=Paper=
{{Paper
|id=Vol-3940/AISD-2024_Paper_2
|storemode=property
|title=AI-Enhanced Swarm Drones: Decentralized Solutions for Sustainable Environmental Monitoring Applications
|pdfUrl=https://ceur-ws.org/Vol-3940/AISD-2024_Paper_2.pdf
|volume=Vol-3940
|authors=Marck Herzon Barrion,Argel Bandala,Jose Martin Maningo,Elmer Dadios,Raouf Naguib
|dblpUrl=https://dblp.org/rec/conf/aisd/BarrionBMDN24
}}
==AI-Enhanced Swarm Drones: Decentralized Solutions for Sustainable Environmental Monitoring Applications ==
https://ceur-ws.org/Vol-3940/AISD-2024_Paper_2.pdf
AI-Enhanced Swarm Drones: Decentralized Solutions for
Sustainable Environmental Monitoring Applications
Marck Herzon C. Barrion1 , Argel A. Bandala1 , Jose Martin Z. Maningo1 , Elmer P.
Dadios1 , and Raouf Naguib2,
1 De La Salle University, 1004 Taft Avenue, Metro Manila, Philippines
2 Liverpool Hope University, Liverpool L69 3BX, United Kingdom
Abstract
Swarm intelligence, a subfield of AI, offers a promising approach for enhancing environmental monitoring,
which is critical for managing and preserving natural ecosystems, particularly in addressing issues like
deforestation, crop health, and soil quality. Traditional centralized monitoring systems are prone to single
points of failure, are less efficient, and are often ecologically disruptive. To address these challenges, we
present a decentralized swarm robotic system using drones that are equipped with AI-based algorithms for
efficient exploration and data integrity. We proposed and tested a hybrid exploration algorithm combining
Correlated Random Walk (CRW) and Levy Flight (LF), which achieved a significantly lower mean absolute
error (3.75%) compared to CRW (7.21%) and LF (11.64%) individually. Additionally, we implemented a two-
factor authentication system to enhance data integrity, reducing the impact of faulty sensors in drones from
a mean absolute error of 30.61% to 20.23%. Our results demonstrate that the decentralized swarm system
outperforms traditional approaches, providing more accurate, efficient, and reliable environmental
monitoring. This research contributes to sustainable land management practices, aligning with UN SDG 15,
and showcases the potential of AI-driven swarm robotics in advancing environmental conservation efforts.
Keywords
swarm robotics, environmental sustainable monitoring, AI-driven systems1
1. Introduction
Artificial intelligence (AI) encompasses a wide range of fields and areas across various domains. In
this study, we explore the field of Swarm Intelligence (SI), particularly swarm robotics, which is a
subdomain of AI that is focused on deploying a decentralized network of robots [1]. We contextualize
the application to environmental monitoring, which is further discussed in this section.
1.1. Background of Study
Environmental monitoring and surveillance are fundamental processes for maintaining and
protecting natural ecosystems and resources worldwide [2], [3]. These practices involve
systematically collecting data to understand and manage the environment's health, which is crucial
for sustaining biodiversity, ensuring food security, and mitigating climate change impacts. Here,
environmental monitoring helps identify ecosystem changes, such as deforestation, pollution, and
land degradation, enabling timely interventions to prevent further damage [4], [5] that may even
apply to sustainable management of resources, such as water [6]. The importance of these activities
aligns with the United Nations Sustainable Development Goal (SDG) #15, which focuses on
AISD-2024: Second International Workshop on Artificial Intelligence: Empowering Sustainable Development, October 2, 2024,
co-located with the Second International Conference on Artificial Intelligence: Towards Sustainable Intelligence (AI4S-2024),
Virtual Event, Lucknow, India.
marck.barrion@dlsu.edu.ph, marckbarrion@gmail.com (M.H.C. Barrion); argel.bandala@dlsu.edu.ph (A.A. Bandala);
jose.martin.maningo@dlsu.edu.ph (J.M.Z. Maningo); elmer.dadios@dlsu.edu.ph (E.P. Dadios); naguibr@hope.ac.uk,
r.naguib@ieee.org (R. Naguib);
0009-0001-6762-2344 (M.H.C. Barrion); 0000-0002-3568-4858 (A.A. Bandala); 0000-0003-2823-2279 (J.M.Z. Maningo);
0000-0002-5751-389X (E.P. Dadios); 0000-0001-6807-7993 (R. Naguib)
© 2024 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
Workshop ISSN 1613-0073
Proceedings
protecting, restoring, and promoting the sustainable use of terrestrial ecosystems, managing forests
sustainably, combating desertification, halting and reversing land degradation, and halting
biodiversity loss.
In this study, we concentrate on three specific scenarios within environmental monitoring: crop
health monitoring, deforestation, and soil quality monitoring, which are shown in Figure 1. Crop
health monitoring is essential for detecting diseases and pests that can devastate agriculture,
affecting food production and security [7]. Deforestation monitoring is crucial for preserving forests,
which are vital for carbon sequestration and biodiversity [8]. Soil quality monitoring helps maintain
fertile lands necessary for agriculture and prevent land degradation [9]. While these scenarios
represent the deployment end goal of our research, the current study assumes that these contexts
are used as sample scenarios where more effective methodologies for environmental monitoring may
be implemented.
(a) (b) (c)
Figure 1: Environmental Monitoring for (a) Crop health [10], (b) Deforestation [11], and (c) Soil
Quality [12].
Classical and conventional environmental surveillance techniques involve manual data collection
and remote sensing technologies. Manual methods, although accurate, are labor-intensive, time-
consuming, and limited in scope. Remote sensing, including satellite imagery and aerial surveys,
offers broader coverage but can be expensive, dependent on weather conditions, and lacks the
necessary resolution for detailed analysis [13], [14], [15]. Additionally, centralized approaches to
environmental monitoring are prone to single points of failure, are less efficient in data processing,
and often disrupt ecological balance due to their intrusive methods. Data integrity is another
significant issue, as centralized systems can be vulnerable to data corruption and loss. Drones,
especially when deployed as a swarm, present a promising solution to these challenges. Swarm
intelligence maximizes decentralized control, enhancing resilience and efficiency and minimizing
ecological disruption [16], [17], [18]. Applying swarm intelligence to drones can revolutionize
environmental monitoring by providing more robust, scalable, and adaptive systems.
1.2. Research Gap
Current centralized environmental monitoring techniques face significant challenges, including
susceptibility to single points of failure, inefficiency, and ecological disruption. Decentralized
systems offer potential benefits such as improved resilience and scalability, yet there is a lack of
efficient swarm exploration algorithms that leverage AI to maximize these benefits [16], [19], [20].
Additionally, ensuring data integrity in decentralized swarm systems remains unresolved. Without
robust methods to secure data, the reliability of the collected information is compromised[21].
In summary, the primary research gaps identified are the limitations of centralized environmental
monitoring, the need for efficient AI-based swarm exploration algorithms, and the importance of
securing data integrity in decentralized systems. Addressing these gaps is crucial for developing
advanced environmental monitoring solutions that align with sustainable development goals.
1.3. Objectives of the Study
The primary aim of this study is to explore and develop innovative methodologies for decentralized
environmental monitoring using swarm robotics. By maximizing the capabilities of artificial
intelligence and swarm intelligence, the research seeks to address the limitations of traditional
environmental surveillance techniques. This study focuses on enhancing monitoring systems'
efficiency, accuracy, and resilience by deploying autonomous drones in a swarm configuration. The
goal is to contribute to sustainable development practices by providing advanced tools for
environmental surveillance that align with the United Nations Sustainable Development Goals (UN
SDGs).
The specific objectives and contributions of this study are as follows:
• Implement a decentralized environmental monitoring system utilizing a swarm of aerial
drones with AI-based algorithms.
• Identify and develop efficient exploration techniques for environmental monitoring,
particularly in crop health monitoring, deforestation, and soil quality assessment scenarios.
• Ensure the integrity of data collected by swarm systems through applying two-factor
authentication approaches, enhancing the reliability and security of the monitoring process.
• Evaluate the performance of the proposed system in various environmental scenarios to
validate its effectiveness and scalability.
• Contribute to the broader field of environmental monitoring by providing insights and
methodologies that can be adapted to different ecological contexts, thereby supporting
sustainable land management practices.
2. Review of Related Literature
Having established the setting and the goals of the study, this section discusses the necessary
concepts and existing works relevant to the paper. Here, we break down the components of the
study, identifying what has been done and what is lacking in the approaches.
2.1. Conventional Environmental Monitoring Techniques
Conventional environmental monitoring techniques primarily involve manual data collection and
remote sensing technologies such as satellite imagery and aerial surveys. While manual methods
provide high accuracy, they are labor-intensive, time-consuming, and limited in spatial coverage.
Remote sensing, although offering broader coverage, often suffers from high costs, dependency on
weather conditions, and sometimes insufficient resolution for detailed analysis [13], [14], [15]. These
centralized approaches are also prone to single points of failure, making the systems vulnerable to
disruptions.
Moreover, traditional methods can be ecologically disruptive, involving significant human
intervention and potentially harming the ecosystems they aim to monitor. The limitations of these
classical techniques highlight the need for innovative solutions that can offer decentralized, efficient,
and minimally invasive monitoring, such as the deployment of drone swarms [16] equipped with
advanced AI algorithms.
2.2. Random Exploration Swarm Algorithms
Random exploration algorithms are pivotal in swarm robotics because they enable autonomous
robots to cover and explore large, unknown environments efficiently. These algorithms help
distribute the exploration tasks among multiple drones, reducing redundancy and increasing overall
coverage.
Correlated Random Walk (CRW) is an exploration strategy where each drone moves in a direction
correlated with its previous movement, balancing exploration and exploitation [22]. On the other
hand, Lévy Flight (LF) involves taking long, straight paths interspersed with short, random
movements, which is particularly effective for searching in environments where targets are sparsely
distributed [23]. Combining these strategies can use both approaches' strengths, leading to more
efficient exploration and data collection in environmental monitoring scenarios [24].
2.3. Vulnerability of Swarm Robots
Swarm robotics systems, despite their robustness and adaptability, are vulnerable to the influence of
malfunctioning or compromised drones, which can affect the behavior and data integrity of the entire
swarm. For instance, a drone with a faulty sensor may transmit incorrect data, leading the swarm to
make erroneous consensus decisions [16], [21], [25]. This scenario is critical in environmental
monitoring, where accurate data collection is essential. A compromised drone might falsely report
healthy conditions in a degraded area, skewing the overall assessment and hindering timely
intervention. Ensuring data integrity within the swarm is thus crucial, necessitating robust
mechanisms to detect and mitigate the impact of such faults, thereby maintaining the reliability of
the swarm's collective decision-making process [26].
3. Proposed System and Algorithms
This paper integrates a drone swarm system with random exploration algorithms to ensure efficient
and secure data collection for the environmental monitoring application. For this section, we discuss
in detail how the decentralized network of functions in environmental collective sensing
scenarios and present the relevant algorithms employed to explore and gather data that is secured
by a two-factor authentication system.
3.1. Swarm Robotics System
The proposed system operates as a distributed network of drones, where each drone acts as a node
participating in the environmental monitoring application. As illustrated in Figure 2, the network of
drones collaborates to cover extensive areas efficiently. Each drone in the swarm is equipped with
ground sensors to detect environmental features, proximity sensors to navigate effectively, and
communication modules such as range and bearing sensors to interact with other drones. These
drones are tasked with reaching a consensus on the environmental state, specifically detecting the
percentage of white tiles in a square grid environment, simulating various environmental features.
Figure 2: Swarm Drone Network
This system achieves consensus through iterative communication and data sharing among the
drones. Each drone independently collects data from its assigned area and shares it with its
neighbors. The drones collectively converge on a shared understanding of the environment through
repeated exchanges and updates. This decentralized approach not only enhances the robustness and
scalability of the system but also mitigates the risks associated with single points of failure in
centralized systems. The consensus mechanism ensures that the collective decision reflects the
aggregated data from all drones, improving the accuracy and reliability of environmental
monitoring.
3.2. Hybrid Correlated Random Walk with Lévy Flight
Random exploration algorithms are essential for efficiently covering unknown and unexplored areas,
which is fundamental in applications like environmental monitoring. Swarm robotics, in particular,
benefits from these algorithms' ability to enhance robustness, scalability, and efficiency. The
movement strategy of drones is crucial for effective exploration, and random movement patterns
help cover larger areas, avoid obstacles, and prevent getting stuck in local minima.
The Correlated Random Walk (CRW) algorithm ensures that each drone's movement direction is
correlated with its previous movement, favoring smaller turning angles. This approach leads to a
tendency for the drone to continue moving in the same direction, which is modeled using a wrapped
Cauchy distribution, as shown in (1). The algorithm updates the drone's position by taking steps
influenced by a correlation factor and a small random deviation, resulting in a path with correlated
consecutive steps, reducing the likelihood of abrupt directional changes.
1 1 (1)
𝑃(𝜙𝑡+1 |𝜙𝑡 ) = ∙ ,
2𝜋 1 + 𝜌2 − 2𝜌 cos(𝜙𝑡+1 − 𝜙𝑡 )
where 𝜌 is the correlation coefficient and 𝜙 is the turning angle. Lévy Flight (LF), another random
walk variation, is characterized by a power-law step-length distribution, resulting in a series of short
turns followed by occasional long straight-line movements. This behavior allows drones to perform
extensive explorations with periodic long jumps, enhancing coverage efficiency. The Lévy Flight
algorithm updates the drone's position using the step-length distribution, as shown in (2).
𝑃(𝛿) ~𝛿 −𝜇 , (2)
where 𝛿 is the step length and 𝜇 is a factor between 1 and 3. Integrating these strategies, the
hybrid approach leverages the strengths of both CRW and LF, ensuring thorough coverage and
efficient navigation. The combined algorithm, presented in Algorithm 1, optimizes the exploration
efficiency of the robotic system by balancing local exploration and long-distance travel.
Algorithm 1
Pseudo-code Algorithm for Hybrid Correlated Random Walk and Lévy Flight
Procedure: HybridRandomWalk(𝑁, 𝜌, 𝜇)
Input: Number of steps 𝑁, Correlation factor 𝜌, Lévy exponent 𝜇
Output: Position (𝑥, 𝑦) after 𝑁 steps
1: INITIALIZE 𝑥 ← 0, 𝑦 ← 0
2: FOR 𝑖 ← 1 to N DO
3: IF 𝑟𝑎𝑛𝑑𝑜𝑚() < 0.5 THEN
4: Randomly choose 𝛿𝜃 from normal distribution 𝒩(0, 𝜎)
5: Update direction 𝜃 ← 𝜌𝜃 + (1 − 𝜌)𝛿𝜃
6: ELSE
7: Randomly choose 𝜃 from a uniform distribution over [0, 2𝜋]
8: Randomly choose L from a Lévy distribution 𝑃(𝐿) ∝ 𝐿−𝜇
9: END IF
10: Set Δ𝑥 ← L ⋅ cos(𝜃) , Δy ← L ⋅ sin(𝜃)
11: Update 𝑥 + Δ𝑥, 𝑦 + Δy
12: END FOR
13: RETURN (𝑥, 𝑦)
3.3. Multi-factor Swarm Data Authentication
We implement a multi-factor data authentication approach to ensure the integrity of data collected
by the swarm. The first verification level occurs within the swarm, where drones cross-verify the
data collected by their peers. This internal verification helps identify and correct discrepancies early
in the data collection process. The second level involves using a decentralized ledger, employing
smart contracts like those used in blockchain technology, to validate the consensus reached by the
swarm. This additional layer of verification ensures that the data integrity is maintained, and the
information is secure from tampering or corruption.
Algorithm 2 presents the pseudocode for multi-factor swarm data authentication. The process
begins with each drone validating its data with neighboring drones, followed by recording the
validated data on the decentralized ledger. The smart contracts facilitate the consensus validation,
ensuring that only verified data is accepted. This two-tiered approach not only secures data integrity
but also enhances the reliability of the environmental monitoring system by preventing the
propagation of erroneous data.
Algorithm 2
Pseudo-code Algorithm for Two-Factor Authentication
Procedure: TwoFactorAuth()
Input: Drone Data (𝑒𝑠𝑡𝑖𝑚𝑎𝑡𝑒𝑠, 𝑣𝑎𝑙𝑖𝑑𝑎𝑡𝑖𝑜𝑛 𝑘𝑒𝑦𝑠)
Output: Authenticated Estimates, Consensus
1: FOR each robot 𝑖 in swarm DO
2: Validate data with neighboring drones (1st Factor)
3: IF validation 𝑠𝑢𝑐𝑐𝑒𝑠𝑠𝑓𝑢𝑙 THEN
4: Submit validated data to the decentralized ledger
5: Record and update consensus estimate using smart contract
6: END IF
7: END FOR
4. Methodology
This section of the paper presents the methods that are implemented to achieve the objectives of the
study. These methods are implemented using different experimental setups, representing
hypothetical scenarios to simulate environmental monitoring applications that include (1) crop
health monitoring, (2) deforestation surveillance, and (3) soil quality monitoring. Here, we
represented specific parameters and characteristics as simplistic quantities and targets, such as white
tile percentages, to serve as proof of concept to test algorithms before actual deployment.
4.1. Experimental Setups
The general goal of these experiments is for the swarm to reach a consensus on the percentage of
white tiles in an unexplored environment. This framework applies to all experiments, with the
representation of what white tiles signify varying per experiment. The black-and-white square grid
used in the experiments represents any unexplored environmental scenario, with white tiles
indicating specific targets or conditions and black tiles indicating different targets or conditions.
Figure 3 shows the consensus task of the swarm with the environmental setup. These experiments
are done via simulation using the ARGoS software in Ubuntu.
Figure 3: Environmental Setup for all the Test Consensus Scenarios with White and Black Tile
Representations
As a summary, Table 1 presents all the experimental setups, together with pertinent descriptions
and configurations for each.
Table 1
Summary of Experimental Setups with Hypothetical Scenarios
Exp Environmental White Tiles Swarm Exploration
Name/Parameter
No. Scenario Representation Size Algorithm
Decentralized Swarm Crop Health Healthy Crops
1 1, 4 Hybrid CRW-LF
Drones Monitoring
Efficient Random Intact Trees in
Deforestation CRW, LF,
2 Exploration Forests 4
Monitoring Hybrid CRW-LF
Algorithms
Nutrient-rich
Soil Quality
3 Data Integrity soil with good 4 Hybrid CRW-LF
Monitoring
vegetation
4.1.1. Experiment #1: Crop Health Monitoring via Single Robot System and
Swarm of Drones
In this experiment, the environmental scenario is set in an agricultural farm where the goal is to
determine the percentage of healthy crops. The white tiles represent healthy agricultural farm, while
the black tiles represent those affected by disease or pests. This is a hypothetical scenario, meaning
that the detailed method by which drones gather information is simplified. Figure 4 shows the setup
for this experiment. We examine and compare the effectiveness of a single drone versus a swarm of
drones in reaching a consensus on the crop health percentage. This experiment is relevant as it
demonstrates the potential advantages of using a swarm of drones over a single drone in terms of
efficiency and accuracy in environmental monitoring.
Figure 4: Experiment #1 Setup for the Single Robot System vs. Swarm of Drones
4.1.2. Experiment #2: Deforestation Monitoring Using Efficient Random
Exploration Algorithms
The hypothetical scenario for this experiment involves deforestation monitoring. In this setup, the
white tiles represent intact and healthy trees and forests, while the black tiles represent areas affected
by illegal logging, forest fires, or other destructive activities. The goal is for the swarm to reach a
consensus on the percentage of white tiles, indicating the health percentage of the forest. Figure 5
shows the setup for this experiment. Here, we test our hybrid algorithm against the correlated
random walk and levy flight algorithms. This experiment is important as it evaluates the efficiency
of different exploration algorithms in accurately and effectively monitoring deforestation.
Figure 5: Experiment #2 Setup for the Efficient Random Exploration Algorithms
4.1.3. Experiment #3: Soil Quality Monitoring Ensuring Data Integrity
This experiment focuses on soil quality monitoring based on vegetation growth. The objective is to
detect good soil quality based on vegetation growth, represented by white tiles, while black tiles
indicate poor soil quality due to factors such as erosion, nutrient depletion, and contamination.
Additionally, this experiment includes a hypothetical scenario where one drone appears to function
properly but has a faulty sensor that constantly reports a 0% white tile value. Figure 6 shows the
experimental setup. This experiment is crucial for assessing the impact of data integrity issues within
the swarm and validating the effectiveness of our two-factor authentication approach in maintaining
accurate environmental monitoring.
Figure 6: Experiment #3 Setup for the Data Integrity Test
4.2. Performance Metric Evaluations
This section discusses the performance metrics measured across all three experiments. The primary
metrics evaluated are the absolute error percentage and the convergence time. The absolute error
(AE) percentage measures the difference between the actual and estimated values of the white tile
percentage, calculated using (3).
|𝜌𝑎𝑐𝑡𝑢𝑎𝑙 − 𝜌̂𝑒𝑠𝑡𝑖𝑚𝑎𝑡𝑒 | (3)
𝐴𝐸 = × 100,
𝜌𝑎𝑐𝑡𝑢𝑎𝑙
This metric provides insight into the accuracy of the consensus reached by the swarm. Additionally,
the convergence time, represented in seconds, is measured in some of the experiments to determine
the time taken for the swarm to reach a consensus. These metrics help evaluate the effectiveness and
efficiency of the proposed algorithms and systems in environmental monitoring applications.
4.3. Statistical Tests and Treatment
We employ a series of statistical tests to ensure the validity and reliability of the conclusions drawn
from the experiments. Initially, the Shapiro-Wilk Test is used to assess the normality of the data
distribution. This test helps determine whether the data follows a normal distribution, which is a
prerequisite for certain parametric tests. Following this, Levene's Test is applied to evaluate the
homogeneity of variances across different groups. This test checks if the variances are equal, which
is crucial for accurate analysis in subsequent steps.
Based on the results of the normality and homogeneity tests, we proceed with either One-way
ANOVA or Welch's ANOVA. One-way ANOVA is used when the data meets the assumptions of
normality and homogeneity of variances, allowing us to compare the means of multiple groups to
see if they differ significantly. If the assumptions are violated, Welch's ANOVA is employed as it is
more robust to unequal variances. These statistical tests provide a rigorous framework for analyzing
the experimental results, ensuring that the findings are statistically significant and reliable.
5. Results and Discussion
In this section, the results obtained from the experiments are presented and discussed. Pertinent
analyses that are related to the objectives of the study are also presented in this section to ensure
that the target contributions are met.
5.1. Implementation of a Drone Swarm Systems
In the first experiment comparing the performance of a single drone versus a swarm of four drones,
the results demonstrate a significant difference in absolute error percentages. As shown in Figure 7a,
the single drone system exhibited a mean absolute error of 12.95%, with a standard deviation of 7.20%.
This error indicates considerable variability and a higher tendency to deviate from the true
percentage of white tiles in the environment. In contrast, the swarm of four drones achieved a much
lower mean absolute error of 3.75% with a standard deviation of 2.42%. This reduction in error
highlights the effectiveness of utilizing multiple drones for consensus tasks, as the swarm is better
equipped to accurately estimate the environmental state by aggregating data from multiple sources.
Additionally, when examining the convergence time, as depicted in Figure 7b, the single drone
system required an average of 236.76 seconds to reach a consensus, whereas the swarm system
required significantly less time, with an average of 77.64 seconds. Based on 20 repetitions, these
findings highlight the advantages of employing a swarm of drones for faster and more accurate
environmental monitoring.
(a) (b)
Figure 7: Experiment 1 Results on (a) Absolute Error Percentage and (b) Convergence Time
Distributions by Swarm Size
Statistical analysis further supports the observed differences between the single drone and swarm
systems. The Shapiro-Wilk test results indicate that the data for both the single drone (p = 0.147) and
the swarm of drones (p = 0.377) follow a normal distribution, allowing for further parametric testing.
between the two groups (p = 0.000545), suggesting that the variability in error is not consistent across
swarm
yielding a significant F-statistic of 27.89 with a p-value of 5.50E-06. These results confirm that the
swarm of drones outperforms the single drone system in terms of both accuracy and efficiency, with
statistically significant differences in performance metrics.
These results have important implications for environmental monitoring applications. In the
context of crop health monitoring, where the white tiles represent healthy crops and vegetables, the
lower absolute error and faster convergence time of the swarm system suggest that using multiple
drones can provide more reliable and timely data. This is crucial for early detection of crop health
degradation, allowing for prompt intervention and better agricultural management. The findings
also emphasize the scalability and robustness of swarm systems, making them more suitable for
large-scale environmental monitoring tasks. Overall, implementing a drone swarm system enhances
the accuracy and efficiency of environmental surveillance, contributing to more sustainable and
effective management of natural resources.
5.2. Efficient Environmental Exploration Algorithms
The results of the experiment comparing different exploration algorithms Correlated Random Walk
(CRW), Lévy Flight (LF), and the Hybrid CRW-LF are shown in Figure 8, which presents the
absolute error percentage distribution. The goal is to minimize the absolute error, with smaller values
indicating more accurate exploration. The descriptive statistics reveal that the Hybrid CRW-LF
algorithm achieved the lowest mean absolute error of 3.75%, with a standard deviation of 2.42%,
outperforming CRW and LF individually. The CRW algorithm had a mean absolute error of 7.21%
with a standard deviation of 4.34%, while the LF algorithm exhibited the highest mean absolute error
of 11.64% with a standard deviation of 4.46%. The results clearly indicate that the Hybrid CRW-LF
algorithm is more effective in reducing error and achieving more accurate consensus in the
environmental monitoring tasks.
Figure 8: Experiment 2 Results on Absolute Error Percentage Distribution by Random Exploration
Algorithm
The statistical tests further confirm the superiority of the Hybrid CRW-LF algorithm. The
Shapiro-Wilk test results suggest that the data for all three algorithms follow a normal distribution,
with p-values of 0.124 for CRW, 0.369 for LF, and 0.377 for the Hybrid CRW-LF, allowing for
ANOVA results yield an F-statistic of 24.07 with a highly significant p-value of 2.65E-08,
confirming that the differences in absolute error among the algorithms are statistically significant.
These findings validate the effectiveness of the Hybrid CRW-LF algorithm in providing more reliable
exploration outcomes in swarm robotics.
In the context of deforestation monitoring, the white tiles represent areas of intact and healthy
trees and forests. In contrast, the black tiles indicate regions affected by illegal logging, forest fires,
or other destructive activities. The lower absolute error achieved by the Hybrid CRW-LF algorithm
suggests that this method is more effective in accurately mapping and identifying areas of
deforestation. This is critical for timely interventions and the effective management of forest
resources. By employing an exploration algorithm with reduced error rates, the monitoring system
can better ensure that areas requiring urgent attention are correctly identified. This contributes
significantly to the broader goal of sustainable environmental management, particularly in
preserving forest ecosystems, which are vital for maintaining biodiversity and combating climate
AI-driven solutions to support sustainable development and environmental conservation.
5.3. Ensuring Data Integrity with Secured Swarm Two-factor Authentication
In the final experiment, the impact of a faulty drone on the swarm's data integrity was assessed by
comparing the absolute error percentages across different configurations, as shown in Figure 9.
When no faulty drones were present, the baseline configuration (without two-factor security) had a
mean absolute error of 5.97% with a standard deviation of 5.64%. The two-factor security
configuration slightly improved accuracy, reducing the mean absolute error to 5.76% with a standard
deviation of 4.22%. However, when introducing a faulty drone where the drone's sensor
consistently reported 0% white tile estimates regardless of actual conditions the baseline
configuration's mean absolute error dramatically increased to 30.61%, with a standard deviation of
5.62%. In contrast, the two-factor security configuration managed to limit the impact of the faulty
drone, reducing the mean absolute error to 20.23%, with a standard deviation of 8.73%. These results
emphasize the effectiveness of the two-factor security system in mitigating the adverse effects of
faulty sensors on the
Figure 9: Experiment 3 Results on Absolute Error Percentage Distribution by Number of Faulty
Drones
The statistical tests further validated these observations. The Shapiro-Wilk test results indicated
that the data was normally distributed for both configurations, with p-values exceeding 0.05 in most
cases, except for the baseline with no faulty drones, with a p-
that variances between the groups were not significantly different when there were no faulty drones
(p = 0.571) but became significant when a faulty drone was introduced (p = 0.014). This justified the
use of -statistic
of 18.99 and a p-value of 9.61E-05. These findings confirm that a faulty drone introduces considerable
variability in data accuracy, and the two-factor security system significantly reduces this variability,
ensuring more reliable data collection.
These findings have important implications in soil quality monitoring through vegetation
surveillance, where white tiles represent healthy vegetation, and black tiles represent poor soil
conditions. Introducing a faulty sensor is a realistic scenario in field operations, where drones may
experience sensor malfunctions due to harsh environmental conditions or wear and tear. The
significant increase in absolute error under the baseline configuration demonstrates the potential
risk of relying on a system without robust data integrity checks. By employing a two-factor
authentication system, we can better safeguard against the propagation of erroneous data, ensuring
that decisions based on drone-collected data are accurate and reflect actual environmental
conditions. This, in turn, supports more effective and sustainable land management practices,
contributing to the overarching goal of environmental sustainability through reliable AI-driven
monitoring systems.
6. Conclusion and Future Work
In this study, we successfully implemented and evaluated a decentralized drone swarm system for
environmental monitoring, focusing on three key scenarios: forest health monitoring, deforestation
surveillance, and soil quality assessment. We developed and tested a hybrid exploration algorithm
combining CRW and LF, demonstrating superior accuracy and efficiency in environmental
exploration compared to individual algorithms. Additionally, we implemented a two-factor
authentication system to ensure data integrity within the swarm, significantly reducing the impact
of faulty drones on overall data accuracy. The findings from these experiments confirm the
effectiveness of using a swarm of drones for more reliable and timely environmental monitoring,
contributing to better decision-making in resource management.
There are several avenues for improving this work in the future. One potential improvement is
to expand the testing to more complex and diverse environmental scenarios, further validating the
proposed system's robustness and scalability. Here, practical deployment is also a possible point of
expansion. Integrating more advanced AI techniques, such as machine learning for anomaly
detection, could enhance the system's ability to identify and adapt to unforeseen challenges in real-
time. Further development of the two-factor authentication system could also involve exploring
more sophisticated consensus algorithms or blockchain-based solutions for even greater security.
The use of swarm robotics in this research aligns well with the objectives of UN SDG 15, which
focuses on the sustainable management of land resources. The promising results indicate that AI-
driven swarm robotics can significantly achieve these goals, offering a scalable, efficient, and reliable
approach to environmental monitoring.
Acknowledgments
The authors would like to thank the Engineering Research and Development for Technology (ERDT)
of the Department of Science and Technology (DOST) for the funding of the study. Additionally,
thank you to De La Salle University (DLSU) Manila for the facilities and constant support in the
conduct of this research.
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