SABEC: Secure and Adaptive Blockchain-Enabled Coordination

Protocol for Unmanned Aerial Vehicles(UAVs) Network

Hulya Dogan

and Anton Setzer

Department of Computer Science, Swansea University, Swansea, U.K.

Keywords: UAVs Network, Byzantine Attack, Swarm Drone, Blockchain, Security, Proof of Work (PoW), Fuzzy

C-Modes Clustering Algorithm, Fault Tolerance.

Abstract: The rapid advancement of drone swarm technology has unlocked a multitude of applications across diverse

industrial sectors, including surveillance, delivery services, disaster management, and environmental

monitoring. Despite these promising prospects, ensuring secure and efficient communication and coordination

among drones within a swarm remains a significant challenge. Key obstacles include maintaining efficiency,

facilitating the seamless sharing of sensing data, and achieving robust consensus in the presence of Byzantine

drones—malicious or faulty UAVs capable of disrupting swarm operations and leading to catastrophic

outcomes. To address these challenges, we introduce SABEC (Secure and Adaptive Blockchain-Enabled

Coordination Protocol), an innovative blockchain-based approach designed to manage multi-drone

collaboration during swarm operations. SABEC improves the security of the consensus achievement process

by integrating an efficient blockchain into the UAV network, coupled with a practical and dynamic consensus

mechanism. The protocol incentivizes network devices through a scoring system, requiring UAVs to solve

intricate problems employing the Proof of Work (PoW) with Fuzzy C-Modes clustering algorithm. Leader

UAVs are dynamically selected within clusters based on a predefined threshold, tasked with transmitting status

control information about neighbouring UAVs to a cloud server. The server consolidates these data through a

robust consensus mechanism, relaying them to the network coordination tier where decision-making

consensus is reached, and the data are immutably stored on the blockchain. To facilitate the dynamic and

adaptive construction of configurable trusted networks, SABEC employs a consensus protocol based on the

blockchain-assisted storage. Comparative experiments conducted using NS3 simulation software demonstrate

SABEC's significant advantages over traditional routing and consensus protocols in terms of packet delivery

rate, coordination overhead, and average end-to-end delay. These improvements collectively enhance the fault

tolerance of UAV networks, ensuring high availability and reliability even in the presence of adversarial nodes.

By augmenting the security of consensus achievement, SABEC substantially improves connectivity, security

and efficiency within intelligent systems, thereby elevating the potential and stability of multi-drone

applications in real-world scenarios.

1 INTRODUCTION

In the era of 4.0 industry, the widespread integration

of autonomous robotic systems has revolutionized

various sectors, such as healthcare (F. Cunico et al.,

2024), self-driving automobiles (Asilian et al., 2023),

smart manufacturing (Yucesoy et al., 2024), and

agriculture (Tang et al., 2024). This paradigm shift in

robotics research has transitioned from developing

and operating sophisticated single-robot systems to

exploring multi-robot or swarm-robot systems. The

https://orcid.org/0009-0000-1841-2968

https://orcid.org/0000-0001-5322-6060

ability to integrate simple individual robot actions

into collaborative missions involving multiple robots

has enabled the accomplishment of higher-level tasks

through interaction and collaboration within vast

robotic systems. Despite individual robots being

relatively uncomplicated and limited in capability,

they can exhibit sophisticated collective behaviours at

the multi-robot level (Pajany et al., 2024). Notably,

drones have emerged as pivotal aerospace robots,

facilitating diverse real-world applications. The

advent of smart manufacturing and smart cities has

Dogan, H. and Setzer, A.

SABEC: Secure and Adaptive Blockchain-Enabled Coordination Protocol for Unmanned Aerial Vehicles(UAVs) Network.

DOI: 10.5220/0013330500003899

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

In Proceedings of the 11th International Conference on Information Systems Security and Privacy (ICISSP 2025) - Volume 1, pages 377-388

ISBN: 978-989-758-735-1; ISSN: 2184-4356

377

underscored the increasing importance of real-time,

efficient, and secure environment monitoring

systems, which rely on Unmanned Arial Vehicles

(UAVs) for enhanced functionality (Chung et al.,

2018). UAV enables collaboration among drones and

their access to restricted airspace, thereby bolstering

air traffic management (Jin et al., 2003), logistics

monitoring (Queralta et al., 2020), smart mobility

(Yang et al., 2024), public safety (Lou et al., 2024),

and environmental applications (Du et al., 2024).

Drones have found extensive utility in numerous

domains, including package delivery (Dogan et al.,

2023), environmental monitoring (Wang et al., 2024),

collaborative operations with other robot types in

smart manufacturing (Silva et al., 2024), traffic

monitoring in smart cities (Amarcha et al., 2024), and

public safety and disaster management. These

applications share a common requirement of

navigation and airspace control (Salim et al., 2024).

Moreover, large-scale environmental monitoring

necessitates the coordination of a group of drones due

to individual drones' limited mobility and

capabilities. Consequently, coordinated control

strategies and practical consensus algorithms are

indispensable to ensure UAV systems' stability,

safety, energy efficiency, and trustworthiness.

However, the inherent heterogeneity and complexity

of UAV systems necessitate the development of

efficient and adaptable network designs to ensure

proper functioning and safety. Blockchain

technology, specifically consensus algorithms, offers

a decentralized and scalable solution for achieving

consensus among multi-drones while enhancing

security and trustworthiness in UAV networks (Chen

et al., 2024; Alsamhi et al., 2022; Jin et al., 2024).

Integrating blockchain into multi-drone systems has

emerged as a prominent research area, providing

solutions for controlling Byzantine drones and

addressing the consensus problem. Furthermore,

specific aspects of collaboration requiring the sharing

of sensitive data among drones can be secured by

incorporating elements of the blockchain stack, such

as the Merkle Tree technique (Jiang et al., 2020).

Consequently, multi-drone systems necessitate

consensus among drones to enable real-time,

collaborative, and efficient task execution.

Subsequent investigations since 2018 have explored

various blockchain applications in the swarm of

UAVs, encompassing consensus achievement of

swarms in the presence of Byzantine drones,

management of collaboration in heterogeneous UAV

systems, and secure data collection. Nonetheless, this

study investigates the utilization of blockchain

technology to manage drone collaboration in a multi-

drone system, emphasizing the sharing of sensor data

capability, which poses a significant challenge in

multi-drone collaboration. Considering that drones

exhibit varying numbers, types, and data analysis

rates, it is crucial to establish an automatic consensus

mechanism for drones. The objectives of applying

consensus algorithms in blockchain systems align

with those of swarm design. Firstly, blockchain

functions as a distributed decision-making system

that operates without the need for trust between

participating entities, mirroring the operating

conditions of swarms (Liang et al., 2024). Secondly,

since blockchain systems incorporate procedures to

maintain information integrity, swarms established

through these procedures do not require additional

nodes for verifying operational records (Khan et al.,

2024). Thirdly, the loss of a single drone, akin to the

loss of an individual node in any decentralized

system, should maintain the consensus-reaching

process (Jiang et al., 2024). Proof of Work (PoW), a

decentralized consensus technique, compels network

participants to invest time in solving arbitrary

mathematical puzzles to prevent malicious influences

(Sedjelmaci et al., 2017). In this study, we

implemented a new practical and dynamic protocol

using PoW consensus to generate the difficulty factor

in the UAV network and the dynamic clustering

selection frequency. This approach provides drones

with enhanced accuracy, usability and mitigates the

risk of malicious attackers/ Byzantine drones sharing

tampered data.

UAV networks possess qualities such as

affordability, easy and flexible deployment, and high

resistance to destruction, making them extensively

utilized in numerous fields (Bertrand et al., 2024). In

recent years, the domestic consumer-grade UAV

market has reached saturation, leading to the

prominence of industrial-grade UAV

s in the emerging

industry. Collaborating with traditional sectors, UAV

networks have become indispensable aerial

platforms, playing irreplaceable and crucial roles in

various specialized environments, including security

monitoring, emergency disaster mitigation, rescue

operations, exploration, and digital cities (Kundu et

al., 2024). Despite progress in swarm drone

technology, drones remain vulnerable to jamming,

trapping (Fang et al., 2022), and attacks (Hughes et

al., 2024) due to their limited resources, the open

nature of wireless communications, and the need for

more aerial countermeasures (Li et al., 2021).

Mission-oriented UAV networks operate in highly

dynamic, complex, and unstructured environments

where network size, topology, and node

trustworthiness constantly change. Enhancing

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network fault tolerance and maintaining

trustworthiness during missions pose significant

challenges for distributed UAV networks, given their

limited resources and lack of central support (He et al.,

2020). UAV networks operating in mission-oriented

environments face three significant unfavourable

conditions: non-security, complex operation

environments, lack of central support, and limited

resources of network nodes. Thus, enhancing fault

tolerance and maintaining trustworthiness during

missions pose major challenges for distributed UAV

networks with limited resources and no central support.

Mission-oriented UAV networks operate in highly

dynamic, complex, and unstructured environments

where network size, topology, and trustworthiness of

network nodes continuously change. Consequently,

unauthorized access by external nodes must be

prevented along with tolerating internal error nodes

that may emerge within UAV nodes due to

consumption, damage, or compromise.

Figure 1: Network Architecture of the System.

2 CONTRIBUTIONS

This paper introduces the Secure and Adaptive

Blockchain-Enabled Coordination (SABEC) protocol,

which addresses the dynamic nature of UAV networks

by leveraging blockchain technology combined with

the Proof-of-Work (PoW) mechanism (Abishu et al.,

2024) and Fuzzy C-Means Clustering (FCM)

algorithm (Sun et al., 2024). SABEC ensures secure

network participation and leader election through

rigorous verification processes, enhancing protection

against Byzantine drones and other security threats.

Leader drones, validated through PoW, are responsible

for securely transmitting data to a base station server,

which aggregates and evaluates data, storing results on

a blockchain for integrity and reliability. The adaptive

consensus mechanism introduced by SABEC

efficiently handles network topology changes and node

reliability by recording health assessments and

facilitating automatic reconfiguration of the network.

The clustering algorithm within SABEC periodically

selects cluster heads based on trust metrics, forming an

upper-layer network to manage operations. This

dynamic clustering approach optimizes resource

usage, enhances fault tolerance, and supports efficient

collaboration among UAVs. SABEC provides an

innovative solution for secure UAVs network, adaptive

leader election, efficient consensus, and reliable data

storage, significantly advancing UAV network coordi-

nation by improving trust, scalability, and resilience.

3 NETWORK ARCHITECTURE

This The network architecture of the Secure and

Adaptive Blockchain-Enabled Coordination Protocol

(SABEC) is presented, an innovative cross-layer

protocol designed to optimize UAV network

performance through adaptive trust management and

blockchain technology. SABEC addresses critical

challenges such as excessive coordination overhead,

dynamic node density, and Byzantine faults, thereby

ensuring high network availability and

trustworthiness. By leveraging advanced blockchain

technology and innovative consensus algorithms,

SABEC provides a scalable and secure framework

adaptable to the dynamic and resource-constrained

environments in which UAV networks operate. The

architecture of SABEC is meticulously designed to

operate across multiple network tiers, facilitating

seamless information exchange and task

collaboration among UAV nodes. The protocol

integrates blockchain technology to enhance security

and trust management, ensuring that only reliable

nodes participate in the network's upper management

layer. The architecture is compartmentalized into

distinct tiers, each responsible for specific

functionalities essential to the framework’s

performance and reliability.

Signal Transmission and Access Coordination

Tiers: At the foundational signal transmission tier,

the Proximal Node Discovery and Monitoring

Component protocol (PDMC) is responsible for the

accurate detection and continuous monitoring of

adjacent UAV nodes. PDMC employs enhanced

signal processing techniques to identify neighbouring

nodes reliably, even in environments with high

SABEC: Secure and Adaptive Blockchain-Enabled Coordination Protocol for Unmanned Aerial Vehicles(UAVs) Network

379

Figure 2: Blockchain-Enhanced for Swarms of drone network Architecture.

interference and node mobility. This component

protocol establishes a dependable foundation for

subsequent routing decisions by maintaining up-to-

date neighbour tables and monitoring the forwarding

behaviours of adjacent nodes.

Data Coordination Tiers: The data coordination tier

integrates three pivotal component protocols that

collectively manage local network and cross-network

communications: Localized Trust Coordination

Component protocol (LTCC): This component

protocol manages local zone communications by

evaluating and prioritizing coordination paths

through trusted nodes based on real-time assessments.

LTCC minimizes internal zone coordination

overhead by selecting optimal paths that reduce

latency and enhance data delivery efficiency.

Hierarchical Trust-Based Coordination Component

protocol (HTCC): Facilitating external

communications, HTCC establishes hierarchical

coordination paths that connect different network

zones through trusted gateway nodes. HTCC employs

dynamic clustering algorithms to form and manage

hierarchical structures, thereby enhancing scalability

and reducing coordination complexity. Secure

Border Coordination Component protocol (SBCC):

Overseeing data transmission across network

boundaries, SBCC ensures secure and efficient

coordination between zones. SBCC integrates

blockchain-based verification mechanisms to

authenticate coordination information and prevent the

dissemination of malicious data.

Service Management and Control Tiers: At the

pinnacle of the architecture, the service management

tier incorporates the Secure and Adaptive

Blockchain-Enabled Coordination Protocol

(SABEC). SABEC serves as the core component for

managing trust and coordination within the network.

It maintains an immutable ledger of node

trustworthiness and network configurations, enabling

real-time network reconfiguration based on trust

assessments and operational requirements. The

control coordination tier ensures that data transmitted

across the network adheres to predefined security

protocols and operational guidelines, further

fortifying the network’s integrity.

SABEC utilizes a Two-Tier Consensus

mechanism (TTC) to ensure efficient and secure

network reconfiguration: Trust Evaluation Tier

(Data Consensus Stage): In this initial tier, nodes

perform real-time monitoring of proximal nodes’

behaviours using the LTCC and HTCC component

protocols. Nodes generate TATs based on observed

behaviours, which are then broadcasted to authorized

nodes within the upper management network. This

tier employs a Lightweight Byzantine Fault

Tolerance (LBFT) algorithm to achieve rapid

consensus on trust assessments with minimal

computational overhead. Network Coordination Tier

(Decision Consensus Stage): The second tier

involves the aggregation and validation of TATs

through the blockchain’s smart contracts. Authorized

nodes execute smart contracts to finalize consensus

on trust scores and determine necessary network

reconfigurations. This tier ensures that only trusted

nodes are involved in critical network operations,

thereby maintaining the integrity and reliability of the

UAV network.

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Figure 3: Simulation of the Proposed System.

4 SIMULATION OF THE

PROPOSED SYSTEM

To rigorously evaluate the performance and

robustness of SABEC, comprehensive simulations

were conducted using the NS-3 Network Simulator, a

widely recognized tool for modelling and analysing

network protocols. The simulation parameters shown

in Figure 5. To emulate realistic operational

conditions, Windows 11 Home 64-bit 13th Gen Intel

Core i7-13650Hx 2.6GHz 32GB RAM were used in

the simulation. During the simulation, the behaviour

of each node of the network is calculated

independently to match the realistic network

operation, providing detailed and various statistical

data analysis functions.

Figure 4: Results of the Simulation.

The simulation environment was meticulously

designed to replicate real-world UAV mission

scenarios, incorporating a range of operational

parameters to assess protocol performance under

diverse conditions.

Figure 5: Simulation Parameters in NS-3.

Furthermore, the proposed protocol was tested on

mission scenarios and the number of UAV nodes was

selected as 1000 in the simulation experiment. Each

testing protocol was run with one hundred scenarios

with different random numbers, and the average of all

runs was used as the basis for evaluation. The results

are shown in the graph in Figure 4. The data obtained

shows that Byzantine devices do not affect the

proposed system, and the packet transmission speed

is quite successful compared to other studies. Various

mission scenarios were simulated by incrementally

introducing byzantine nodes (ranging from 0 to 35) to

evaluate SABEC's resilience against compromised,

selfish, and failure-prone nodes. Each scenario was

executed thrice with different random node

trajectories to ensure statistical validity, and the

average results were employed for comprehensive

analysis. Malicious nodes exhibited behaviours such

as packet dropping, data tampering, and false

coordination information dissemination to simulate

realistic attack vectors.

SABEC Protocol Implementation

Let X = {x₁, x₂, ..., xₙ} represent the set of UAV nodes

in the network, where each xᵢ contains trust metrics:

Message forwarding accuracy (f), Energy

consumption (e), and Protocol adherence (p). The

FCM algorithm minimizes the objective function:

J(U,V) =

∑∑

(μᵢⱼ)ᵐ ||xᵢ  vⱼ||²









where is U = [μᵢⱼ] is the fuzzy membership matrix, V

= {v₁, v₂, ..., vₖ} represents cluster centers, m > 1 is

the fuzziness coefficient, ||xᵢ - vⱼ|| is the Euclidean

distance between node xᵢ and cluster center vⱼ . The

objective function J(U,V) is the standard formulation

used in the FCM algorithm. It aims to minimize the

weighted sum of squared distances between data

points and cluster centers, where the weights are the

fuzzy membership degrees raised to the power of m.

Trust Metric Calculation for each UAV node, trust

metrics are computed as:

T(xᵢ) = w₁f + w₂e + w₃p

SABEC: Secure and Adaptive Blockchain-Enabled Coordination Protocol for Unmanned Aerial Vehicles(UAVs) Network

381

where is w₁, w₂, w₃ are weight coefficients, 0 ≤ f, e,

p ≤ 1,

∑wᵢ = 1. The trust value T(xᵢ) is computed as a

weighted sum of normalized trust metrics, which is a

common approach in trust assessment models.

Ensuring that ∑wᵢ = 1 allows the trust value to remain

within a consistent scale. Algorithm steps as follows.

Step 1: Initialize membership matrix U⁽⁰⁾ randomly

FOR each iteration t: Step 2: Calculate cluster

centres:

vⱼ =

∑(

ᵢⱼ

)

ᵐ ᵢ





∑(

ᵢⱼ

)

ᵐ





Step 3: Update membership values:

μᵢⱼ =



∑



||ᵢ  ⱼ||

||ᵢ  ₖ||



/()





Step 4: Check convergence:

IF ||U⁽ᵗ⁾ - U⁽ᵗ⁻¹⁾|| < ε THEN stop. END FOR

Trust-based Cluster Formation algorithm categorizes

nodes into c clusters (c = 3):

 High-trust cluster (CH): μᵢⱼ ≥ 0.7

 Medium-trust cluster (CM): 0.3 < μᵢⱼ < 0.7

 Low-trust cluster (CL): μᵢⱼ ≤ 0.3

The trust threshold (τ) is dynamically adjusted:

τ(t) = τ₀ + α∑(ΔT/Δt)

where is τ₀ is the initial threshold, α is the adjustment

coefficient, ΔT/Δt represents trust value change rate.

The effectiveness of FCM clustering is evaluated

using Silhouette_Score defined as (b - a) / max(a,b)

where is a: mean intra-cluster distance, b: mean

nearest-cluster distance. The algorithm incorporates

Byzantine fault tolerance by defining the

Trust_Threshold as mean(TV) + α * std(TV) where α

is the security parameter (ranging from 1.5 to 2.0),

and std represents the standard deviation. Setting the

threshold based on the mean and standard deviation

allows the protocol to dynamically adjust to the

distribution of trust values, enhancing resilience

against Byzantine faults. The time complexity is O(N

* C * I * D) where N is the number of nodes, C is the

number of clusters, I is the number of iterations, and

D is the dimension of the feature vector. The

parameters and algorithms presented are correct and

appropriately formulated for the implementation of

the FCM algorithm within the SABEC protocol. They

accurately reflect standard methodologies in fuzzy

clustering and trust management, and their integration

into the SABEC framework is logically sound. The

detailed steps and formulas provide a robust

foundation for dynamic trust assessment, efficient

cluster formation, and resilience against Byzantine

attacks in UAV networks. The fundamental

membership verification is based on a fuzzy logic

approach combined with blockchain-based

validation. The primary membership vector MV(i)

represents the degree of belonging for each drone i to

available clusters, expressed as: MV(i) = [μi1, μi2, ...,

μic] where μij is the membership degree of drone i to

cluster j, c is the number of clusters. This vector

incorporates multiple parameters including drone

positioning, trust metrics, and performance

indicators.

The protocol employs a trust-weighted

membership strength calculation, MS(i,j) = μij *

w(𝑇



) where w(𝑇



) is the trust-weighted coefficient,

𝑇



represents the trust value of drone i in cluster j.

This formulation ensures that membership

assignment is influenced by both fuzzy clustering

results and established trust metrics.

The algorithm for Cluster Membership Validation

is as follows: Input: Drone 𝐷



, Cluster Set C . Output:

Validated Cluster Assignment and Proof. First,

calculate the Feature Vector F(i) = [Position(i),

Energy(Ū), Trust(i), Performance(i)]. Next, compute

the distance metrics for each cluster 𝐶



in C: D(i,j) =

|| 𝐹



- Centroid(j)||. Then, calculate the degrees of

membership for each group cluster 𝐶



in C:

μᵢⱼ =



∑





(,)



(,)



/()





Finally, validate the proof. If 𝐴𝐶



≥

threshold_membership&ValidateSignature( 𝑃𝑟𝑜𝑜𝑓

()

) and VerifyConsensus(𝑃𝑟𝑜𝑜𝑓

()

) all hold true, then

return VALID. Otherwise, return INVALID.

Leader Selection Metrics

The primary selection metric is calculated using a

weighted composite score SS(i) = α1 * MS(i,j) + α2

* TR(i) + α3 * PS(i) where SS(i) is the selection score

for drone i, MS(i,j) is the membership strength in

cluster j, TR(i) is the trust rating, PS(i) is the

performance score, α1, α2, α3 are weight coefficients

where ∑α = 1. The membership strength (MS) is

defined as:

MS(i,j) = μij * w(Tij) where μij is the

fuzzy membership degree, w(Tij) is the trust-

weighted coefficient, and Tij is the historical trust

value. The characteristics features are reflecting

drone's belonging degree to specific clusters,

ICISSP 2025 - 11th International Conference on Information Systems Security and Privacy

382

incorporating historical performance and accounting

for drone distribution. The trust rating calculation

(TR) is defined as:

TR(i) =

(∑

𝑇𝑉(𝑘,𝑖)





)

/ n * β

where the components are TV(k,i) representing the

trust value from drone k to drone i, n is the number of

evaluating drones, β is the trust decay factor (0 < β ≤

1). Peer evaluation impact, temporal relevance and

network consensus are considered. The performance

score (PS) is defined as:

PS(i) = w1* EC(i) + w2 * CC(i) + w3 * NS(i)

where is EC(i) is the energy capacity, CC(i) is the

communication capability, NS(i) is network stability,

and w1, w2, w3 are weight factors. The weight

adaptation formula is 𝛼



= 𝛼



+ η * ΔP

where η is the learning rate, ΔP represents the

performance change. The threshold adjustment is

given by threshold(t+1) = threshold(t) * (1 + λ*ΔE)

where λ is the adjustment coefficient, ΔE is the

environmental change factor.

When the cluster head selection, the cluster head

score (CH_score) is calculated as:

CH_score(i) = SS(i)*(𝐸



/𝐸



)*(1/𝐷



)

where𝐸



is the current energy level, 𝐸



is the

maximum energy capacity, and 𝐷



is the

average distance to cluster members. The role

assignment formula is

Role_fitness(i) = SS(i) * CF(i) * AF(i) where CF(i) is

the capability factor, and AF(i) is the availability

factor.

Proof of Work (PoW) and Leader

Election

At the core of SABEC's security mechanisms is the

integration of the PoW mechanism with leader

election. PoW serves as a fundamental principle for

defending the network and incentivizing legitimate

participation. Each node capable of solving a valid

PoW receives recognition as the legitimate leader.

The PoW mechanism uses a cryptographic puzzle,

which provides fairness in terms of computational

effort and fosters scalability among autonomous

nodes, deterring collusion. This combined approach

improves resilience against Sybil attacks, ensures

decentralized governance, and provides more

scalability in consensus leadership roles, ultimately

contributing to improved security and critical

network performance.

The Difficulty Factor D is dynamically adjusted

to regulate computational effort required by each

UAV. It is recalculated in response to network

changes to ensure fairness and maintain appropriate

security provisioning. The expression for D is:

D = 𝐷















where 𝑇



is the target time for discovering a hash

value that meets the condition. This inclusion of a

target time ensures the unpredictability of PoW

solutions. Nodes solve the difficulty puzzle, and the

UAV broadcasts the result along with its unique

identification to all nearby nodes. Each UAV verifies

the solution by hashing its assigned identifier, 𝐼𝐷



, the

current timestamp 𝑡



, and a generated nonce 𝑁



, as G

= H(𝐼𝐷



|| 𝑡



|| 𝑁



). Difficulty verification requires

that G < 𝐶



, which is the network difficulty

component: 𝐶



= 𝐶



x 𝑇



. This

condition ensures that only UAVs investing

significant computational effort can find a valid

solution. Upon finding a valid nonce 𝑁



, the UAV

broadcasts its solution, including 𝐼𝐷



, 𝑡



, and 𝑁



, to

neighboring nodes. Neighboring UAVs independently

verify the solution by recomputing 𝐶



and

checking the difficulty condition. This step prevents

fraudulent claims of PoW resolutions. The solution is

valid, and the UAV proceeds to the next operation of

leader election. The criteria to rank and elect the

leader involves the highest score in a pre-existing

metric calculated as the total assessment, historical

performance, operational validity, and peer

evaluation: 𝑅



= α



* 𝑇



+ α



* 𝑃



+ α



* 𝐶



+ α



* 𝐻



where 𝑇



is trust score of UAV node i, 𝑃



performance score, 𝐶



is communication capability,

and 𝐻



is historical accuracy. Every authenticated

UAV node with a verified computational difficulty

solution is included in the leadership process, and a

unique identifier set {𝐼𝐷



, 𝑡



, 𝑁



} is broadcast to

verify identity and ensure consistency.

Security Analysis of SABEC Protocol

The robustness of the Secure and Adaptive

Blockchain-Enabled Coordination (SABEC) protocol

against specific attacks is paramount for ensuring the

reliability and security of UAV networks. By

conducting a comprehensive security analysis, we can

elucidate how SABEC addresses potential threats

such as Sybil attacks, collusion, replay attacks, and

Byzantine faults. This analysis highlights the

protocol's resilience and the mechanisms by which it

safeguards the network's integrity. One of the critical

threats in UAV networks is the Sybil attack, where a

SABEC: Secure and Adaptive Blockchain-Enabled Coordination Protocol for Unmanned Aerial Vehicles(UAVs) Network

383

malicious entity generates multiple fake identities to

gain disproportionate influence over the network.

SABEC mitigates this risk through a multifaceted

approach that combines unique identity verification,

blockchain-based identity management, and trust

evaluation adjustments. The trust evaluation process

incorporates identity verification by assigning lower

trust scores to nodes with no or limited history—a

common characteristic of newly created Sybil

identities. The trust rating for a node i is adjusted

using a new identity factor ɣ



, where ɣ



= 0.5 for new

nodes and ɣ



= 1 for established nodes. The trust

rating is then calculated as:

TR(i) =



∑

(,)









x β x ɣ



where 𝑇𝑉(𝑘,𝑖) is the trust value from node k to node

i, n is the number of evaluating nodes, and β is the

trust decay factor.

In addressing collusion attacks, where multiple

malicious nodes collaborate to manipulate trust

assessments or disrupt network operations, SABEC

employs distributed trust assessment, adaptive

weighting mechanisms, and selective consensus

participation. Trust evaluations are aggregated from

multiple independent nodes, reducing the influence of

any colluding group. Each node k assesses node i and

computes 𝑇𝑉(𝑘,𝑖). The global trust score TR(i) is

calculated as:

TR(i) =



∑

(,)









x β

An anomaly detection mechanism computes the

variance 𝜎





of the trust values for node i. If 𝜎





exceeds a threshold 𝜃



, collusion is suspected,

and appropriate measures are taken. Adaptive

weighting further diminishes the impact of colluding

nodes by weighting trust scores based on the

trustworthiness of the evaluating nodes. The weighted

trust aggregation is:

TR(i) = 

∑





 

(

,

)





∑









 x β

where 𝜔



= 𝑇𝑅



is the trust rating of node k. Nodes

with lower trust ratings have less influence on the

global trust score, making it difficult for malicious

nodes to skew trust evaluations. Moreover, only

nodes exceeding a trust threshold

𝜏



participate in the consensus process, limiting the

ability of malicious nodes to influence critical

network decisions. The trust threshold is dynamically

set as:

𝜏



= mean(TR) + α * std(TR) where α

is a security parameter, and std(TR) is the standard

deviation of trust ratings.

To counter replay attacks, where valid messages

are maliciously retransmitted to deceive the network,

SABEC includes timestamps 𝑡



and nonces 𝑁



messages to ensure freshness. The message structure

is: 𝑀





𝐷𝑎𝑡𝑎,𝑡



,𝑁



,𝑆𝑖𝑔𝑛𝑎𝑡𝑢𝑟𝑒



. Recipients verify

that the timestamp is within an acceptable window

and that the nonce has not been previously used,

preventing attackers from replaying old messages.

Addressing Byzantine faults, where nodes behave

arbitrarily or maliciously, SABEC implements a

lightweight Byzantine Fault Tolerance (LBFT)

consensus algorithm. This algorithm ensures that the

network can reach consensus even when a fraction of

nodes is faulty or malicious. The LBFT algorithm

tolerates up to ƒ faulty nodes in a network of ƞ nodes,

provided that ƞ ≥ 3ƒ + 1. The consensus process

involves pre-prepare, prepare, and commit phases,

where nodes validate proposals, broadcast

verifications, and agree on decisions after receiving

sufficient confirmations.

Dynamic leader election, based on trust scores and

rotated periodically, prevents any single node from

exploiting a leadership position. Key parameters

within SABEC play a vital role in the protocol's

security. The security parameter α affects the

sensitivity to trust deviations in threshold

calculations, impacting the detection of anomalies

and potential attacks. The trust decay factor β controls

the influence of past trust evaluations, ensuring that

recent behaviors are weighted appropriately in trust

assessments. The new identity factor ɣ



reduces the

trust influence of new nodes, mitigating the impact of

Sybil attacks by preventing newly introduced

identities from gaining immediate significant

influence. The variance threshold 𝜃



aids in

detecting potential collusion by identifying

inconsistencies in trust evaluations. The adjustment

coefficient ʎ allows for dynamic adaptation of

thresholds in response to environmental changes,

ensuring that the protocol remains effective under

varying network conditions. The Secure and Adaptive

Blockchain-Enabled Coordination (SABEC) protocol

represents a significant advancement in securing

Unmanned Aerial Vehicle (UAV) networks. It

enhances the integrity and operational resilience

through the use of Proof of Work (PoW) mechanisms,

lightweight hierarchical leader election, and adaptive

security policies specifically designed to protect

nodes against critical threats. The detailed security

threats, such as Sybil attacks, DoS attacks, and

Byzantine faults, in the following sections shed light

on the intricacies of the SABEC framework. The

protocol provides significant measures of security

and reliability.

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384

5 PERFORMANCE ANALYSIS

The comparative analysis underscores SABEC's

superiority in maintaining high performance and

reliability under adverse conditions. While traditional

protocols like AODV(Tan et al., 2020), OLSR(Proto et

al., 2011), and ZRP(Khan et al., 2021) exhibit

satisfactory performance in benign environments, their

capabilities deteriorate rapidly in the presence of

malicious nodes. SABEC exhibits superior fault

tolerance by dynamically isolating malicious nodes

and reconfiguring the network topology. This

proactive approach prevents faulty or malicious nodes

from disrupting network operations, ensuring

continuous and reliable data transmission. Traditional

protocols lack such dynamic isolation mechanisms,

making them vulnerable to network destabilization

under high adversarial conditions. SABEC optimizes

resource utilization through its hierarchical network

structure and efficient consensus mechanisms. By

minimizing redundant coordination paths and reducing

coordination overhead, SABEC ensures that limited

UAV resources are allocated effectively, enhancing

overall network performance and longevity. In

contrast, traditional protocols often suffer from

excessive routing overhead and inefficient resource

allocation, particularly as network size increases.

Traditional protocols generally lack integrated security

features, rendering them susceptible to various attacks.

SABEC’s integration of blockchain technology

provides robust security enhancements, including

immutable trust records and secure consensus

operations. This integration effectively mitigates

threats such as black hole attacks, gray hole attacks,

node impersonation, and collusion, thereby preserving

the integrity and reliability of the UAV network.

Figure 6: Packet Delivery Rate vs. Number of Malicious

Nodes.

The results, depicted in Figure 6, illustrates the

Packet Delivery Rate (PDR) across different

protocols as the number of malicious nodes increases.

Initially, AODV (Tan et al., 2020) demonstrates the

highest PDR in the absence of malicious nodes,

closely followed by ZRP(Khan et al., 2021) and

SABEC. However, as malicious nodes are introduced,

the PDR of AODV, OLSR, and ZRP declines sharply

due to their inability to effectively isolate

compromised nodes. In contrast, SABEC maintains a

high PDR even with an increasing number of

malicious nodes, thanks to its dynamic trust

blockchain-based consensus mechanisms.

Figure 7: Coordination Overhead vs. Number of Malicious

Nodes.

Figure 7 presents the coordination overhead across

different protocols under varying numbers of byzantine

nodes. Classical protocols like OLSR and AODV

exhibit low coordination overhead in benign

conditions; however, their overhead surges drama-

tically as malicious nodes are introduced, primarily due

to the proliferation of invalid routing information and

continuous route maintenance. Conversely, SABEC

demonstrates a consistently low and decreasing

coordination overhead. This efficiency is achieved

through the isolation of untrustworthy nodes and the

reliance on a trusted upper management network,

which minimizes redundant coordination information

and optimizes resource utilization.

Figure 8: End-to-End Delay vs. Number of Malicious

Nodes.

SABEC: Secure and Adaptive Blockchain-Enabled Coordination Protocol for Unmanned Aerial Vehicles(UAVs) Network

385

The End-to-End Delay (E2E Delay), depicted in

Figure 8, is a crucial metric for time-sensitive UAV

operations. In environments without malicious nodes,

ZRP achieves the lowest latency, followed by OLSR

and AODV. However, the introduction of malicious

nodes leads to a rapid increase in E2E Delay for these

classical protocols, ultimately causing network

instability beyond 30 malicious nodes. SABEC,

leveraging its trusted coordination mechanisms and

hierarchical network structure, maintains low E2E

Delay even under high adversarial conditions,

ensuring timely data delivery essential for mission-

critical UAV applications.

Figure 9: Blockchain Storage Growth Comparison.

Storage and energy efficiency are critical for UAV

networks, which operate under stringent resource

constraints. SABEC addresses these challenges

through its two-tier consensus mechanism and

efficient blockchain integration. Figure 9

demonstrates that SABEC significantly reduces

blockchain storage growth by retaining only essential

consensus results and aggregated trust scores. This

approach contrasts sharply with traditional

blockchains, which require continuous storage of all

transaction data, leading to rapid ledger expansion.

Figure 10: Energy consumption vs. Number of Nodes.

Energy consumption analysis, presented in Figure

10, reveals that SABEC outperforms traditional

blockchain consensus algorithms such as Proof-of-

Work (PoW), Proof-of-Stake (PoS), and Practical

Byzantine Fault Tolerance (PBFT). By minimizing

computational and communication overhead through

trusted coordination and periodic network

reconfiguration, SABEC ensures sustainable energy

usage, thereby extending the operational lifespan of

UAV nodes. Traditional consensus mechanisms,

particularly PoW, incur high energy costs due to their

computationally intensive nature, making them less

suitable for resource-constrained UAV environments.

The comparative performance evaluation of

SABEC against Enhanced AODV (Tan et al., 2020),

Adaptive OLSR (Proto et al., 2011), and Secure ZRP

(Khan et al., 2021) highlights its superior resilience,

scalability, security, and efficiency under adverse

conditions. SABEC's blockchain-based trust

mechanisms not only enhance its ability to maintain a

high Packet Delivery Rate but also reduce

coordination overhead, ensure low End-to-End

Delay, and provide scalability, security, and energy

efficiency even under challenging conditions. These

advantages position SABEC as a highly suitable

protocol for UAV networks where security,

efficiency, and responsiveness are paramount.

6 CONCLUSIONS

The comparative analysis The implementation and

evaluation of the Secure and Adaptive Blockchain-

Enabled Coordination Protocol (SABEC) demonstrate

its efficacy in enhancing the performance, scalability,

and security of UAV networks. By integrating

blockchain technology with advanced coordination

protocols, SABEC effectively mitigates coordination

overhead, ensures high packet delivery rates,

maintains low end-to-end delays, and optimizes

energy consumption. The framework's ability to

dynamically reconfigure the network in response to

changing node states and malicious activities further

underscores its suitability for mission-critical UAV

applications. Simulation results validate SABEC's

superior performance compared to traditional

coordination protocols, highlighting its resilience and

efficiency in complex operational environments. The

adoption of a two-tier consensus mechanism and

hierarchical network structure ensures that SABEC

can scale effectively while maintaining robust

security and trust management. Future work may

explore the integration of machine learning

algorithms for predictive trust assessments, further

ICISSP 2025 - 11th International Conference on Information Systems Security and Privacy

386

optimization of the consensus mechanism for

enhanced energy efficiency, and real-world

deployment of SABEC in diverse UAV mission

scenarios to validate its performance in practical

applications.

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