=Paper=
{{Paper
|id=Vol-3922/paper4
|storemode=property
|title=A Hybrid Consensus Mechanism for Enhancing Security and Efficiency in IoV Networks
|pdfUrl=https://ceur-ws.org/Vol-3922/paper4.pdf
|volume=Vol-3922
|authors=Radouane Baghiani,Lyamine Guezouli
|dblpUrl=https://dblp.org/rec/conf/iam/BaghianiG24
}}
==A Hybrid Consensus Mechanism for Enhancing Security and Efficiency in IoV Networks==
https://ceur-ws.org/Vol-3922/paper4.pdf
A Hybrid Consensus Mechanism for Enhancing Security
and Efficiency in IoV Networks⋆
Radouane Baghiani1,*,† , Lyamine Guezouli2
1
Kasdi Merbah University, Ouargla, Algeria
2
HNS-RE2SD, Batna, Algeria
Abstract
The advancement of the Internet of Vehicles (IoV) necessitates secure, scalable, and energy-efficient networking
solutions to support seamless, real-time data exchange among connected vehicles. This paper introduces a
tailored hybrid consensus mechanism, Delegated and Authorized Proof of Stake (DPA-PoS), which addresses these
needs by combining Delegated Proof of Stake (DPoS) with Proof of Authority (PoA). Enhanced with advanced
cryptographic techniques such as Zero-Knowledge Proofs (ZKP) and homomorphic encryption, DPA-PoS offers
significant improvements in security, privacy, and efficiency. By minimizing latency and lowering energy demands,
this approach proves well-suited for critical IoV applications like autonomous vehicle coordination and secure
inter-vehicle communication. Performance tests demonstrate that DPA-PoS surpasses traditional consensus
protocols (PoW, PoS) in efficiency metrics, including reduced latency, faster transaction processing, and improved
energy savings, highlighting its potential as a foundational solution for next-generation IoV systems.
Keywords
Internet of Vehicles (IoV), Delegated Proof of Stake (DPoS), Proof of Authority (PoA), Zero-Knowledge Proofs
(ZKP), Encryption, Data Security
1. Introduction
The Internet of Vehicles (IoV) represents a transformative advancement in connected transportation,
establishing a sophisticated network system where vehicles can communicate seamlessly with each
other (V2V), with surrounding infrastructure like traffic signals and sensors (V2I), and with various
other entities such as pedestrians and smart devices (V2X) [1]. This level of interconnectivity is designed
to create an intelligent, data-driven transport ecosystem that operates in real-time, enhancing safety,
optimizing traffic flow, and enriching the overall driving experience [2].
In recent years, IoV has shown significant potential to improve road safety by facilitating real-
time alerts about hazards and traffic conditions, thereby enabling drivers and autonomous vehicles
to make informed, split-second decisions [3]. Additionally, IoV can reduce traffic congestion through
dynamic routing, manage traffic flows more efficiently, and provide personalized services to drivers and
passengers, such as adaptive navigation and location-based services. As such, IoV not only advances
mobility but also aligns with broader goals of smart cities to create safer, more efficient, and user-centric
urban environments [4].
To achieve its full potential, IoV must address several objectives related to data security, communica-
tion efficiency, and interoperability [5]. At the core of these objectives is the necessity to ensure secure
and reliable data exchanges across the IoV network. Protecting data from cyberattacks and maintaining
confidentiality of user information are fundamental, given the sensitive nature of vehicle and user
data [6]. Additionally, efficient communication is crucial for real-time decision-making, requiring
minimized latency and high-speed data transfer to support applications such as collision avoidance and
traffic management [7]. Seamless interoperability is also essential, as IoV systems integrate various
Proceedings of the 7th International Conference on Informatics and Applied Mathematics IAM’24 , December 4-5, 2024, GUELMA,
ALGERIA.
⋆
Corresponding author.
⋆
† These authors contributed equally.
$ baghiani.radouane@univ-ouargla.dz (R. Baghiani)
© 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
types of vehicles, infrastructure, and management systems from diverse manufacturers and technology
platforms [8].
This paper addresses these challenges by proposing a hybrid consensus mechanism, Delegated
and Authorized Proof of Stake (DPA-PoS). The mechanism combines the scalability and low latency
of Delegated Proof of Stake (DPoS) with the energy efficiency and security of Proof of Authority
(PoA). Enhanced with advanced cryptographic techniques such as Zero-Knowledge Proofs (ZKP) and
homomorphic encryption, DPA-PoS ensures robust data security and operational efficiency, making
it suitable for real-time IoV applications. By leveraging these features, DPA-PoS offers improvements
in critical metrics such as transaction latency, throughput, energy consumption, and data integrity,
addressing key gaps in existing IoV solutions.
The rest of the paper is structured as follows: Section 2 reviews related work and highlights limitations
of existing blockchain-based solutions for IoV. Section 3 analyzes key challenges in IoV systems related
to safety and efficiency. Section 4 provides an overview of traditional consensus mechanisms and their
limitations. Section 5 introduces the DPA-PoS mechanism and its theoretical framework. Section 6
presents case studies and simulation results, comparing DPA-PoS with other mechanisms. Section
7 discusses implementation challenges and future perspectives. Section 8 addresses limitations of
the proposed mechanism and suggests directions for future research. Finally, Section 9 concludes by
summarizing the contributions and emphasizing the potential of DPA-PoS to transform IoV systems.
This class depends on the following packages for its proper functioning:
2. Related Work
The Internet of Vehicles (IoV) has garnered significant attention as an advanced communication network
between vehicles, road infrastructure, and surrounding entities. In [9], As B. Ji et al. (2020) define,
IoV is fundamentally a dynamic network designed to enhance road safety, improve traffic efficiency,
and optimize user experience by enabling real-time data exchanges. Early studies, such as those by
Siuhi and Mwakalonge (2016), focused on the initial applications of IoV, highlighting its transformative
potential in intelligent transport systems (ITS) and autonomous vehicle networks, where real-time data
transfer is critical for safe and efficient operations [10].
Research has since expanded to explore the diverse applications and benefits of IoV. Guerrero-
Ibáñez, Zeadally, and Contreras-Castillo (2018) discuss how IoV supports practical applications, such as
intelligent transport systems, shared mobility services, and commercial logistics. They emphasize that
IoV improves road safety and traffic management while also contributing to environmental sustainability
by reducing greenhouse gas emissions[11]. Similarly, Hamid, Zamzuri, and Limbu (2019) underscore
the essential role of IoV in autonomous vehicles, where real-time communication enables decision-
making and safe navigation, highlighting IoV’s critical function in supporting the future of automated
transportation[4].
However, as IoV has advanced, researchers have identified several challenges that must be addressed
to fully realize its potential. Lu et al. (2014) outline key issues, including data security, user privacy,
communication efficiency, and the interoperability of different systems and devices[12]. These challenges
underscore the need for robust and secure communication protocols to ensure that IoV networks can
function effectively. Qiu et al. (2018) further elaborate on these vulnerabilities, especially focusing on
weaknesses in existing communication protocols that could expose IoV networks to security threats[13].
The insights from these studies demonstrate the necessity of developing enhanced security measures to
protect IoV systems against cyber threats.
In recent years, blockchain technology has emerged as a promising solution for addressing security
and efficiency challenges in IoV. Several researchers have explored its integration with IoV to enhance
security, data integrity, and operational efficiency. For example, Arushi Aroraa and Sumit Kumar Yadav
(2018) propose a blockchain-based security mechanism for IoV, emphasizing how blockchain’s decen-
tralized architecture can improve authentication and data transfer security across the IoV network[14].
Their work also introduces smart contracts to automate connected car services, boosting system effi-
ciency. In a similar vein, the authors of “Security Mechanism for Vehicle Identification and Transaction
Authentication in the Internet of Vehicles (IoV) Scenario” discuss a blockchain model that employs public-
key cryptography and secure transaction protocols to protect sensitive data and maintain confidentiality
within the IoV network, highlighting critical aspects of secure identity management [15].
Further developments in blockchain-enabled IoV networks focus on optimizing communication
and reducing computational demands. The authors of “Blockchain-Based Internet of Vehicles (IoV)
Information Transmission Mechanisms” propose using blockchain functionalities, such as distributed
registries and smart contracts, to authenticate data transmissions securely [16]. Their approach prior-
itizes essential messages and minimizes required authentications, thereby enhancing efficiency and
reducing computational costs. [17] add to this line of research by presenting an optimized method for
vehicle authentication using Proof of Authority (PoA). Their method enhances privacy and reduces the
time required for vehicle authentication, which is especially beneficial in high-traffic IoV scenarios [17].
A more recent study, “Enhancing Security using Trusted Blockchain Method for Internet of Vehicles,”
expands on these concepts by suggesting a distributed access control system within blockchain-enabled
IoV. This method allows vehicles to participate in secure, scalable applications that support decentralized
decision-making, demonstrating blockchain’s potential to enhance transparency and data security
across IoV networks. Such applications illustrate how blockchain can foster a secure and trusted IoV
environment [18].
Despite the progress made in integrating blockchain with IoV, a gap remains in the development
of a specific hybrid consensus mechanism tailored to IoV’s unique requirements [19]. Our proposed
Delegated and Authorized Proof of Stake (DPA-PoS) seeks to address this gap by combining the benefits
of Delegated Proof of Stake (DPoS) and Proof of Authority (PoA) mechanisms. By incorporating
advanced technologies like Zero-Knowledge Proofs (ZKP) and homomorphic encryption, DPA-PoS
enhances IoV security, scalability, and efficiency, providing a robust solution for secure data exchange
and reliable communications in connected vehicle networks.
This related work highlights the growing interest in leveraging blockchain to overcome IoV’s security
and efficiency challenges, and DPA-PoS stands as a distinct contribution in advancing these efforts,
offering a comprehensive, hybrid approach that aligns with the IoV network’s complex needs. These
studies highlight the need for a tailored consensus mechanism that can enhance scalability, security,
and efficiency in IoV systems. The proposed DPA-PoS aims to bridge this gap.
3. Analysis of Safety and Efficiency Challenges in IOV
The Internet of Vehicles (IoV) faces critical challenges in both security and efficiency that must be
addressed to achieve safe and effective network operations.
3.1. Security Challenges
IoV networks are vulnerable to cyber threats like DDoS attacks, data interception, and intrusions due to
weak communication protocols and interoperability issues. Ensuring data confidentiality is crucial to
maintain user privacy, requiring data anonymization, encryption, and adherence to regulations such as
GDPR[20]. Addressing security vulnerabilities is crucial to ensure trust and reliability in IoV systems,
which also impacts overall efficiency, as discussed next.
3.2. Efficiency Challenges
• Data Management: The vast amount of data generated in IoV demands efficient storage, synchro-
nization, and real-time processing to enable accurate and timely decision-making.[21]
• Latency: Low latency is essential for safety-critical applications, making high-speed networks
like 5G critical for rapid data transmission.[22]
• Scalability: IoV systems must handle increasing numbers of connected devices and data volumes
without losing performance, requiring robust resource management and adaptability.[23]
• By addressing these challenges, IoV can meet the demands of secure, efficient, and scalable modern
transportation systems.
These challenges necessitate innovative solutions, such as a robust consensus mechanism, which we
explore in the following section.
4. Exploring Current Blockchain Mechanisms
Current blockchain mechanisms each offer distinct advantages and drawbacks for IoV:
• Proof of Work (PoW): Secure but energy-intensive and slow, making it unsuitable for real-time
IoV needs.[24]
• Proof of Stake (PoS): More energy-efficient but prone to centralization and security challenges.[25]
• Delegated Proof of Stake (DPoS): Improves scalability and latency but risks centralization among
elected delegates.[26]
• Proof of Authority (PoA): Fast and energy-efficient, yet may compromise security and decentral-
ization due to reliance on a few trusted nodes.[27]
These limitations highlight the need for a tailored, hybrid consensus mechanism to meet IoV’s specific
requirements for low latency, security, and scalability.
While these mechanisms offer distinct advantages, none fully address the unique requirements of
IoV systems, such as real-time performance and scalability. This gap motivates the development of the
proposed DPA-PoS mechanism.
5. Proposed Improvements
The proposed Delegated and Authorized Proof of Stake (DPA-PoS) mechanism addresses the unique
challenges of the Internet of Vehicles (IoV), focusing on enhancing scalability, security, and energy
efficiency. By integrating the scalability and low latency of Delegated Proof of Stake (DPoS) with the
energy-efficient and secure characteristics of Proof of Authority (PoA), DPA-PoS provides a tailored
consensus mechanism for real-time, secure IoV applications.
5.1. Operational Framework of DPA-PoS
The DPA-PoS mechanism operates through the following steps:
1. Delegate and Validator Selection:
• IoV participants, such as connected vehicles and roadside units (RSUs), vote for delegates
based on their reputation and stake [28].
• Validators are randomly selected from the pool of delegates to ensure fairness and mitigate
risks of centralization [29].
2. Block Proposal and Validation:
• Delegates propose blocks containing transaction or communication data within the IoV
network [30].
• Validators authenticate blocks using Zero-Knowledge Proofs (ZKP) to maintain data privacy
without revealing sensitive details.
3. Consensus Formation:
• Validators evaluate the block and reach a consensus based on a predefined threshold, ensur-
ing the inclusion of validated transactions in the blockchain.
• If the block is invalid, it is rejected and re-proposed by another delegate.
4. Dynamic Security and Role Rotation:
• To prevent centralization and improve resilience, the roles of delegates and validators are
rotated periodically.
• Continuous network monitoring and cryptographic protocols (e.g., homomorphic encryp-
tion) protect against threats such as Sybil attacks and Distributed Denial of Service (DDoS)
attacks.
5. Energy-Efficient Operations:
• By limiting computational tasks to a small number of trusted nodes, DPA-PoS achieves
significantly reduced energy consumption while maintaining high transaction throughput
and low latency.
• Continuous network monitoring and cryptographic protocols (e.g., homomorphic encryp-
tion) protect against threats such as Sybil attacks and Distributed Denial of Service (DDoS)
attacks.
5.2. Practical Applicability in Real-World Scenarios
DPA-PoS is particularly suited for critical IoV applications, including:
1. Urban Traffic Management:
• RSUs act as delegates to validate and synchronize traffic data, enabling dynamic control of
traffic flow and congestion reduction.
• Low-latency operations ensure real-time updates to traffic lights and navigation systems.
2. Autonomous Vehicle Coordination:
• ToAutonomous vehicles communicate securely using encrypted messages validated through
DPA-PoS.
• The mechanism supports reliable decision-making for tasks such as collision avoidance,
lane changes, and platooning.
3. Secure Payment Systems for Electric Vehicle Charging:
• DPA-PoS enables secure, low-latency financial transactions between electric vehicles and
charging stations.
• Cryptographic techniques such as ZKP and homomorphic encryption ensure transaction
confidentiality and integrity.
5.3. Flowchart of DPA-PoS Mechanism
Below is a high-level flowchart illustrating the operational steps of DPA-PoS for IoV systems:
5.4. Advantages of DPA-PoS
The DPA-PoS mechanism demonstrates significant advantages over traditional consensus methods:
1. Scalability: Supports up to 2000 transactions per second (TPS), outperforming Proof of Work
(PoW) and Proof of Stake (PoS) systems [30].
2. Energy Efficiency: Consumes only 0.5 J per transaction, significantly less than PoW and compara-
ble to PoA [30].
3. Security: Combines ZKP and homomorphic encryption to ensure data integrity, confidentiality,
and robustness against cyberattacks [30].
4. eal-Time Communication: Achieves sub-second transaction latency, enabling real-time operations
essential for IoV applications such as traffic management and autonomous vehicle coordination
[30].
By addressing these critical needs, DPA-PoS offers a robust solution for secure, scalable, and efficient
IoV systems, bridging gaps in current blockchain-based consensus mechanisms and paving the way for
enhanced connected mobility.
Figure 1: Flowchart illustrating the operational steps of DPA-PoS for IoV systems
6. Case Studies and Simulations
To address the computational demands of advanced cryptographic methods in the DPA-PoS mechanism,
we assess the impact of Zero-Knowledge Proofs (ZKP) and homomorphic encryption. While ZKP
enhances data privacy by enabling verification without exposing details, it introduces latency as
transaction volume increases. Homomorphic encryption allows encrypted data processing but requires
considerable computational resources, which can impact throughput in high-traffic IoV environments.
These trade-offs are considered in our performance simulations.
6.1. Methodology for Testing DPA-PoS
Our simulations evaluate DPA-PoS for performance, safety, and energy efficiency across test scenarios
in dense urban areas and for electric vehicle payments. Using traffic and network simulators (SUMO and
NS-3) alongside blockchain performance tools (Hyperledger Caliper), we measure latency, throughput,
energy consumption, and security.
The validation results, presented in the next section, highlight the performance of DPA-PoS in terms
of latency, throughput, and energy efficiency.
6.2. Validation Process
Simulation of Urban Mobility (SUMO) and Network Simulator 3 (NS-3) to replicate realistic IoV con-
ditions. SUMO was employed to model traffic dynamics in a virtual urban environment, simulating
vehicle mobility, traffic density, and road conditions. The simulation involved creating a city layout
with intersections, traffic lights, and diverse road types, where vehicles followed predefined routes
over a simulation period of 3600 seconds. With a total of 1000 vehicles moving at an average speed of
40 km/h, SUMO generated mobility traces representing real-time vehicle positions, which were later
integrated into the network simulation.
NS-3 was then utilized to simulate communication among vehicles and infrastructure, incorporating
the mobility traces from SUMO to ensure accurate vehicle movements. The IEEE 802.11p standard,
designed for vehicular ad hoc networks (VANETs), was used to model data exchanges. The network
setup included a communication range of 300 meters, a channel bandwidth of 10 MHz, a packet size
of 512 bytes, and a transmission interval of 100 milliseconds. Metrics such as latency, throughput,
packet delivery ratio, and energy consumption were measured to evaluate the performance of the
communication network under real-world IoV scenarios.
The simulation parameters used in this study are summarized in the following table:
Table 1
Simulation Parameters
Parameter Value
Traffic Area Size 10 km²
Number of Vehicles 1000
Simulation Time 3600 seconds
Vehicle Speed Range 20–80 km/h
Communication Range 300 meters
Channel Bandwidth 10 MHz
Packet Size 512 bytes
Transmission Interval 100 ms
Consensus Mechanism DPA-PoS
Mobility Model SUMO-generated traces
The DPA-PoS mechanism was integrated into the simulation to handle transaction validation and
block generation. Testing scenarios included high-density urban traffic, electric vehicle charging
station payments, and emergency vehicle prioritization. Key performance indicators such as average
transaction latency, throughput in transactions per second (TPS), energy consumed per transaction,
system scalability under increasing loads, and security resilience against cyberattacks were analyzed to
assess the effectiveness of the mechanism.
By combining SUMO and NS-3, the validation process provided a comprehensive framework to
test DPA-PoS in realistic IoV settings, demonstrating its relevance and effectiveness in addressing the
challenges of real-time communication, energy efficiency, and network scalability. This integration
ensured an accurate evaluation of the proposed mechanism’s performance and applicability in modern
IoV systems.
6.3. Results Achieved: Performance, Energy Efficiency , and Safety
The evaluation of the proposed DPA-PoS mechanism demonstrated significant improvements across
key metrics, including latency, throughput, energy efficiency, and security, making it a robust solution
for real-time and scalable IoV applications. Table 2 summarizes the performance metrics and provides a
comparative analysis with traditional consensus mechanisms such as Proof of Work (PoW), Proof of
Stake (PoS), and Proof of Authority (PoA).
Performance Metrics. The DPA-PoS mechanism achieved a latency of less than 1 second, signif-
icantly outperforming PoW, which requires 50 seconds, and PoS, which averages 3.5 seconds. This
ultra-low latency is essential for IoV systems that rely on real-time data exchanges, such as autonomous
navigation and emergency response applications. Additionally, DPA-PoS demonstrated a throughput of
2000 transactions per second (TPS), far exceeding PoW (15–20 TPS), PoS (1250 TPS), and PoA (1500
TPS). This high throughput ensures the system can accommodate the increasing number of connected
vehicles in modern IoV networks. In terms of energy efficiency, DPA-PoS consumed only 0.5 Joules per
transaction, a dramatic improvement over PoW’s 3 Joules and comparable to PoA. This energy efficiency
makes the mechanism sustainable, especially for battery-constrained devices like electric vehicles and
roadside units. Furthermore, DPA-PoS exhibited strong security capabilities, achieving an attack failure
rate of less than 0.01% and 99.9% confidentiality through advanced cryptographic techniques such as
Zero-Knowledge Proofs (ZKP) and homomorphic encryption. These attributes ensure the integrity and
privacy of sensitive data, safeguarding IoV systems against cyber threats such as Distributed Denial of
Service (DDoS) and Sybil attacks.
Scalability was another key strength of DPA-PoS, which demonstrated the ability to handle an annual
network growth rate of 25%, outperforming PoW (5%), PoS (15%), and PoA (20%). This scalability high-
lights the mechanism’s capacity to support the rapid expansion of IoV networks without performance
degradation.
Practical Implications and Scenarios. These results highlight several practical implications for IoV
systems. The low latency enables real-time decision-making in critical applications, such as autonomous
vehicle coordination and dynamic traffic management. High throughput ensures the efficient handling
of traffic surges in urban environments, preventing congestion and enabling smooth traffic flow. The
energy efficiency of DPA-PoS supports sustainable operations, reducing the energy burden on IoV
devices and contributing to the environmental goals of smart cities.
To illustrate the potential applications, consider a smart city traffic management system. During
rush hours, DPA-PoS can dynamically adjust traffic signals and reroute vehicles to optimize traffic flow.
In emergency response scenarios, such as an ambulance navigating heavy traffic, DPA-PoS enables
seamless communication with traffic lights and nearby vehicles to create a clear path. In decentralized
electric vehicle charging networks, DPA-PoS facilitates secure, low-latency payments while optimizing
energy distribution to meet user demand efficiently.
The results underline the transformative potential of DPA-PoS for IoV systems, offering a unique
combination of low latency, high throughput, energy efficiency, and robust security. These characteris-
tics make it well-suited for addressing the challenges of real-world IoV applications, paving the way
for enhanced scalability, efficiency, and sustainability in connected transportation networks. Further
exploration through real-world implementations could validate these findings and uncover additional
use cases.
Performance
1. Latency Comparison Graph
The graph illustrates the average latency for transaction validation across different consensus mecha-
nisms. The x-axis is labeled as "Consensus Mechanism" (e.g., DPA-PoS, PoW, PoS, PoA), while the y-axis
is labeled as "Latency (seconds)." Consistent colors or patterns are used to represent each mechanism.
fig 2. This graph compares the average transaction latency of various consensus mechanisms. DPA-
PoS achieves a latency of less than 1 second, significantly outperforming PoW (50 seconds) and PoS
(3.5 seconds) while matching PoA. This low latency is crucial for real-time IoV applications such as
collision avoidance and traffic management.
2. Throughput Comparison Graph This graph displays the throughput achieved by each consensus
mechanism. The x-axis represents "Consensus Mechanism," and the y-axis represents "Throughput
(TPS)." A legend is included to clarify the representation of each mechanism.
fig 3. This graph highlights the throughput in transactions per second for different consensus
mechanisms. DPA-PoS achieves the highest throughput at 2000 TPS, significantly exceeding PoW
Figure 2: Average Latency of Consensus Mechanisms
Figure 3: Average Latency of Consensus Mechanisms
(15–20 TPS), PoS (1250 TPS), and PoA (1500 TPS). This high throughput ensures scalability in handling
the increasing number of IoV transactions.
3. Energy Efficiency Comparison Graph The energy consumption graph uses a bar chart to highlight
the efficiency differences between consensus mechanisms. The x-axis is labeled "Consensus Mechanism,"
and the y-axis is labeled "Energy Consumption (Joules per transaction).
fig 4. This graph shows the energy consumption per transaction for each consensus mechanism.
DPA-PoS and PoA are the most energy-efficient, consuming only 0.5 Joules per transaction, while
PoW is significantly less efficient, consuming 3 Joules. This energy efficiency supports sustainable IoV
operations, especially for battery-constrained devices.
Figure 4: Energy Consumption of Different Consensus Mechanism
Safety
Resistance to Attacks: Simulations showed robustness against DDoS attacks and intrusions, thanks to the
combination of PoS and PoA. Data Integrity: Transactions remained intact and unaltered, guaranteeing
the integrity of communications. Privacy Protection: The use of ZKP and homomorphic encryption
ensured the confidentiality of user data. Security Graph: The graph compares the attack resistance of
different consensus mechanisms. The DPA-PoS mechanism demonstrates very high security, particularly
due to the use of Zero-Knowledge Proofs (ZKP) and homomorphic encryption.
Figure 5: Energy Consumption of Different Consensus Mechanism
6.1. Quantitative Comparison of DPA-PoS with Traditional Consensus Mechanisms
The DPA-PoS mechanism was evaluated for key metrics with quantitative estimates to enhance com-
parison:
Energy Consumption: DPA-PoS uses approximately 0.5 Joules per transaction, about 40% less energy
than PoW and comparable to PoA.
Security: DPA-PoS demonstrated resilience, with an attack failure rate below 0.01% and enhanced data
integrity, outperforming typical PoS systems by up to 30%.
Confidentiality: With Zero-Knowledge Proofs (ZKP) and homomorphic encryption, DPA-PoS achieves
99.9% confidentiality, a 20% improvement over PoS.
Scalability: DPA-PoS supports high transaction throughput (around 2,000 TPS) and can accommodate
an annual network growth rate of 25%, significantly higher than PoW and PoS.
This quantitative approach provides a clearer, data-backed comparison of DPA-PoS with PoW, PoS,
and PoA systems, emphasizing its benefits in energy efficiency, security, confidentiality, and scalability.
The following table provides a comparison of the proposed DPA-PoS mechanism with traditional
consensus mechanisms in terms of key performance metric:
Table 2
Comparison with Traditional Consensus Mechanisms
Metric DPA-PoS (Proposed) PoW PoS PoA
Latency (seconds) <1 50 3.5 <1
Throughput (TPS) 2000 15-20 1250 1500
Energy Consumption (J) 0.5 3.0 0.7 0.5
Attack Failure Rate (%) <0.01 10 2 3
Confidentiality (%) 99.9 80 90 85
Scalability (Annual Growth) 25% 5% 15% 20%
The results of case studies and simulations show that the DPA-PoS hybrid mechanism offers a
viable and improved solution for the specific needs of the Internet of Vehicles (IoV). It offers superior
performance, enhanced security, and optimal energy efficiency compared with traditional consensus
mechanisms such as PoW, PoS, and PoA. By combining the strengths of Delegated Proof of Stake
and Proof of Authority, and integrating advanced technologies such as Zero-Knowledge Proofs (ZKP)
and homomorphic encryption, DPA-PoS can revolutionize the way connected vehicles interact and
communicate, paving the way for safer, more efficient, and more sustainable mobility.
6.2. Real-World Applicability of DPA-PoS in IoV Systems
The DPA-PoS mechanism offers significant potential to enhance scalability and security in smart city and
IoV applications. In traffic management, it can dynamically coordinate real-time communication between
vehicles and infrastructure, reducing congestion and improving safety in projects like Singapore’s
intelligent traffic systems. For decentralized EV charging networks, as seen in cities like Amsterdam,
DPA-PoS facilitates secure, real-time payments and supports scalability to meet growing demand.
Autonomous vehicle coordination, such as Detroit’s Mobility Innovation Corridor, benefits from DPA-
PoS’s low-latency and high-security communication, ensuring safe navigation and efficient platooning.
During disasters, the mechanism enables rapid validation of evacuation routes, as seen in disaster-prone
cities like Tokyo, ensuring reliable and secure coordination of emergency vehicles. In rural areas
connected to smart city networks, DPA-PoS’s energy-efficient design supports long-term operation for
IoV devices reliant on limited power sources.
These scenarios illustrate how DPA-PoS addresses IoV challenges, providing scalable, secure, and
efficient solutions for modern transportation systems. Future pilot projects will further demonstrate its
practicality and refine its capabilities for widespread adoption.
7. Challenges and Perspectives
The implementation of DPA-PoS in IoV systems faces technical challenges, including interoperability,
scalability, security, privacy, and achieving low latency for real-time applications. Operationally, high
implementation costs, adoption hurdles, and regulatory compliance pose significant barriers. Addressing
these requires collaboration among stakeholders, including manufacturers, governments, and service
providers.
Future advancements in AI, 6G, and smart cities present opportunities to enhance DPA-PoS. AI can
optimize resource allocation and transaction validation, while 6G’s ultra-low latency and high data
rates will improve real-time performance. Pilot projects and collaborations will be essential to refine
the mechanism and test its applicability in traffic management, EV charging, and autonomous vehicles.
With continuous innovation and stakeholder cooperation, DPA-PoS can become a cornerstone for
secure and scalable IoV systems in smart cities.
While DPA-PoS addresses several critical challenges, certain limitations persist, which are explored
in the following section.
8. Discussion on Limits and Future Work
The proposed DPA-PoS mechanism, while showcasing significant improvements in scalability, security,
and energy efficiency, faces several limitations that must be addressed to ensure its practical applicability
in real-world IoV systems. One of the primary challenges lies in the integration costs associated with
deploying the mechanism in existing vehicular communication infrastructures. These costs, which
include the implementation of blockchain frameworks and hardware upgrades, may discourage adoption,
particularly among smaller stakeholders with limited resources.
Another concern is the mechanism’s susceptibility to Sybil attacks, where malicious actors can
create multiple fake identities to manipulate the consensus process. Although DPA-PoS combines the
strengths of Delegated Proof of Stake (DPoS) and Proof of Authority (PoA), the reliance on node selection
processes exposes it to this vulnerability. Addressing this issue is critical to maintaining the integrity
and fairness of the network. Additionally, the scalability of DPA-PoS, while superior to traditional
consensus mechanisms, can be challenged under high transaction volumes or peak traffic conditions.
These bottlenecks may hinder real-time communication and decision-making in safety-critical IoV
applications.
The computational overhead introduced by advanced cryptographic techniques, such as Zero-
Knowledge Proofs (ZKP) and homomorphic encryption, also presents a limitation. While these tech-
niques enhance security and privacy, they demand significant processing power, which could strain
resource-constrained IoV devices like autonomous vehicles and roadside units. Reducing this overhead
is essential to ensure the widespread adoption of the mechanism across diverse IoV environments.
To overcome these challenges, several solutions and future directions are proposed. Cost optimization
can be achieved through collaborative funding models involving governments, automotive manufactur-
ers, and IoV service providers. Additionally, using modular and open-source blockchain frameworks
may reduce development costs and simplify integration. To enhance security against Sybil attacks,
robust identity management systems based on decentralized identifiers (DIDs) can be implemented.
Integrating machine learning algorithms to detect and prevent malicious behavior can further improve
network integrity.
Improving scalability is a priority, and techniques such as sharding and sidechains can be introduced
to distribute transaction loads across multiple chains. For example, transactions in high-density traffic
areas can be processed on sidechains, leaving the main chain free for critical operations. Dynamic
resource allocation mechanisms can also be employed to prioritize time-sensitive transactions during
high-load conditions, ensuring real-time performance in critical IoV scenarios.
Leveraging artificial intelligence (AI) offers another promising avenue for optimization. AI can be
integrated with DPA-PoS to enhance resource allocation, predict potential bottlenecks, and streamline
transaction validation processes. This integration can ensure consistent performance under varying
network conditions while minimizing computational demands. Additionally, research into lightweight
cryptographic protocols can address the challenges posed by the computational overhead of ZKP and
homomorphic encryption, making DPA-PoS more suitable for devices with limited processing power.
Finally, real-world pilot projects in smart cities can validate the mechanism’s practicality and perfor-
mance. Collaborations with automotive manufacturers, urban planners, and IoV service providers can
provide empirical data to refine the mechanism and demonstrate its scalability and adaptability. These
initiatives will be critical in translating the theoretical benefits of DPA-PoS into tangible improvements
in connected transportation systems.
By addressing these limitations and exploring the proposed solutions, DPA-PoS can evolve into a
comprehensive and adaptable technology for IoV systems. Future research should focus on integrating
emerging technologies such as AI and IoT to further enhance its robustness and efficiency. Continuous
innovation and collaboration among stakeholders will ensure that DPA-PoS meets the demands of
secure, scalable, and energy-efficient IoV systems, paving the way for its adoption as a cornerstone of
connected mobility.
9. Conclusion
The DPA-PoS mechanism demonstrates strong potential as a scalable, secure, and energy-efficient
solution for IoV systems, addressing challenges in real-time communication, data privacy, and net-
work resilience. By combining DPoS and PoA with advanced cryptographic techniques like ZKP and
homomorphic encryption, it outperforms traditional mechanisms in latency, throughput, and energy
efficiency, making it ideal for connected transportation networks.
Future research should validate DPA-PoS through pilot projects in traffic management, autonomous
vehicle coordination, and EV charging networks, providing insights into practical deployment challenges.
AI integration can further optimize resource allocation and transaction validation, while scalability
enhancements via sharding and sidechains will support growing IoV networks. Efforts to improve
cryptographic efficiency will ensure the mechanism’s suitability for resource-constrained devices. With
these advancements, DPA-PoS can become a foundational technology for smart, connected mobility
systems.
Declaration on Generative AI
The authors have not employed any Generative AI tools.
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