INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
Enhancing VANET Mobility Management Through Intelligent Relay  
Vehicle Optimization  
1 Janardan Prasad, 1 Arvind Kumar, 1 Sharad Kumar, 2 Vikas Sharma  
1 School of Engineering & Technology, Shri Venkateshwara University, Gajraula, U.P. India  
2 Department of Computer Applications, SRM Institute of Science and Technology, Delhi NCR Campus,  
Ghaziabad, U.P. India  
DOI : https://doi.org/10.51583/IJLTEMAS.2025.1412000071  
Received: 20 December 2025; Accepted: 25 December 2025; Published: 05 January 2026  
ABSTRACT  
Vehicular Ad Hoc Networks (VANETs) play a crucial role in enabling intelligent transportation systems by  
supporting real-time vehicle-to-vehicle and vehicle-to-infrastructure communications. However, high node  
mobility, frequent topology changes, and intermittent connectivity significantly affect communication  
reliability and network performance. To address these challenges, this paper proposes an intelligent relay  
vehicle optimization approach aimed at enhancing mobility management in VANET environments. The  
proposed scheme dynamically selects optimal relay vehicles based on key parameters such as vehicle mobility  
patterns, relative speed, link stability, and network connectivity conditions. By intelligently adapting to rapidly  
changing vehicular scenarios, the approach improves data forwarding efficiency, reduces packet loss, and  
enhances overall communication reliability. Simulation-based performance evaluation demonstrates that the  
proposed method outperforms conventional relay selection techniques in terms of packet delivery ratio, end-to-  
end delay, and network throughput. The results indicate that intelligent relay vehicle optimization is an  
effective solution for robust and efficient mobility management in VANETs, particularly in high-speed and  
dense traffic conditions.  
Keywords—Vehicular Ad Hoc Networks (VANETs), Mobility Management, Intelligent Relay Selection,  
Relay Vehicle Optimization, Link Stability, Packet Delivery Ratio, Intelligent Transportation Systems  
INTRODUCTION  
The rapid growth of intelligent transportation systems (ITS) has significantly increased the demand for  
reliable, low-latency, and scalable vehicular communication networks. Vehicular Ad Hoc Networks  
(VANETs), a specialized form of mobile ad hoc networks (MANETs), enable direct communication among  
vehicles and between vehicles and roadside infrastructure to support a wide range of applications, including  
traffic safety, congestion management, infotainment services, and autonomous driving assistance. Despite their  
potential, VANETs face several inherent challenges due to the highly dynamic nature of vehicular  
environments, where frequent topology changes, variable vehicle speeds, and intermittent connectivity  
severely impact network performance and communication reliability. One of the key challenges in VANETs is  
efficient mobility management. Unlike traditional wireless networks, vehicles move at high speeds and in  
unpredictable patterns, leading to frequent link breakages and route failures. These mobility-induced  
disruptions result in increased packet loss, higher end-to-end delay, and reduced throughput, thereby degrading  
the quality of service (QoS) required by time-critical ITS applications. Effective mobility management  
mechanisms are therefore essential to maintain stable communication links and ensure seamless data  
transmission in rapidly changing vehicular scenarios. Relay-based communication has emerged as a promising  
solution to address connectivity issues in VANETs, particularly in sparse networks or high-mobility  
environments. By selecting suitable relay vehicles to forward data packets, VANETs can extend  
communication range, improve link stability, and enhance overall network connectivity. However, the  
performance of relay-based communication heavily depends on the selection of appropriate relay vehicles.  
Conventional relay selection approaches often rely on static metrics such as distance or signal strength, which  
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are insufficient to cope with the dynamic and heterogeneous nature of vehicular networks. These approaches  
may result in suboptimal relay choices, leading to frequent route failures and inefficient use of network  
resources. To overcome these limitations, intelligent relay vehicle optimization has gained increasing attention  
in recent years.  
Intelligent Relay Vehicle Optimization in VANETs  
By incorporating mobility-aware and context-aware parameters—such as relative speed, direction of  
movement, link lifetime, vehicle density, and traffic conditions—intelligent relay selection mechanisms can  
make more informed decisions. Such approaches enable the network to dynamically adapt to real-time  
vehicular conditions, thereby improving communication reliability and network performance. The integration  
of intelligent decision-making techniques, including heuristic optimization and data-driven methods, further  
enhances the effectiveness of relay selection in complex VANET environments. Enhancing mobility  
management through intelligent relay vehicle optimization is particularly important for safety-critical  
applications, where timely and reliable data dissemination can prevent accidents and save lives. Applications  
such as collision avoidance, emergency message dissemination, and cooperative driving require stable and  
low-latency communication links. An optimized relay selection strategy helps maintain robust communication  
paths even under high-speed or dense traffic conditions, ensuring consistent network performance and  
improved QoS. In this context, this paper focuses on enhancing VANET mobility management by proposing  
an intelligent relay vehicle optimization approach. The proposed method dynamically selects optimal relay  
vehicles based on multiple mobility and network-related parameters to ensure stable and efficient data  
forwarding. By continuously adapting to changes in vehicle movement and network topology, the approach  
aims to minimize link breakages and improve overall communication efficiency. Comprehensive simulation-  
based evaluations are conducted to assess the performance of the proposed scheme in comparison with existing  
relay selection techniques. The results demonstrate significant improvements in key performance metrics,  
including packet delivery ratio, end-to-end delay, and throughput, highlighting the effectiveness of intelligent  
relay vehicle optimization in managing mobility in VANETs shown in above Fig. 1. The remainder of this  
paper is organized as follows: Section II reviews related work on relay selection and mobility management in  
VANETs. Section III describes the proposed intelligent relay vehicle optimization model. Section IV presents  
the simulation setup and performance evaluation results, and Section V concludes the paper with future  
research direction.  
LITERATURE REVIEW  
Recent research in Vehicular Ad Hoc Networks (VANETs) has extensively focused on improving mobility  
management, routing efficiency, traffic monitoring, and overall network performance under highly dynamic  
vehicular conditions. Habelalmateen et al. [1] proposed an effectual routing approach for VANETs in urban  
environments by integrating mobility and traffic monitoring mechanisms. Their work demonstrated improved  
routing reliability by adapting to real-time traffic conditions; however, it primarily focused on routing protocol  
enhancement and did not explicitly address optimal relay vehicle selection for sustained link stability. Mobility  
modeling plays a crucial role in evaluating VANET performance. Ramamoorthy et al. [2], [3] conducted a  
simulation-based analysis of different mobility models for Infrastructure-to-Highway VANETs (IH-VANETs).  
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Their study highlighted that realistic mobility models significantly influence network performance metrics such  
as delay and packet delivery. While the work provides valuable insights into mobility behavior, it does not  
propose a mobility-aware relay optimization mechanism, leaving scope for intelligent relay-based mobility  
management approaches. Software Defined Networking (SDN) has also been explored to enhance traffic and  
mobility management in VANETs. Hashim et al. [4] introduced a hybrid traffic management framework in  
SDN-enabled multilayer VANETs, demonstrating improved traffic control and adaptability. Similarly, Ramesh  
and Punniakodi [6] presented a comprehensive study on QoS enhancement in SDN-based VANET  
architectures, emphasizing centralized control and flexible network management. Although SDN-based  
solutions improve global network visibility, they often introduce additional control overhead and dependency  
on infrastructure, which may not be feasible in all VANET scenarios. To address traffic congestion and data  
transmission challenges, Habelalmateen et al. [5] proposed a hybrid traffic management and multipath data  
transmission approach for vehicle-to-vehicle communication. Their method improves data reliability through  
multipath mitigation; however, relay vehicle stability under high mobility conditions was not a primary focus.  
Wu et al. [7] investigated topology optimization for autonomous intersection management systems using a  
periodic intervention-based approach. Their work demonstrated that topology-aware optimization enhances  
communication efficiency, but it is tailored toward intersection management rather than general-purpose  
mobility-aware relay selection. Several studies have also addressed emerging challenges and future trends in  
VANETs. Karimullah et al. [8] discussed advancements in connectivity and mobility to address modern  
VANET challenges, emphasizing the need for adaptive and intelligent networking solutions. Khalifa et al. [9]  
provided a comprehensive analysis of VANET security threats and countermeasures, highlighting that secure  
and stable communication links are essential for reliable mobility management. However, security-centric  
solutions alone do not address frequent link failures caused by vehicular mobility. The integration of  
intelligence into VANET systems has gained increasing attention. Mohanty et al. [10] proposed a cognitive  
intelligence-based VANET framework for effective traffic congestion detection in smart urban mobility,  
demonstrating the potential of intelligent decision-making in vehicular networks. Abdulsattar et al. [11] focused  
on latency reduction and traffic management in hybrid surface-enabled VANETs, achieving improved delay  
performance but relying on hybrid infrastructure support. At a broader level, Li et al. [12] presented a  
comprehensive survey on network management for xANETs, outlining key challenges, evolution trends, and  
future research directions, including the need for adaptive, mobility-aware, and decentralized management  
solutions.  
PROPOSED METHODOLOGY  
This section presents the proposed intelligent relay vehicle optimization methodology designed to enhance  
mobility management in Vehicular Ad Hoc Networks (VANETs). The primary objective of the proposed  
approach is to dynamically select the most suitable relay vehicle for data forwarding by considering mobility-  
aware and network-aware parameters, thereby improving communication reliability, reducing link failures, and  
optimizing overall network performance in highly dynamic vehicular environments.  
1. System Model and Assumptions: The VANET system considered in this study consists of vehicles  
equipped with onboard units (OBUs) that communicate using Dedicated Short-Range Communications  
(DSRC) or IEEE 802.11p standards. Vehicles periodically exchange beacon messages containing information  
such as position, speed, direction, and timestamp. It is assumed that each vehicle is aware of its geographical  
location through GPS and can obtain neighbourhood information within its communication range. Roadside  
units (RSUs) may be present but are not mandatory for the proposed relay selection process, making the  
approach suitable for both infrastructure-assisted and infrastructure-less scenarios.  
2. Problem Formulation: In highly mobile VANET environments, frequent link disconnections occur due to  
rapid changes in vehicle speed and direction. The relay selection problem can be formulated as an optimization  
task, where the goal is to select an optimal relay vehicle that maximizes link stability and communication  
efficiency while minimizing packet loss and transmission delay. Given a set of neighbouring vehicles within  
the transmission range of a source vehicle, the challenge lies in identifying the relay vehicle that can sustain a  
reliable communication link for the longest possible duration under dynamic mobility conditions.  
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3. Mobility and Network Parameter Selection: To achieve intelligent relay optimization, multiple  
parameters influencing link reliability and mobility are considered:  
ï‚·
ï‚·
ï‚·
ï‚·
ï‚·
Relative Speed (RS): The speed difference between the source and candidate relay vehicle, where  
lower relative speed indicates longer link lifetime.  
Direction Similarity (DS): Vehicles moving in the same or similar direction are preferred to ensure  
stable connectivity.  
Inter-Vehicle Distance (IVD): Shorter distances generally offer better signal strength and reduced  
packet errors.  
Link Stability (LS): Estimated using predicted link lifetime based on relative motion and  
communication range.  
Neighbour Density (ND): Vehicles in moderately dense regions are preferred to avoid network  
fragmentation or excessive contention.  
These parameters are normalized to ensure uniform contribution during relay evaluation.  
4. Intelligent Relay Selection Algorithm: The proposed methodology employs a weighted decision-making  
mechanism to compute a Relay Selection Score (RSS) for each candidate relay vehicle. Each parameter is  
assigned a weight based on its relative importance to mobility management. The RSS is calculated as:  
where  
are weighting coefficients satisfying  
.
The candidate vehicle with the highest RSS is selected as the optimal relay. The weighting factors can be  
dynamically adjusted based on traffic conditions, such as high-speed highways or dense urban environments,  
enabling adaptability and context awareness.  
5. Dynamic Relay Maintenance: Due to continuous vehicle movement, the initially selected relay may  
become suboptimal over time. To address this, the proposed methodology includes a dynamic relay  
maintenance mechanism. Vehicles periodically re-evaluate relay suitability using updated mobility  
information. If the RSS of the current relay falls below a predefined threshold, a relay handover is initiated to  
select a new optimal relay, ensuring uninterrupted communication and minimizing packet loss during mobility  
events. Security and privacy considerations are critical in VANET environments due to the open and  
decentralized nature of vehicular communications. The proposed methodology can be extended to incorporate  
security-aware relay selection by integrating trust and authentication metrics into the relay evaluation process.  
Secure beacon authentication, pseudonym-based identity protection, and trust scoring mechanisms can be  
employed to ensure that only legitimate and reliable vehicles participate in relay selection. By incorporating  
these security and privacy measures, the proposed relay optimization framework can mitigate threats such as  
malicious relay selection and false information dissemination, thereby enhancing the reliability and  
trustworthiness of VANET communications.  
6. Performance Evaluation Setup: The proposed methodology is evaluated using a simulation-based  
approach under varying traffic densities and vehicle speeds. Key performance metrics such as packet delivery  
ratio, end-to-end delay, throughput, and relay lifetime are used to assess effectiveness. The results demonstrate  
that intelligent relay vehicle optimization significantly enhances mobility management compared to  
conventional relay selection methods.  
RESULT & ANALYSIS  
This section presents the performance evaluation of the proposed Intelligent Relay Vehicle Optimization  
(IRVO) methodology for enhancing mobility management in VANETs. Simulation-based experiments were  
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conducted to analyze the effectiveness of the proposed approach in comparison with conventional relay  
selection techniques.  
1. Simulation Environment and Dataset Description: The performance evaluation of the proposed approach  
was conducted using a realistic VANET simulation environment that integrates a vehicular mobility generator  
with a network communication simulator. Vehicular movement patterns were generated using SUMO-based  
urban and highway scenarios to accurately capture real-world traffic behavior, including varying vehicle  
speeds, densities, and road layouts. The communication characteristics of the network were modeled using the  
IEEE 802.11p standard, which is widely adopted for vehicular communications and supports low-latency data  
exchange among vehicles. The simulation area was defined as a 1500 m Ă— 1500 m region, with the number of  
vehicles varying from 50 to 200 to represent different traffic density conditions. Vehicle speeds ranged from  
20 km/h to 120 km/h, reflecting both urban traffic and high-speed highway environments. Each vehicle was  
assigned a communication range of 300 m, and Constant Bit Rate (CBR) traffic was used to ensure consistent  
data transmission during the simulation. Data packets of size 512 bytes were transmitted over a simulation  
duration of 300 seconds, allowing sufficient time to observe network performance under dynamic mobility  
conditions. This comparative analysis enables a comprehensive assessment of the proposed method against  
conventional relay selection techniques under identical simulation conditions. The practical applicability of the  
proposed Intelligent Relay Vehicle Optimization (IRVO) methodology can be realized in real-world VANET  
deployments, as it relies on mobility and network parameters that are readily available through onboard units  
(OBUs), GPS, and IEEE 802.11p communication standards. Parameters such as vehicle speed, direction,  
position, and neighbor information are already exchanged via periodic beacon messages in existing vehicular  
communication systems. Therefore, the proposed relay selection and maintenance mechanisms can be  
integrated into current ITS frameworks without requiring additional hardware modifications. The use of  
realistic mobility patterns generated through SUMO further ensures that the simulation environment closely  
reflects real traffic behavior observed in urban and highway scenarios, thereby validating the feasibility of  
deploying the proposed approach in practical vehicular networks.  
2. Packet Delivery Ratio Analysis : Packet Delivery Ratio (PDR) is a critical performance metric in VANETs  
that represents the ratio of successfully received data packets at the destination to the total number of packets  
transmitted by the source. It directly reflects the reliability and effectiveness of data dissemination in highly  
dynamic vehicular environments. Due to frequent topology changes, high vehicle speeds, and intermittent  
connectivity, VANETs often experience packet losses caused by link breakages and route failures. An efficient  
mobility management and relay selection mechanism is expected to maintain stable communication links,  
thereby improving PDR. Intelligent relay selection strategies that consider mobility-aware parameters such as  
relative speed, direction of movement, and link stability are theoretically more capable of sustaining reliable  
paths, resulting in higher packet delivery ratios compared to random or distance-based relay selection methods.  
Packet Delivery Ratio Comparison Under Varying Vehicle Densities  
No. of Vehicles  
50  
RRS  
78.4  
DBRS  
84.6  
Proposed IRVO  
92.8  
100  
150  
200  
72.1  
65.7  
59.8  
80.3  
75.4  
70.1  
90.2  
87.9  
85.3  
The proposed IRVO method consistently achieves higher PDR across all network densities. This improvement  
is attributed to mobility-aware relay selection, which minimizes frequent link breakages common in high-  
speed VANET scenarios.  
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Performance Comparison of Routing Approaches Across Vehicle Densities  
Fig. 2. showing Packet Delivery Ratio (%) versus Number of Vehicles (50, 100, 150, 200) for three routing  
schemes: RRS, DBRS, and Proposed IRVO. For all vehicle densities, the Proposed IRVO consistently  
achieves the highest packet delivery ratio, followed by DBRS, while RRS shows the lowest performance.  
Packet delivery ratio decreases for all schemes as the number of vehicles increases.  
3. End-to-End Delay Analysis: End-to-end delay refers to the average time taken by a data packet to travel  
from the source vehicle to the destination vehicle across the network. This delay includes transmission,  
propagation, processing, and queuing delays, as well as delays caused by route discovery and retransmissions.  
In VANETs, high mobility and frequent link disruptions significantly increase end-to-end delay, particularly  
when relay vehicles are poorly selected. Effective mobility management aims to minimize this delay by  
ensuring stable relay paths and reducing the need for packet retransmissions and route rediscovery.  
Theoretically, intelligent relay vehicle optimization reduces end-to-end delay by selecting relay nodes with  
longer predicted link lifetimes and similar mobility characteristics, enabling faster and more reliable data  
forwarding, which is essential for time-sensitive safety applications.  
Average End-To-End Delay Performance of Relay Selection Schemes  
No. of Vehicles  
RRS  
84.6  
DBRS  
72.3  
Proposed IRVO  
49.5  
50  
100  
150  
200  
96.8  
81.9  
95.6  
108.2  
56.2  
63.8  
71.4  
110.5  
124.3  
IRVO significantly reduces delay by selecting relay vehicles with stable links and similar mobility patterns.  
Dynamic relay maintenance further prevents retransmissions and route rediscovery delays.  
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End-to-End Delay Comparison of Relay Selection Schemes  
Fig. 3. illustrating Average End-to-End Delay (ms) versus Number of Vehicles (50, 100, 150, 200) for three  
relay selection schemes: RRS, DBRS, and Proposed IRVO. The delay increases with vehicle density for all  
schemes. The Proposed IRVO consistently shows the lowest end-to-end delay across all vehicle counts,  
followed by DBRS, while RRS exhibits the highest delay.  
4. Throughput Analysis: Throughput measures the average rate of successful data delivery over the  
communication channel and is an important indicator of network efficiency. In VANETs, throughput is heavily  
influenced by packet loss, congestion, and frequent link failures caused by rapid vehicle movement. Poor relay  
selection often results in repeated packet drops and retransmissions, reducing overall throughput. An optimized  
relay selection approach enhances throughput by maintaining stable communication links and ensuring  
continuous data flow. From a theoretical perspective, intelligent relay vehicle optimization improves  
throughput by minimizing packet loss, reducing communication interruptions, and efficiently utilizing  
available bandwidth, particularly in dense or high-speed vehicular scenarios.  
Throughput Comparison of Vanet Relay Selection Methods  
No. of Vehicles  
RRS  
620  
DBRS  
740  
Proposed IRVO  
910  
50  
100  
150  
200  
580  
520  
470  
690  
640  
590  
860  
810  
760  
The proposed method achieves higher throughput due to reduced packet loss and improved relay stability,  
making it suitable for data-intensive and safety-critical VANET applications.  
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Throughput Performance Comparison Across Vehicle Densities  
Fig. 4. showing Throughput (kbps) versus Number of Vehicles (50, 100, 150, 200) for three VANET relay  
selection methods: RRS, DBRS, and Proposed IRVO. Throughput decreases as vehicle density increases for  
all methods. The Proposed IRVO consistently achieves the highest throughput, followed by DBRS, while RRS  
records the lowest throughput across all vehicle densities.  
5. Average Relay Lifetime Analysis: Average relay lifetime refers to the duration for which a selected relay  
vehicle remains suitable and connected for reliable data forwarding. This metric is especially important in  
VANETs, where relay vehicles frequently move out of communication range due to high mobility. Short relay  
lifetimes lead to frequent relay reselection, increasing control overhead and negatively impacting network  
performance. A robust mobility management strategy seeks to maximize relay lifetime by choosing vehicles  
with similar speed and direction to the source, thereby prolonging link stability. Theoretically, intelligent relay  
vehicle optimization enhances average relay lifetime by predicting mobility behavior and selecting relays with  
longer expected link durations, resulting in fewer relay handovers and improved communication reliability.  
Average Relay Lifetime for Different Vehicle Densities  
No. of Vehicles  
DBRS  
Proposed IRVO  
50  
18.6  
15.4  
12.9  
10.7  
31.2  
27.8  
24.1  
21.6  
100  
150  
200  
The IRVO approach significantly increases relay lifetime by prioritizing vehicles with low relative speed and  
similar direction of movement, leading to more stable communication paths.  
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Relay Lifetime Comparison Under Varying Vehicle Densities  
Fig. 5. illustrating Average Relay Lifetime (seconds) versus Number of Vehicles (50, 100, 150, 200) for DBRS  
and Proposed IRVO schemes. For both schemes, relay lifetime decreases as vehicle density increases. The  
Proposed IRVO consistently achieves a significantly higher relay lifetime compared to DBRS across all  
vehicle densities. In comparison to advanced machine learning and deep learning-based relay selection  
techniques, the proposed IRVO approach offers a lightweight and computationally efficient solution suitable  
for highly dynamic VANET environments. While ML and DL methods require extensive training data, higher  
computational resources, and continuous model updates, the proposed method relies on real-time mobility  
metrics and deterministic decision-making, enabling faster adaptation to rapid topology changes. This makes  
IRVO particularly effective for delay-sensitive and safety-critical applications, while also reducing processing  
overhead on vehicular onboard units.  
CONCLUSION  
This paper presented an intelligent relay vehicle optimization approach to enhance mobility management in  
Vehicular Ad Hoc Networks (VANETs) under highly dynamic traffic conditions. By incorporating mobility-  
aware and network-aware parameters such as relative speed, direction similarity, link stability, and neighbor  
density, the proposed method effectively selects optimal relay vehicles to maintain reliable communication  
links. Simulation results demonstrate that the proposed approach significantly improves packet delivery ratio,  
reduces end-to-end delay, increases throughput, and extends relay lifetime when compared with conventional  
relay selection techniques. These improvements highlight the effectiveness of intelligent relay optimization in  
addressing key challenges associated with frequent topology changes and high vehicle mobility in VANET  
environments. As future work, the proposed methodology can be extended by integrating advanced machine  
learning or deep learning models for predictive relay selection, incorporating real-time traffic and road  
condition data, and evaluating performance in large-scale city-wide scenarios. Additionally, future research  
may explore the applicability of the proposed approach in emerging V2X and 5G/6G-enabled vehicular  
networks, as well as its integration with security and privacy-aware mechanisms for next-generation intelligent  
transportation systems. Although the proposed methodology demonstrates significant performance  
improvements under varying traffic densities, the current evaluation is limited to medium-scale simulation  
scenarios. Large-scale city-wide VANET deployments may introduce additional challenges such as  
heterogeneous traffic patterns, complex road topologies, and increased signaling overhead. Evaluating the  
proposed approach under large-scale metropolitan environments would provide deeper insights into its  
scalability and robustness. This limitation will be addressed in future work by extending the simulation  
framework to city-wide scenarios involving thousands of vehicles and diverse traffic conditions.  
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