Evaluation of Redundancy Mitigation Rules in V2X Networks for Enhanced Collective Perception Services Article Swipe
Collective perception enables connected vehicles to share detailed environmental data, significantly enhancing situational awareness and safety. This data sharing is crucial for the functioning of modern vehicular networks, but it introduces the challenge of managing redundant information, which can congest communication channels and degrade network performance. To address this challenge, several redundancy mitigation rules have been proposed and extensively evaluated to filter out unnecessary data. This work investigates the impact of different redundancy mitigation rules on the performance of connected vehicular networks with collective perception under different market penetration rates. Additionally, the study introduces a set of hybrid rules designed to optimize this balance for collective perception services in vehicular networks. These hybrid rules are compared to scenarios without object filtering and other existing redundancy mitigation rules. Key performance metrics include channel busy ratio, environment awareness ratio, redundancy level, and the age of information. By analyzing the metrics as a function of the distance between the reported object and the receiving connected vehicle, the study identifies key trends in balancing redundancy reduction with information freshness under diverse network conditions. The results demonstrate that hybrid redundancy mitigation rules outperform existing approaches by effectively balancing channel load, redundancy level, and environment awareness, while maintaining lower age of information values. This balance is particularly crucial for safety-critical objects in close proximity to the connected vehicle. The findings highlight the importance of intelligent redundancy mitigation strategies in enhancing the timeliness and reliability of information in densely populated vehicular networks, ensuring the efficient and safe operation of connected vehicles.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2024.3464514
- OA Status
- gold
- Cited By
- 3
- References
- 17
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402673574
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4402673574Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2024.3464514Digital Object Identifier
- Title
-
Evaluation of Redundancy Mitigation Rules in V2X Networks for Enhanced Collective Perception ServicesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-01Full publication date if available
- Authors
-
Ahmed Hamdi SakrList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2024.3464514Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1109/access.2024.3464514Direct OA link when available
- Concepts
-
Redundancy (engineering), Computer science, Perception, Computer security, Artificial intelligence, Operating system, Biology, NeuroscienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3Per-year citation counts (last 5 years)
- References (count)
-
17Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.particularly | 210 |
| abstract_inverted_index.performance. | 45 |
| abstract_inverted_index.Additionally, | 90 |
| abstract_inverted_index.communication | 40 |
| abstract_inverted_index.environmental | 8 |
| abstract_inverted_index.significantly | 10 |
| abstract_inverted_index.safety-critical | 213 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 96 |
| corresponding_author_ids | https://openalex.org/A5052891361 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 1 |
| corresponding_institution_ids | https://openalex.org/I74413500 |
| citation_normalized_percentile.value | 0.83551888 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |