Engineering of Digital Innovation in Edge Computing and Industry 4.0:

An Experience Report

Michael Chima Ogbuachi

, Murad Huseynli

and Udo Bub

Faculty of Informatics, E

otv

os Lor

and University (ELTE), P

azmany P

eter s

any 1/C, H-1117 Budapest, Hungary

Keywords:

Information Systems, Innovation Engineering, Edge Computing, Industry 4.0, Design Science Research.

Abstract:

This paper illustrates the use of an innovative process model that represents a systematic and scientiﬁc ap-

proach, using well-established methodologies and techniques from the ﬁeld of Information Systems and

telecommunications. Organizations in the ﬁeld are pushing for innovation and proposing new technologies

and standards, but these proposals often have fundamental differences and tend to primarily target industrial

consumers, which have use cases that require reliable and stable systems and methodologies. Despite the

known potential beneﬁts of Edge Computing in industrial settings, there is still a lack of scientiﬁc rigor in

related research and development processes. We contribute to the ﬁeld by addressing this shortcoming, and

by providing a scientiﬁc evaluation and a tailored version of a pre-existing method, which allowed us to build

a practical solution that tackles the needs of industrial partners, while following a scientiﬁc approach. As a

result, we managed to build an innovative design for a large industrial company operating in Edge Computing,

while thoroughly assessing the progress from idea formulation to the complete solution.

1 INTRODUCTION

1.1 Problem Statement and Motivation

In recent years we have experienced an increasing

push for digital transformation in industry, and more

often the enabling technologies of this innovation are

rooted in the ﬁeld of Telecommunications. Indus-

try 4.0 (or the fourth industrial revolution) is more

than ever characterized by a strong reliance of “tra-

ditional” industries (e.g. manufacturing, logistics,

etc.) on the Internet of Things, which implies the

use of devices, network technologies and distributed

infrastructures to achieve automation through the ex-

change and processing of (often very) large amounts

of data, in a quasi-real-time manner. The major in-

dustry verticals that operate in the ﬁeld of telecom-

munication are not idle on the matter. In fact, they are

the major proponents of technologies and standard-

ization efforts that are meant to bring Cloud Com-

puting much closer to the users, in the paradigm

known as Edge Computing (and even further, with

IoT Edge). Such include companies that are mostly

https://orcid.org/0000-0002-3826-5499

https://orcid.org/0000-0001-5945-9420

https://orcid.org/0000-0002-6018-2411

operating as parts of large standardization consortia,

of the likes of GSMA, ETSI, 5GFF, and CAMARA,

which are organizations built to promote the standard-

ization and practical implementation(s) of Edge Com-

puting. Academia and governmental bodies are also

participating in the technical research, and a notable

example is represented by the OpenFog Consortium, a

worldwide ecosystem trying to push the creation and

adoption of Fog Computing platforms, architectures

and use cases (fog an “intermediate layer” between

cloud and edge) (OpenFog Consortium Architecture

Working Group et al., 2017). Some further examples

are listed here. The International Telecommunication

Union (ITU) coordinates operations and research at

a global scale, and has dedicated major efforts to the

design of standards for edge computing on 5G and

mobile networks (Gupta et al., 2016). The European

Telecommunications Standards Institute (ETSI) and

the Edge Computing Consortium (ECC), independent

non-proﬁt organizations, have been working on stan-

dardizing communication protocols, network infras-

tructures and interfaces at the application level for

edge computing, especially targeting use cases that

involve IoT, Industry 4.0, and smart cities (Khan et al.,

2019; Hamm et al., 2019). Notable mentions regard-

ing highly heterogeneous networks and Machine-to-

Machine(M2M) communications are the Open Net-

Ogbuachi, M., Huseynli, M. and Bub, U.

Engineering of Digital Innovation in Edge Computing and Industry 4.0: An Experience Report.

DOI: 10.5220/0011994300003467

In Proceedings of the 25th International Conference on Enterprise Information Systems (ICEIS 2023) - Volume 2, pages 211-218

ISBN: 978-989-758-648-4; ISSN: 2184-4992

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

211

working Foundation (ONF) and the Industrial Internet

Consortium (IIC). The ONF has worked on standards

for Software Deﬁned Networks (SDN) and Network

Function Virtualization (NFV), while the IIC worked

on advancing industrial human-computer interaction,

by promoting a large-scale adoption of interconnected

intelligent machines, and real-time analytics.

While they do share many of their fundamental

aspects, some of the standards proposed by these or-

ganizations often also come with fundamental differ-

ences, e.g. related to how the user data is handled,

or what type of integration should be realized with

the Virtual Network Functions (VNFs) that consti-

tute the 5G core network. Therefore independent re-

search groups are often proposing converging mod-

els (Yannuzzi et al., 2017). Most experimental use

cases are developed in controlled industrial environ-

ments, often with dedicated on-premise 5G network

setups, and target applications such as VR/AR, mul-

timedia streaming, geo-localized services for smart

cities and possibly Cellular Vehicle-to-Everything (C-

V2X) communication. In industrial settings, there has

always been a strong need for highly available and

reliable systems to perform tasks such as monitor-

ing, actuation and general infrastructure management.

Modern Edge Computing proposals offer the techno-

logical baseline (e.g., having the 4G LTE/5G network

as the core networking infrastructure) to allow this

sort of application, and also to cover use cases that can

be mission-critical. Nonetheless, there exists research

that is already deﬁning the foundations for 6G net-

works and Extreme Edge Computing infrastructures

(You et al., 2020). Moreover, as these advancements

happen quickly, they are often based on many parallel

efforts, based on a lot of trial and error, that end up be-

ing potentially very time-consuming and often redun-

dant (across enterprises, many organizations strive to

achieve the same things, in different manners). In

short, there are some methodical approaches in this

research ﬁeld, but hardly scientiﬁcally grounded ones.

1.2 Proposed Contributions

While working with a prominent telecommunications

company, we saw an opportunity to deﬁne an inno-

vative concept for industrial partners that were inter-

ested in a solution to automatically manage devices

that belong to their general infrastructure. The inno-

vative idea was to have a mechanism that would allow

machinery of any type and embodiment to connect to

the premise’s internal network and, in a secure man-

ner, perform an automated setup with minimal human

interaction. This would be a baseline for a technol-

ogy that provides a private cloud of industrial equip-

ment, in which management and operations could be

highly automated, as in a complement/substitute for

SCADA. An example use case would be for robots

that are meant to roam warehouses that need to be set

up, to perform monitoring and actuation tasks (e.g.,

moving around cargo, alerting in the occurrence of

accidents, ﬁres, etc.). The tasks would be deployed

as any cloud/edge-native application, and the devices

would know “by themselves” which computing in-

frastructure they should join. This innovative concept

for the physical infrastructure is what we called the

“Edge-native device”.

As mentioned in the previous subsection, though,

one of the main problems in this ﬁeld is the seem-

ingly widespread lack of scientiﬁc rigor in innovative

processes. This is a problem that has been discussed

since the 1990s, and is best explained by (David et al.,

1992) as the “problem of translation”, that afﬂicts es-

pecially the ﬁeld of research in Information Systems.

The issue can be summarized as the difﬁculty of trans-

forming an invention into real innovation (something

that brings beneﬁts to business and society), which of-

ten results in long periods of time spanning between

an idea proposal and its actual productization.

Therefore, we wanted to contribute in this regard,

by providing an experience report, showing that it

is possible to carry out an innovative effort in the

ﬁeld of Edge Computing for Industrial IoT (IIoT),

in a way that follows scientiﬁc rigor. The discipline

that seemed most suitable to fulﬁl this, is Design Sci-

ence Research (DSR). In particular, the approach we

followed is strongly based on the work described in

(Huseynli et al., 2022), which integrates the Stage-

gate process (Cooper, 1990) with foundational de-

sign theories, and follows the DSR process through

the three main stages Problem Identiﬁcation, Solu-

tion Design and Evaluation, as described in (Offer-

mann et al., 2009). Moreover, we practicalized the

knowledge extraction tasks of (Huseynli et al., 2022)

by using the main building block of the framework de-

scribed in (Ogbuachi et al., 2022), the Reduced Frag-

ment Descriptor (RFD).

2 LITERATURE REVIEW

In this section, we outline some of the literature

that represents state-of-the-art research on the mat-

ter of Digital Innovation. Some of these works pro-

pose frameworks and tools for innovation process

management, showing use cases for Digital Innova-

tion/Transformation.

(Nyl

en and Holmstr

om, 2015) propose a frame-

work for supporting digital innovation management

ICEIS 2023 - 25th International Conference on Enterprise Information Systems

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Idea

Improvement

IP protection /

Productization

Selection of IISF

Strategy

Reuse

Knowledge

Extraction

Innovation process

Stage-gated Process

G1 G2 G3 G4

Figure 1: Implementation of (Huseynli et al., 2022) for DI in Edge Computing.

as a solution for controlling and predicting emerging

product and service innovations in organizations. The

managerial framework that resulted from this research

encompasses the ﬁve areas “user experience, value

proposition, digital evolution scanning, skills, and im-

provisation”. Another notable contribution from the

paper is a diagnostic tool that assists ﬁrms in identi-

fying areas that need technological improvement and,

thus, shows possibilities to start applying digital in-

novation practices. Furthermore, (Wang, 2019) notes

that the environment in which digital innovations are

shaped can be intuitively called an “ecosystem”. The

author writes about the main similarities between so-

ciotechnical and natural ecosystems and suggests the

idea that digital innovation research could gain an ad-

vantage if the research took the ecological perspective

seriously by treating it as part of the theory. The paper

contributes to research in digital innovation by build-

ing a comprehensive theory – a multilevel framework

for digital innovation ecosystems which can allow

practitioners to ﬁgure out digital innovation prospects

in their organizations.

In addition, (H

aiki

o and Koivum

aki, 2016) build

a value generation process framework in order to im-

prove understanding and promote the formation of a

knowledge base for value creation and service inno-

vation within the digital service innovation process.

The framework is assessed by checking its applica-

bility in an actual networked retail service innovation

context. The study shows that in digital service in-

novation, multiple business, process, and information

technology-related factors have an impact on value

creation.

A notable study applied in industry is described in

(vom Brocke et al., 2017), which is about the jour-

ney that the company Hilti went through for its digital

transformation and innovation process. As a result of

a successful endeavour, Hilti became a globally in-

tegrated enterprise and ended up with greater opera-

tional and customer-service excellence by further re-

deﬁning business processes and the way their work

was performed. Hilti used several phases to trans-

form itself into a digital enterprise: ﬁrstly a digital

basis was established, and then an actual utilization

of the resulting digital potential followed. The au-

thors show that digital innovation needs a backbone,

putting it as a prerequisite for further development.

Moreover, they show that a strategy is a vital player

to direct, and encompasses “related actions, key ob-

jectives, and expected developments”.

Likewise, (Anderson et al., 2012) explore the re-

lationship between Innovation and DSR by focusing

particularly on Information Systems. They investi-

gate the synergies between the research streams of

two topics and concentrate essentially on how to iden-

tify the differences and common aspects. They then

perform a case study in Chevron where an innova-

tion process is implemented, and the ﬁndings from

this process show that key insights arising from DSR

guidelines (Hevner et al., 2004) can potentially im-

Engineering of Digital Innovation in Edge Computing and Industry 4.0: An Experience Report

213

prove innovation processes within organizations. The

paper identiﬁes ﬁve potential areas of DSR that can

beneﬁt innovation processes.

Similarly to the work outlined in these papers, we

also conducted innovation process management, but

with a focus on Edge Computing for Industry 4.0, ad-

dressing both scientiﬁc rigor and practical relevance.

3 METHODOLOGY

As explained in Section 1.2, a DSR process was

adopted from the planning to the design phase of

this innovation project. This followed through the ﬁ-

nal submission to the internal enterprise stakeholders,

who accepted to invest in the solution through a patent

ﬁling (Figure 1).

The strategy we adopted is based on the applica-

tion of two DSR tools: the Method for the Engineer-

ing of Digital Innovation proposed by (Huseynli et al.,

2022) for the constitution of the whole research and

development process, and the construct proposed by

(Ogbuachi et al., 2022) to aid the documentation of

the process steps. The RFDs were also used to aid part

of the knowledge extraction efforts from (Huseynli

et al., 2022).

Speciﬁcally, (Huseynli et al., 2022) presents a

method to guide enterprises through a process for

Digital Innovation, using Design Science Research.

The team(s) involved in the process should ﬁrst iden-

tify a problem and deﬁne the initial proposal for an

Idea, categorizing it also in terms of design range.

These design ranges were originally described in (Of-

fermann et al., 2011), and were applied with some

modiﬁcations in the Integrated Innovation Strategies

Framework (IISF) proposed in (Huseynli et al., 2021).

They deﬁne an initial conceptual focus of the innova-

tion project (or research) within Narrow-, Mid-, and

Wide-range innovation strategies, where the terms do

not identify a temporal scope, but rather a high-level

estimation of feasibility and efforts. The choice of a

target design range and a speciﬁc innovation strategy

allows to produce the ﬁrst output of this process: an

abstract that introduces the idea to stakeholders.

This abstract is one of the initial documents sub-

mitted at the “entry” of the stage-gated innovation

process and is considered in the evaluation that hap-

pens at Gate 1. The innovation process itself takes

strong inspiration from the original work of (Cooper,

1990), the Stage-gate process, and expands it by

adding phases based on the DSR process of (Offer-

mann et al., 2009) between each gate.

(Huseynli et al., 2022) deals with the process def-

inition at a high level, and it is up to the team to

decide how to implement each intermediate stage,

and produce the documentation required at each gate,

namely:

• Gate 1: Idea proposal (including design range, in-

novation strategy, and the corresponding abstract).

• Gate 2: Project Scheme

• Gate 3: Project Plan

• Gate 4: Results of the Agile project execution (e.g,

report of the fulﬁlled milestones)

In the case of this innovation process, to aid a team

that approached the method of (Huseynli et al., 2022)

for the ﬁrst time, we integrated the use of a Method

Engineering (ME) construct, the Method Fragment.

In particular, we decided to use the construct

proposed in (Ogbuachi et al., 2022), the Reduced

Fragment Descriptor (RFD). This is a representa-

tional tool meant to visually simplify the illustra-

tion of ISO/IEC 24744 method fragments (ISO/IEC

24744:2014, 2014; Henderson-Sellers et al., 2008),

by highlighting the key aspects of the proposed frag-

ment, to make them easily digestible also to people

that are more conversant with development, rather

than business. In addition to the information provided

by traditional Method Fragments, RFDs highlight se-

quentiality by the describing Input (requirements to

actuate the method described by the fragment) and

Output (results of the method fragment) in the vi-

sual representation (valid for both process and prod-

uct Fragments (Henderson-Sellers et al., 2008)). This

makes it so that RFDs can be used for the constitu-

tion of processes, where they act as “building blocks”

of temporal/functional sequences, similarly to the ap-

proach used in Business Process Modeling.

The next section will explain in detail how a prac-

tical implementation of the DSR process and the

method from (Huseynli et al., 2022) was performed

in this speciﬁc scenario. It will use the structure pro-

posed by (vom Brocke and Mendling, 2017) to de-

scribe the conducted innovation process and evaluate

the aforementioned method.

4 INNOVATION PROCESS

4.1 Situation Faced

Innovation Process. A notable telecommunication

company working on Edge Computing and Industrial

IoT solutions faced the problem of proposing a

standardizable method for the constitution of com-

putational clusters in private networks. The Edge

computing paradigm, as of now, does not target User

ICEIS 2023 - 25th International Conference on Enterprise Information Systems

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Equipment (UE) as direct computational nodes of

an Edge Computing infrastructure, but rather just

as the end-users of Edge-native services, which

produce/consume information either in the form

of streamed data or responses to/from pre-deﬁned

exposed services. In Industrial IoT (IIoT), needs

differ from those of the typical consumer, and it is

important that data is transmitted consistently, at high

rates, and in a manner that can allow for a trustworthy

and coherent representation of the status of the

equipment (resource observability and monitoring).

The connected devices, therefore, need to be active

providers of information that can help manage the

infrastructure that resides at the industrial premise.

Another “nice to have” feature of this research

stemmed from ideas such as (Kang et al., 2017;

Chen et al., 2018), where the operational costs for an

industrial partner would be decreased by enabling an

automated conﬁguration of the equipment, lifting part

of the setup work from human labour. This additional

feature was given the preliminary name of “cluster

autoconﬁguration”. The telecommunication company

wanted to make sure that all the devices capable of

connecting to the internal network infrastructure

of an industrial premise, having enough logical

hardware resources to perform extra computational

tasks, could join a cluster of devices. Said cluster

would be based on MicroK8s (a lightweight version

of Kubernetes for IoT devices) and managed by an

orchestrator node running the control plane, and the

devices would receive applications packaged as Ku-

bernetes “Pods”. The infrastructure would allow for

tasks such as telemetry reporting, task actuation and

occasionally performing unrelated computing tasks

(to make the devices useful while in their “idle” state).

Research Process. The major stakeholders in this

project were Experts and Heads of the Technology &

Innovation group within the company. These people

were particularly knowledgeable about trends in the

industry, being in direct contact with industrial part-

ners that rely on the services of their company to sat-

isfy speciﬁc networking needs. They had enough in-

sight not only to tell if the proposed idea was viable,

but also to estimate marketability in the short/mid-

term. Throughout the innovation process, they helped

keep the idea development realistic both in terms of

technology and expected efforts/resources.

We also observed that, though current edge com-

puting (and similar) standards do not specify a base-

line for the technologies to be used in the orchestra-

tion of services running on the edge infrastructure, big

industrial players such as Intel have been working on

implementations based on the production-grade or-

chestrator Kubernetes, to deploy not only the Edge

applications, but the virtualized Network Access In-

frastructure itself (Intel Smart Edge Open). By tar-

geting UE as the ﬁnal computing node, the company

wanted to push things even further, and contribute to

the state of the art with a solution that could be a pre-

cursor to highly distributed Edge applications in 6G.

4.2 Action Taken

Innovation Process. Our process followed the struc-

ture described in Figure 1. The ﬁrst step was to pro-

duce a Gate 1 proposal for the stakeholders. This pro-

posal would describe the general statements/claims

and show that the team had gathered enough back-

ground knowledge to justify the feasibility of the

idea and its practical relevance. These claims would

later be fundamental for the actual patent claims, in

case the idea and project went through successfully.

The experts that followed the team’s work provided

enough insight to help build a high-level view of the

requirements and a potential system design. After

successfully passing Gate 1, a similar strategy was

followed to satisfy Gate 2.

For this second gate, the level of detail required

by the stakeholders (who acted as “gatekeepers”) was

higher, leading to the drafting of a Project Scheme.

The role of this second stage is to ensure that the

team has a clear strategy for the rest of the DI process

and that the members know and are able to commu-

nicate the expected design outputs of the innovation

project. The output could be DSR artifacts, and one

or more selected (or entirely new) development pro-

cesses. The ﬁnal decision at this stage is also of the

“go/no go” type (as a “ﬁltering gate”), and the out-

come was successful.

At Gate 3 the expectation is a ﬁnalized project

plan, including an estimation of required resources for

the design proposal and prototypes, the deﬁnition of

a budget to satisfy such requirements, a stable deﬁni-

tion of the human resources (team members and idea

owners), and a project timeline (mostly based on the

quarterly meeting held in the company for the eval-

uation of patentable ideas). The plan included also

the ﬁrst architectural and functional deﬁnitions for a

more detailed design of the proposed system, in the

form of sequence, class, and components diagrams in

the Uniﬁed Modeling Language (UML). These dia-

grams outlined the base features of the system and a

clear deﬁnition of the characteristics of a successful

proof of concept.

Finally, at Gate 4 the stakeholders evaluated the

consistency of the submitted artifacts to the original

plan. The evaluation included the proof of concept

Engineering of Digital Innovation in Edge Computing and Industry 4.0: An Experience Report

215

(implementation of the main features of the system),

metrics for the satisfaction of the detailed project

plan and consistency with the initial proposal and the

suggested novelty/business relevance.

Research Process. The scientiﬁc contribution of Gate

1 was an assessment of the state-of-the-art in regard

to Multi-access Edge Computing (MEC) and the in-

dustrial needs for industrial distributed computing and

real-time operations. The assessment served as jus-

tiﬁcatory knowledge to motivate the proposal of our

innovation idea. The idea itself was then framed

through an IISF strategy, speciﬁcally as a mid-range

Exaptation of the type “Derive from”, and the corre-

sponding abstract was added to the documentation for

the stakeholders (Table 1, 2).

The phase preceding Gate 2 focused on the DSR

steps of Problem Identiﬁcation (following from phase

1) and Solution Design (at a relatively high level),

and the expected output was an artifact of the type

Method (Bucher and Winter, 2008). At this stage, the

idea was also “refreshed” and structured to accom-

modate the needs of two types of users: industrial

IT managers setting up on-premise clusters of com-

putational devices, and telecommunication operators

wanting to create future-proof hybrid clouds, hosting

the UE of their cell plan subscribers into their clusters

to create highly available and super-low-latency net-

works. Therefore, the design was split into two dif-

ferent “Facets”, the second design was framed into a

mid-range Exploitation of the type “Increase scope”,

and the corresponding abstract was added to the doc-

umentation (Table 1, 2).

Gate 3 required settling the team members and

their responsibilities in a timely manner. This meant

having a deﬁnition of a set of sequential milestones

and corresponding deadlines (following an Agile de-

sign paradigm). These milestones were deﬁned on

a per-feature basis. Another important aspect of

this phase was establishing contacts with “comple-

mentary teams”. These were the teams that could

provide speciﬁc information or even practical help

in terms of networking, security and development.

This last part was especially important to ﬁnd peo-

ple/resources from which the main innovation team

could gather knowledge about embedded systems

development, since the UE were expected to be

resource-constrained devices, based on hardware such

as ARM microcontrollers). During this step, the deci-

sion to rely on MicroK8s was ﬁnalized.

Finally, the work for the presentation at Gate 4 tar-

geted an implementation of the core functionalities

of the system, meaning the deﬁnition of user func-

tions, security (e.g., key exchange and storage), APIs

for automated network discovery, and the protocols

for automated UE evaluation and onboarding. This

part of the process was conducted through multiple

iterations of design, development and evaluation, typ-

ically meant to fulﬁl the milestones that were deﬁned

in the Project Plan. Gate 4 was concluded with the

ﬁnal evaluation, in which internal stakeholders were

present, together with the Heads of the Patent Devel-

opment unit at the company.

4.3 Results and Lessons Learned

This strategy brought several beneﬁts. Having a

stage-gated background allowed for keeping the

project in focus, especially during the deﬁnition of

the core functionalities, while also enabling a ﬂexible

development workﬂow, thanks to its compatibility

with the Agile lifecycle model (Cooper, 2014). This

proved particularly true for the innovation team in

the Project Plan and Project Execution phases, during

the deﬁnition of the structural and functional details

of the system. Following (Offermann et al., 2009;

Huseynli et al., 2022), the iterations and evaluations

in the Project Execution phase also allowed the ex-

traction from the development efforts of experiential

design knowledge of the prescriptive type (vom

Brocke et al., 2020), that could be stored and reused

for future projects or as a base for improvements.

This prescriptive knowledge could be stored as DSR

artifacts as well, namely in the form of method

fragments (using RFDs) and Design Principles.

5 CONCLUSIONS

A telecommunications company faced the challenge

of proposing a standardizable method for the forma-

tion of computational clusters in private networks for

Edge computing and industrial IoT solutions. The

company aimed to ensure that all devices capable

of connecting to the internal network infrastructure

of an industrial premise and having enough logical

hardware resources, could join a cluster of devices.

The cluster would be managed by a MicroK8s con-

trol plane-based orchestrator node and devices would

receive applications in the form of Kubernetes “Pods”

to perform workloads such as telemetry reporting and

task actuation. The research process involved key

stakeholders from the company’s Technology & In-

novation group, who were knowledgeable about the

direction of the edge computing industry and had in-

sight into the viability and marketability of the pro-

posed solution. As a result, the project was devel-

ICEIS 2023 - 25th International Conference on Enterprise Information Systems

216

Table 1: Application of the IISF Strategies (Huseynli et al., 2021).

Design Contributions Applied Innovation Strategy Implementation

Design of an infrastructure to support

direct utilization of UE in an indus-

trial Edge Computing infrastructure

Exaptation: Derive from Derive core characteristics of Multi-

access Edge Computing and apply

them to Industry-speciﬁc use cases,

to innovate IIoT infrastructures with

novel, hardened networking capabili-

ties

Extension of the proposed design to

support Telco operators in creating

highly dynamic and highly reliable

clusters of UE

Exploitation: Increase scope Take the proposed design and im-

prove it in terms of security, scalabil-

ity (e.g., federation of control planes)

and infrastructure management , for

potential massive IoT Edge use cases.

Table 2: Innovation strategies and corresponding abstracts.

Chosen Innovation Strategy Corresponding Abstract

Exaptation: Derive from In the ﬁeld of distributed systems, the idea of Edge Com-

puting proposes solutions to the centrality of modern

cloud computing, which still relies heavily on massive

computing infrastructure handled by Hyper-scale cloud

providers. This can cause issues related to security (e.g.,

data ownership and distribution) and performance (e.g.,

latency that can be too large for sensitive industrial ap-

plications). Based on these issues, we developed a new

solution for the IoT Edge, in form of a mid-range design:

an infrastructure scaling model that can facilitate control

and expansion of industrial computing and actuation fa-

cilities.

Exploitation: Increase scope In the ﬁeld of Multi-access Edge Computing, Edge in-

frastructure is meant to bring the computing power much

closer to the User Equipment, to decrease latency and the

load on the Hyperscale providers. This design though

doesn’t allow the provider to ofﬂoad computation. In

this paper, we propose a model inspired by Extreme and

IoT Edge, in which the UE can participate in the compu-

tational power of the Edge cloud, under the supervision

of the network provider.

oped within the expected time limits, and passed all

the stages of the gated process, resulting in the com-

pany investing in a patent ﬁling. Future work on

this topic will include further development and testing

of the proposed solution and/or extended method in

other real-world industrial environments. This could

involve conducting pilot studies with industrial part-

ners to gather feedback on the solution’s performance

and identify any areas of improvement. Additionally,

research could be done to evaluate the scalability and

robustness of the solution proposed here in large-scale

deployments and investigate potential security risks

and develop mitigation strategies. Another direction

could be to investigate the integration of other tech-

nologies, such as Artiﬁcial Intelligence, to enhance

the solution’s functionality. Additionally, it would be

important to evaluate the potential cost savings and

efﬁciency improvements that can be achieved by us-

ing this solution in diverse industrial environments.

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