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

Keywords:

Internet of Things, Decentralized, Autonomous, Dynamic, Smart.

Abstract:

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.

1 INTRODUCTION

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

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

367

non-functional requirements for IoT systems. Finally,

Section 4 summarizes this paper and gives an outlook

on future work.

2 RELATED 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.

3 REQUIREMENTS FOR

AUTONOMOUS

DECENTRALIZED INTERNET

OF THINGS

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

Portabil-

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

messages.

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

368

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

Flow

(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)

Location-

Awareness/-

Sensitivity

(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?

369

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.

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370

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

Secu-

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-

lowing.

NFR

– 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.

NFR

– 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.

NFR

– 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?

371

Table 2: Non-Functional Requirements and associated references.

Requirement Name References

NFR

Privacy (Tankard, 2016) (Warren and Brandeis, 1890) (Weber, 2010)

(Selander et al., 2017)

NFR

Trust (Yan et al., 2014) (Chen et al., 2011) (Sicari et al., 2015)

NFR

Safety (Celik et al., 2018)

NFR

Security (Weber, 2010) (Stankovic, 2014) (Sicari et al., 2015)

NFR

Reliability (Kyriazis and Varvarigou, 2013) (Qiu et al., 2016)

(Newman and Watts, 1999)

NFR

Manageability (Aceto et al., 2013) (Chiang and Zhang, 2016)

NFR

Adaptability (Kyriazis and Varvarigou, 2013)

NFR

Real-Time Capabilities (Yasumoto et al., 2016)

NFR

– 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

challenges.

NFR

– 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.

NFR

– 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.

NFR

– 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.

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372

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.

NFR

– 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.

4 CONCLUSION AND FUTURE

WORK

We see the most research gaps in the non-functional

requirements, especially NFR

, 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

enough.

In the future, we aim to do more work in FR

NFR

, and NFR

, building on our previous work.

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