Fulfilling the IoT Vision: Are We There Yet?
Daniel Del Gaudio and Pascal Hirmer
Institute for Parallel and Distributed Systems, University of Stuttgart, Universit
atsstraße 38, Stuttgart, Germany
Internet of Things, Decentralized, Autonomous, Dynamic, Smart.
The vision of the Internet of Things is enabling self-controlled and decentralized environments, in which hard-
ware devices, equipped with sensors and actuators communicate with each other trough standardized internet
protocols to reach common goals. The device-to-device communication should be decentralized and should
not necessarily require human interaction. However, enabling such complex IoT applications, e.g., connected
cars, is a big challenge, since many requirements need to be fulfilled. These requirements include, for exam-
ple, security, privacy, timely data processing, uniform communication standards, or location-awareness. Based
on an intensive literature review, in this overview paper, we define requirements for such environments and,
in addition, we discuss whether they are fulfilled by state-of-the-art approaches or whether there still has to be
work done in the future. We conclude this paper by illustrating research gaps that have to be filled in order to
realize the IoT vision.
In the Internet of Things (IoT), devices communicate
with each other through standard internet protocols
to reach common goals (Vermesan and Friess, 2013).
These devices are usually attached with sensors and
actuators to measure or alter the physical properties
of the environment (Gubbi et al., 2013). For example,
measuring a high humidity in a smart home could lead
to opening a window.
Hence, the overall goal of the IoT is making peo-
ple’s lives easier and safer without requiring elaborate
configuration or management tasks. Devices should
seamlessly integrate themselves into everyday activi-
ties by autonomously collaborating (Stankovic, 2014;
Brumitt et al., 2000).
However, in current IoT software solutions, the
human user is still the core actor and has to make
most of the decisions. For example, in smart home
applications, turning on lights through a mobile ap-
plication is already considered as an IoT application.
In the IoT vision, however, devices should work to-
gether to make such decisions themselves. In secu-
rity critical scenarios, a final decision of the human
user could still be necessary, however, the decision
making process should be supported by the devices.
An example for an IoT application, which should
work autonomously regarding decision making is au-
tonomous driving, where cars (the devices) communi-
cate with each other to reach a common goal, securely
arriving at a destination. Figure 1 depicts a smart car
that shares information about an upcoming obstacle
Figure 1: Connected cars sharing information about an up-
coming obstacle.
directly with cars close by and via the cloud.
To enable completely autonomous IoT environ-
ments, different requirements need to be fulfilled,
including security, privacy, timely data processing,
communication standards, or location-awareness. In
this overview paper, we aim at defining these require-
ments based on an intensive literature review. The
requirements are split into functional requirements
- FR
and non-functional requirements NFR
By doing so, we further investigate which require-
ments are already fulfilled and which requirements
still need to be worked on in more depth in the future.
Finally, we give a conclusion about research gaps that
need to be filled in order to enable complex IoT sce-
narios, such as autonomous driving.
This paper is structured as follows: Section 2 con-
tains related work. Section 3 describes functional and
Del Gaudio, D. and Hirmer, P.
Fulfilling the IoT Vision: Are We There Yet?.
DOI: 10.5220/0009439603670374
In Proceedings of the 5th International Conference on Internet of Things, Big Data and Security (IoTBDS 2020), pages 367-374
ISBN: 978-989-758-426-8
2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
non-functional requirements for IoT systems. Finally,
Section 4 summarizes this paper and gives an outlook
on future work.
This section describes related work to our paper.
Kyriazis and Varvarigou (Kyriazis and Var-
varigou, 2013) describe challenges and enablers of
smart, autonomous and reliable IoT environments.
Furthermore, they propose an architecture for cross-
platform IoT applications.
Gubbi et al. (Gubbi et al., 2013) define a vision of
the IoT as a cloud-centric system to collect and ana-
lyze data from multiple sensors. In contrast, we con-
sider the IoT as a highly distributed, decentralized,
and autonomous system, where users and applications
are the focus instead of data analysis.
The vision of the IoT stated by Miorandi et
al. (Miorandi et al., 2012) is similar to ours. They see
the IoT as a dynamic and distributed system of smart
objects, which do not only collect data but also inter-
act with the physical world. The vision described in
this work is based on the one of Miorandi et al. Addi-
tionally to them, we evaluate our vision from a current
state-of-the-art view. Furthermore, we define a set of
functional and non-functional requirements that are,
in our opinion, necessary to reach this vision.
In this section, we introduce requirements that need to
be fulfilled in order to enable autonomous Internet of
Things scenarios, such as autonomous driving. These
requirements are based on an intensive literature re-
view in the area of autonomous Internet of Things.
We divided the requirements into functional and non-
functional requirements.
3.1 Functional Requirements
We consider the following functional requirements:
Discovery, FR
Interoperability, FR
ity, FR
Controllability, FR
Data Storage, and FR
Location-Awareness. Table 1 lists all functional re-
quirements and their associated References. These
requirements are described in detail in the following.
– Discovery
An important requirement for decentralized, self-
controlled Internet of Things is discovery. In order
for devices to collaborate autonomously, they need to
be aware of each other and the services they provide.
When a new device enters the environment, other de-
vices need to be notified about its existence to inte-
grate it into the IoT environment. Furthermore, the
device itself must be informed about the environment
it is involved in. When a device leaves the environ-
ment or fails, other devices must also be informed
about it to react accordingly, e.g., stop forwarding
In centralized smart environments, devices regis-
ter via a central component, similar to the discovery in
service-oriented computing via UDDI (Weerawarana
et al., 2005). However, this is not sufficient for the
IoT vision, since this approach requires either setting
up a discovery component for each environment, or
each device being connected to an upper-lying net-
work. This could lead to issues in terms of connectiv-
ity, and thus, flexibility, privacy, and scalability. De-
vices must be able to handle new or leaving devices
themselves in a decentralized manner.
Datta et al. (Datta et al., 2015) categorize related
work in the area of discovery into the following areas:
distributed and peer-to-peer discovery services, cen-
tralized architectures, CoAP-based service discovery,
semantic-based discovery, search engines for resource
directory, and utilization of ONS and DNS.
In previous work, we introduce a life cycle method
for device management in dynamic IoT environments
that also includes informing new devices about al-
ready existing devices and vice versa (Del Gaudio
et al., 2020).
Fredj et al. (Fredj et al., 2014) propose a semantic-
based service discovery using ontologies. A semantic
model that can be used to achieve discovery in such a
manner is IoT Lite (Bermudez-Edo et al., 2016).
CoAP-based discovery mechanisms can make use
of the resource discovery (/.well-known/core) inter-
face of a CoAP server, through which provided ser-
vices of the server can be retrieved (Shelby et al.,
2014; Shelby et al., 2013). Cirani et al. (Cirani et al.,
2014) propose an architecture for peer-to-peer-based
autonomous resource and service discovery in the
IoT. The architecture utilizes a central IoT gateway
and the CoAP resource discovery interface.
In conclusion, the area of discovery, especially
in the service domain is very well researched. For
the IoT, many specialized approaches already exist.
Hence, we can conclude that the discovery require-
ment can already be considered as fulfilled.
– Interoperability
IoTBDS 2020 - 5th International Conference on Internet of Things, Big Data and Security
Table 1: Functional Requirements and associated references.
Requirement Name References
Discovery (Weerawarana et al., 2005) (Datta et al., 2015) (Fredj et al., 2014)
(Bermudez-Edo et al., 2016) (Shelby et al., 2014) (Shelby et al., 2013)
(Cirani et al., 2014) (Del Gaudio et al., 2020)
Interoperability (Akyildiz et al., 2002) (Shelby et al., 2014) (Hunkeler et al., 2008)
(Meng et al., 2016)
Portability and Soft-
ware Provisioning
(Binz et al., 2012) (Franco da Silva et al., 2016) (Franco da Silva et al., 2017)
Controllability of
(Meyer et al., 2013) (Andrews et al., 2003) (White, 2004) (Seeger et al., 2018b)
(Seeger et al., 2018a) (DelGaudio and Hirmer, 2019)
Efficient Data Stor-
age and Processing
(Madden et al., 2005)
(Mahfouz et al., 2009) (Wang et al., 2013) (Witrisal et al., 2016)
IoT environments typically consist of numerous het-
erogeneous devices. Thus, we need communication
standards that also constrained devices are capable of.
In the IoT, standard internet protocols are used for
communication. However, for constrained devices,
adapted protocols and standards are required that are
more lightweight, for example, using UDP for com-
munication instead of TCP.
Interoperability can be divided into multiple lay-
ers (Akyildiz et al., 2002): Physical layer, data link
layer, network layer, transport layer, and applica-
tion layer. Protocols to enable communication be-
tween devices on the application layer have been
developed in the past, e. g., the Constrained Appli-
cation Protocol (CoAP) (Shelby et al., 2014) and
the Message Queuing Telemetry Transport proto-
col (MQTT) (Hunkeler et al., 2008). MQTT adapts
the publish-subscribe pattern (Eugster et al., 2003)
and requires a central message broker.
In decentralized and self-controlled IoT environ-
ments, the message broker must be hosted by devices
themselves, which contradicts to the asynchronous
communication concept of the publish-subscribe pat-
tern. CoAP is similar to HTTP but simpler and based
on UDP. Hence, it is more appropriate for communi-
cation in IoT environments.
Another protocol that can, for example, be used
for machine-to-machine communication in industrial
IoT applications is ZeroMQ (ZMQ), as Meng et al.
show in (Meng et al., 2016). ZMQ is specifically de-
signed for machine-to-machine messaging communi-
cation in the IoT.
In conclusion, it can be stated that there are al-
ready many standards that exist for communication
in IoT environments. Many of them are already es-
tablished in research and industry (such as MQTT or
CoAP). Hence, the requirement Interoperability can
also be considered as fulfilled.
– Portability and Software Provisioning
Since devices in heterogeneous IoT environments
tend to have different physical and computational ca-
pabilities, portability is an important issue. More pre-
cisely, IoT devices communicating to reach a com-
mon goal need to support the same software solutions,
i.e., the software needs to be portable amongst hetero-
geneous IoT devices. Therefore, software must be de-
signed to be quickly deployable on different kinds of
devices. Thus, an important requirement is portable
software for IoT systems.
Since smart devices tend to be highly heteroge-
neous, portability of software is still a challenge.
Low-level devices, such as the Arduino Boards or
other micro controllers need to be flashed, so software
usually cannot be deployed on them automatically.
For higher-level devices, software deployment and
management approaches, such as the TOSCA (Binz
et al., 2012) standard can be utilized to model ap-
plication stacks and dynamically deploy them on de-
vices (Franco da Silva et al., 2016).
Even though portability is mostly provided by IoT
devices and corresponding standards, automated pro-
visioning of software in IoT environments is still be-
ing researched. There are first efforts to adapt the
TOSCA standard to provision software into IoT en-
vironments (Franco da Silva et al., 2017), however,
there still needs to be work done to enable reliable
software provisioning in such complex environments.
– Controllability of Flow
To control distributed heterogeneous devices in the
IoT, the flow of data and execution must be controlled.
Fulfilling the IoT Vision: Are We There Yet?
In centralized environments, a processing engine is
typically used (Meyer et al., 2013). However, since
requiring a central component is contradicting to the
IoT vision, flow must be controlled by devices them-
selves. Thus, the data flow has to be controlled im-
plicitly in the sense that every device knows its prede-
cessor and successor.
Control flow and data flow are typically governed
by a central instance using, e.g., BPEL (Andrews
et al., 2003) or BPMN (White, 2004) engines. Since
such central instances contradict with the vision of a
decentralized and autonomous Internet of Things, we
must seek for other solutions to control data and exe-
cution flow of IoT applications. For example, Seeger
et al. propose a solution based on the choreography
model (Seeger et al., 2018b; Seeger et al., 2018a).
In previous work, we introduce a messaging engine,
which is able to control the flow without any central
component necessary (DelGaudio and Hirmer, 2019).
Similarly, Bumgardner et al. introduce a decentral-
ized IoT system, in which devices can exchange data.
However, current state of the art approaches focusing
on IoT are still mostly rudimentary and prototypical.
Most established concepts originate from peer-to-peer
systems, where file transfer is the usually the main
goal. Hence, there are still missing approaches that
are purely focused on the IoT.
– Efficient Data Storage and Processing
To process data in smart environments in a decentral-
ized manner, devices must be able to store data effi-
ciently in time and place. Since smart devices tend to
be much more vulnerable and mobile than larger ma-
chines or servers, data loss must be expected and han-
dled by devices themselves. Traditional database sys-
tems are usually heavy-weight and come with many
features, such as replication, scalability, or efficient
data querying capabilities. However, the enhanced
number of features and the consequently increasing
size of these database systems makes them unsuitable
for constrained IoT devices.
Hence, several database systems have been devel-
oped that are tailored for low-resource IoT devices.
Those databases include, for example, in-memory
databases, decentralized databases, key-value-stores,
or lightweight document stores. Famous database
systems for the IoT include TinyDB (Madden et al.,
2005), SQLite, MongoDB Mobile, or HarperDB.
Consequently, database developers have noticed the
need for such lightweight systems that do only pro-
vide a subset of features in contrast to traditional
database system, but are much more lightweight.
In conclusion, there are already many systems
available that can even be deployed onto very low-
resource wireless sensor networks, such as TinyDB.
The requirement for efficient data storage on IoT de-
vices can, therefore, be considered as fulfilled.
Regarding data processing, it is necessary to con-
duct data operations on the devices themselves in-
stead of moving all the data to a central component.
This is generally referred to as edge or fog comput-
ing. By doing so, latency can be reduced and, fur-
thermore, the decentralized IoT system can be pre-
served. Consequently, there is a need for data pro-
cessing techniques in edge cloud environments, espe-
cially focusing on stream processing. To realize this,
lightweight stream and event processing systems have
been developed, such as CEP Esper. However, there
still needs to be work done regarding data process-
ing on resource-limited devices. Oftentimes, only mi-
nor data operations can be conducted and data still
needs to be transferred to a backend cloud environ-
ment for processing. This, however, contradicts with
the idea of decentralization. Hence, there still is a
need for more sophisticated concepts for data process-
ing in IoT environments, enabling data processing as
close to the data sources as possible.
– Location-Awareness/-Sensitivity
In IoT environments, the location of each device plays
an important role. For example, in connected car
scenarios, localization needs to have a very high ac-
curacy, especially if complete autonomy should be
achieved. Furthermore, in Smart Factory or Smart
Home scenarios, the location of sensors and actua-
tors are crucial to develop functioning IoT applica-
tions. Especially moving devices, for example, smart
phones or cars need to be locatable at all times.
There is a lot of research conducted regarding lo-
calization of IoT devices. The most common way for
localization is GPS, which can nowadays provide a
good accuracy. However, GPS has the limitation that
it works only outside of buildings and, for example,
not in a Smart Factory environments. Furthermore,
for some scenarios (connected cars), the accuracy of
GPS is not sufficient.
To cope with these issues, new localization ap-
proaches are currently being researched, for example,
using Bluetooth Low Energy triangulation or UWB
RTLS, achieving a high accuracy of ca. 8.5 centimeter
derivation (Wang et al., 2013; Mahfouz et al., 2009).
However, such accuracy can only be achieved in well-
defined, closed environments.
The new communication standard 5G also pro-
vides localization features, which should be used for
connected car solutions, since the accuracy is high
and it can also be applied to scenarios outside and in-
side of buildings (Witrisal et al., 2016).
Currently, there is still a lot of research going on
to increase the accuracy of localization approaches.
IoTBDS 2020 - 5th International Conference on Internet of Things, Big Data and Security
The experiences with 5G are still limited and need to
be gathered in the future through the application in
various IoT use cases.
In conclusion, in order to achieve completely self-
controlled and decentralized IoT applications, there
still needs to be work done regarding localization of
IoT devices.
3.2 Non-functional Requirements
Additionally to the functional requirements, we
evaluated the non-functional requirements: NFR
Privacy, NFR
Trust, NFR
Safety, NFR
rity, NFR
Reliability, NFR
Manageability, NFR
Adaptability, and NFR
Real-Time Capabilities. Ta-
ble 2 lists all non-functional requirements and their
associated references. These are specified in the fol-
– Privacy
Regulations, such as the GDPR show that privacy
becomes increasingly important to users (Tankard,
2016). However, the right of users to decide over
their information is also a common one in the United
States (Warren and Brandeis, 1890). Avoiding cen-
tral components always leads to an increase in pri-
vacy, since no single instance has knowledge about
the whole environment. Nevertheless, this does not
ensure privacy for users in all cases sufficiently. Of-
tentimes privacy and quality of service need to be
weighted against each other.
One way to improve privacy in decentralized
IoT environments is to encrypt communication be-
tween devices. Many technologies to do this have
been developed independently from the Internet of
Things. Those are among others Virtual Private Net-
works, Transport Layer Security (TLS), DNS Secu-
rity Extension, and Onion Routing (Weber, 2010).
Since many IoT devices tend to have similar comput-
ing capabilities than typical computers, conventional
privacy-ensuring standards can also be applied in the
Internet of Things. Especially MQTT, which is based
on the TCP transport protocol, can be encrypted with
TLS. In regard to IoT-specific protocols like CoAP,
efforts to encrypt communication is still in progress,
i. e., Object Security of CoAP (OSCOAP) (Selander
et al., 2017) for CoAP.
Since the goal of decentralized and autonomous
IoT environments is that data must not be sent to cen-
tral services hosted by third parties, privacy is ensured
much more by design, because no single organization
is able to collect all data from every user. Nonethe-
less, the problem gets partially shifted to a trust issue,
because if two devices interact, there is also no central
instance that controls who the respective other devices
identity is.
– Trust
In decentralized environments, no controlling in-
stance verifies transactions between parties. Thus,
means are necessary to create trust between parties,
i. e., the devices and their owners.
Yan et al. (Yan et al., 2014) identify the following
objectives in regard to trust management in the IoT:
Trust relationship and decision, data perception trust,
privacy preservation, data fusion and mining trust,
data transmission and communication trust, quality of
IoT services, system security and robustness, gener-
ality, human-computer trust interaction, and identity
trust. In their opinion, there are open issues espe-
cially in the areas trust evaluation, in terms of context
awareness and user’s subjective properties, compre-
hensive trust management frameworks, interoperation
or integration of data transmission and communica-
tion trust technologies with other trust management
mechanism, and human-computer trust interaction.
Chen et al. (Chen et al., 2011) describe a fuzzy
trust model, where trust is evaluated by devices via
observing its neighbor’s packet forwarding behavior.
The ratio of correctly forwarded packets to the to-
tal number of forwarded packets decides about how
much a node can be trusted.
Sicari et al. (Sicari et al., 2015) state the following
open issues in IoT trust management: a lack of a com-
mon trust negotiation language, object identity man-
agement, and trust negotiation mechanisms to handle
data stream access control. This shows that there are
still many open topics in terms of trust in the IoT that
need further research.
– Safety
The more smart devices are integrated inside peoples
lives, the more their lives rely on them. Especially in
critical scenarios, such as autonomous driving, safety
is an important requirement.
Currently, there are only a few approaches focus-
ing on safety in IoT systems. For example, Celik et
al. (Celik et al., 2018) introduce a system to conduct a
static analysis on IoT application and check whether
the application fulfills a set of predefined properties.
Consequently, safety is one of the aspects that still
needs further research in order to enable the IoT vi-
sion we aim for.
Fulfilling the IoT Vision: Are We There Yet?
Table 2: Non-Functional Requirements and associated references.
Requirement Name References
Privacy (Tankard, 2016) (Warren and Brandeis, 1890) (Weber, 2010)
(Selander et al., 2017)
Trust (Yan et al., 2014) (Chen et al., 2011) (Sicari et al., 2015)
Safety (Celik et al., 2018)
Security (Weber, 2010) (Stankovic, 2014) (Sicari et al., 2015)
Reliability (Kyriazis and Varvarigou, 2013) (Qiu et al., 2016)
(Newman and Watts, 1999)
Manageability (Aceto et al., 2013) (Chiang and Zhang, 2016)
Adaptability (Kyriazis and Varvarigou, 2013)
Real-Time Capabilities (Yasumoto et al., 2016)
– Security
To ensure not only privacy, security, trust and safety
but also integrity, IoT environments must be secure in
terms of attacks (Weber, 2010; Stankovic, 2014).
Important requirements to achieve security are re-
silience to attacks, authentication, confidentiality, and
access control (Weber, 2010; Sicari et al., 2015). En-
cryption standards like TLS and Datagram Transport
Layer Security (DTLS) can be applied to secure au-
thentication, increase confidentiality, and prevent spe-
cific kinds of attacks.
The existing security standards that are applied to
the Internet, can also be transferred in IoT environ-
ments. However, oftentimes, these approaches are
not applied in order to avoid the overhead. Find-
ing a good trade-off between overhead due to secu-
rity mechanisms and efficiency is still one of the great
– Reliability
Physical things tend to be volatile and smart de-
vices cannot ensure the same availability than pure
cyber-physical systems. The real world tends to be
much more dynamic (Kyriazis and Varvarigou, 2013).
Thus, we need means to achieve robustness and fail-
ure safety in smart environments. Devices must be
able to handle failures, exceptions, and data loss
themselves autonomously, to ensure safety for users.
In their survey paper Smart, autonomous and
reliable Internet of Things, Kyriazis and Var-
varigou (Kyriazis and Varvarigou, 2013) introduce the
necessity of links between smart objects and their
circumstances like administration domains, condi-
tions, and events to derive the reliability of those ob-
jects. Furthermore, they recommend to use models
and knowledge generation methods to evaluate the
reliability of devices dependent on specific circum-
stances. Such methods can be used to raise con-
sciousness when interacting with volatile smart ob-
jects. Nonetheless, we need means to increase their
reliability, despite of their physical weaknesses.
Qiu et al. (Qiu et al., 2016) propose a solution to
increase robustness in IoT networks by adding short-
cuts between nodes, which is based on the small-
world network model (Newman and Watts, 1999).
In conclusion, there are already many works fo-
cusing on reliability and fault tolerance in IoT envi-
ronments, however, coping with the failure of devices
is still an important issue which requires further work
in the future.
– Manageability
In centralized environments, management tasks, like
monitoring, introducing new devices or new soft-
ware, and network management, is done via central
instances. This is not sufficient for our previously de-
fined vision of the IoT. Thus, new concepts regarding
manageability are required. Although IoT environ-
ments tend to be highly distributed, monitoring still
is a task that is centralized by nature. A technique
of cloud monitoring to increase scalability that can be
adopted to IoT environments are agents to perform
data collection, filtering and aggregation directly on
devices (Aceto et al., 2013). An issue when manag-
ing IoT applications is to keep software and creden-
tials up to date, especially when devices are mobile
and the internet connection is error-prone(Chiang and
Zhang, 2016). Since many techniques of Cloud mon-
itoring can be adopted to the IoT, there is no need
for fundamental research in terms of IoT monitoring.
Nonetheless, we need means to decentralize and au-
tomatize monitoring tasks.
– Adaptability
Situations in smart environments are not consistent
by nature. Devices can be mobile or destroyed by
physical interactions. Thus, devices need to be able
to mutually react to changing situations. Smart envi-
ronments need to be designed in such way that they
are flexible in terms of changing situations.
IoTBDS 2020 - 5th International Conference on Internet of Things, Big Data and Security
Kyriazis and Varvarigou (Kyriazis and Var-
varigou, 2013) state that devices should be able to
exchange information about experienced situations to
overcome them in the future. To achieve this, devices
must have means to store and communicate context
information. They should be able to autonomously
learn how to adapt to changing situations in a pre-
emptive manner.
Adaptability to new situations and considering
concept drifts is still an open research issue, which
needs to be considered in future approaches.
– Real-Time Capabilities
Devices must be able to mutually react to changing
situations in a timely manner. Thus, devices need to
be able to process data in real-time, since they must
react to real-time changing situations. Relocating data
processing from the cloud to the edge of the network
enables low latency for data stream processing. Ya-
maguchi and Shigeno (Yasumoto et al., 2016) survey
the state-of-the-art of real-time data stream process-
ing in the IoT. They conclude that real-time capabili-
ties, i.e., the guarantee to process data in a given time,
cannot yet be achieved in real-world scenarios. Es-
pecially for autonomous cars, this however is crucial
and requires new technologies, such as 5G networks.
We see the most research gaps in the non-functional
requirements, especially NFR
and NFR
. Privacy is probably one of the biggest
challenges in terms of our vision, since IoT appli-
cations must balance between governmental regula-
tions, user’s preferences, and service quality. Also,
trust is an important issue. We must evaluate whom
users trust more: centralized systems or each other.
We see much potential in blockchains or related sys-
tems to solve this. In terms of reliability, systems
like smart cars are already more reliable than hu-
man beings. However, when systems become more
distributed, handling failures is not yet investigated
In the future, we aim to do more work in FR
, and NFR
, building on our previous work.
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