-

max normalization from (2) with its minimum and

maximum values from Table 1. Then the parabolic

function from (3) was used to convert the normalized

value of 0.57 to the WEI of 98.04.

4.1.4 Step 4: Aggregation

Once all measured values have been converted, the ﬁ-

nal step is to aggregate them to calculate the overall

WEI for a speciﬁc workplace. As previously men-

tioned, there may be cases where not all the calcu-

lated values contribute equally to the overall WEI. For

this reason, weights are used to emphasize one result

or environmental measure over another. This means

that speciﬁc environmental factors can be given more

weight than others, depending on their impact on the

workplace environment. Based on the weighted ap-

proach, the ﬁnal score provides a tailored assessment

of the workplace environment that reﬂects the unique

priorities and needs of each workplace. As a result,

the partial WEI results for the temperature, humidity

and the CO

level as well as the strategic weighting of

the environmental variables are incorporated into the

subsequent equation:

W EI =

W EI

∗W

+W EI

∗W

+W EI

∗W

(4)

Using the example environmental measurement

data from Table 1 and giving equal weight to each

metric, the overall WEI result is 90.85.

4.2 WEI Applicability

The calculated WEI can be used as an indicator

to evaluate different environmental conditions of a

workplace and calculate an overall score. This ap-

proach enables to monitor the conditions of a work-

place in real-time and to take action to improve or op-

timize the conditions of a workplace. In addition, the

use of IoT technology demonstrates how a physical

work environment can become smart by dynamically

adapting to the user needs.

One of the key advantages of the WEI is its

utility in enhancing user experience within a shared

workspace environment. Employees within the Smart

Ofﬁce can select workplaces that perfectly match

their personal preferences and comfort requirements

based on the calculated WEI. This capability is not

only beneﬁcial for satisfying employees’ comfort

needs, but also for optimizing their productivity and

well-being. In addition, the WEI can be used to rank

workplaces, providing a clear, data-driven guide to

the best performing environments within a facility.

Such a feature democratizes workplace choice and en-

sures that all users have access to the spaces that best

suit their needs, thereby promoting a more equitable

and satisfying working environment.

Beyond the beneﬁts to the individual user, the

WEI can be an invaluable tool for facilities man-

agement, acting as an early warning system to high-

light potential problems at speciﬁc workstations. For

example, abnormal temperature readings could indi-

cate a malfunction in the ventilation system, while

elevated CO

levels could indicate overcrowding in

a particular area. Furthermore, by correlating WEI

data with user booking patterns, facility managers can

gain insight into the environmental conditions that are

most conducive to employee comfort and productiv-

ity.

This improved understanding can contribute to

more efﬁcient control of facility systems for heating,

cooling and lighting based on real-time occupancy

and usage patterns. Such tailored environmental ad-

justments not only improve the overall workplace ex-

perience but also contribute to energy efﬁciency and

sustainability goals. In the area of predictive analyt-

ics, trends in WEI data can predict the need for main-

tenance or indicate potential failures in facility ser-

vices, enabling pre-emptive action to minimize dis-

ruption and improve overall workplace functionality.

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372

5 CONCLUSIONS AND FUTURE

WORK

The COVID-19 pandemic has been the driving force

behind an unprecedented shift towards hybrid and

remote working models, opening up a wide range

of possibilities for reimagining the traditional ofﬁce

space. At the heart of this transformation is the chal-

lenge of optimizing these new working environments

to meet the diverse needs of a dispersed workforce,

while maximizing efﬁciency and fostering a sense of

belonging among employees. In this paper we pro-

posed a cloud-based Smart Ofﬁce solution for Shared

Open Workspaces, with a focus on individualization,

efﬁciency within adaptive work environment.

First, we presented a technological framework us-

ing microservice architecture to manage, personal-

ize, and book ofﬁce spaces, complemented by mo-

bile applications for end users and administrators.

Next, we presented a model that uses different en-

vironmental measurement metrics (measured by an

IoT device with sensors) and combines them to cal-

culate an overall WEI score. Finally, we discussed

how the calculated WEI can be used to monitor and

optimize workplace conditions, improving employee

well-being, and productivity.

The proposed Smart Ofﬁce solution bridges the

gap between workspace ﬂexibility and personaliza-

tion needs in hybrid working models. Based on a

microservice architecture and IoT integration, the so-

lution enables scalable, user-centric ofﬁce environ-

ments. In addition, the WEI score provides actionable

insights into workplace conditions, promoting opti-

mal workspace utilization and employee satisfaction.

To further support the proposed Smart Ofﬁce so-

lution, it is essential to conduct empirical studies or

pilot implementations. Therefore, it is planned to im-

plement the Smart Ofﬁce solution in a real-world of-

ﬁce environment, incorporating adjustable desks and

digital frames for a personalized touch. This empir-

ical real-world implementation serves as a founda-

tional step not only for assessing the solution’s effec-

tiveness and practicality but also for analysing critical

aspects such as cost efﬁciency, security, privacy con-

cerns, and scalability. By evaluating these facets, the

implementation aims to ensure the solution’s viability

in diverse settings and its adaptability to various user

needs and ofﬁce environments.

The empirical approach will be complemented by

a user experience study to correlate user satisfaction

with the WEI outcomes, thereby enabling iterative re-

ﬁnement of the solution based on actual usage data.

A thorough evaluation of the WEI in a live environ-

ment will help to identify and improve both optimal

and underperforming workspaces. The adaptability of

the Smart Ofﬁce solution to ofﬁces of varying sizes

and organizational structures is a key consideration.

A detailed scalability study will be conducted to eval-

uate how the solution can be effectively scaled up or

down, depending on the speciﬁc needs of an organiza-

tion. This will include exploring modular aspects of

the microservice architecture and the IoT framework

to ensure that the Smart Ofﬁce solution can be tai-

lored to accommodate different workspace environ-

ments and employee densities.

This position paper paves the way for future re-

search into Smart Ofﬁce environments, focusing on

sustainability, user engagement, and the integration of

advanced technologies to further enhance the ofﬁce of

the future.

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