Enhancing negative messages broadcasting with Meet-Table and TTL in VANET Article Swipe
YOU?
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· 2018
· Open Access
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· DOI: https://doi.org/10.1186/s13638-018-1237-8
In VANET (vehicular ad hoc network), a negative message has an objective vehicle and describes the negative attributes of the vehicle. Broadcasting negative messages in VANET is essential to make the VANET secure for these messages are about the untrustworthy vehicles. The epidemic model distributes negative messages rapidly with unlimited broadcasting, but uncontrolled epidemic may cause flooding storm. TTL (time-to-live) can be used to control broadcasting, but it may heavily decrease the coverage of negative messages in VANET. Meet-Table is used with TTL to solve the problem. A Meet-Table is a data structure held by a vehicle to record vehicles it met. When a vehicle receives a message, it checks whether the message’s objective vehicle is in its Meet-Table. If it is, the vehicle resets the TTL of the message to the maximum TTL to extend the transmission of the message. The negative message, coverage percentage and accurate coverage percentage of the negative message, and the Meet-Table are formally defined. The algorithm for broadcasting negative messages with Meet-Table and TTL is given. The simulation results show that Meet-Table can increase the coverage and decrease the delay of negative messages. More importantly, the simulation results show that the coverage increased by Meet-Table is accurate.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s13638-018-1237-8
- https://jwcn-eurasipjournals.springeropen.com/track/pdf/10.1186/s13638-018-1237-8
- OA Status
- gold
- Cited By
- 1
- References
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2892319355
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2892319355Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1186/s13638-018-1237-8Digital Object Identifier
- Title
-
Enhancing negative messages broadcasting with Meet-Table and TTL in VANETWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-09-12Full publication date if available
- Authors
-
Xiaolu Cheng, Jiawei Mo, Baohua HuangList of authors in order
- Landing page
-
https://doi.org/10.1186/s13638-018-1237-8Publisher landing page
- PDF URL
-
https://jwcn-eurasipjournals.springeropen.com/track/pdf/10.1186/s13638-018-1237-8Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://jwcn-eurasipjournals.springeropen.com/track/pdf/10.1186/s13638-018-1237-8Direct OA link when available
- Concepts
-
Computer science, Broadcasting (networking), Table (database), Vehicular ad hoc network, Flooding (psychology), Computer network, Wireless ad hoc network, Telecommunications, Wireless, Psychotherapist, Psychology, Data miningTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2020: 1Per-year citation counts (last 5 years)
- References (count)
-
18Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.with | 49, 82, 167 |
| abstract_inverted_index.VANET | 2, 26, 32 |
| abstract_inverted_index.about | 38 |
| abstract_inverted_index.cause | 56 |
| abstract_inverted_index.delay | 186 |
| abstract_inverted_index.model | 44 |
| abstract_inverted_index.solve | 85 |
| abstract_inverted_index.these | 35 |
| abstract_inverted_index.VANET. | 78 |
| abstract_inverted_index.checks | 110 |
| abstract_inverted_index.extend | 136 |
| abstract_inverted_index.given. | 172 |
| abstract_inverted_index.record | 99 |
| abstract_inverted_index.resets | 125 |
| abstract_inverted_index.secure | 33 |
| abstract_inverted_index.storm. | 58 |
| abstract_inverted_index.control | 65 |
| abstract_inverted_index.heavily | 70 |
| abstract_inverted_index.maximum | 133 |
| abstract_inverted_index.message | 9, 130 |
| abstract_inverted_index.rapidly | 48 |
| abstract_inverted_index.results | 175, 194 |
| abstract_inverted_index.vehicle | 13, 97, 105, 115, 124 |
| abstract_inverted_index.whether | 111 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.accurate | 148 |
| abstract_inverted_index.coverage | 73, 145, 149, 182, 198 |
| abstract_inverted_index.decrease | 71, 184 |
| abstract_inverted_index.defined. | 160 |
| abstract_inverted_index.epidemic | 43, 54 |
| abstract_inverted_index.flooding | 57 |
| abstract_inverted_index.formally | 159 |
| abstract_inverted_index.increase | 180 |
| abstract_inverted_index.message, | 108, 144, 154 |
| abstract_inverted_index.message. | 141 |
| abstract_inverted_index.messages | 24, 36, 47, 76, 166 |
| abstract_inverted_index.negative | 8, 17, 23, 46, 75, 143, 153, 165, 188 |
| abstract_inverted_index.problem. | 87 |
| abstract_inverted_index.receives | 106 |
| abstract_inverted_index.vehicle. | 21 |
| abstract_inverted_index.vehicles | 100 |
| abstract_inverted_index.accurate. | 203 |
| abstract_inverted_index.algorithm | 162 |
| abstract_inverted_index.describes | 15 |
| abstract_inverted_index.essential | 28 |
| abstract_inverted_index.increased | 199 |
| abstract_inverted_index.messages. | 189 |
| abstract_inverted_index.network), | 6 |
| abstract_inverted_index.objective | 12, 114 |
| abstract_inverted_index.structure | 93 |
| abstract_inverted_index.unlimited | 50 |
| abstract_inverted_index.vehicles. | 41 |
| abstract_inverted_index.(vehicular | 3 |
| abstract_inverted_index.Meet-Table | 79, 89, 157, 168, 178, 201 |
| abstract_inverted_index.attributes | 18 |
| abstract_inverted_index.percentage | 146, 150 |
| abstract_inverted_index.simulation | 174, 193 |
| abstract_inverted_index.Meet-Table. | 119 |
| abstract_inverted_index.distributes | 45 |
| abstract_inverted_index.message’s | 113 |
| abstract_inverted_index.Broadcasting | 22 |
| abstract_inverted_index.broadcasting | 164 |
| abstract_inverted_index.importantly, | 191 |
| abstract_inverted_index.transmission | 138 |
| abstract_inverted_index.uncontrolled | 53 |
| abstract_inverted_index.broadcasting, | 51, 66 |
| abstract_inverted_index.untrustworthy | 40 |
| abstract_inverted_index.(time-to-live) | 60 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5037485851 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I150807315 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.8799999952316284 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.49074156 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |