IoT-based Systems Actuation Conﬂicts Management Towards DevOps:

A Systematic Mapping Study

ephane Lavirotte

, G

erald Rocher

, Jean-Yves Tigli and Thibaut Gonnin

Universit

e C

ote d’Azur, CNRS, Laboratoire I3S, 06903 Sophia-Antipolis, France

Keywords:

Internet of Things, Cyber-Physical Systems, Conﬂict Identiﬁcation, Conﬂict Resolution, DevOps.

Abstract:

The Internet of Things (IoT) has long been understood as an infrastructure layer allowing to gather environ-

mental data through sensors. However, it also provides means to physically interact with our living environ-

ments through actuators. To the extent that actuation effects are not without risks on safety and trustworthiness,

providing the IoT infrastructure layer with merely sensors access control mechanisms is no longer sufﬁcient.

It is also required to prevent conﬂicting (and possibly unsafe) actuation effects to occur in the physical en-

vironment and deploy means to resolve them. In this paper, we consider actuation conﬂicts management as

part of the DevOps approach, which aims to harmonize tools and objectives of actors involved in IoT-based

systems life cycle from their design to their deployment. In this context, a systematic mapping study (SMS) is

conducted to better understand the actuation conﬂicts management approaches and to what extent they could

be integrated into the DevOps life cycle.

1 INTRODUCTION

Internet of Things (IoT) based systems generally fol-

low a layered architecture: (1) a shared IoT infras-

tructure layer consisting of a set of connected and

distributed resources (e.g., processing units, memory,

sensors, actuators, etc.) likely to be embedded in ev-

eryday objects (chair, lamp, etc.) and/or things (room,

building, etc.), (2) a top layer where applications are

deployed and, (3) one or more intermediate layers

managing communications and ensuring the overall

coherency between applications and the shared infras-

tructure. Thereby, the literature refers to three-layer

architecture, middleware architecture, service-based

architecture, ﬁve-layer architecture etc. (Kumar and

Mallick, 2018). The notion of coherence, here, has a

strong connection to that of conﬂict, “[...] ...a con-

text change that leads to a state of the environment

which is considered inadmissible by the application

or user’” (Tuttlies et al., 2007).

Following these architectural schemes, IoT-based

systems have long been limited to collecting ﬁeld in-

formation from sensors; in this context, the problem

the intermediate layers must solve is merely tech-

nological; it aims to provide the infrastructure layer

with a sensors access control mechanism (Cecchinel

https://orcid.org/0000-0002-3341-6577

https://orcid.org/0000-0002-3874-6276

et al., 2014). The notion of coherence then refers to

the problem of managing direct conﬂicts (i.e., con-

current accesses). Managing actuation conﬂicts is a

much more challenging task. It is not only a ques-

tion of managing direct conﬂicts but also indirect con-

ﬂicts that arise from the concurrent interactions of

actuators with a common physical system and that

can lead to an undesirable evolution of some of its

properties (Teixeira et al., 2011) (e.g., simultaneously

heating and cooling a room). In this context, the

problem the intermediate layers must solve, beyond

being technological, lies in the semantic interpreta-

tion of the effects produced in the environment and

their consequences for humans in terms of safety and

trustworthiness. From that viewpoint, by making ex-

plicit the interactions with the physical environment

through the IoT shared infrastructure layer, IoT-based

Cyber-Physical Systems (CPS/IoT) (Shih et al., 2016;

Damjanovic-Behrendt et al., 2018), an “integration of

computation with physical processes, intersection of

the physical and the cyber” (Lee and Seshia, 2016),

are most likely to face with this problem.

Actuation conﬂicts management is a ﬁrst class

concern in the realm of thrustworthy and safe IoT-

based systems, justifying the efforts put by the Eu-

ropean Union on this topic (e.g. ENACT project

https://www.enact-project.eu

Lavirotte, S., Rocher, G., Tigli, J. and Gonnin, T.

IoT-based Systems Actuation Conﬂicts Management Towards DevOps: A Systematic Mapping Study.

DOI: 10.5220/0009355102270234

In Proceedings of the 5th International Conference on Internet of Things, Big Data and Security (IoTBDS 2020), pages 227-234

ISBN: 978-989-758-426-8

227

Brain-IoT project

and SecureIoT

). However, to

date, only few surveys, systematic mappings and lit-

erature reviews have been conducted on the actuation

conﬂicts management problem in the context of IoT-

based systems. In (Resendes et al., 2014), the authors

conducted a Systematic Literature Review (SLR) on

the conﬂict detection and resolution problem in Home

and Building Automation Systems (HBAS). They

propose a taxonomy that classiﬁes conﬂicts accord-

ing to four different dimensions: (1) source, (2) inter-

venients, (3) time of detection and (4) solvability. Al-

though it provides an overview of the research on con-

ﬂicts detection and resolution, the study is restricted

to HBAS and does not provide recent information on

how far these topics are covered in research.

Actuation conﬂict management is crucial and calls

for a methodological break in the development pro-

cess of IoT-based systems. In this context, the De-

vOps approach, an agile and incremental develop-

ment approach that aims to harmonize the practices of

actors involved in all stages of a system life cycle (i.e.

from development to deployment and maintenance),

is promising.

The goal of this paper is to clarify the interest

and scope of recent research on the management of

actuation conﬂicts in the area of IoT-based systems.

In particular, we are interested in analyzing actua-

tion conﬂict management in the perspective of the De-

vOps approach. To this end, we rely on the system-

atic mapping approach, well established in evidence

based medicine and dedicated to provide researchers

with the ability to build a classiﬁcation scheme and

structure a ﬁeld of interest from which speciﬁc re-

search questions can be answered (Heinz, 2014; Sny-

der, 2019).

2 BACKGROUND

In this section, we provide an overview of Internet

of Things, Cyber Physical Systems and DevOps ap-

proach underlying the scope of this study.

2.1 Internet of Things (IoT)

Thanks to constant innovations in electronics and

communication technologies, the use of our surround-

ing physical entities (chair, lamp, houses, cities, etc.)

is transcended. Being connected to the Internet, new

forms of interaction are emerging from these physi-

cal entities thanks to sensors and actuators, embodied

http://www.brain-iot.eu/

https://secureiot.eu

in remotely accessible software services and resulting

in the fusion of the cyber and physical dimensions of

our environments. In this context, IoT is “the infras-

tructure enabling advanced services by interconnect-

ing (physical and virtual) things based on existing and

evolving interoperable information and communica-

tion technologies” (International Telecommunication

Union, ITU).

From an architectural point of view, IoT-based

systems are generally multi-layered. The Fig. 1

includes some examples from (Kumar and Mallick,

2018). Here, it is interesting to note the purpose given

to the shared IoT infrastructure layer:

Perception layer

Network layer

Application layer

Perception layer

Transport layer

Processing layer

Application layer

Business layer

Security layer

Storage layer

Pre-processing layer

Monitoring layer

Physical layer

Transport layer

Three layer Five layer

Six layer (FOG)

Figure 1: IoT architectures layers (Kumar and Mallick,

2018).

Perception Layer. Provides the ability to detect, col-

lect and gather information about the physical envi-

ronment and the connected objects within it.

Environmental Layer. Provides the ability to detect

objects or places that are under observation. This in-

cludes the ability to observe moving physical entities,

such as humans, cars, etc. and environmental proper-

ties such as temperature or humidity.

This semantics is somehow representative of the

prevailing idea that consists in associating IoT to envi-

ronmental sensing/monitoring capabilities. Although

this vision has led to tremendous improvements in

human well-being and assistance, optimization of re-

sources, etc., it is important not to forget the action ca-

pabilities offered by actuators. Only recently has this

capacity, and the associated risks to both humans and

their environments, been seriously considered. This is

witnessed by some on-going European projects. For

instance, ENACT project acknowledges that “[...] IoT

system innovations have until now mainly been con-

cerned with sensors, device management and connec-

tivity, with the mission to gather data for process-

IoTBDS 2020 - 5th International Conference on Internet of Things, Big Data and Security

228

ing and analysis in the cloud” and consider actuation

as a ﬁrst class concern in IoT “[...] The next gen-

eration IoT systems need to [..] manage the closed

loop from sensing to actuation” (Ferry et al., 2018).

BRAIN-IoT project speciﬁcally “aims to support the

integration into an IoT environment of devices and

subsystems with actuation features that could possi-

ble give rise to mixed-critically situations” (Conzon

et al., 2019).

2.2 Cyber-Physical Systems (CPS)

Cyber-Physical Systems (CPS) are a generalization of

the concept of embedded systems to that of connected

things with the objective of making them collaborate

for the control of physical processes (Rajkumar et al.,

2010). CPS ﬁnd applications in the optimization of

resources, their means of supply, etc. at the heart

of the Industry4.0 and Industrial IoT (IIoT) revolu-

tions. However, by controlling physical processes,

these systems are not without risks for humans and

their environment, as evidenced by the SecureIoT Eu-

ropean project. This project aims to develop secure

services targeting the areas of digital automation in

manufacturing (Industry 4.0), social assistance robots

for coaching and health and connected cars and au-

tonomous driving. Such mechanisms are “highly de-

manded by the industry in order to secure a whole new

range of IoT applications that transcend the bound-

aries of multiple IoT platforms, while involving au-

tonomous interactions between intelligent CPS sys-

tems and networks of smart objects”.

2.3 DevOps Approach

The DevOps (Sharma and Coyne, 2017) approach

aims to harmonize the practices of software devel-

opment (development, integration and testing) and

systems administration (deployment, operation and

maintenance) stakeholders (Fig. 2). This harmoniza-

tion is justiﬁed by the conﬂicting objectives of these

actors; on the one hand, software developers are con-

strained by cost and time, with the negative impacts

that this can have on the quality of the software de-

livered. On the other hand, IT administration actors

seek to achieve stability and quality objectives, at the

expense of costs and deadlines.

This approach is based on agile and lean manage-

ment methods and results in the collaboration of busi-

ness managers, developers, operations and quality

stakeholders in order to continuously deploy differ-

ent software versions. In this context, this approach

aims to pool the tools for implementing software ap-

plications, from their design to their deployment. The

Release

Deploy

Monitor

Operate

Code

Test

Build

Plan

Dev

Ops

Figure 2: DevOps life cycle.

underlying interest of this harmonization lies in the

repetability of the processes implemented throughout

the DevOps loop (Fig. 2) and paves the way for their

automation (Lwakatare et al., 2015). The latter is im-

portant in order to accelerate and maintain the conver-

gence towards a system that meets the requirements of

all actors involved. Automation is of particular impor-

tance in IoT-based systems where connected devices

are constantly providing feedback. Automation then

brings reactivity in updating these systems throughout

the DevOps loop as soon as they need to be.

In this paper, we seek to understand to what ex-

tent the tools for identifying and resolving actuation

conﬂicts can be integrated into IoT-based systems life

cycle in the context of DevOps. To this end, we rely

on the systematic mapping approach (Snyder, 2019)

consisting in establishing research questions and or-

ganizing the answers obtained from the study of sci-

entiﬁc publications. The methodology and results are

exposed in the sequel.

3 RESEARCH METHOD

This systematic mapping study was developed follow-

ing the guidelines proposed in (Petersen et al., 2015).

On the basis of the context and the motivations pre-

sented in §1 and 2, we deﬁne the research questions

(RQ) in §3.1. In order to deﬁne the scope of the study

and reduce possible biases in the selection process, we

explain the inclusion and exclusion criteria in §3.2.

3.1 Research Questions

This study aims to answer the set of research ques-

tions described in Table 1. The questions relating

to conﬂict management (RQ2) remain rather general,

the objective being to understand the limitations of

the current actuation conﬂicts management methods

towards their implementation within the DevOps ap-

proach.

IoT-based Systems Actuation Conﬂicts Management Towards DevOps: A Systematic Mapping Study

229

Table 1: Research questions.

RQ1 What are the primary studies statistics?

RQ1.1 What is the publication rate over years?

RQ1.2

In which types of venue (workshop, confer-

ence, journal) were the studies published?

RQ1.3

How studies are distributed in terms of aca-

demic and industrial afﬁliation and location?

RQ1.4 What application domains are concerned?

RQ2 How actuation conﬂicts are managed?

RQ2.1

What actuation conﬂicts are considered (di-

rect/indirect)?

RQ2.2

At what stage of the IoT-based systems life cy-

cle are they implemented?

RQ2.3 What is their automation level?

RQ2.4 What is their maturity level?

3.2 Search Strategy

The study was conducted using three different

databases: ACM-DL, IEEE Xplore and Scopus. Dur-

ing the selection process, the inclusion and exclusion

criteria deﬁned in tables 2 and 3 were applied.

Table 2: Inclusion criteria.

Inclusion criteria

Primary peer-reviewed paper

The scope of the paper is fully related to the research

questions

Paper written in English language

Publication year ≥ 2008

Table 3: Exclusion criteria.

Exclusion criteria

White paper, technical report, thesis, book chapter,

patent and presentation

The content of the paper is not appropriate to answer the

research questions

Duplicate

The queries associated to each database are given

in table 4. The scope of the search is restricted to

papers related to IoT and CPS domains published in

2008 onward. 2008 was a pivotal year in the ﬁeld

of IoT. This is the year from which the number of

IoT-related publications has started to increase signif-

icantly (Fig. 3). It was also the year in which the

ﬁrst international conference on IoT was held (Flo-

erkemeier et al., 2008).

The second conjunctive part of the queries re-

stricts the scope of the search to papers dealing with

actuation, source of the direct and indirect conﬂicts.

While the notion of actuation conﬂict has a strong

meaning in the ﬁeld of robotics (the physical side), in

the software engineering domain (the cyber side), it is

more about the one of Feature Interaction (FI) (Bruns,

2005), a feature “being a unit of functionality that can

2006

2008 2010 2012 2014

2016

2018

0.5

1.5

·10

#publications

Figure 3: Evolution of publications related to the Internet

of Things (IoT).

be developed and evolved independently” (Bocovich

and Atlee, 2016). Therefore, it is worth taking into

account these different semantic interpretations in the

search, IoT-based systems developers having a strong

software engineering culture. Finally, the third con-

junctive part of the queries expresses the notion of

conﬂict through a set of synonyms.

On the basis of the papers found using the search

string, a backward snowballing technique was used

(Wohlin, 2014) in order to identify additional relevant

papers. By following this approach, three more pa-

pers were added. Taking the guidelines and applying

the exclusion criteria, an extensive review of the se-

lected papers was made by three researchers analyz-

ing the title, the abstract and the content of each ex-

tracted paper. Consensus on keeping or rejecting pa-

pers was found during meetings conducted through-

out the selection process. Finally, a total of 26 papers

(Table 10) were selected as a result of this classiﬁ-

cation process while 2842 papers were excluded, as

shown in Table 5.

4 RESULTS HIGHLIGHTS

The following sections are devoted to providing an

analysis of the selected publications according to the

research questions identiﬁed in §3.1.

4.1 Overview

This section provides answers to the research question

RQ1 and associated sub-questions RQ1.1, RQ1.2,

RQ1.3 and RQ1.4.

Answering RQ1.1: Until 2011, there were only few

papers dealing with actuation conﬂict management in

IoT-based systems as depicted in Fig. 4.

IoTBDS 2020 - 5th International Conference on Internet of Things, Big Data and Security

230

Table 4: Search strings and ﬁlters for each database.

Library Advanced /Command search query string Added ﬁlters

ACM-DL

(”internet of things” OR iot OR ”cyber-physical system” OR cps)

AND (actuation OR actuator OR feature) AND (conﬂict OR inter-

action OR interference OR shared OR concurrency)

Published 2008 onward

IEEE Xplore

(”internet of things” OR iot OR ”cyber-physical system” OR cps)

AND (actuation OR actuator OR feature) AND (conﬂict OR inter-

action OR interference OR shared OR concurrency)

Year range: from 2008 to 2019

Conferences and Journals

Scopus

TITLE-ABS-KEY((”internet of things” OR iot OR ”cyber-physical

systems” OR cps) AND (actuation OR actuator OR feature) AND

(conﬂict OR interaction OR interference OR shared OR concur-

rency))

Year: limit to ”2008 to 2019”

Subject area: limit to Computer Science

Document type: limit to Conferences

and Journals

Table 5: The search and selection stages for primary studies.

Source SearchResults Removing duplicates Reviewing titles Reviewing abstract Scanning content

ACM-DL 866 834 112 29 12

IEEE Xplore 970 943 92 28 9

Scopus 1032 505 40 16 5

Total 2868 2107 269 78 26

Figure 4: Primary studies per year.

Based on the Fig.4, although limited, one can de-

note an increasing publication trend on the subject

from 2011 onward, demonstrating the growing in-

terest of the research community to the IoT-based

systems actuation conﬂict management problem.

We conducted our search process in November 2019

which can explain the low amount of papers published

for this year.

Answering RQ1.2: Fig. 5 depicts the distribution of

primary studies published over years and per venue

type. There is a constant number of publications dur-

ing the period 2014-2018. 24% are journal papers,

62% are conference papers and the remaining 14%

are workshop papers.

Answering RQ1.3: by focusing on authors’ afﬁlia-

tion, one can denote in Fig. 6 that most of the authors

of the primary studies are researchers (83%).

The involvement of industry in this research area

is still limited (3%). There are also joint research be-

Figure 5: Primary studies per venue type.

Figure 6: Primary studies per afﬁliation type.

tween academic and industry (14%). This underline

the raising importance of actuation conﬂict manage-

ment concern in industrial use cases, still at an aca-

demic research level. These results tend to conﬁrm

that IoT-based systems actuation conﬂict manage-

IoT-based Systems Actuation Conﬂicts Management Towards DevOps: A Systematic Mapping Study

231

ment problem is still in its infancy. The top most

countries are United States of America (8), Austria

(4), following by Canada, France and Japan (3). Re-

searchers from other countries are also involved in

this research for a total of 19 countries.

Answering RQ1.4: in terms of application areas, a

short predominance can be observed for Smart Home

relative use-cases (and, in the broad sense, smart-*

systems), ahead of the automotive use-cases. The ap-

plications speciﬁc to CPS as deﬁned in §2.2 will be

found under the terms Automotive, Smart Factory and

Robotics. Bottom-line, IoT-based systems actua-

tion conﬂict management problem affects all socio-

economical layers ranging from humans (Smart

Health), houses (Smart Home) to cities (Smart

City) and industry (Smart Factory).

Figure 7: Primary studies per domains.

4.2 Analysis towards the DevOps

Perspective

Answering RQ2.1: Direct and indirect conﬂicts are

taken into account and are well balanced (Table 6).

Papers on IoT deal with direct conﬂicts more than

indirect conﬂicts. This trend is reversed for papers

on CPS that are more focused on indirect conﬂicts.

By making interactions with the physical environment

explicit, it is expected to observe such a trend for CPS.

Table 6: Actuation conﬂicts types considered in studies.

IoT CPS Total

Direct 34.48% 13.79% 48.27%

Indirect 20.69% 31.03% 51.72%

Total 55.17% 44.82% 100%

Answering RQ2.2 & RQ2.3: The table 7 shows the

Dev/Ops dichotomy in the identiﬁcation of direct and

indirect conﬂicts. Whether at design time (Devs) and

operation (Ops), the analysis shows a well-balanced

Table 7: Actuation conﬂicts identiﬁcation.

@design @runtime Total

Direct 27.58% 20.69% 48.27%

Indirect 24.14% 27.58% 51.72%

Total 51.72% 48.27% 100%

handling to the identiﬁcation of direct and indirect

conﬂicts.

Concerning the resolution, the picture is not as

good (Table 8). Conﬂict resolution is far from being

addressed in all the papers, 20.69% of them discuss

actuation conﬂicts identiﬁcation without proposing a

solution to resolve them. On the other hand, 51.71%

of the papers assume manual actuation conﬂict reso-

lution. In addition to the ad-hoc nature that this type

of management implies, this raises also the question

of the scalability of the proposed approaches. In-

deed, the number of the possible combination of inter-

actions and their effects increase exponentially with

the number of interactions considered. Scalability is

considered a ﬁrst-class concern for only three papers

(S15, S19, S22).

Besides scalability, it is also worth noting that au-

tomation is at the heart of the DevOps approach by

allowing reactive and timely update of these systems

throughout their life cycle, all the more important as

IoT devices never stop providing feedback. Given

the large number of distributed actuators (and sen-

sors) likely to be involved, a non automated solution

is likely to fail in insuring these timely updates. In

this context, only 27% of the papers rely on a param-

eterized approach or synthesize a conﬂict manager to

resolve them, the latter type of approach representing

10% of the papers. These latter approaches therefore

seem to be the most relevant for addressing the scala-

bility and automation issues and deserve further work.

Table 8: Actuation conﬂicts resolution strategies.

None

Manual

Parameterized

Synthesis

Direct 6.89% 31.03% 3.45% 6.89%

Indirect 13.80% 20.68% 13.80% 3.44%

Total 20.69% 51.71% 17.25% 10.33%

Table 9: Actuation conﬂicts management maturity.

Theoretical

Use-case

In silico

In vitro

In vivo

Direct 3.45% 6.89% 17.24% 20.70% 0.00%

Indirect 0.00% 13.80% 31.03% 6.89% 0.00%

Total 3.45% 20.69% 48.27% 27.59% 0.00%

IoTBDS 2020 - 5th International Conference on Internet of Things, Big Data and Security

232

Table 10: List of the papers selected for the study (J: Journal, C: Conference, W: Workshop).

# Title Year v # Title Year v

1 Formal Veriﬁcation of Cyber-physical Fea-

ture Coordination with Minimalist Qualitative

Models

2019 J 16 Towards a model-based veriﬁcation method-

ology for Complex Swarm Systems

2016 C

2 IoTC2: A formal method approach for de-

tecting conﬂicts in large scale IoT systems

2019 C 17 An Application Conﬂict Detection and Reso-

lution System for Smart Homes

2015 W

3 A spatially aware policy conﬂict resolution

for information services

2018 J 18 Dependable control systems with Internet of

Things

2015 J

4 Automata-Based Generic Model for Interop-

erating Context-Aware Ad-Hoc Devices in

Internet of Things

2018 J 19 Safe Composition in Middleware for the In-

ternet of Things

2015 W

5 Context Aware Virtual Assistant with Case-

Based Conﬂict Resolution in Multi-User

Smart Home Environment

2018 C 20 Coordination of ECA rules by veriﬁcation

and control

2014 C

6 IotSan: Fortifying the safety of IoT systems 2018 C 21 DepSys: Dependency aware integration of

cyber-physical systems for smart homes

2014 C

7 Taming and optimizing feature interaction in

software-intensive automotive systems

2018 C 22 Distributed programming framework for fast

iterative optimization in networked cyber-

physical systems

2014 J

8 Continuous variable-speciﬁc resolutions of

feature interactions

2017 C 23 ECA rules for IoT environment: A case

study in safe design

2014 C

9 Event management for simultaneous ac-

tions in the Internet of Things

2017 C 24 Harnessing evolutionary computation to en-

able dynamically adaptive systems to man-

age uncertainty

2013 W

10 Modeling architectures of cyber-physical

systems

2017 C 25 Component-oriented Interoperation of Real-

time DEVS Engines

2011 C

11 Synchronization abstractions and separa-

tion of concerns as key aspects to the in-

teroperability in IoT

2017 C 26 Programming support for distributed opti-

mization and control in cyber-physical sys-

tems

2011 C

12 Towards Model Checking of Network Appli-

cations for IoT System Development

2017 J 27 Semantic Web-based policy interaction de-

tection method with rules in smart home for

detecting interactions among user policies

2011 J

13 Detection of Runtime Conﬂicts among Ser-

vices in Smart Cities

2016 C 28 Toward a programming model for safer per-

vasive spaces

2010 W

14 Minimalist qualitative models for model

checking cyber-physical feature coordina-

tion

2016 C 29 Feature interaction detection in the automo-

tive

2008 C

15 SPIRE: Scalable and Uniﬁed Platform for

Real World IoT Services with Feature Inter-

action

2016 C

Answering RQ2.4: We can see here the maturity

problem already mentioned in the research/industry

dichotomy (Fig. 6). In particular, none of the pro-

posed approaches for identifying and resolving actua-

tion conﬂicts have been validated in vivo. This obser-

vation is aggravated for indirect conﬂicts whose val-

idation does not go beyond the in silico stage. This

conﬁrms the difﬁculty of implementing the manage-

ment of indirect actuation conﬂicts, and therefore the

importance of evaluating them to reach the maturity

required for a transfer.

5 CONCLUSION

This SMS study highlighted recent work on the ac-

tuation conﬂict management in the ﬁeld of IoT-based

systems. More speciﬁcally, we have been interested

in studying their applicability into the DevOps ap-

proach. While direct/indirect conﬂicts identiﬁcation

methods applicability is well balanced from develop-

ment to execution stages, most of the current resolu-

tion methodologies lacks important properties to pre-

tend to their exploitation within the DevOps loop.

Indeed, most of them are not automated failing to

scale and ensure timely and reactive systems updates

throughout their life cycle (which is one of the main

reason for promoting the DevOps approach).

Furthermore, the maturity of the identiﬁcation and

resolution of actuation conﬂict does not go beyond in

vitro and in silico stages respectively. No experimen-

tation has been conducted in-vivo.

This study will need to be complemented by a

Systematic Literature Review (SLR) to analyse the

IoT-based Systems Actuation Conﬂicts Management Towards DevOps: A Systematic Mapping Study

233

different methods used for conﬂict identiﬁcation and

resolution.

ACKNOWLEDGMENTS

The research has received funding from the European

Commission’s H2020 Program under grant agreement

numbers 780351 (ENACT).

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