Integrating a Multi-Agent System Simulator and a Network Emulator to

Realistically Exercise Military Network Scenarios

Dante A. C. Barone

1 a

, Juliano Araujo Wickboldt

1 b

, Maria Claudia Reis Cavalcanti

2 c

David Moura

2 d

, Julio Cesar C. Tesolin

2 e

, Andr

e M. Demori

2 f

, Julio C. S. dos Anjos

3 g

Leonardo Filipe Batista Silva de Carvalho

4 h

, Jo

ao Eduardo Costa Gomes

5 i

and Edison Pignaton de Freitas

1 j

Graduate Program on Computer Science, Federal University of Rio Grande do Sul, Porto Alegre, Brazil

Military Institute of Engineering, Rio de Janeiro, Brazil

Graduate Program in Teleinformatics Engineering (PPGETI/UFC), Federal University of Cear

a, Fortaleza, Brazil

Federal Institute of Rio Grande do Sul, Canoas, Brazil

Graduate Program on Electrical Engineering, Federal University of Rio Grande do Sul, Porto Alegre, Brazil

Keywords:

Integration, MAS, Military Network.

Abstract:

Modern battleﬁeld scenario are complex environment in which a myriad of equipment and people interact to

accomplish a given mission. Most of this interaction is performed by means of wireless communication via

Command and Control Systems, which efﬁciency represent a critical factor the mission success. The assess-

ment of these systems, and their supporting networks, is of primal interest to decide for the best equipment

and military maneuver approach. However, there is a lack of tools that provide all the necessary behavioral

and network features to perform the task. Observing this fact, this work presents an alternative to simulate a

battleﬁeld environment model by means of integrating a network emulator and a Multi-Agent System simula-

tor. By combining both software, it is possible to assess speciﬁc characteristics of each area without limiting

the model, thus providing the necessary data for an informed military network setup assessment.

1 INTRODUCTION

Battleﬁelds have been dynamic and hostile environ-

ments throughout history that are constantly subjected

to unexpected changes. The advance of technology

has increased the size and scope of battles. Now, bat-

tleﬁelds can spread over multiple domains where ad-

versaries contest their forces over land, air, sea, space

and cyberspace. Such evolution has signiﬁcantly in-

https://orcid.org/0000-0002-5133-0144

https://orcid.org/0000-0002-7686-8370

https://orcid.org/0000-0003-4965-9941

https://orcid.org/0000-0002-1153-3879

https://orcid.org/0000-0002-0240-4506

https://orcid.org/0000-0002-0533-3395

https://orcid.org/0000-0003-3623-2762

https://orcid.org/0009-0001-7032-5850

https://orcid.org/0000-0003-1418-0658

https://orcid.org/0000-0003-4655-8889

creased the role of communications in military ac-

tivities and with it, has created the basic concept of

Network-Centric Warfare (NCW) (Cebrowski, 1999).

This has made it possible to deploy distributed Com-

mand and Control (C2) Systems to support Multi-

Domain Operations (MDO) (Townsend, 2018).

The complexity and extension of modern military

operations make them costly and cumbersome to as-

sess. Therefore, simulation becomes a useful tool to

evaluate military scenarios and help armed forces to

test new approaches. However, the effectiveness and

accuracy of a simulation depend on how close to the

real-world operation its model is. Moreover, as new

details are added, more complex becomes the model

and the demands from the simulator.

An alternative is to break the model into smaller

ones, isolating and assessing each aspect of the sce-

nario with a speciﬁc simulator. This separation, how-

ever valid, removes the interaction between different

aspects of the model from the assessment. In order to

194

Barone, D., Wickboldt, J., Cavalcanti, M., Moura, D., Tesolin, J., Demori, A., Anjos, J., Silva de Carvalho, L., Gomes, J. and Pignaton de Freitas, E.

Integrating a Multi-Agent System Simulator and a Network Emulator to Realistically Exercise Military Network Scenarios.

DOI: 10.5220/0012051600003546

In Proceedings of the 13th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2023), pages 194-201

ISBN: 978-989-758-668-2; ISSN: 2184-2841

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

get a better picture of what is happening in the mili-

tary operation as a whole, it is necessary to broaden

the scope instead of isolating its parts. Unfortunately,

there are not many simulators capable of assessing

very complex military operations models. This is the

case when the goal is assess the performance of troops

in Network-Centric Operations, in which communi-

cation network aspects directly affect the unfolding

of the military units on the ﬁeld.

Thus, a solution to model such complex environ-

ment is to integrate two types of simulators, each

covering one aspect of the scenario: unit behavior

and network communication between units. Albeit

simple, the idea of integrating two different simu-

lators is easier said than done since both programs

might run in different operating systems, use differ-

ent programming languages, and have different time

approaches (real-time, simulated time, event-driven),

among many other speciﬁcities.

Observing the need for simulations that exercise

C2 communication network features in realistic mil-

itary operation scenarios, this work proposes the in-

tegration of a Multi-Agent System (MAS) simulator

with a Network emulator. This paper presents an

overview of the proposed Simulator-Emulator Inte-

grated System, highlighting the main contributions:

• The proposal of an integration between a behav-

ioral model based on Multi-Agents Systems with

computer network paradigms able to deploy a re-

alistic behavior of a military network;

• An integrated execution environment entitled Sys-

tem of Systems of Command and Control (S2C2)

Emu-Sim;

• The design of timing and decision mechanisms

that allows the joint operation of the integrated

software.

This work is divided in the following structure:

Section 2 brings the main concepts regarding the ad-

dressed military scenarios, then Section 3 presents

the main challenges involved in integrating two dif-

ferent simulation paradigms, and Section 4 follows

with the proposal of the integrated software. Section

5 presents the solutions for the challenges previously

presented. Section 6 shows the results obtained on a

case study and the paper is concluded with Section 7.

2 CONCEPTS REVIEW

The key concepts for military communications on the

ﬁeld are brieﬂy summarized next.

2.1 Command and Control

The fundamental core of Command and Control re-

gards the structure and decision making to enable a

team of individuals to accomplish a mission (Alberts

and Hayes, 2006).

The idea of military Command used to be pro-

jected into a single individual (the leader, the ge-

nius commander) but the Information Age has shifted

that idea from centralized Command to de-centralized

Command (Alberts and Hayes, 2003). Therefore,

shifting authority and decision power to individuals at

the edge of the organization (Boone, 2021) in a new

approach that has been called Network-Centric War-

fare (NCW) (Alberts and Hayes, 2006).

2.2 Military Communication Networks

To modern military conﬂicts, the communication net-

work infrastructure is highly important. While it

includes several methods, it relies mostly on wire-

less connections (Pawgasame and Wipusitwarakun,

2015). As consequence, there has been a consider-

able general increase in data rates to support mod-

ern applications such as maps, friendly positions and

real-time video feeds (Jalui et al., 2019), which has

become even more pressing after the introduction of

Internet of Battle Things (IoBT) (Kott et al., 2016),

which must be capable of dealing with different types

of users and data requests. A contemporary exam-

ple of that are the debilities of the Russian Armed

Forces to communicate in the war of Ukraine, which

points that most of the difﬁculties faced by the Rus-

sians are based on communication network problems,

particularly in regard to acquiring timely data and to

the coordination of their actions - clearly C2 business

(Cranny-Evans and Withington, 2022).

To overcome the challenges of a variable network

conﬁguration and topology, new network approaches

have been developed based on new paradigms that

aim to aim to improve network connectivity and data

transfer between nodes while providing robustness,

such as Information-Centric Network (ICN) (Campi-

oni et al., 2019), Disruption/Delay-Tolerant Network

(DTN) (Amin et al., 2015), Software Deﬁned Net-

work (SDN) (Nobre et al., 2016) and combinations

of them (Wang et al., 2017), (Zacarias et al., 2017),

(Leal et al., 2019).

2.3 Decision Making Process

Every military group has its own hierarchy and dis-

cipline. However, as NCW advances, responsibility

and decision power shift to de-centralized individu-

Integrating a Multi-Agent System Simulator and a Network Emulator to Realistically Exercise Military Network Scenarios

195

als at the edge of the organization. Hence, each troop

must be capable to make its own decisions in view of

the main objective of the operation.

One way to simulate such behavior is to use Multi-

Agents System (MAS) simulators. The core abstrac-

tion of agent is deﬁned by (Dorri et al., 2018, p. 2)

as “an entity which is placed in an environment and

senses different parameters that are used to make a de-

cision based on the goal of the entity. The entity per-

forms the necessary action on the environment based

on this decision”. This deﬁnition perfectly models the

military units and the military operation scenario in

which their are inserted.

However, while MAS simulators create communi-

cations between agents to allow them to interact with

each other, those are simple and aim only to exchange

knowledge of the environment. Thus, simulators usu-

ally do not assess real network parameters.

2.4 Conceptual Data Modeling

It is essential for the success of software development

endeavors to conciliate both developers’ and experts’

perceptions about a domain while capturing it. Con-

ceptual Data Modeling aims to provide such align-

ment, bringing semantic-rich concepts to represent re-

ality as accurately as it can be, becoming more un-

derstandable and implementation independent. In this

sense, the modeling language must provide the essen-

tial constructs for such task. Otherwise, some impor-

tant concepts may be left out of the ﬁnal model.

Ontological Conceptual Data Modeling (OCDM)

can be used on large and complex information sys-

tem, instead of Traditional Conceptual Modeling

(TCM), to bring substantial beneﬁts. In (Verdonck

et al., 2019), authors observed in their empirical

study that novice modelers (modelers without pre-

vious data modeling knowledge) using OCDM tech-

niques brought higher quality models when compared

to the ones brought by novice modelers using a TCM

technique. Hence, ontology-based conceptual model-

ing tools, such as OntoUML (Guizzardi et al., 2015),

are able to deliver better domain conceptual models,

improving not only their reading clarity and imple-

mentation, as well to improve the reasoning capabil-

ities of a system. Using ontology also reduces ambi-

guity and avoids semantic conﬂicts within the model.

3 PROBLEM STATEMENT

Modern battleﬁeld scenarios cover aspects of

decision-making and network communications

that make realistic simulation models complex.

As more details and parameters are added, more

is required from simulators. Although there are

network simulators and emulators able to efﬁciently

assess network parameters, and to evaluate different

network paradigms, they have poor support to

realistic represent the behavior of military troops on

the ﬁeld. This includes making decisions regarding

the presence of enemies and performing coordinated

maneuvers to avoid natural or man-made obstacles.

On the other hand, while making decisions and co-

ordinating the movement of units are tasks better eval-

uated by MAS simulators, they are unﬁt to assess/test

new network paradigms or parameters. Hence, to in-

tegrate these two types of simulators provides a way

to improve complex military operation models to net-

work communications and decision-making as well.

To use those two tools in synchrony requires to

account some key factors, such as the simulation

progress time having to occur at the same pace in both

programs. Otherwise, events occurring in one pro-

gram (and their consequences) are not properly re-

ﬂected at the other. This is not a simple task when

combining an emulator and a simulator. A simulator

can accelerate (or slow down) time, while an emulator

uses real-world clock time. This creates restrictions to

how fast (or if) the scenario can be sped up to.

The kind of information shared between software,

how it is shared and the physical position of units on

the ﬁeld are also matters to consider. Particularly,

continuous and synchronous update of positions on

both software is necessary so that aspects like signal

loss/variation or loss of packages can be added to the

model and to the decision-making process. That way,

movement decisions are dealt by the MAS simula-

tor while the network emulator uses the position of

units to ﬁgure the network behavior. Thus, a common

data interface to share information between these two

types of tools becomes a requirement. However, that

is not part of the design goals of either of them.

One way to synchronize data communication be-

tween those programs is to write and read data from

external ﬁles, e.g. .txt or .csv ﬁles. That way, data

generated and registered by one program can be ac-

cessed by to the other. Yet, this solution might lead to

issues like semantic conﬂicts (see Section 2.4), con-

current ﬁle requests and ﬁle management problems.

To overcome such problems, an alternative is to

write and read data from databases as the use of a

well-conceived data model might facilitate interoper-

ability. This makes it possible to register the conﬁgu-

ration of the simulation and of each step of its steps,

therefore, making it easier to compare, debug or iden-

tify outliers. The drawback of this approach is that

if often requires the use of external scripts or third-

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196

party software (such as DBMS) to enable direct ac-

cess database ﬁles. Other than, that, the permission

to read/write access into the database ﬁle is also an

important concern.

The simulation of military scenarios requires to

use information from real locations, with natural ge-

ographic elements such as rivers, mountains and val-

leys, as well as man-made elements such as bridges

and roads. Hence, the use of maps with Geographic

Information System (GIS) is a good option. This

because, MAS simulators can be tuned to interpret

each geographic information and use them in path-

ﬁnding algorithms like Dijkstra or A*. On that mat-

ter, weather conditions may also be another source of

interference for units’ movement and network com-

munications that should be taken into account.

Combining all these concerns described above in

a single setup that enables the assessment of network

and military doctrine parameters is not a trivial task

and requires the integration of different software sys-

tems. In turn, this integration has a number of chal-

lenges that have to be handled in order to work prop-

erly. The approach presented in this paper faces these

problems as detailed in the following.

4 INTEGRATION PROPOSAL

This work presents the integration of a MAS simula-

tor and a Network emulator to realistic represent bat-

tleﬁeld environments and the accurate behavior of its

troops, including the interoperability issues that might

occur in communications in such scenarios.

4.1 Architectural Overview

The general structure of the proposed solution de-

ﬁned by the “S2C2 EmuSim - Command and Con-

trol Simulation Conﬁguration and Orchestration” sys-

tem is shown on the component diagram of Figure

1 and are described next from a objective point-of-

view. Though most of the inner components of the

diagram are omitted, Section 4.2 discusses the sub-

components of the “EmuSim Script” component.

• “S2C2 Menu”: the component tasked to initialize

the system. It provides the “Start” interface used

by other components to execute the each or theirs

corresponding functionality.

• “ManageSimulation”: the component used to cre-

ate, load and edit a “Scenario (Simulation)” ob-

ject. It uses the “Run” interface provided by the

“EmuSim Script” to run the scenario over the

“MAS Simulator” and the “Network Emulator”.

• “Scenario (Simulation)”: the battleﬁeld scenario

to be simulated. Each scenario is built over a bat-

tleﬁeld ontology based on the Web Ontology Lan-

guage (OWL)

, a domain ontology for the rich

representation of entities, individuals, categories,

inferences, attributes, and relationships on the bat-

tleﬁeld. It also enables checking the logical con-

sistency between the simulated entities and infer-

ring rules and constraints of the battleﬁeld sce-

nario.

• “Simulation Log”: an object created by “Manage

Simulation” after a scenario has been run and had

its data saved to the database.

• “S2C2 OWL Loader”: a component used to con-

vert to/from OWL data through its “Parse” inter-

face.

• “MAS Simulator”: the Multi-agent System Sim-

ulator used to simulate the troops and the battle-

ﬁeld environment of the military scenario under

analysis, including the decision-making and path-

ﬁnding algorithms used to choose routes and ac-

tions that should be taken to reach the goal.

• “Network Emulator”: a program used to em-

ulate the communication signals (and the suc-

cess/failure of their delivery) of military troops

simulated by the MAS Simulator.

• “EmuSim Script”: a component tasked to syn-

chronize the “MAS Simulator” and the “Network

Emulator” and to run the input battleﬁeld Sce-

nario.

• “EmuSim Persistence”: a component used to in-

sert, update or read data base data through its pro-

vided “DB Query” interface.

• “Manage Simulation Report”: a component used

to read the “Simulation Log” object and view the

resulting data from the execution of a Scenario in

a user-friendly way. It is also used to conﬁgure the

report, selecting which statistics should be shown.

• “Manage Simulation Set”: a component tasked to

build and read a “Simulation Set” object and to

sequentially run each of its scenarios.

• “Simulation Set”: the object ﬁle containing a set

of different conﬁgurations of the same “Scenario

(Simulation)” ﬁle that should be run to collect

their data and to produce reports to identify the

best resulting strategies.

• DBMS: the database of the system.

The need to synchronize the “MAS Simulator”

and the “Network Emulator” comes from the fact that

https://www.w3.org/OWL/

Integrating a Multi-Agent System Simulator and a Network Emulator to Realistically Exercise Military Network Scenarios

197

Figure 1: S2C2 EmuSim - Command and Control Simulation Conﬁguration and Orchestration.

both use co-simulation (Gomes et al., 2018) to simul-

taneously run the same military scenario over their re-

spective settings. This is critical to this work since

that, different from a simulator, an emulator works in

real-world time only.

4.2 EmuSim Script Component

The “EmuSim Script” component manages the gen-

eral functioning of the S2C2 System. It also bridges

the events triggered by the use of the system and the

external applications responsible to them, such as the

“MAS simulator” and the “Network emulator”, re-

spectively NetLogo and Mininet WiFi.

NetLogo is a multi-agent programmable modeling

environment (Wilensky, 1999). It is a robust open-

source platform based on Logo programming lan-

guage and implemented in Java and Scala. Mininet-

WiFi is a lightweight open-source emulator developed

in Python used to create realistic virtual networks that

runs at the same computer real kernel, switch, and ap-

plication code(Lantz et al., 2010), as well as wireless

connections with access points, ad hoc communica-

tion, and mesh networks (Fontes et al., 2015).

When executing a scenario, the “MAS Simulator”

is tasked to start and end the simulation, as well as

to set the number of nodes (agents), their environ-

ment, primary objective, and behavior when encoun-

tering enemies. Meanwhile, the “Network Emulator”

has the task of asserting the nodes’ success/failure to

communicate in order to track the chance of friendly

ﬁre.

The “EmuSim Script” is triggered by the “Manage

Simulation” component whenever a scenario simula-

tion must be run. Next, it simultaneously start the

“MAS simulator” and the “Network emulator”. This

task is carried out by its two sub-components seen in

Figure 1: “PyNetLogo” and “Socket”.

The “PyNetLogo” sub-component is a Python

Script to send instructions to NetLogo and to Mininet-

Wiﬁ. It provides the “Command” interface to send

messages that deliver these instructions. However, the

distributed nature of the Network Emulator requires

that messages are delivered to network nodes, which

may not be physically located on the same computer.

For that reason, messages sent to the “Network Emu-

lator” through the “Command” interface are ﬁrst de-

livered to the “Socket” sub-component that, in turn,

forwards them through its own provided “Command”

interface to the network nodes emulated by the “Net-

work Emulator”. Details of the complete ﬂow of this

process are described in Section 4.3.

Once the simulation is over the resulting data is

collected through the “SIM log” interface connecting

the “PyNetLogo” sub-component to the “MAS Sim-

ulator”, and the “Emu log” interface connecting it to

the “Socket” sub-component. In turn, this component

uses its own provided “Emu log” interface, gathers

data from the emulator and delivers it back.

4.3 System Workﬂow

To run a simulation scenario on the “S2C2 EmuSim -

Command and Control Simulation Conﬁguration and

Orchestration” system requires a number of steps.

The ﬂow of this functionality is detailed next.

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1. The user starts the System using the “S2C2

Menu” component and selects to manage a sim-

ulation (“Manage Simulation component”).

2. The user requests the “Manage Simulation” com-

ponent to load a previously built simulation.

3. The “Manage Simulation” component” retrieves

the scenario data and gives the “EmuSim Script”

component charge of the simulation.

4. The “EmuSim Script” initializes the database of

the system, the “MAS Simulator” and the “Net-

work Emulator” to set them ready to store data

and to run commands.

5. The “Network Emulator” instantiates every net-

work node of the emulated scenario, each with its

own client and server objects, so it can monitor

the success/failure of their exchange of messages.

Each loaded scenario may have any number of

emulated network nodes that, in turn, communi-

cate with one another. Here, an abstraction is used

to represent this unknown number of nodes as

the “ClienteNodeA”, “ClienteNodeB”, “ServerN-

odeA” and “ServerNodeB” objects.

6. Next, the system runs the simulation to collect its

results. This starts a loop that is repeated one tick

at a time, until all the nodes of the simulation have

reached their goal. It should be noted that, while

the simulation moves forward one tick at a time

the simulator can be set to run any number of ticks

per second. The next sub-items detail this loop.

(a) The “EmuSim Script” requests from the

“Database” the communication messages of all

nodes (if any) that were written at the last tick.

(b) The retrieved data is sent to the “MAS Simula-

tor” to use it to update for every node of the

simulation the information of each ally node

they are aware of.

the data about any hill between two simulated

nodes. This step is essential to simulate ge-

ographical interference that may affect troop

members and lead to friendly ﬁre.

(d) The “EmuSim Script” calls the “MAS Simula-

tor” to run the present tick. Next, it retrieves

from it the current positions of the simulated

nodes and writes it to the “Database”.

(e) The “EmuSim Script” calls the “MAS Simu-

lator” to retrieve the information of every hill

located between the current position of any two

nodes. It then writes the data to the “Database”.

The internal time frame unit of the simulation.

(f) The “EmuSim Script” calls the “Network Em-

ulator” to start the communication of the nodes

using the MQTT (Message Queuing Teleme-

try Transport) protocol, a machine-to-machine

network protocol for message queue/message

queuing service (FairCom, 1999). This action

starts a new loop (detailed next) that runs on a

single tick to every simulated node.

i. The “Network Emulator” sends an asyn-

chronous message to “ClientNodeA” via

MQTT protocol.

ii. The message is received by “ClientNodeA”

which sends an asynchronous UDP message

to “ServerNodeB” to asses communication

success/failure.

iii. To every successfully received message,

“ServerNodeB” sends an asynchronous mes-

sage to “ClientNodeB” to write that message.

iv. “ClientNodeA” requests the database to per-

sist every message it sent at the current tick.

v. “ServerNodeB” requests the current simula-

tion tick from the database. Then, it writes

to it every message it received at that tick.

vi. Next, steps 6(f)i to 6(f)v repeat themselves

to the opposite client-server pairs. First,

the “Network Emulator” asynchronously mes-

sages the “ClientNodeB” via MQTT protocol.

vii. When a message is received by “ClientN-

odeB” it sends an asynchronous UDP mes-

sage to “ServerNodeA” to asses communica-

tion success/failure.

viii. To every message it successfully received,

“ServerNodeA” sends an asynchronous mes-

sage to “ClientNodeA” to write that message.

ix. “ClientNodeB” requests the database to per-

sist every message it sent at the current tick.

x. “ServerNodeA” requests from the database

the value of the current simulation tick. After-

ward, it writes to the database every message

it received at that tick.

7. The “EmuSim Script” ﬁres messages to stop the

“Network Emulator” and the “MAS Simulator”

and to disconnect the database.

5 CHALLENGES

Time synchronization is crucial to integrate the “MAS

simulator” and the “Network emulator”. Contrary

to the parameters evaluated by the former, the ones

assessed by the latter require real-world clock time.

Thus, to optimize the execution time of the simulation

the “EmuSim Script” acts as a pacemaker to keep both

Integrating a Multi-Agent System Simulator and a Network Emulator to Realistically Exercise Military Network Scenarios

199

tools running at real-world time speed. Hence, while

this avoids disturbing the assessment of the parame-

ters of the emulator (e.g. transmitted packets, estab-

lished connection, etc) it allows the quick transfer of

the position of units from the MAS to the emulator.

To enable the “EmuSim Script” to control the

simulation pace and to achieve synchronization, the

smallest time fraction of the “MAS simulator” was set

to one (1) second. This also made it possible to track

parameters of interest along the run of the simulation.

The management of the database is a task of the

“DBMS” as it controls the read and write opera-

tions and the overall access. This is required due

to the “MAS simulator”, “Network emulator” and

“EmuSim Script” having different read/write privi-

leges to most database tables. Other than that, the

use of a database also allowed saving data to different

conﬁgurations of the simulation, such as the number

of units or broadcast communication intervals.

The last challenge faced in co-simulate the battle-

ﬁeld scenarios was scale. Mininet-WiFi uses real-life

metrics while NetLogo uses different scales accord-

ing to the map ﬁle. Yet, while NetLogo can use real

geographic data from GIS ﬁles, the map scale has to

be set within the simulator. To integrate these differ-

ent scales, it was required to use a scale factor to keep

the position of units proportional in both software.

6 CASE STUDY

Sections 4 and 5 show that the problem described in

Section 3 is addressed by integrating Mininet-WiFi

and NetLogo via database and orchestration scripts.

Figure 2 illustrates the interfaces of the two synchro-

nized software and is meant to represent a friendly ﬁre

case study scenario to validate that solution.

At the top of Figure 2 is the Mininet-WiFi emu-

lator, displaying the telemetry of its emulated nodes

in real-world distances. At the bottom is the NetLogo

simulator, showing the geography of the current map

being simulated and its agents/nodes. As shown, the

two programs have the same nodes at the same posi-

tions (considering scale correction).

The scenario consists of 29 units (blue hexagons

with military symbols) that carry broadcast radios and

walk through a real-world terrain marked by plains

(light green), hills (darker shades of green), and water

bodies (blue). When in execution, NetLogo (bottom

map) must consider these aspects and ﬁnd either the

shortest or fastest path to take troops from their initial

position (bottom-left red square) over their mid-goals

(red squares on the map diagonal) to their goal (top-

right red square). To units, water bodies and steep

Figure 2: Mininet and NetLogo synchronized interfaces.

hills are impassable while plains and leaser hills have

different effects over their movement speed.

To assess possible friendly ﬁre situations, each

unit broadcasts its current location every few seconds

until it reaches its goal, thus, creating a blue force

tracking. This is a necessity since there are elements

of the map that might block the sight of an agent and

lead to friendly ﬁre whenever an agent detects an un-

known agent in its presence. The same scenario can

also be customized to run under different time inter-

vals to analyze the impact these differences have over

the friendly ﬁre.

Particularly, longer intervals provide less informa-

tion to agents about their peers, thus increasing the en-

counter of agents unaware of the other’s nature (friend

or foe) and the potential of friendly ﬁre. At the end of

each simulation, the value of the used test-parameter

interval and the results of the simulation are shown in

a report with a set of batches that lists for each agent

the average number of encounters leading to friendly

ﬁre and their standard deviation.

7 CONCLUSIONS

Despite the complexity to simulate modern military

operations, this paper proposes how to handle some

of that complexity by integrating two software. More-

over, while this architecture allows to simultaneously

assess bigger complexities and larger number of pa-

rameters, it also prompts a model behavior closer to

reality and avoids oversimpliﬁcations and loss of in-

formation.

SIMULTECH 2023 - 13th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

200

To that end, aspects such as timing, data for-

mat, shared information, and the orchestration of the

involved software had to be considered. Synchro-

nizing these software was crucial so that the model

could take into account communication and decision-

making factors, while the database assured the evalu-

ation and register of these factors for further analysis

and comparison as well as the build of the case study.

Due to the success of this work, further develop-

ments are expected to communications and decision-

making. This includes the testing of new network

paradigms (such as ICN, DTN) and the addition of

new elements to inﬂuence the behavior of agents (e.g.

limited resources, enemies, etc.).

ACKNOWLEDGEMENTS

This study was ﬁnanced in part by CAPES - Brazil

- Finance Code 001 and in part by CNPq - Brazil,

Projects 309505/2020-8. We also thank the Brazilian

Army via the research project S2C2, ref. 2904/20.

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