A Novel Authentication and Communication Protocol for Urban Traffic Monitoring in VANETs Based on Cluster Management
Abstract
:1. Introduction
- Authentication and registration using third-party certification authority.
- V2I and V2I Communication Channel.
- Graph-Based Resource Sharing in Vehicular Communication.
2. Literature Review
- We introduce a secure authentication mechanism that relies on a trusted third-party certification authority. It ensures the integrity and authenticity of vehicles and infrastructure units participating in the communication network. By leveraging this mechanism, we enhance the overall security of V2I and V2V communications.
- Our protocols establish reliable and secure communication channels for V2I and V2V interactions. They enable efficient and seamless communication between vehicles and infrastructure units, facilitating the exchange of critical data and messages.
- We propose a novel resource-sharing approach based on graph theory principles. By modeling the VANET as a graph, we optimize the allocation of resources, such as bandwidth and power, among vehicles and infrastructure units. This ensures fair and efficient resource utilization, enhancing the overall performance of the communication network.
- By addressing these essential aspects, our research advances the security, reliability, and efficiency of V2I and V2V communications in vehicular networks. The proposed protocols and resource-sharing mechanism pave the way for more robust and secure communication systems, fostering the development and deployment of intelligent transportation solutions.
3. Methodology
3.1. Explanation of Notations
- The range of in varies from 1 to , i.e., , but in other notations, it varies from to i.e.,
- MD5-based secure hash function 128-bit hash value ranges from
- such that presents a set of random numbers generated using the LCG algorithm
- Linear congruential generators can be defined through recurrence relation as:
- will generate a six-digit code.
- is generated using the Fisher–Yates shuffle algorithm. Here is the pseudo representation of this algorithm:
- ○
- function fisherYatesShuffle(array)
- ○
- = length(array)
- ○
- for i from down to 1 do
- ○
- = random integer between 0 and (inclusive)
- ○
- swap and
- ○
- return array
- Explanation of Fisher–Yates Shuffle Algorithm:
- The fisherYatesShuffle function takes an array as input and returns the shuffled array.
- It initializes the variable n with the length of the array.
- The algorithm starts a loop from down to 1. This loop iterates over the elements of the array in reverse order.
- A random integer is generated inside the loop between 0 and (inclusive), where is the current iteration index.
- The elements at indices and are swapped using a temporary variable.
- After the loop completes, the shuffled array is returned.
- and are two extractor functions that extract latitude and longitude from the input location.
- contains message templates. These messages might contain the following commands:
- ○
- Please give me the way. I am on your back!
- ○
- Speed up!
- ○
- Danger ahead!
- ○
- Traffic is jammed on the road. Please adopt an alternative way.
- ○
- I run short of fuel. Please help!
- ○
- I need a mechanic.
- ○
- The tire is punctured.
- ○
- There is an accident on the road near my location.
- ○
- There is a crowd protesting on the way.
- ○
- Please give way to the ambulance!
- ○
- Stop on the way. There is a check post.
- ○
- The weather condition is bad.
- contains three values, including 0, 1, and 2. (0) means the priority of this message is nothing. It might be an informative message. 1 means normal priority, while 2 means a very high priority.
3.2. Motivations and Deployment Considerations
3.2.1. Third-Party CA for Traffic Monitoring
3.2.2. Separate Authentication Communication Scheme
3.2.3. Influence of Capacity and Speed on Network Signaling Overhead
3.2.4. Cluster Management
4. Proposed Protocol
4.1. Registration Phase
- E-mail;
- Password;
- Vehicle Registration Number.
4.2. Authentication Phase
4.3. V2I Communication
4.4. V2V Communication
4.5. Vehicle Clustering and Monitoring
4.6. Cluster Head Selection Algorithm
- Each vehicle inside a cluster announces itself as a “Cluster Head“ and displays the broadcast signal:
- Every vehicle displays the list of closest vehicles () after getting from
- is estimated by
- The weighted sum is calculated by
- The vehicle calculates the above equation’s arguments, and the range of weighted constants varies from 0 to 1. Since the weighted sum is derived from these arguments, the Cluster Head based on this sum will be the most efficient and trustworthy.
- In the end, the with the lowest is selected as the Cluster Head.
- Pseudo code for the Cluster Head Selection Algorithm:
- function select cluster head (clusterVehicles)
- lowest weight = infinity
- selected cluster head = null
- for each vehicle in clusterVehicles do
- = get Closest Vehicles List ()
- weighted sum = calculate Weighted Sum (, )
- if weighted sum < lowest weight then
- lowest weight = weighted sum
- selected cluster head =
- return selected cluster head
- Explanation:
- The select cluster head function takes the list of vehicles within the cluster as input and returns the selected cluster head.
- It initializes the lowest Weight variable with a high value (infinity) and sets the selected Cluster Head to null.
- For each vehicle in the clusterVehicles list, the algorithm retrieves the list of closest vehicles using the get Closest Vehicles List function.
- The weighted sum is calculated using the arguments (the current vehicle) and (the closest vehicles list).
- If the calculated weighted sum is lower than the current lowest Weight, the lowest Weight is updated, and the corresponding vehicle is selected as the potential cluster head.
- After processing all vehicles, the algorithm returns the selected cluster head with the lowest weighted sum.
5. Simulation Setup and Experiments
5.1. Varying the Attackers
5.2. Transmission Range
5.3. Baseline Graph-Based Resource Allocation
Algorithm 1: Baseline Graph-Based Resource Allocation Algorithm |
1. Arbitrarily allocate one V2V link to every cluster from C clusters (C1, …… Cn). 2. for m = 1 to M do 3. for n = 1 to N do 4. for f = 1 to F do 5. Compute the V2I optimal transmit power using the algorithms provided by [66]. 6. Compute the V2V optimal transmit power using the algorithms provided by [66]. 7. Compute the V2I and V2V power gain capacity from the base station resource blocks with the optimized power control parameters. 8. end for 9. end for 10. end for 11. Construct a tripartite graph where M represents V2I links and N represents V2V links. 12. Return the power allocation using resource blocks. |
- Initialization: Assign an arbitrary V2V link to each cluster based on predefined criteria or randomly.
- Graph Construction: Create a graph representation where each cluster is represented as a node, and the V2V links between clusters are represented as edges. This graph captures the connectivity and relationships between clusters.
- Resource Evaluation: Evaluate the resources available within each cluster and the requirements of the V2V communication links. This assessment may include available bandwidth, signal strength, channel conditions, and quality-of-service (QoS) metrics.
- Resource Allocation: Utilize graph-based algorithms to allocate resources based on connectivity and performance requirements. Various graph algorithms, such as minimum spanning tree, shortest path, or maximum flow algorithms, can be employed to optimize resource allocation.
- Optimization: Continuously evaluate and optimize the resource allocation based on dynamic changes in the network, such as variations in vehicle density, traffic conditions, or communication demands. This ensures adaptability and efficient resource utilization in real-time scenarios.
5.4. Greedy Resource Allocation
Algorithm 2: Greedy Resource Allocation Algorithm |
1. Obtain V2V and V2I clustering results from the baseline graph-based resource allocation algorithm. 2. Repeat the process. for k = 1 to K do Initialize all zero vectors of length N. for n = 1 to N do If the kth V2V is not only in its current cluster C, then set Ck = n end if end for end for Return the power allocation using resource blocks. |
- Initialization: Begin with an initial set of available resources and an open allocation plan.
- Vehicle Selection: Select a vehicle from the pool of vehicles requiring resource allocation.
- Resource Evaluation: Evaluate the available resources and the requirements of the selected vehicle. This evaluation may include available bandwidth, signal quality, channel conditions, proximity to other vehicles, and communication demands.
- Resource Allocation: Allocate the resources to the selected vehicle based on the evaluation. The allocation decision is made greedily, considering the local optimization criteria. For example, the algorithm may prioritize allocating resources to vehicles with higher communication demands or vehicles experiencing poor signal quality.
- Update Resource Pool: Update the pool of available resources by subtracting the allocated resources from the total available resources.
- Repeat Steps 2–5: Repeat the vehicle selection process, resource evaluation, allocation, and resource pool update for the remaining vehicles until all vehicles have been allocated resources.
5.5. V2I and V2V Communications
6. Conclusions
7. Future Recommendations
- As security and privacy concerns remain critical in VANETs, future work should focus on developing advanced encryption and cryptographic techniques to safeguard sensitive information and prevent unauthorized access. Additionally, privacy-preserving mechanisms, such as secure data aggregation and anonymization, should be explored to protect the privacy of vehicle and driver identities.
- Conducting real-world deployments and field testing of the proposed protocol will provide valuable insights into its practical feasibility and effectiveness. Evaluating the protocol’s performance under various traffic scenarios, road conditions, and network densities will help identify potential limitations and areas for further improvement.
- In order to achieve uninterrupted communication and practical cooperation between diverse vehicles and infrastructure systems, it is imperative to establish interoperability standards. These standards will promote compatibility and collaboration among various Vehicular AdHoc Networks (VANETs), facilitating enhanced traffic management and safer interactions between vehicles and infrastructure components.
- Energy Efficiency Considerations: Energy efficiency is crucial to VANETs, as vehicles operate on limited power resources. Future research should explore energy-aware strategies and mechanisms to optimize power consumption, prolong the network’s lifetime, and reduce the carbon footprint associated with vehicular communication.
- As urban areas continue to expand, the scalability and robustness of the proposed protocol need to be thoroughly investigated. Future work should address the challenges of large-scale VANET deployments, including managing a vast number of vehicles, efficient cluster formation, and effective communication in highly dynamic environments.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Keyword | Meanings |
---|---|
ITS | Intelligent Transportation System |
VANETs | Vehicular AdHoc Networks |
V2V | Vehicle-to-Vehicle |
V2I | Vehicle-to-Infrastructure |
V2X | Vehicle-to-Everything |
RSU | Road Side Units |
E2E | End-to-End |
PDR | Packet Delivery Ratio |
RR | Route Reliability |
P2P | Peer-to-Peer |
TRPs | Topological Routing Protocols |
GRPs | Geographic Routing Protocols |
5G | Fifth Generation |
SDN | Software-Defined Network |
DDoS | Distributed Denial of Services |
MANETs | Mobile AdHoc Networks |
CA | Certification Authority |
LCG | Linear Congruential Generator |
FYS | Fisher–Yates Shuffle |
API | Application Programming Interface |
OTP | One-Time Password |
HTTP | Hypertext Transfer Protocol |
UR | Ultra-Reliability |
IoT | Internet of Things |
AI | Artificial Intelligence |
MD5 | Media-Digest Algorithm for Encryption |
SUMO | Simulation of Urban Mobility |
API | Application Program Interface |
TCP | Transfer Control Protocol |
Transmission Strategy | Route Formation | Routing Protocols |
---|---|---|
Single-hop Broadcasting | Proactive | Beacon-Vector (BV) |
Global Internet Connectivity for Vehicular AdHoc Networks (GLOVE) | ||
Position-based Opportunistic Routing (POR) | ||
Reactive | Intersection-based Routing (IBR) | |
Traffic-aware Routing for Isolated Nodes (TRAIN) | ||
Emergency Message Transmission Protocol (EMTP) | ||
Multi-hop Unicast | Proactive | Distance-Based Clustering (DBC) |
Clustering Routing Protocol (CRP) | ||
Dynamic Source Routing (DSR) | ||
Reactive | AdHoc On-Demand Distance Vector (AODV) | |
Temporally Ordered Routing Algorithm (TORA) | ||
Dynamic Destination-Sequenced Distance Vector (DSDV) | ||
Multi-hop Broadcast | Proactive | Flooding-based Routing (FBR) |
Geographical and Temporal Routing (GTR) | ||
Vehicular Opportunistic Routing (VOR) | ||
Reactive | Broadcast Storm Avoidance Routing (BSA) | |
Counter-Based Broadcast Scheme (CBS) | ||
Probability-Based Broadcasting (PBB) |
Ref. No. | Key Contribution | V2V | V2X |
---|---|---|---|
[31] | AI transmission scheduling in cognitive vehicular communication and vehicular communication modes | ✔ | ✔ |
[32] | Resource allocation and power-sharing with several optimal resource allocation algorithms | ✔ | ✔ |
[33] | Mobile edge computing framework and a network paradigm with predictive off-loading | ✔ | ✔ |
[34] | Hierarchy of wireless networks and network standards, cumulative smart grid model, and IoT security analysis | ✽ | ✽ |
[35] | Models and communication channel measurement metrics for wireless infrared networks | ✽ | ✽ |
[36] | Longitudinal safety assessment of connected vehicles and intelligent driver model in a connected vehicle network | ✔ | ✽ |
[37] | Survey on V2V-based vehicular sensor networks communications and discussion on issues and challenges in V2C communication | ✔ | ✽ |
[38] | A physical layer perspective of vehicular communications | ✽ | ✔ |
[39] | A short-range model for vehicle-to-vehicle communication on Vehicular AdHoc Networks through the showcase of Vehicle-to-Vehicle communication probability analysis | ✔ | ✗ |
[40] | A hybrid clustering algorithm and simulation of the proposed algorithm on SUMO | ✽ | ✽ |
[41] | A distributed cloud structure for urban traffic management through a cloud service handled message algorithm | ✽ | ✽ |
[42] | Implementation of the Markov renewal process and classification of message passing between Road Side Units (RSU) and vehicles | ✽ | ✔ |
[43] | Proposed a Cell Transmission Model for expressway traffic | ✽ | ✔ |
[42] | Proposed a distributed cloud-based architecture for Vehicular AdHoc Networks performance and an additional mathematical model for solid communication of vehicles | ✗ | ✔ |
[42] | A secure communication scheme for Vehicular AdHoc Networks | ✔ | ✔ |
[44] | Highlighted the difficulties in implementing VANET systems due to traffic, communication, and safety concerns, and investigated how machine learning techniques may help address these challenges | ✔ | ✔ |
[45] | Maximized global criteria while simultaneously boosting class longevity, information transmission speed, and lowering inter-class overload and provided an Efficient Key Management Scheme (KMSUNET) based on symmetric and asymmetric encryption to solve the performance and security issues of the UVANET environment | ✔ | ✽ |
[46] | The suggested approach aims to establish reliable and steady clusters contributing to the network’s overall reliability | ✔ | ✔ |
[47] | Over time, the percentage of malicious nodes in a vehicle AdHoc network may be reduced thanks to this scheme’s capability to identify and remove them | ✔ | ✽ |
[48] | A novel self-adaptive Angular-based k-medoid Clustering Algorithm (SAACS) is created to generate flexible clusters. The network latency decrease, and clusters are built using informed predictions about route lengths and signal ranges. | ✔ | ✔ |
[49] | This comprehensive review encompasses the many methods explored in current literature as prospective solutions for addressing the pervasive problem of traffic congestion. | ✔ | ✽ |
[50] | Scholars are now more responsible for safeguarding individuals’ private data and information and put forward the Key Agreement Protocol for Urbanized Block Chains (UB-KAP) | ✔ | ✽ |
[51] | An innovative REST web service for visualizing data is proposed, and also the Fundamental Safety Messages were put through their paces in a Big Data lab. The authors discussed proper behavior concerning packet loss, packet delivery, and communication latency was shown. | ✔ | ✔ |
[52] | For this reason, the AWCP EE-WOA model analysis includes a vehicular network at a predetermined velocity and location. | ✔ | ✽ |
Notation | Description |
---|---|
A Vehicle | |
Certification Authority Server | |
Vehicle ID | |
Vehicle Registration Number | |
Extract Numeric Values from a String | |
Vehicle Numeric Code extracted from the Registration Number | |
Vehicle e-mail | |
Vehicle Password | |
Concatenation | |
One-Way Hash Function | |
Vehicle Timestamp | |
Server-Side Timestamp | |
LCG-Based Random Numbers | |
Vehicle Code generated by the server | |
Shuffled Vehicle Code using the FYS algorithm | |
The Result computed on the Server-Side | |
Secured Password for Vehicle | |
Updated Vehicle Code | |
Updated Vehicle Code generated by the server | |
Vehicle Reference | |
Shuffling of Updated Vehicle Code generated by the server | |
Vehicle Code for Authentication generated by the server | |
OTP-Like Vehicle Code generated by the server | |
Vehicle Session Key generated by the server | |
Vehicle Authentication Method | |
Vehicle Authentication Status | |
Server Response Against HTTP Request | |
Not Equal Operator | |
Latitude | |
Longitude | |
Current location of the vehicle moving on the road | |
Vehicle Threshold Location Interval | |
Server Interval Table | |
Server Log Table | |
The function that will extract latitude from the location | |
The function that will extract longitude from the location | |
Message Priority | |
Message Template | |
Receiver ID | |
Sender ID |
OBU | CA Server | |
---|---|---|
Registration: Computes | ||
inside vehicle OBU | Generates |
Vehicle OBU | CA Server | |
---|---|---|
Login and authentication: Computes | Checks with DB Updates logs table with DB Updates logs table | |
Vehicle | CA Server | |
---|---|---|
V2I Communication: Computes | Updating logs |
Vehicle A | Vehicle B | |
V2V Communication: Computes | Replies: Computes |
Notations | |
---|---|
A Vehicle | |
Vehicle Unique Identity | |
Closest Vehicle | |
Vehicle-ID | |
Closest Vehicles List | |
Distance Between Vi and Vj | |
Number of Closest Vehicles to Vj | |
Range of Dynamic Transmission | |
Moving Vehicle Direction | |
Vehicle Speed | |
Assumed Weights |
Vehicle-to-Infrastructure (V2I) | |||||
---|---|---|---|---|---|
Attributes | 1st Request | 2nd Request | 3rd Request | 4th Request | 5th Request |
Status (status code) | 200 | 200 | 200 | 200 | 200 |
Response Size (bytes) | 289 | 289 | 289 | 289 | 289 |
Socket Initialization (milliseconds) | 2.18 | 2.07 | 1.77 | 2.12 | 1.40 |
DNS Lookup (milliseconds) | 4.11 | 3.19 | 2.95 | 2.17 | 1.62 |
TCP Handshake (milliseconds) | 1.47 | 1.25 | 1.06 | 0.92 | 0.76 |
Transfer Start (milliseconds) | 91.38 | 98.18 | 88.02 | 82.33 | 80.91 |
Download (milliseconds) | 20.24 | 4.19 | 3.35 | 4.81 | 3.73 |
Vehicle-to-Vehicle (V2V) | |||||
Status (status code) | 200 | 200 | 200 | 200 | 200 |
Response Size (bytes) | 289 | 289 | 289 | 289 | 289 |
Socket Initialization (milliseconds) | 11.24 | 4.22 | 1.68 | 1.36 | 1.04 |
DNS Lookup (milliseconds) | 1.19 | 0.48 | 0.77 | 0.47 | 0.89 |
TCP Handshake (milliseconds) | 3.03 | 1.48 | 2.49 | 2.63 | 2.41 |
Transfer Start (milliseconds) | 91.75 | 93.56 | 83.26 | 62.03 | 58.95 |
Download (milliseconds) | 12.61 | 4.49 | 2.89 | 3.32 | 3.45 |
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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Latif, R.M.A.; Jamil, M.; He, J.; Farhan, M. A Novel Authentication and Communication Protocol for Urban Traffic Monitoring in VANETs Based on Cluster Management. Systems 2023, 11, 322. https://doi.org/10.3390/systems11070322
Latif RMA, Jamil M, He J, Farhan M. A Novel Authentication and Communication Protocol for Urban Traffic Monitoring in VANETs Based on Cluster Management. Systems. 2023; 11(7):322. https://doi.org/10.3390/systems11070322
Chicago/Turabian StyleLatif, Rana Muhammad Amir, Muhammad Jamil, Jinliao He, and Muhammad Farhan. 2023. "A Novel Authentication and Communication Protocol for Urban Traffic Monitoring in VANETs Based on Cluster Management" Systems 11, no. 7: 322. https://doi.org/10.3390/systems11070322