Heuristic Path Search and Multi-Attribute Decision-Making-Based Routing Method for Vehicular Safety Messages
Abstract
:1. Introduction
- For the identification of a global optimal transmission path, HMDR initiates by constructing a path tree model spanning from the destination intersections back to the source intersection. It then pinpoints the most efficient route through a heuristic path search that emphasizes road section connectivity;
- To accurately evaluate the performance of candidate relays, the HDMR method employs a multi-attribute decision-making-based approach. This calculates the subjective and objective weights of candidate relays based on the ordinal relationship analysis method and the Criteria Importance Through Intercriteria Correlation (CRITIC) weighting method, respectively;
- The innovative HMDR strategy synergistically blends path search with relay selection, equipping it to manage safety message transmission in multifaceted traffic scenarios. Our experimental results underscore the effectiveness and proficiency of HMDR in this domain.
2. Related Work
3. Heuristic Path Search and Multi-Attribute Decision-Making-Based Routing Method
3.1. System Model and Assumptions
- Initially, the vehicles with Poisson distribution travel in the urban traffic scenario with multiple lanes in both directions;
- Each vehicle is equipped with an On-board Unit (OBU) that utilizes a Dedicated Short-range Communication (DSRC) interface for Vehicle-to-vehicle (V2V) communication and obtains location information based on GPS and electronic map;
- Each vehicle receives road section information from the equipped electronic map, as well as periodical Beacon messages (including vehicle ID, speed, position, timestamp, etc.) from its neighbors, and maintains a road section information table and a neighbor information table;
- Both buildings and trees attenuate the RSS of safety messages, and each vehicle records the RSS values of monitored neighboring vehicles in the neighbor information table.
3.2. Overview of HDMR
3.3. Optimal Path Search
3.3.1. Path Tree Model
- Each path tree starts at a destination intersection and ends at the source intersection;
- When an intersection is added to a path tree, it is removed from the set of intersections to which it belongs;
- The children of an intersection in layer can only be its neighboring intersections in layer i;
- The construction of all path trees is completed when the intersection set is .
3.3.2. Connectivity Analysis of Road Sections
3.3.3. Heuristic Path Search
3.4. Optimal Relay Selection
3.4.1. Data Preprocessing
3.4.2. Relay Evaluation
3.4.3. Relay Selection
4. Experiments and Analysis
4.1. Experimental Scenario and Parameters
4.2. Experimental Results and Analysis
4.2.1. Sensitivity Analysis of the Value
4.2.2. Road Connectivity Analysis
4.2.3. Comparative Results and Analysis
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
HDMR | Heuristic path search and multi-attribute decision-making-based routing method |
RSS | Received signal strength |
CRITIC | Criteria Importance through Intercriteria Correlation |
VANET | Vehicular Ad Hoc Network |
V2V | Vehicle-to-vehicle |
V2I | Vehicle-to-infrastructure |
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Variable | Description |
---|---|
denotes road intersections, is the source, and and are the targets. | |
, | The road section that joins two intersections , and its length is |
denotes the candidate relays of the current forwarding vehicle. | |
denotes the evaluation metrics of candidate relays. | |
The location of the current forwarding vehicle. | |
The location of . | |
R | The transmission radius of the vehicles. |
The speed of the current forwarding vehicle. | |
The speed of . | |
The maximum speed limit of vehicles. | |
The direction angle between the current forwarding vehicle and . | |
The RRS value of recorded by the current forwarding vehicle. | |
, | The minimum and maximum thresholds of the RRS value. |
The area density of . | |
The maximum area density of vehicles. | |
The connectivity of . |
The Value of | Description |
---|---|
1.0 | is as equally important as . |
2.0 | is slightly more important than . |
3.0 | is significantly more important than . |
4.0 | is more important than . |
5.0 | is extremely more important than . |
Parameter | Value |
---|---|
Transmission radius R/m | 270 |
Vehicle speed | 8∼16 |
Maximum vehicle density | 250 |
Minimum RSS threshold | −85 |
Beacon sending interval/s | 1 |
Minimum distance between vehicles/m | 1 |
The value of | 0.4 |
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Nie, L.; Zhang, J.; Bao, H.; Huo, Y. Heuristic Path Search and Multi-Attribute Decision-Making-Based Routing Method for Vehicular Safety Messages. Sensors 2023, 23, 9506. https://doi.org/10.3390/s23239506
Nie L, Zhang J, Bao H, Huo Y. Heuristic Path Search and Multi-Attribute Decision-Making-Based Routing Method for Vehicular Safety Messages. Sensors. 2023; 23(23):9506. https://doi.org/10.3390/s23239506
Chicago/Turabian StyleNie, Lei, Junjie Zhang, Haizhou Bao, and Yiming Huo. 2023. "Heuristic Path Search and Multi-Attribute Decision-Making-Based Routing Method for Vehicular Safety Messages" Sensors 23, no. 23: 9506. https://doi.org/10.3390/s23239506