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|title=Evaluation of the Effectiveness of Network-centric Control of Mobile Agents in a Dynamic Environment Using Neural Networks
|pdfUrl=https://ceur-ws.org/Vol-3887/paper16.pdf
|volume=Vol-3887
|authors=Vadym Mukhin,Valerii Zavgorodnii,Anna Zavgorodnya,Oleksandr Yarovyi,Lesia Baranovska,Oleg Mukhin
|dblpUrl=https://dblp.org/rec/conf/its2/MukhinZZYBM23
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==Evaluation of the Effectiveness of Network-centric Control of Mobile Agents in a Dynamic Environment Using Neural Networks==
https://ceur-ws.org/Vol-3887/paper16.pdf
Vadym Mukhin, Valerii Zavgorodnii, Anna Zavgorodnya, Oleksandr Yarovyi,
Lesia Baranovska and Oleg Mukhin
National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 37, Prospect Beresteiskyi, Kyiv, 03056,
Ukraine
Abstract
This paper examines the network-centric approach for enhancing the efficiency of mobile agent control. The
primary distinction of this approach is the use of a "wandering center." If communication with the active
center is lost, a new center is appointed to continue control, ensuring the system's survivability and
improving the overall efficiency of mobile agent management.
An experimental study was conducted to evaluate the parameters and effectiveness of network-centric
control of mobile agents. The study utilized a developed model of mobile agent interaction based on network-
centric control, along with a simulation model of agent behavior in a dynamic environment. Additionally, a
neural network was created that accurately predicts the probability of hitting the target (%) under changing
dynamics. The use of this neural network also helped identify input parameters with minimal impact on the
outcome.
Keywords
Mobile agent, network-centric control, dynamic environment, neural networks, simulation model 1
1. Introduction
Today, most existing mobile agents are controlled manually using remote controls that operate on
radio channels. However, this manual control poses several challenges, including the need for
specialized operator training, limited operational range, and dependency on weather conditions [1,
2].
A mobile agent can be a software or hardware entity capable of performing various tasks within
a network or on a device, such as carrying information, conducting computations, and interacting
with other agents or the environment [3, 4].
Controlling mobile agents requires qualified specialists. For example, in the U.S. military,
experienced Air Force pilots undergo a full year of training to become proficient mobile agent
operators – a task that, in some cases, can be more demanding than piloting an aircraft. Operator
errors and mechanical failures account for most mobile agent accidents [5, 6].
A new technological trend involves the development of mobile agents equipped with an even
number of rotors that rotate diagonally in opposite directions. Mobile agents are just one component
within a complex, multi-functional system [7, 8]. Unlike manned aircraft, operating mobile agents
requires additional support system components, including the agent itself, the operator’s workstation,
software, data lines, and other elements necessary to achieve mission objectives. Current
development trends favor compact mobile agents, with a focus on simplicity of control, reliability,
and maneuverability [9, 10].
ITS-2023: Information Technologies and Security, November 30, 2023, Kyiv, Ukraine
v.mukhin@kpi.ua (V. Mukhin); zavgorodniivalerii@gmail.com (V. Zavgorodnii);
annzavgorodnya@gmail.com (A. Zavgorodnya); getem13@ukr.net (O. Yarovyi);
lesia@baranovsky.org (L. Baranovska); o.mukhin01@gmail.com (O. Mukhin)
0000-0002-1206-9131 (V. Mukhin); 0000-0002-8347-7183 (V. Zavgorodnii);
0000-0001-8523-1761 (A. Zavgorodnya); 0000-0002-3889-5730 (O. Yarovyi);
0000-0003-0024-8180 (L. Baranovska); 0009-0005-5301-8276 (O. Mukhin)
© 2023 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
184
Workshop ISSN 1613-0073
Proceedings
In model aviation and professional applications such as the civil sector, agriculture, military, law
enforcement, and other fields, mobile agents are in high demand. Selecting the optimal models and
control systems is crucial for effectively monitoring ground-based targets [11, 12].
Thus, pilotless mobile agents present a more efficient and economical alternative to manned
aircraft for many tasks [13–15].
The main challenge in controlling mobile agents lies in the reliance on centralized control systems,
where the control center serves as a vulnerable element; if communication with it is lost, further
control becomes impossible. In contrast, decentralized control faces issues with coordinating and
circulating large volumes of information, leading to slower response times. A promising alternative
is a network-centric approach, which allows control to be transferred to an alternative center when
needed [16–18].
An analysis of recent scientific literature reveals that, although modern information technologies
have advanced considerably, the network-centric approach to control remains underdeveloped
compared to centralized and decentralized methods.
2. A network-centric approach to enhancing the efficiency of mobile
agent control systems
Three main approaches to the control of mobile agents can be identified: centralized, decentralized,
and network-centric. In centralized control, a single command center issues control signals to all
mobile agents. If this center is disabled or compromised, all mobile agents lose connectivity and
cannot be controlled.
Decentralized control, while reducing dependence on a single command point, has the drawback
of coordination challenges. For example, if mobile agents need to rapidly reconfigure, a large amount
of information must be transmitted to them, significantly reducing the overall speed of the control
system [19, 20].
The network-centric approach is designed to address the limitations of the previous models. This
approach integrates all forces and resources into a single information system, enabling control
objectives to be met even in dynamic, complex environments that are subject to unpredictable
interference. Such interference can be irregular, intermittent, and variable, yet network-centric
control is still effective under these conditions [21, 22].
Consider a control network that a subset of mobile agents follows. Among the entire group of
mobile agents, approximately 10% are selected as nodes that hold partial control information (Fig. 1).
These nodes, equipped with control functions, form a network, and from them, one is designated as
the primary control center [23, 24].
Only the node mobile agents (NMA) are coordinated directly by the control center; these nodes,
in turn, control the remaining mobile agents (MA). This setup minimizes communication interference
since individual control of each node could overwhelm communication channels. For example, if the
command is to change direction quickly, adjusting the coordinates for each mobile agent individually
would require significant time. Furthermore, if there is only one primary control agent and it
suddenly stops responding, is damaged, or is compromised, all mobile agents would lose direction,
become vulnerable to interception, or cease to operate [25–27].
A network-centric system consists of a primary control mobile agent (MMA) that continuously
sends commands to other mobile agents, setting parameters such as movement direction [28, 29].
Alongside commands, it also relays information from the control center to the node mobile agents. If
the primary control agent fails for any reason, copies of all information, including control data for all
mobile agents, are preserved within the node mobile agents. In this way, an adversary would need to
disable every node agent to compromise the entire network – a challenging task since the enemy
cannot readily identify which agents’ function as nodes. This network organization enhances the
resilience of the control system.
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Figure 1: Mobile agent control system based on network-centric control
A key feature of a network-centric system is its “wandering center.” If the primary control mobile
agent is identified and disabled by an adversary, the stability of the system remains unaffected, as
control can quickly be reassigned. A new control agent is selected from the remaining active node
agents, allowing for seamless continuity. This flexibility means that control can be handed over to
another node at any moment, ensuring sustained operation. In this network-centric mobile agent
control system, two types of information are managed:
control: this includes global coordinates transmitted from the control center to the main
control agent.
command: this includes the coordinates transmitted by the control agent to node agents,
which then relay local coordinates to other agents. Thus, mobile agents navigate based on local
rather than global coordinates, as provided by the node agents.
In summary, a key strength of the network-centric approach is the use of a roving control center.
If connections to the current center are lost, a new center is assigned to maintain control. This design
ensures system survivability, thereby greatly enhancing the overall effectiveness of mobile agent
control.
3. Evaluation of the Effectiveness of Network-Centric Control for
Mobile Agents in a Dynamic Environment
Based on the network-centric control algorithm model for mobile agents in dynamic environments
and the simulation model of interacting mobile agents, it is essential to conduct experimental studies
to assess parameters and evaluate the effectiveness of network-centric control.
186
To validate the scientific and methodological framework developed, we performed mathematical
modeling to analyze the effectiveness of network-centric control for mobile agents in a dynamic
environment.
The software tools used in this study include:
Simulations were performed on a personal computer with an Intel® Celeron® processor (3.2 GHz)
and 16 GB of RAM running on Microsoft Windows 10.
Microsoft SQL Server 2022 was utilized as the database management system.
Microsoft Visual Studio 2022 Community Edition served as the development environment.
Development was conducted in C# using an object-oriented approach.
Experiments in the simulation environment included setting initial parameters, performing
intermediate calculations, and visualizing results with Windows Presentation Foundation.
The minimum required percentage of node mobile agents % (nodePercentage) needed to reach the
target was calculated as follows. A target is considered "hit" if both of the following conditions are
met:
The percentage of mobile agents that reached the target (targetHitPercentage) must be at least
equal to the required percentage of mobile agents needed to hit the target (targetHitPercentage).
Among the mobile agents that reached the target, there must be at least one node mobile agent
(targetReachedCount > 0).
To evaluate these conditions, 100 experimental trials were conducted, using the following
parameters:
Number of mobile agents (numberOfAgents): 100.
Minimum percentage of mobile agents required to hit the target (targetHitPercentage): 5%.
Percentage of node mobile agents (nodePercentage): ranging from 1% to 20% of the total number
of mobile agents.
Initial (startLatitude, startLongitude) and target (targetLatitude, targetLongitude) geographic
coordinates for node mobile agents.
Percentage of mobile agents that lost communication (lossPercentage), randomly generated
between 50% and 90%. For each agent that lost communication, the geographic coordinates were set
to NULL.
The experimental results were used to plot graphs illustrating the relationship between the
percentage of node mobile agents % (nodePercentage) and the probability of hitting the target
(targetHitPercentage) in each experiment (Fig. 2).
Figure 2: Relationship between the percentage of node mobile agents and the probability of hitting
the target
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Based on the experimental results, the average effectiveness of target hits with varying numbers
of node mobile agents was analyzed (see Table 1 and Fig. 3).
Table 1
Target-Hitting Effectiveness with Varying Numbers of Node Mobile Agents
Number of Number of mobile agents with which communication was lost, %
nodal mobile
up to 50% up to 60% up to 70% up to 80% up to 90%
agents, %
0 24 22 17 12 6
1 29 27 24 19 22
2 31 31 32 22 19
3 33 44 53 38 28
4 78 66 62 53 47
5 87 82 54 47 34
6 84 78 82 72 62
7 92 83 92 88 72
8 91 83 88 76 70
9 90 92 91 78 72
10 99 94 97 82 84
11 100 98 98 84 83
12 100 100 96 92 88
13 100 100 100 91 94
14 100 100 100 97 92
15 100 100 100 100 97
16 100 100 100 100 100
17 100 100 100 100 100
18 100 100 100 100 100
19 100 100 100 100 100
20 100 100 100 100 100
Figure 3: Average Effectiveness of Target Hits with Varying Numbers of Node Mobile Agents
Figure 3 indicates the average effectiveness of target hits based on the percentage of node mobile
agents:
188
16% or more node mobile agents: 100% effectiveness.
12–15% node mobile agents: 95% effectiveness.
10–11% node mobile agents: 90% effectiveness.
7–9% node mobile agents: 80% effectiveness.
0–6% node mobile agents: less than 80% effectiveness.
From the above, it can be concluded that to achieve effective target damage (80% and above), the
minimum required number of node mobile agents (nodePercentage) is between 7% and 9%. Therefore,
using more than 9% of node mobile agents out of the total number of mobile agents is not advisable.
4. Evaluation of the Effectiveness of Network-Centric Control for
Mobile Agents in a Dynamic Environment Using Neural Networks
Evaluating the effectiveness of network-centric control for mobile agents in a dynamic environment,
using neural networks, plays a crucial role in determining the suitability of this approach for various
tasks. The main stages of this method can be described as follows:
1. Training Data Preparation: collect and prepare a large dataset representing different
scenarios and dynamic environmental conditions.
2. Creating a Dataset for Network Training: split the collected data into training and test sets to
evaluate the network's adaptability to new situations.
3. Designing and Configuring the Neural Network:
develop a neural network architecture suited for controlling mobile agents in dynamic
environments.
set the parameters and functions to optimize training.
4. Neural Network Training: use the training set to teach the network to recognize and solve
agent control tasks in various environments.
5. Validation and Performance Evaluation:
use the test set to assess the network’s accuracy and efficiency under real-world
conditions.
analyze the results in the context of specific tasks and environmental constraints.
6. Improvement and Adaptation:
use the results to refine the network architecture and its parameters.
adapt the network to changes in environmental dynamics and control tasks.
The process of training and creating the neural network was carried out using the Deductor Studio
Academic analytical platform, which provides highly efficient tools for data analysis and processing.
A critical step in this process was utilizing a specially created and carefully prepared dataset to train
the network.
The dataset included information from solving 1,000 different problems, which played a key role
in enhancing the adaptability and accuracy of the neural network. This approach to data preparation
contributed significantly to the network’s ability to solve diverse tasks and ensured its high efficiency
across various contexts.
For the parameters listed in Table 2, a mechanism for estimating the probability of target damage
(%) based on the neural network can be applied.
Table 2
The main parameters of the task
Parameter Description
number Mobile agent number
numberOfAgents Number of mobile agents, units
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targetHitPercentage The number of mobile agents that will be enough to hit the target, %
targetHitCount The number of mobile agents that will be enough to hit the target, units.
nodePercentage Number of nodal mobile agents, %
nodeCount Number of nodal mobile agents, units
startLatitude Initial geographic coordinates of the mobile agent (latitude), degrees.
startLongitude Initial geographic coordinates of the mobile agent (longitude), degrees.
targetLatitude Final geographical coordinates of the mobile agent (latitude), degrees.
targetLongitude Final geographic coordinates of the mobile agent (longitude), degrees.
lossPercentage Number of mobile agents with which communication was lost, %
lossCount The number of mobile agents with which communication was lost, units.
targetHitProbability Probability of hitting the target, %
The parameters from Table 2 were provided as input data for building a neural network (Fig. 4).
Figure 4: Input Data Set for Neural Network Construction
Based on the results of the correlation analysis of the raw data, it was determined that not all fields
should be used for neural network training. Only the fields listed in Table 3 are relevant, as the
remaining fields have an insignificant impact on the resulting value (Fig. 5).
Table 3
Input data for neural network training
Parameter Description
number Mobile agent number
numberOfAgents Number of mobile agents, units
targetHitPercentage The number of mobile agents that will be enough to hit the target, %
targetHitCount The number of mobile agents that will be enough to hit the target, units.
nodePercentage Number of nodal mobile agents, %
nodeCount Number of nodal mobile agents, units
lossPercentage Number of mobile agents with which communication was lost, %
lossCount The number of mobile agents with which communication was lost, units.
targetHitProbability Probability of hitting the target, %
targetHitProbability Probability of hitting the target, %
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Figure 5: Correlation Analysis of Input Data
The obtained dataset was used to train a multilayer perceptron neural network. The following
network structure was established: five layers of neurons, with the first (input) layer containing 7
neurons, three hidden layers containing 7, 3, and 5 neurons respectively, and the fifth (output) layer
containing 1 neuron. The activation function used is sigmoid. The architecture of the network –
defined by the number of layers, their sizes, and the activation function – determines its ability to
solve specific tasks. This framework demonstrates how data moves through the network, from the
input to the output layer, with each layer performing calculations using weights and the activation
function. The backpropagation method was employed for training.
The schematic representation of the neural network structure is shown in Figure 6, where the line
colors indicate the values of the weighting factors. The output of the neural network is the probability
of a target being hit (%). By applying the parameters of a different task to the input, the network
generates a predictive output for the probability of hitting the target (%).
Figure 6: The structure of a neural network
During the training of the neural network, the probability values of hitting the target (%) were
obtained. On average, the deviation across 1,000 experiments was 3.8%. This suggests that the neural
network trained using this methodology yields results that closely match the efficiency of the mobile
agent control system (Table 4).
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Table 4
Comparison of the results of the neural network with the original data
numberOfAgents 73 99 87 44 66 89 79 50 91
targetHitPercentage 5 5 5 5 5 5 5 5 5
targetHitCount 4 5 4 2 3 4 4 2 5
nodePercentage 10 11 4 5 15 6 19 15 15
nodeCount 7 11 3 2 10 5 15 8 14
lossPercentage 63 51 69 69 90 63 86 63 73
lossCount 46 50 60 30 59 56 68 31 66
targetHitProbability 74 88 70 84 55 16 57 17 100
Neural network
77 86 68 83 58 15 60 16 99
data
Error, % 4.05 2.27 2.86 1.19 5.45 6.25 5.26 5.88 1.00
The structure of the neural network selected for training resulted in the smallest relative
calculation error, while errors for other network configurations exceeded 5%. Therefore, the
constructed neural network can be effectively used to solve similar problems, as it accurately reflects
the results and allows for the estimation of the probability of hitting the target (%) in dynamically
changing environments. Additionally, the use of the neural network helped identify input parameters
with minimal impact on the outcome.
5. Conclusions
This study reviews the network-centric approach as a method for enhancing the efficiency of mobile
agent control systems. The key distinction of this approach, compared to others, is the use of a
"wandering center." If communication with the current center is lost, a new center is appointed,
ensuring the continuity of the control process. This structure guarantees the system's survivability
and, as demonstrated, offers significant potential for improvement, ultimately increasing the overall
efficiency of mobile agent control.
The research highlights the effectiveness of network-centric control when compared to centralized
control (in which no nodal mobile agents are used). It was found that as the number of nodal mobile
agents increases, the effectiveness of hitting the target improves. Notably, effective performance (80%
or higher) is achieved with just 9% of the total number of mobile agents functioning as nodal agents,
suggesting that exceeding this threshold would not provide additional benefits.
Finally, the results from the mobile agent control system were compared with the output from the
neural network training, which estimates the probability of hitting the target with a relative deviation
of 3.8%. This confirms that the neural network, trained using the proposed methodology, produces
results that closely align with the efficiency of the mobile agent control system.
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