=Paper=
{{Paper
|id=Vol-2530/paper2
|storemode=property
|title=SmartWorkplace: A Privacy-based Fog Computing Approach to Boost Energy Efficiency and Wellness in Digital Workspaces
|pdfUrl=https://ceur-ws.org/Vol-2530/paper2.pdf
|volume=Vol-2530
|authors=Fatima Z. Benhamida,Joan Navarro,Oihane Gómez-Carmona,Diego Casado-Mansilla,Diego López-de-Ipiña,Agustín Zaballos
|dblpUrl=https://dblp.org/rec/conf/iot/BenhamidaNGCLZ19
}}
==SmartWorkplace: A Privacy-based Fog Computing Approach to Boost Energy Efficiency and Wellness in Digital Workspaces==
https://ceur-ws.org/Vol-2530/paper2.pdf
SmartWorkplace: A Privacy-based Fog Computing Approach to
Boost Energy Efficiency and Wellness in Digital Workplaces
Fatima Z. Benhamida Joan Navarro Oihane Gómez-Carmona
Laboratoire des méthodes de Grup de Recerca en Internet MORElab – Deusto Institute of
conception des systèmes–Ecole Technologies & Storage, La Salle – Technology
Nationale Superieure d’Informatique Universitat Ramon Llull Bilbao, Spain
Oued Smar, Algeria Barcelona, Spain oihane.gomezc@deusto.es
f_benhamida@esi.dz jnavarro@salle.url.edu
Diego Casado-Mansilla Diego López-de-Ipiña Agustín Zaballos
MORElab – Deusto Institute of MORElab – Deusto Institute of Grup de Recerca en Internet
Technology Technology Technologies & Storage, La Salle –
Bilbao, Spain Bilbao, Spain Universitat Ramon Llull
dcasado@deusto.es dipina@deusto.es Barcelona, Spain
agustin.zaballos@salle.url.edu
ABSTRACT 1 INTRODUCTION
The massive digitalization of modern society has transformed hu- With the advent of new ubiquitous technologies and the emerging
man lifestyles in several dimensions ranging from social interac- creation of a new interconnected world, digital transformation is
tions to healthcare and wellness, including transportation systems, playing a crucial role in modern societies [52]. Several fields and
jobs, machinery, or energy management. However, physical envi- domains such as healthcare [2], business [6], Industry 4.0 [48], trans-
ronments and people have not evolved at the same pace, leaving a portation [20], or even education [15] have already taken advantage
challenging gap between the advances in technology and how soci- of the never-ending advances in the Information and Communi-
ety efficiently interact with it. One specific case is the workplaces cation Technologies (ICT).
where digital literacy is not widespread among all employees (e.g. Under this context, the predominant presence of technology can
blue or grey collars) and the advent of such digitalization is a reality. play a relevant role in bringing added value services in a way
This work presents an architectural approach to improve energy never imagined before and facing existing societal challenges [33].
efficiency and wellness at work (by suggesting new behaviours However, despite this continuous progress in services and
and dynamics) while maintaining user comfort and keeping user’s technology, human beings seem to struggle on keep-ing the pace of
privacy. More specifically, this approach—inspired by the Fog com- such digital achievements.
puting paradigm—features a hierarchical scheme based of privacy An example can be found in office-based workplaces (i.e. those
maintenance which (1) collects real-time data from the users at the spaces in which employees perform their working duties in a
workplace environment; (2) processes these data in either in the Fog workstation) [7] that basically have kept the same physical
or Cloud infrastructure depending on the data sensitiveness; and (3) layout and configuration—typically composed of a table, a lamp, a
provides feedback to the user along with a set of recommendations (desktop) computer and a monitor—for the last three decades but
related to energy usage. As such, the user is included in the whole the digital services and data they use have changed enormously.
data-cycle which allows employees to decide what information can Besides the digital gap that some white and grey collars have, this
be monitored, where it can be computed and the appropriate ICT traditional set-up, contributes to increasing the large number of
channels to receive the feedback. health issues related to the interactions of workers and their en-
vironment [40].
KEYWORDS In particular, the unconscious habits and behaviors associated to
Fog computing, energy efficiency, privacy, confidence, smart envi- this spaces take a primary role on the physical, mental and social
ronments well-being of the worker [36], including long periods of inactivity
and sitting times [10], [38], ergonomic related problems [50],[17]
or the development of computer vision syndrome because of the
exponential screen time exposure [43], [28].
1st Workshop on Cyber-Physical Social Systems (CPSS2019),
October 22, 2019, Bilbao, Spain.
Copyright © 2019 for this paper by its authors. Use permitted under
Creative Commons License Attribution 4.0 International (CC BY 4.0).
9
CPSS2019, October 22, 2019, Bilbao, Spain F. Z. Benhamida, et al.
Workplaces are very sensitive places to conduct experiments [5] This idea of "Smart Workplace" seeks to turn a working space
since they might affect to workers performance and, most impor- into a more efficient, trustworthy and acceptable environment by
tantly, compromise critical data from companies and their employ- its workers. Thus, transform the way we work and we interact
ees. For instance, installing a camera in an office might provide mas- with our environment while promoting more healthier
sive amounts of valuable data to enhance user’s health and energy behaviours or increasing levels of comfort and the productivity to
consumption (e.g., worker’s postures, office illumination, hazardous their occupants in return. To summarize, this paper contributes
situations, etc.) but it also may reveal private data regarding con- with a two-fold approach:
tents of contracts, meetings, etc. (i) We present our concept of a Fog Computing architecture,
Also, it has to be considered that people in modern societies designed both from the technical and the user perspective, to
are averse to be continuously tracked and monitored without contribute to the transformation of smart workplaces while
knowing which data they are sharing [1]. Therefore, securing keeping users’ privacy. Through this implementation, we
users’ privacy while making them aware which data they are seek to convert the workplace into an appropriate setting
disclosing is a critical issue to be addressed when considering to to motivate workers toward more sustainable and healthier
design or deploy any (new) device in a workplace or using data behaviours while promoting changes that hopefully persist
generated in it [19]. over time.
The purpose of this work is contributing to this open challenge (ii) We illustrate the principal challenges associated with ev-
by discussing the feasibility of conceiving a platform able to trans- ery layer of the architecture and the needs that should be
form the digital workplace into a proactive entity (coined as Smart taken into account for succeeding in the transformation of
Workplace) while ensuring user’s data privacy. In the proposed workplaces.
architecture, the worker decides which data wants to disclose and The remainder of this paper is organized as follows. The next
until up to what extent and, from there, it is continuously monitored section reviews the related work on smart workplaces. Later, the
and then advised on the best actions to increase his/her comfort proposed system architecture required to transform a digital work-
while optimizing energy usage. place into a smart workplace is outlined. Finally, a discussion on
More specifically, instead of con-ceiving expensive and new ad the limitations of the proposed approach and some conclusions are
hoc gadgets, we aim to benefit from the off-the-shelf technology provided.
already deployed in digital workplaces (e.g., desktop computers,
smartphones) to sense the environmen-tal status and worker
2 RELATED WORK
dynamics and naturally interact with them. To overcome the
data storage and computing limitations associ-ated to this From occupational risk assessment and ensuring safety in the work-
continuous monitoring, the key idea of this proposal is to build a places [34], the idea of transforming the workplace has progres-
Fog Computing domain composed of all the digital devices sively evolved from a safety-first concern to a more holistic ap-
deployed in a workplace (that can join or leave at will) and a Cloud proach that includes persistent lifestyle changes within such spaces
Computing layer that will be used whenever the devices need to [37]. Since technology allows the possibility to provide context-
carry more complex computations. aware guidance and influence on the users, technology-based solu-
The combination of Fog and Cloud layers enables the system tions can be considered appropriate drivers to promote wellness and
to limit the scope of the sensed data according to the worker’s energy awareness on the workplace. Therefore, several attempts
preferences in relation to the privacy they wanted to preserve, have been made to design enhanced workplaces [5] through the
while obscuring its data when needed (i.e., splitting the adoption of ICTs, offering different solutions for facing indirect
computation process in several distributed nodes improves data risks associated with these spaces and bringing energy awareness
security [31, 32]). while reaching large audiences. The PEROSH initiative [22] studied
Hence, privacy is considered throughout the whole data-cycle: how wearable devices could be part of wellness promotion inter-
since data collection, through data intensive computing and user ventions and elaborated a decision support framework for selecting
feedback. The user is then in-volved from the very beginning of useful sensors and a proper data collection strategies for avoiding
the design of the architectural approach presented. Thus, an sedentary behaviors neglecting data privacy issues. In the same
employee can decide in real-time which information is sensible to way, Jimenez et al. [26] presented some guidelines to promote work-
him/her and what information can be more or less flexible to share place health by using electronic and mobile health tools to provide
or compute. This intermediate step will help to decide if the easier administration for campaign proposers while considering
computation of the activity recognition or the best moment to data privacy from a technical and psychological points of view.
send feedback can be computed on a secured and reliable Fog However, no specific ICT architectures are proposed to conduct
environment or it can be sent to the Cloud layer. this processes.
With this, the novelty of the proposed system strives on our goal Indeed, assessing occupational sedentary behavior standouts as
to combine innovative data processing architectures, distributed one of the most relevant factors to consider in these spaces and
intelligence processes and advanced immersive interaction inter- several initiatives have been designed from diverse perspectives to
faces between users and things to give place to a more healthy and face this problem. In this direction, in 2018, Taylor et Al. reviewed
secure working environments. the existing literature addressing interventions designed to reduce
10
SmartWorkplace: A Privacy-based Fog Computing Approach to Boost Energy CPSS2019, October 22, 2019, Bilbao, Spain
Efficiency andWellness in DigitalWorkplaces
sitting time and the role of the organizational culture [47]. Digi- operations close to where data were generated and, thus, minimiz-
tal technologies have also been proposed for reducing sedentary ing the amount of information sent to the Cloud. Many studies
behaviours[23] as well as to increase energy expenditure [39]. show that users, enterprises and stakeholders are more keen to
Moreover, other approaches have addressed the influence of share and collaborate if that sensitive data are managed locally at
interruptions on high cognitive loads [45], and non-intrusive moni- the edge of the network [29].
toring systems specifically designed to avoid lower back injuries The smart workplace transformation unavoidably requires using
have been proposed [51], modeled physical fatigue in workplaces personal data about users’ behavior to make better decisions ac-
[34] or designed a smart chair to improve the sitting behavior [44]. cordingly (see Section 2). For this reason, the proposed architecture
More in particular, some works have already explored how people aims to define a system model able to transform a workplace into a
and the devices that populate Smart Workplaces can cooperate smart environment using personal data while considering workers’
towards higher energy efficiency [13] or bringing health aware- privacy. The architecture is composed of four main layers: (1) Sens-
ness to the workplace by increasing technology acceptance [18]. ing Layer responsible of data collection, (2) Early Stage Computing
Whilst other works have considered how to enhance safety [41] and Layer represented by a Fog network used for local computation,
comfort[3] in working spaces through 5G and IoT. Other platforms, (3) Intensive Computing Layer deployed in a Cloud infrastructure
such as Comfy 1, which proposed a cloud-enabled platform to con- and responsible for data aggregation, which is used to obtain more
nect employees directly to their physical and digital workplace accurate recommendations, and (4) Worker-Workplace Layer that is
through the captured data. used to optimize the interaction between the users and the devices
Similar advances have been proposed for energy-awareness in while giving recommendations. This four layers are supervised by
these spaces and to guide workers in their routine. In this regard, a Decision Support System (DSS) that with the aid of the worker,
Irizar-Arrieta et al. [24] proposed a digital interface able to inform defines through intents the scope of every datum according to some
workers about their performance related to energy consumption. rules such as privacy, presence or availability. This intent-based
Similarly, the GreenSoul project [25] designed an enhanced object DSS is based on S3OiA architecture [49]. We explain hereafter the
for office environments—an interactive coaster—to persuade work- role of functionality of each layer.
ers to be more aware of the energy consumption of the electrical
devices surrounding them in their desktop. Also, there are works 3.1 Sensing layer
focused on how to reach larger audiences and several strategies
The Sensing layer is committed to collect as much data as possible
have been implemented to address this goal: from measuring shared
from the workplace. It can be best seen as an IoT sub-domain where
lab equipment usage [35] to projecting real-time energy statistics
every digital object does its best to sense as many environmental
of a factory in the physical environment [27] and convert work
variables as possible. For instance, a smart phone can easily sense
equipment into persuasive devices which raise eco-awareness [12].
ambient light intensity, background noise or the amount of phone
In contrast to addressed literature, our proposal puts the focus
calls interrupting worker’s activity. Analogously, a desktop com-
on the requirements to design an open new innovative architec-
puter can easily infer user activity by counting keystrokes (or clicks
ture able to allocate interactive interventions in the workplaces
in the mouse) in a period of time. It can also detect user presence,
while considering system scalability, users’ privacy, and low-cost.
sitting posture, eye gaze, eye blinking by using the built-in camera
Additionally, this approach addresses both, energy consumption
[11]. Additionally, other smart devices such as smart plugs, smart
and workers health, as a whole rather than individually fighting
watches, or smart speakers (digital assistants) can be easily recon-
them with expensive or commercial (e.g., Comfy Enlighted 2) ad figured to report all the data that they seamlessly capture. These
hoc devices. This links the Fog Computing paradigm with the work
data will be later used to be processed and matched to a certain
environment and represents a new way of envisioning the basis
behavior at the upper layers.
for the digital transformation of these spaces. In the following, the
proposed architecture will be described together to the different
challenges associated with each corresponding layer. 3.2 Early Stage Computing Layer
Similarly to a Fog architecture, the Early Stage Computing layer will
3 SYSTEM MODEL receive data from the sensing layer and conduct local non-intensive
To address the transformation of a digital workplace into a smart computations. From a data privacy point of view, this layer can be
workplace in a generic and widely adoptable way, we proposed the best seen as the frontier in which sensible data shall not go beyond.
following hierarchical architecture (see Figure 1) inspired by the Hence, as long as the data privacy policies allow it, this layer will
Fog computing approach. send encrypted data to the upper layer for strong recommendations
Fog Computing is defined as a highly virtualized platform that that require much computing power and more robust models.
provides processing, storage and networking services between ter- Devices located at the edge of the network can be typically iden-
minals and data centers used by traditional Cloud Computing, typi- tified as gateways, computers, or local servers. To further explain
cally located, but not exclusively, on the edge of the network [8]. the role of the Early Stage Computing Layer, suppose that a smart
Fog Computing comes to alleviate those fears related to sharing plug sends the power consumption of a heater. When the gate-
sensitive and private data on the Cloud by conducting computation way detects that the heater has been turned on uninterruptedly
for a specified number of hours, it might suggest to turn off the
1 https://www.comfyapp.com/ heater, which would result in energy saving. In the next computing
2 https://www.enlightedinc.com/ layer (i.e., Intensive Computing Layer), the power consumption
11
CPSS2019, October 22, 2019, Bilbao, Spain F. Z. Benhamida, et al.
Figure 1: Proposed system architecture
of the heater will be correlated with other variables (e.g., ambi- 3.3 Intensive computing and storage layer
ent temperature, office hours, office occupancy, etc.) to make the At this layer, the power of a Cloud computing infrastructure is ex-
recommendation stronger. ploited by (1) logging and aggregating all the collected data, (2) us-
Additionally, it is worth mentioning the situation in which the ing a Learning Classifier System able to build a set of user-readable
same physical device—due to its advanced sensing, computing and rules (i.e., recommendations), and (3) forwarding these rules to the
communication capabilities—belongs to the Sensing and Early Stage devices that have sensing but also acting capabilities from the Early
Computing Layers at a time. For instance, suppose that a smart Stage Computing Layer (i.e., Worker-Workplace interaction Layer).
phone collects (Sensing Layer) data regarding ambient light inten- These recommendations resulting from data analysis, will be mainly
sity. When it detects an excess of ambient light (Early Stage Com- transmitted by means of the Worker-Workplace interaction Layer,
puting Layer) it might suggest to turn off the office light, which which will be in charge of finding the best moment/manner to send
would result in energy saving. However, these data should also be recommendations to the worker (for instance, worker’s presence
again cross-checked with data from other sources (e.g. the desktop must be guaranteed before making a recommendation).
screen is momentarily displaying bright images) in order to make
a strong recommendation. This is why the early stage layer will 3.4 Worker-workplace interaction layer
transfer sensed data to the upper layer for intensive computing and
The availability of a large amount of data provides the opportunity
global storage.
to use this information to influence workers and guide their actions
Finally, it is also worth considering the situation in which a
towards more healthier and sustainable behaviors. For this reason,
camera is used to track workers’ postures and, thus, user privacy
this layer oversees optimizing the interaction between the users and
is of paramount importance. In this case, we propose to take an
the devices by delivering contextualized feedback. This depends on
alternative approach by encrypting and sending to the following
when and how to interact with the workers to effectively influence
layer the worker’s body/face landmarks [11] instead of the whole
their behaviour. On the one side, by choosing the right recommen-
video stream (as done in [51]). Note that this strategy intrinsically
dation mechanism (e.g persuasive strategies based on personalized
boosts worker’s privacy since it is guaranteed that (1) the whole
messages [12]). On the other side, by selecting the right moment
image stream cannot be reconstructed from the landmarks (i.e., no
to provide the recommendations: trough anticipation (about-to-do
plain images are sent) and (2) no other environmental information
moments) and reflection on action (just-in-time moments). The first
of the workplace leave the physical building. Additionally, the over-
one is based on anticipation, consisting of recognizing pre-action
all amount of data transferred to the communications network is
patterns that allow providing immediate interaction to redirect
greatly reduced.
the activity through context-aware signals (lights, sounds or vibra-
tions, among others). The second one consists on providing the
worker with all the information related to his behavior and their
performance, analyzing in depth patterns and changes over time
12
SmartWorkplace: A Privacy-based Fog Computing Approach to Boost Energy CPSS2019, October 22, 2019, Bilbao, Spain
Efficiency andWellness in DigitalWorkplaces
and showing the possible consequences of this trend. Unlike the consider ethical concerns of personal data collection [9]. In par-
previous type of action, in this case we seek to influence future ticular, users are more reluctant to be monitored in these spaces
habits through personal inquiry. as it can be associated with their schedules or work performance
[4]. Secondly, data needs to be gathered without affecting workers
3.5 Illustrative Example routine and minimizing their attention theft. Thus, it needs to be
To better understand the functionality of the proposed architec- as non-intrusive as possible, creating an ecosystem surrounding
ture, we give a simple scenario of a worker using a set of standard the employee that allows collecting data without any effect on its
devices (i.e., desktop computer with in-built camera, smart phone, routine[46].
smart plug, fan, and voice assistant) with sensing capabilities in the The proposed architecture considers both factors. Privacy con-
workplace environment. cerns are covered ensuring the security of the data in every layer of
On the one hand, the desktop computer continuously monitors the architecture, with special focus on the way sensitive informa-
(i.e., Early Stage Computing Layer) the worker position and period- tion is processed and sent to the cloud. Therefore, no personal data
ically triggers alerts when no significant movement is detected for is available and the privacy of the workers is preserved. The second
long periods of time. Additionally, the face/body landmarks are sent aspect is avoided by using digital devices deployed in a workplace
via HTTPS to the Intensive Computing Layer to precisely analyse so that space is not over-instrumented with disruptive elements. In
the worker’s gaze, eye blinking and sitting posture. This layer sends general terms, a successful ICT initiative should have a strong point
back recommendations to the desktop in order to complement their in ensuring how the user interacts with the technology, promoting
local decisions. its adherence while creating a sense of confidence and trust.
On the other hand, the smart plug is continuously sending the
power consumption (via JSON messages) to the same desktop ap- 4.2 Early Stage Computing Layer
plication that locally monitors worker’s movements. At this point The Early Stage Computing Layer is similar to the fog Scheme
(Early Stage Computing Layer) the system can infer that if there where components in the edge of the network are used to make
is no movement and the fan is turned on (i.e., there is power con- local computations, preliminary data analysis, and decompose in-
sumption) the worker might have forgotten to turn off the fan and, formation to make it harder to retrieve in upper layers. This Fog
thus, might decide to trigger a warning via the voice assistant, just architecture ensures sharing resources and services in the neighbor-
in case the worker is still in the office. Additionally, at the Intensive hood of a network while enhancing their secrecy and availability.
Computing Layer, the power consumption of the smart plug can be Indeed, sharing data to the Cloud raises fears when it comes to
correlated with the worker agenda (via Microsoft Outlook Calendar disclose sensitive and private data. Since user is involved in the
API) to check whether the worker shall be elsewhere and, thus, whole chain of the proposed system model, he/she might be more
decide to turn off the fan by means of the smart plug. keen to share and collaborate if that sensitive data were managed
Overall, with this example it can be seen how worker comfort and locally at the edge of the network. In this regard, the Early Stage
energy efficient can be addressed with the proposed architecture. Computing Layer is introduced as an intermediate layer that offers
local decisions based on data collected at the Sensing Layer.
4 DRIVERS AND CHALLENGES However, edge devices are limited in computational and energy
We recall that the basic idea behind the system model proposed resources. Hence, they can only cover common decisions (e.g., turn
above is mainly to offer a n a rchitecture w here: ( 1) t he s ystem is off office lights when sensing a high ambient light intensity). For
able to propose strategies offering wellness (user perspective) and this reason, the proposed system model still requires sending data
energy efficiency (resource consumption), (2) the user is strongly in- to an upper layer with more computing and storage capabilities.
volved in the whole system, from sensing data to applying suggested This layer is the most critical point to consider data privacy.
strategies so his preferences and/or privacy are most respected, and Therefore, we propose to (1) Filter/transform personal data, and/or
(3) sensitive data protection is a major key to consider while trans- (2) encrypt data before sending it to the upper layer (i.e. Cloud
mitting information through different layers of the system model. services). Many existing security schemes can be used in this fog-
Hence, each layer is responsible to satisfy the objectives bellow. inspired architecture. For instance, SKES-Fog can be implemented
We explain hereafter how the design of the proposed architecture in the proposed system model as the architecture could be pre-
offers flexibility and privacy for the user. We also discuss how to sented using domains as suggested in [14]. Besides, data filtering
overcome open challenges related to the same objectives. Figure 2 or transformation allows to delete unnecessary data during the
depicts the drivers (left side) and challenges (right side) for each decision making process (e.g., user’s identity). Later, the interaction
layer of the proposed architecture. layer will assign the anonymized data to its corresponding worker
to send accurate recommendations (based on the decisions from
4.1 Sensing layer the Intensive Computing and Early Stage Computing layers).
Transforming the digital workplace to pursue wellness and sustain-
ability in these spaces involves quantifying physical metrics of both 4.3 Intensive computing and storage layer
the employees and their interaction with the work environment. The Intensive Computing and Storage Layer takes advantage of
Work environments are especially challenging scenarios when the power of Cloud computing infrastructures where: (1) the great
technology is the primary way to collect data and obtain informa- amount of collected data is stored as a whole to be all exploited and
tion about the workers. First, it must preserve their privacy and analysed for accurate decisions, (2) all the sensed data transmitted
13
CPSS2019, October, 2019, Bilbao, Spain F. Z. Benhamida, et al.
from the Early Stage Computing Layer are aggregated, (3) work-
place recommendations are inferred by using a Learning Classifier
System, and (4) rules are forwarded to both Early Stage Computing
Worker-Workplace Interaction
and Interaction layers, which will merge all data (protected and Communicate
Recommendations
Layer
HCI Strategy
aggregated) and then use a strategy to communicate recommenda-
tions to the workers.
The Intensive Computing and Storage Layer allows to run com- Intensive Computing & Storage
plex algorithms and use a huge amount of data to make accurate strong/cross
recommendations
Layer Complexity of
decision making
decisions. Some of Learning Classifier Systems could be based on
KNN (K-Nearest Neighbors) [16], LSDT for reinforcement learning
[30], etc. Early Stage Computing Layer
However, in view of the sensitivity of the information shared in Early/local decisions Limited computations
Fog/Cloud applications, safety and availability are sine qua non con-
ditions for the development and adoption of both Early Stage and
Intensive Computing layers. Security schemes proposed in previous Sensing layer
User Resilience
Collect data Privacy
sections cover data privacy for Fog and Cloud architectures, and
hence, can also be applied for both layers in the proposed system.
4.4 Worker-workplace interaction layer Figure 2: Drivers and Challenges of the proposed architec-
Besides the technological requirements of the architecture that sup- ture
ports this system, the central pillar of the strategy goes through en-
gaging the users and leading them to appropriate lifestyle changes.
increasing rates of participation and attachment levels that will
In particular, the habits and behaviours that we maintain in the
contribute to bringing health-awareness and energy efficiency to
workplace are very entrenched and our tendency is to concentrate
the workplace. There are many directions for future research that
on our tasks and leave other factors aside. Thus, any system or
have arisen as a result of this proposed architecture. On the one
architecture designed to promote new habits in these spaces needs
side, find a middleware based on micro-services that can hold and
to consider the role of the user as a key factor.
orchestrate the proposal investigating the work that Pore et al. [42]
The basis of the change-management process are the way the
carried on in design issues for Fog and Edge middlewares. On the
information used as an awareness mechanism and how this infor-
other side, formally model the different classification or inference
mation is provided to the workers. In particular, information needs
tasks within a digital workplace to better understand the way a
to be delivered effectively and digital feedback is an appropriate
DSS can decide where (Edge / CLoud ) and how to compute (Serial
way to influence i n t he r eceiver [ 21]. I n t his p roposal, t he role
On-the-fly / Parallelization) the incoming sensitive data.
of the user is boosted by the Worker-Workplace layer, in charge
of optimizing the interaction between the users and the system
through contextualized feedback [12] and privacy-based user inten-
ACKNOWLEDGMENTS
tions [49]. The former pursues involving the workers in the process This work has been partially funded by the Aristos Campus Mundus
and influencing their behaviour through the application of tech- under research grant ACM2019_25.
nological persuasion techniques that increase their engagement
and motivation. The latter allows the user to expresses the data REFERENCES
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