An Enterprise Architecture-Based Approach Towards More Agile

and Resilient Command and Control

Ovidiu Noran

and Peter Bernus

IIIS Centre for Enterprise Architecture Research and Management, Griffith University, Brisbane, Australia

Keywords: Defence, Command and Control, Resilience, Architecture Frameworks, Enterprise Architecture, Power and

Energy, Autonomous Hybrid Power and Energy Intelligent Mobile Module, Hybrid Intelligent Tactical

Microgrid.

Abstract: Challenges to the global power balance leading to the possibility of peer warfare and technological advances

enabling autonomous and swarming-capable vehicles performing increasingly complex operations in

contested environments have prompted a stringent need towards more agile and resilient Defence doctrines.

On the other hand, Disaster Response efforts also require a more versatile and robust Command and Control

(C2) approach in the context of increasing intensity and frequency of natural disasters triggered by climate

change. The efforts towards addressing these C2 challenges typically consider the required aspects in

isolation, although more often than not they are closely related and as such, changes to one C2 aspect may

have unintended effects on the others. Therefore, a holistic approach is required considering the overall effects

of the envisaged transformation, so as to maintain the consistency of the C2 evolution effort. This paper

proposes such an integrated method that employs Enterprise Architecture modelling artefacts facilitating an

overarching approach towards more agile and resilient C2 evolution. A case study is employed to illustrate

the concepts proposed and further analyse the relation between the changed warfare and disaster response

paradigms and more agile and resilient C2 approaches.

1 INTRODUCTION

The world is changing at an accelerating pace. World

powers attempting to change the global post-war

order and impose a model similar to their forms of

government (Adler et al., 2023) bring the prospect of

near-peer warfare. Ever more present unconventional

warfare (Kilcullen, 2019) is increasingly using AI-

enabled robotic and autonomous systems displaying

self-organisation and swarming behaviours often

surpassing their manned counterparts. On the civilian

side, the increasing intensity and frequency of natural

calamities owing to climate change also requires a

shift in strategy so as to ensure continued effective

disaster response and management.

As there is a similarity between conditions in a

contested military operation environment and

unstable, rapidly changing situations during a natural

disaster, a synergy between the two domains emerges.

Thus, both of the above-mentioned domains need

https://orcid.org/0000-0002-2135-8533

https://orcid.org/0000-0001-5371-8743

more agile and resilient Command and Control (C2)

approaches (Australian Defence Force, 2020), also

considering changed human computer interaction,

trust and risk aspects. Beyond mere automation,

existing processes must be actively redesigned in

view of the new approaches (Pilling, 2015).

Current attempts at tackling this challenge appear

to look at these aspects in isolation, although they are

typically related and as such changes in one area may

produce unintended ripple effects in others. This

paper proposes an integrated approach that aims to

preserve the integrity of the required C2 evolution

effort, using Enterprise Architecture artefacts.

The rest of the paper is structured as follows: a brief

description of main C2 concepts and challenges is

refined into concrete lines of action towards C2

evolution. The EA artefacts and principles to be used

are introduced next, followed by the presentation of

the case study and definition of a novel concept that

requires evolved C2 approaches. The outlined C2

622

Noran, O. and Bernus, P.

An Enterprise Architecture-Based Approach Towards More Agile and Resilient Command and Control.

DOI: 10.5220/0012443700003690

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024) - Volume 2, pages 622-633

ISBN: 978-989-758-692-7; ISSN: 2184-4992

lines of action are then employed for the case study,

using the proposed EA approach. The paper closes

with conclusions and proposed further work.

2 STATE OF THE ART

The military domain has defined the term ‘Command

and control’ as the application of […] direction by a

suitably designated authority over assigned resources

with the purpose of accomplishing a common goal

(Stanton, Baber, & Harris, 2018). Command is

considered more high level, communicating intent,

while control is typically specifying more detail in

how that intent is to be accomplished. There is an

extensive body of knowledge in relation to C2, which

has been reviewed in synthesizing some of the most

important C2 challenges in view of the changed

nature of conflict and disaster response.

2.1 Defence HQ C2 - Issues

Faced with the significant challenges described in the

Introduction, an evolved Defence HQ (currently

designated as ‘5

Generation’ (Yue, Kalloniatis, &

Kohn, 2016)) should be agile - i.e., be able to

promptly adopt suitable organisational structures

matching the complexity of-, and dealing with a set

of plausible scenarios (Ashby, 1958; Mintzberg,

1979). As such, extensive research has been

performed into the agility of Defence HQ and

especially C2. NATO’s C2 Agility report (NATO,

2014a) features prominently in this specific body of

research, outlining ‘Approach archetypes’ tied to the

levels of a well-defined ‘NATO Network Enabled

Capability C2 Maturity Model’ (N2C2M2) (NATO

RTO, 2010). The approaches are represented as areas

within a system of tri-dimensional axes comprising

levels of information distribution, patterns of

interaction and allocation of decision rights

respectively. The approaches deemed to perform the

best in the presence of uncertainty typical in rapidly

changing and contested environments are those

featuring the higher values along this axis system, i.e.

the C2 ‘Collaborative’ and ‘Edge’ approaches.

The N2C2M2 model has been tied to C2 agility

(Moffat, Huber, & Alberts, 2012) and has also been

validated in several real situations (Farrell et al.,

2013); however, traditional reluctance and resulting

slow progress present in the military towards

adopting the more inclusive and collaborative

approaches depicted in the model (Meddings, 2020)

significantly affect current efforts towards more

Agile C2. Moreover, it is highly likely that several C2

approaches may need to coexist at the same time

within Joint Forces (Alberts & Conley, 2015; NATO,

2014b) often featuring heterogeneous participants

such as international (Allied) Forces, Defence Force

components (Army, Navy, Air Force), or Army units

interacting with civilian organisations (e.g. in case of

disaster response and relief (Ries, 2022)), which adds

to the complexity of the situation.

As a result, there is a need to acquire preparedness

towards a) adopting each of these C2 approaches but

also towards b) the ability to change the C2 approach

following changed internal and/or external

conditions. The chosen C2 approach must match the

situation and be underpinned by adequate information

where and when required, collected using appropriate

communication and interaction (Vassiliou & Alberts,

2013). Note that the apparently simplest solution of

going directly for the most agile C2 approach rather

than building capability to switch on demand is

typically not the best solution as it is neither trivial

nor inexpensive to achieve (NATO, 2014a). Thus, a

highly agile C2 approach requires organisational

preparedness involving a substantial degree of trust

featuring highly distributed decision rights,

information sharing and the existence of suitable

policies and processes (Vassiliou, 2010) supporting

adequate interaction patterns; importantly, it also

carries an increased amount of risk (NATO, 2014a).

Hence, the best strategy is to match the C2 approach

agility to the situation and be prepared to switch as

required.

The impending perspective of near-peer warfare

situations requires enhanced control so as to maintain

initiative in a complex operations environment

(Systematic, 2023). In addition, the firmer

establishment and evolution of unconventional

warfare as seen in recent and current conflicts

requires doctrine evaluation, update and if necessary,

even discontinuation (Kilcullen, 2019).

Another current C2-related issue is the need to

separate Control from Command in order to allow for

proper AI involvement (Alberts & Conley, 2015), as

AI is intended to be involved mainly in the Control

part. To accomplish this task, one needs a tool that

allows clear modelling of the boundaries between

Command, Control and Execution.

The increasingly automated and autonomous

systems’ capabilities also require new suitable C2

approaches (ibid.) which will not arise naturally but

need to be deliberately designed and adopted.

Swarming is an increasingly important aspect of

the autonomous paradigm involving a group of

simply-behaving entities that together achieve

desired results or behaviour (Bürkle, Segor, &

An Enterprise Architecture-Based Approach Towards More Agile and Resilient Command and Control

623

Kollmann, 2011). Despite noise in the environment,

errors in processing information and performing

tasks, and a lack of a global communication system

(highly likely in an anti-access (A2) or area denial

(AD) situation), swarm-capable assets remain

efficient at performing group-level tasks, which is

paramount in successful execution of missions. Thus,

the swarming paradigm needs to be considered as

well in the C2 evolution effort.

The Science, Technology and Research (STaR)

Shots Agile Joint C2 program (Defence Science and

Technology Group, 2020) defines additional facets of

C2 agility that have to be addressed, which, together

with the other above-mentioned aspects, have been

compiled in Table 1. Note that, in the table, ‘sub-

issue’ indicates a specific area of the issue and ‘scope’

designates the context / area of application.

Table 1: Compiled C2 Issues.

It is hypothesised that these aspects typically

influence each other and therefore, should be

analysed in an integrated manner.

2.2 Lines of Action (LOAs) Towards

More Agile and Resilient C2

The previously identified issues can be used towards

synthesizing several more concise Lines of Action

(LoA) in regards to C2 evolution.

1. Support an improved C2 manoeuvre agility

combining the use of an Extended OODA (Observe,

Orient Decide and Act) paradigm (Meddings, 2020),

the NATO NEC C2 maturity model (N2C2M2,

(NATO, 2014a)), the Cynefin complex situation

decision-making framework (Snowden & Boone,

2007) and suitable trust and risk models. This will

facilitate understanding the factors and requirements

involved in achieving and maintaining a suitable C2

manoeuvre agility as part of an iterative process.

2. Evolve C2 towards supporting a harmonious co-

existence of several C2 agility approaches and

interoperability (Alberts & Conley, 2015) as required

e.g. in Joint and Disaster Relief operations (Ries,

2022).

3. Clearly delineate Command from Control in order

to make use of significant recent AI developments

(Alberts & Conley, 2015) and to represent the

paradigm change required to deal with peer warfare

involving A2 or AD (Russell, 2017) situations,

typically encountered in contested environments

featuring high uncertainty.

4. Clarify how C2 must adapt to the new paradigms

of Autonomy and Swarming (Campion, Ranganathan,

& Faruque, 2019; Madey & Madey, 2013). Due to

technological advances, intelligent multiagent

systems play an increasing role in military and

disaster relief operations, both as an advantage and as

a risk (McLennan-Smith & Adams, 2023).

5. Promote C2 Resilience via Distributed Control.

The A2 / AD situations likely in a near-peer warfare

situation or an ongoing natural disaster event imply a

degraded network environment (Farrell et al., 2013)

typically resulting in confusion and isolation. In this

context, C2 resilience becomes paramount in order to

execute the full required range of military or disaster

relief operations (Hostage & Broadwell, 2014). In

addition, the current Centralised Control paradigm

needs to be evolved to a Distributed Control pattern

(ibid.) so as to control the increasing complexity and

required bandwidth of modern warfare. Uncertainty

and high dynamics require representing current and

emerging resilience needs, to enable adequate design.

6. Assist C2 Evolution. Explicitly represent the

transition to the new C2 approaches and artefacts

required for the new warfare and disaster

management paradigms, importantly including

temporary co-existence of legacy- and new solutions.

3 ENTERPRISE ARCHITECTURE

The increasing complexity encountered in the C2

domain in the context of peer, asymmetrical and

accelerated warfare can be tackled by dividing and

organising the relevant concepts according to various

Issue Sub-issue Scope

Approach

Approach Switch

Coexistence

Human-machine

teaming

Human Computer

Interaction

Artificial Intelligence

Autonomy Swarming

Real time mission

simulation

Information

Visualisation

Augmented Reality

Virtual Assistants

Resilience Swarming

AA / A2

environment

Organisational

Structures

Organisational Cultures HQ Agility

C2 Architectures Agility Warfighter

Data Analytics

Large and Diverse Data

Sets

Information

Human

Social

Cultural

Technical

Enable rapid

execution of

command intent

Situational Awareness

and Sense - Making

Innovative Systems

OverallAgility

Decision making

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suitable criteria. This endeavour can be assisted by

using a framework that will ideally make use of a

metamodel supporting the integrity and consistency

of this taxonomy. Within such a framework, the

above-mentioned criteria would constitute viewpoints

reflecting stakeholder group concerns.

This paper proposes adopting this approach for

the C2 Agility endeavour, by using an artefact

originating in the domain of Enterprise Architecture,

namely ISO15704:2019 Annex A: The Generalised

Enterprise Architecture and Methodology (GERAM)

(ISO/IEC, 2019). GERAM has been chosen because

it is an overarching Enterprise Architecture

Framework (EAF) that synthesizes the elements of

several other mainstream EAFs. In addition, GERAM

is an established and proven concept, having been

used in several projects in domains including Disaster

Management (Noran & Bernus, 2011) and C2 Agility

(Noran, 2023).

The Reference Architecture component of

GERAM, namely GERA, contains a Modelling

Framework (MF) comprising a set of viewpoints

which can be used to produce a ‘shopping list’ of

required aspects for a specific modelling endeavour,

while maintaining the consistency of the resulting

models. By tying the specific aspects into a shared

underlying metamodel, the proposed MF supports a

common stakeholder understanding of the present,

future and necessary transition steps, which are

essential in the current C2 endeavour. The GERA MF

is represented in Figure 1, which also shows an

example of how to create a modelling construct by

selecting a set of viewpoints appropriate to a specific

task.

The metamodel and ontology underpinning the

C2 modelling effort can be expressed using the

Generic Instantiation level, while the contents and

suitability of candidate templates and relevant

standards can be represented using the Partial model

level (see Figure 1 top). The life cycle concept

(deemed essential here as a context for managing the

C2 agility aspects) is represented through the vertical

dimension of the GERA MF.

Note that, although the life cycle aspect in the GERA

MF does not include time, GERA itself does include

a temporal aspect in the form of life history, which

can be used to follow the relevant entities along their

evolution (see Section 4.3.6 for an example).

The GERA MF can represent the extents of agility

and resilience for the entities involved along their

entire lives and how they relate to- and may influence

the other entities’ agility and resilience. This in turn

shapes their desired co-existence (see Section 4.3 for

a concrete example).

Figure 1: GERA MF and the example creation of a

modelling construct for dynamic business models.

In addition, standards employed in this area (see e.g.

STANAG 4603 (NATO, 2015)) able to support

essential aspects such as federated interoperability in

the IoT context (Brink, Vasilache, Wrona, & Suri,

2022; Manso et al., 2022) can also be represented by

the chosen GERA MF using the Partial model level.

4 EA ARTEFACTS APPLICATION

TO THE C2 LOA: CASE STUDY

The manner in which EA artefacts can assist the C2

LoAs is exemplified below within a case study that is

relevant to Defence and Disaster Response but also

applies to other situations involving uncertainty and

high dynamics.

Management

and Control

Cust Service

Entity

Simplify

Human

Machine

Resource

Information

Function

Particular level

Hardware

Software

Design

m. design

ed design

ntification

Concept

entation

peration

mission

uirements

Organisation

Management

and Control

Product or Service

Human

Machine

Resource

Organisation

Information

Function

Generic

Partial

Particular

Hardware

Software

LC phases

Views

Instantiation

Design

Prelim. design

Detailed design

Identification

Concept

Implementation

Operation

Decommission

Requirements

Management

and Control

Product or Service

Human

Machine

Resource

Organisation

Information

Function

Generic

Partial

Particular

Hardware

Software

LC phases

Views

Instantiation

Design

Prelim. design

Detailed design

Identification

Concept

Implementation

Operation

Decommission

Requirements

MF of GERA

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4.1 Setting

Continuous and adequate Power and Energy (P&E)

availability is essential in order to complete missions

(NAVFAC, 2016) whether military, or for disaster

relief. This aspect involves three main requirements:

reliability, resilience and efficiency (NAVFAC,

2021). Among these, resilience is especially

important as P&E infrastructure is typically subjected

to disruptions, either to starve the opponent of energy

in a conflict (Samaras, Nuttall, & Bazilian, 2019) or

as an after-effect of natural disasters.

There are several solutions proposed to this

problem, usually employing fixed or portable backup

power generators of various types. Such solutions

have limitations in terms of mobility, prompt

assembly into grids and subsequent dispersal, human

operator presence requirement and typical mismatch

between the power delivered and required (Matthews,

2015). These shortcomings become paramount in the

context of near-peer, accelerated and other

unconventional types of warfare.

Various concepts of mobile and intelligent

microgrid type P&E delivery models are being

investigated as possible answers to the above

problems in the relevant literature (Matthews, 2015;

Roza, 2023; Wood, 2020). However, it appears that

currently there is no solution put forward that

integrates all these concepts. Moreover, it also

appears that an important aspect of this situation,

namely how existing C2 approaches are to be evolved

so as to adequately cope with the proposed P&E

models, is not tackled.

Within the case study, this paper aims to answer

these issues by advocating a potential integrated

solution and subsequently investigating how an EA-

focused approach may assist in evolving C2 to deal

with such a situation.

4.2 Scenario

The situation proposed to test the concepts involves

an accelerated warfare situation or conversely, a

rapidly changing natural disaster situation, where the

environment is either contested or unstable (e.g. due

to cascading disaster events); these circumstances call

for agile C2 and prompt execution. Thus, as per

previous Section, there is a need for adequate and

resilient P&E delivery solution in order to execute the

necessary missions.

4.2.1 Concepts Used for the Scenario

In the scenario, a monolithic and predominantly

stationary P&E delivery infrastructure type is not an

ideal solution due to its low defensibility and

resilience within an A2 / AD situation. Rather, these

constraints would be better met by the ‘small tactical

microgrid’ (Matthews, 2015) or nanogrid concepts,

depending upon the desired granularity (Hamidieh &

Ghassemi, 2022; NAVFAC, 2016; Peterson, Van

Bossuyt, Giachetti, & Oriti, 2021; Varley, Van

Bossuyt, & Pollman, 2022). In addition, a fully

mobile solution has to be adopted, such as e.g. the

Vehicle Centric Microgrid (VCM) (Heuvers, 2019),

whether fully autonomous or partly manned (Juling,

2023). This will enable swift relocation in case of

impending danger, or sudden change in environment

adversely affecting proper operation.

To further improve defensibility and resilience,

the P&E infrastructure should ideally be distributed

and able to rapidly form – in other words, to promptly

be assembled, deliver P&E and disperse upon mission

completion, or in the presence of a threat of incoming

attack. In addition, the P&E components should be

modular and independent of the transport solution

(i.e. vehicle type) and thus able to be readily

integrated into a scalable P&E delivery solution.

In terms of matching the P&E delivery with the

load, for moderate variations, an elected leader model

used within the microgrid may activate more or less

resources to match demand (Jane, Goldsmith, Parker,

Weaver, & Rizzo, 2021). If the variations exceed the

self-adjustment capabilities of the microgrid, a

possible solution would involve networking (Chen,

Wang, Lu, Chen, & Zhao, 2021), in this particular

case with other relocatable VCMs. The above-

mentioned modularity and networking requirements

require adequate interoperability (Bower et al., 2014),

which must also be present in order to support Joint

Operations for military and disaster relief missions.

AI would greatly assist in the efficient integration

and management of microgrids (Talaat, Elkholy,

Alblawi, & Said, 2023) as well as in the

reconfigurations required by load variation or by

some components failing or being damaged.

Another significant enhancement in regards to

mobility is represented by swarming. Previously

analysed in the context of electrification (Sheridan,

Sunderland, & Courtney, 2023), swarming would

also assist e.g. in the load management (Singh, Ding,

Raju, Raghav, & Kumar, 2022) and potential

reconfigurations in the proposed scenario.

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4.2.2 Proposed Integrated Concept

In response to the above, the paper proposes the

concept of ‘Autonomous Hybrid Power and Energy

Intelligent Mobile Modules’ (AHPEIMMs), able to

form Hybrid Intelligent Tactical Mobile Microgrids

(HITMMs, see Figure 2). This represents a coherent

and integrated answer to the above-mentioned

separate requirements and solutions. The proposed

modules would be autonomous, mobile, easy to

deploy, field rugged intelligent machines able to

relocate, swarm and interconnect where and when

required and seamlessly scale delivery to the demand.

There are two main questions that arise in regards

to the scenario and the proposed concept:

1. How do the above identified C2 LoAs apply?

2. How can EA artefacts assist this endeavour?

Figure 2: Autonomous Hybrid Power and Energy

Intelligent Mobile Module (AHPEIMM) concept enabling

the Hybrid Tactical Intelligent Mobile Microgrid (HTIMM)

model.

4.3 C2 LoA Application to the Scenario

4.3.1 Support Better C2 Manoeuvre Agility

C2 manoeuvre agility expresses the capability of

moving between various C2 approaches featuring

various degrees of agility, as required by the actual

situation. This is expressed for the scenario in the

model described in Figure 3. The approach shown

makes use of the Extended OODA paradigm

(Meddings, 2020) whereby the Command Staff assist

the Commander in observing the situation so as to

provide a shallower, albeit wider perspective of the

circumstances, which may yield additional

information and situation awareness. In the chosen

scenario, this means observing how well the current

C2 approach works for the selected P&E solution and

if required, choose a more suitable C2 approach from

the Reference Models (RM) repository, which among

others contains the NATO NEC C2 maturity model

(N2C2M2) C2 approaches. The interoperability

required with each C2 approach can be selected and

customised from the RM as well, as it also contains

various applicable NATO standards such as e.g.

STANAG 4603 (NATO, 2015); this is highly relevant

for the AHPEIMM and HTIMM concepts involved in

the scenario, as they rely on ‘day zero’ (ready to go

from first day) federated interoperability (NATO,

2023a). In this regard, the degree of autonomy of

entities such as Operation, Mission or AHPEIMMs is

reflected in the degree of detail specified in the RM

used: the more autonomous the entity, the less

specific the RM would be (see Figure 4 in Section

4.3.2 for an example).

The EA MF viewpoints such as Function,

Information, Resource and Organisation (see Figure

1, top) can also help explain in more detail the

requirements of each N2C2M2 tier. For example,

Edge C2 requires wide Information Sharing

(Information viewpoint) and Delegation of Decisions

with a matching Interaction pattern (with the last two

requirements expressed in the Function viewpoint).

The Cynefin complex situation decision-making

framework (Snowden & Boone, 2007) is used to

classify the specific situation and guide an

appropriate degree of C2 agility in the model of the

C2 Universe of Discourse (‘Endeavour Space’ in

Figure 3). In the most difficult Cynefin areas, namely

Complex and Chaotic, the only feasible approach is

to Probe / Act, Sense and Respond, a cyclic approach

akin to OODA. This process may also result in new

RMs being created and added to the Repository, with

the EA MF helping structure and classify them.

Finally, the Confusion area of Cynefin may be

resolved into one of the other areas by sense-making,

with appropriate modelling provided by the GERA

MF viewpoints.

The C2 Approach RMs repository can be also

classified by levels of trust applied to individual team

members and the networked collective (Evans,

Cianciolo, Hunter, & Pierce, 2010), whether human,

machine or hybrid, including autonomous types

(Abbass, Scholz, & Reid, 2018) . The risk aspect of

the adopted C2 approach also needs to be explored

and included in the respective RMs. The above can be

accomplished by adding Trust and Risk viewpoints to

the EA MF, which will provide the life cycle context.

Artificial

Intelli-

gence

Nanogrid

Defensible

Distributed

Autono-

mous

Modular

Scalable

Mobile

Networked

Agency,

Independence,

rationality

Integratable

Vehicle type-

independent

Self-Evolving

Rapid

Forming and

Dissolution

Interoperable

(Federated)

Hybrid

Tacti ca l

Intelligent

Mobile

Microgrid

AHPEIMM

An Enterprise Architecture-Based Approach Towards More Agile and Resilient Command and Control

627

Figure 3: A multi-pronged iterative method for C2 Approach Agility (Meddings, 2020; NATO, 2014a; Noran, 2023).

Policies and procedures that must underpin a more

decentralised C2 and, if necessary, overcome mid-

level institutional cultural resistance (Vassiliou,

2010) also need to be considered. GERA MF can

model such artefacts via its Organisation and

Function viewpoints.

The iterative method proposed above can guide

the selection of a more suitable C2 approach, which

will be then used in practice via the Missions created

in the Real C2 Endeavour Space.

4.3.2 Evolve C2 Towards Supporting the

Co-Existence of Several C2

Approaches and Interoperability

Joint and disaster response operations typically

involve heterogeneous organisations (or departments

thereof), each featuring C2 approaches with various

levels of agility. The harmonious co-existence of

these approaches is paramount in enabling effective

joint operation. In order to achieve this co-existence

however, one must first understand the C2 style of

each participating entity. For example, in the case

study the Civilian Organisation (CO) has a less agile

C2 style compared to the participating DFs. This is

shown in Figure 4 by the arrows going back to their

upper life cycles to a lesser (CO) or more (Joint DF)

extent. For more detail, one may represent the

components of the C2 Approach Spaces involved in

regards to their Decision Rights allocation and

Interaction patterns as well as Information Sharing

using GERA MF’s Function and Information

viewpoints, respectively.

In the scenario, the co-existence of legacy P&E

delivery solutions with the proposed AHPEIMM

concept within an evolutionary approach requires

modelling the C2 agility extent of both alternatives.

This is done in Figure 4 in a combined AS-IS / TO-

BE (present / envisaged future) representation due to

the GERA MF atemporal approach; a time-bound

perspective of this evolution is represented using the

GERA Life History in Section 4.3.6. The figure also

shows the differences between the autonomy and

agility of proposed (AHPEIMM) vs. legacy P&E

delivery (LPED) solutions and the corresponding

restricted vs. extended influence on their life cycle

phases, respectively by the Operation C2.

The interoperability aspect of AHPEIMMs is

assisted here by specific reference models such as

STANAG 4603 (Technical Interoperability), or

NATO’s Generic Vehicle Architecture (NGVA

(NATO, 2023b)) and Reference Mobility Model

Development (NATO, 2018). These can be integrated

in the representation using GERA MF’s Partial Model

level of the framework (see Figure 1, top). In Figure

4., they are represented as part of the C2 Reference

Models (C2RM) used in setting up the Operation and

further on, creating the proposed (AHPEIMM,

HTIMM) and legacy (LPED) P&E delivery solutions.

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Figure 4: Co-Existence of various C2 Approaches and P&E solutions (AS-IS and TO-BE combined representation).

4.3.3 Distinguish Command from Control

Due to the complexity and workload involved, there

is a high C2 risk in micro-managing humans and

especially autonomous systems such as the proposed

AHPEIMM, which are to be numerous; hence, there

is a need to shift from Centralised- to Distributed

Control, which will also enable a more agile C2

(NATO, 2014a); the AI involvement in AHPEIMM

(see Figure 2) is also typically intended for the C2

Control aspect. In order to achieve the above goals,

there is a need to separate the Command and Control

components of C2. From the EA point of view, the

GERA MF addresses C2 in an amalgamated fashion

through the Management (termed ‘Command’ in C2)

and Control, vs. Production / Execution

classification, as shown in the construct in Figure 1

and subsequently used in Figure 4. However, an

additional division can be created in the modelling

construct, such as shown in Figure 5 for the

Operation entity (See Command ‘Cd’ and Control

‘Ct’); this allows to represent how a significant part

of the Control is relinquished when using autonomous

and intelligent entities such as AHPEIMMs, as

opposed to legacy P&E delivery (for rationale see

Section 4.3.4).

Figure 5: GERA MF Command / Control Separation and

Autonomy and Swarming effect on C2 influence extent.

If necessary, additional C2 detail can be modelled

using the GERA MF using the Functional viewpoint;

in addition, C2 can also be decomposed using the

level of detail they convey: thus, high level decisions

Mission

Joint

DFHQ

Operation

HQJOC

HTIMM

MCD

C2RM

Life cycle phases: Id: Identification; C=concept; R=requirements, PD=prelim. design; DD=detailed design,

I=implementation, Op=operation, D=decommissioning; Others: E=Execution, C2=Command and Control

: Agility extent - high

Legend:

DF: Defence Forces

DFHQ: DF Headquarters

CO: Civilian Organisation

C2RM: C2 Reference Models

PF: Projected Force

HQJOC: Headquarters Joint Op

Ctrl

LPED Legacy P&E Delivery

HTIMM: Hybrid Tactical

Intelligent Mobile

Microgrid

AHPEIMM: Autonomous Hybrid

Power and Energy

Intelligent Mobile

Module

MC: Mission Capabilities

MCD: Mission Command

: alternative Agility extent

: Agility extent - low

Trust,

Risk,

Policies

: use of Ref. Models

AHPEIMM

LPED

Operation

AHPEIMM

LPED

Legend:

C2 = Command and Control, Cd = Command

Ct = Control

= Command Influence

= Control Influence

= C2 Influence (combined)

= Agility Extent – high

= Agility Extent - low

An Enterprise Architecture-Based Approach Towards More Agile and Resilient Command and Control

629

(where the implementation detail is left to the lower

tiers) are Commands, while lower level decisions

(where all details are specified) are akin to Controls.

4.3.4 Autonomy and Swarming Effect on C2

In the case study, autonomy and swarming allow

AHPEIMM to self-organise in order to cope with load

variations and to congregate into HITMMs when and

where there is a need for P&E delivery.

The research in swarming is ongoing; however, it

has been established that careful selection of the C2

decision intent and proper adjustment of the Control

extent can give a significant benefit when at a Force

disadvantage, whether in a warfare, or disaster relief

situation (McLennan-Smith & Adams, 2023).

There are several swarm C2 models, depending on

the centralisation degree (ibid.). In this case,

considering the potential A2/AD environment and to

take full advantage of AHPEIMM autonomy and

intelligence, it has been decided to enable either the

consensus or emergent coordination C2 models, both

resulting in a reduced extent of the Operation Control

influence on AHPEIMM life cycle phases, as shown

in Figure 5. As this representation relies on the

separation of command and control, it is once again

evident that the LoAs identified are related to, support

and influence each other.

4.3.5 Promote C2 Resilience Through

Distributed Control

C2 resilience can be increased through the use of

AHPEIMMs and HITMMs, which reduce C2

fragility by limiting communication bandwidth and

workload. Resilience and agility are inherently

linked; thus, adaptive and transformative resilience

approaches (Folke et al., 2010) enable more agile C2,

which is required in complex, confusing and chaotic

situations (see Figure 3) typically encountered in

contested environments.

The GERA MF can help represent and achieve a

common understanding of the required C2 resilience.

An example is shown in Figure 6, whereby adaptive

C2 resilience is achieved by the Mission itself during

operation; for more significant environment changes,

transformative C2 resilience is achieved based on an

adequate RM under directions from the Operation.

In the scenario, the change from LPED to HITMMs

will support Operation C2 resilience by shifting some

of the Control load over to the autonomous modules

(as shown in Figure 5), in line with the Distributed

Control research findings (Hostage & Broadwell,

2014). As can be seen, Resilience is linked to the C2

Autonomy and Separation aspects.

Figure 6: Adaptive and Transformative Resilience.

4.3.6 Assist C2 Evolution

While the GERA MF does not contain an explicit

time dimension, it is possible to depict the combined

present / future situation from Figure 4 in a temporal

representation. Thus, in Figure 7, one can distinguish

the creation of an Operation and the HTIMM concept

and requirements by the Joint DF, followed by the

creation of Missions, AHPEIMMs and the initial

HITMM design by an Operation. Additional temporal

detail such as concurrency and succession can also be

shown. For example, Figure 7 shows the transition

from legacy to new P&E delivery models involving

their temporary parallel operation. This in turn

requires the co-existence of several C2 approaches,

again confirming the LoA interdependence.

5 CONCLUSIONS AND FURTHER

WORK

The nature of armed conflict and natural disasters is

changing and involving an increased use of

autonomous and intelligent solutions. The existing

C2 approaches need to also evolve in order to

effectively cope with these changes. The paper has

proposed and tested several main directions towards

achieving this endeavour.

Legend:

C2=Command &Control; C2 RM= C2 Reference Models;

STD = standards; E=Execution;

: Self-evolution

: Adaptive Resilience (high)

: Transformative Resilience

using Reference Models

Mission

C2 RM

(STD)

Operation

: Adaptive Resilience (low)

ICEIS 2024 - 26th International Conference on Enterprise Information Systems

630

Figure 7: Life histories of the entities relevant to the LPED to HTIMM transition (excerpt).

The application of these directions to the case study

has shown that they influence each other and as such,

they need to be accomplished in an integrated

manner, so that changes in all required areas occur

coherently. This confirms the holistic, EA-based

approach taken.

The contribution of this paper is twofold: a) it

proposes practical directions for C2 evolution and

investigates the role of EA artefacts in assisting C2 in

dealing with the necessary changes and b) it analyses

the application of these directions materialised as C2

evolution Lines of Action, tested through a case study

involving the novel concepts of Autonomous Hybrid

Power and Energy Intelligent Mobile Module and

Hybrid Tactical Intelligent Mobile Microgrid, which

represent the synthesis of several research directions

in the area.

Future research will consider applying the

existing Lines of Action to additional case studies in

other domains featuring uncertainty and high

dynamics in order to validate and potentially increase

the applicability of this method to other areas.

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