A Machine Intelligent Cloud Based Framework to Monitor Driving Behavior of Vehicles Using Internet of Things Article Swipe
Sourav Kumar Bhoi
,
Krishna Prasad K
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.7298089
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.7298089
Monitoring driving behavior of vehicles is a very important area of research in the field of Intelligent Transportation Systems (ITSs). As vehicles are rising tremendously on the roads of cities, it is very much essential to track the driving behavior of a vehicle to reduce the accidents in the cities. The drivers who are not driving properly, for example if a driver is continuously performing sudden acceleration, sudden deceleration, sudden left turn, sudden right turn, etc. then these types of vehicles need to be monitored and detected. In this work, a machine intelligent cloud-based framework is proposed to monitor the driving behavior of vehicles in the city using internet of things (IoTs). The vehicles are installed with an on-board unit which is responsible for collecting all readings from the sensors and sending these readings to cloud using IoT for detection of driving patterns. Here, the cloud is deployed with a supervised machine intelligent model that is responsible for identifying the driving pattern after receiving the readings from a vehicle. The model is selected by conducting training and testing over a standard dataset. The performance of the framework is tested using Python tool. From the results, it is found that Random Forest (RF) model performs better in identifying the accurate vehicle behavior with highest classification accuracy (CA).
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://zenodo.org/record/7298089
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4308358342
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4308358342Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5281/zenodo.7298089Digital Object Identifier
- Title
-
A Machine Intelligent Cloud Based Framework to Monitor Driving Behavior of Vehicles Using Internet of ThingsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-08-02Full publication date if available
- Authors
-
Sourav Kumar Bhoi, Krishna Prasad KList of authors in order
- Landing page
-
https://zenodo.org/record/7298089Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://zenodo.org/record/7298089Direct OA link when available
- Concepts
-
Internet of Things, Cloud computing, Computer science, Embedded system, Computer security, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4308358342 |
|---|---|
| doi | https://doi.org/10.5281/zenodo.7298089 |
| ids.doi | https://doi.org/10.5281/zenodo.7298089 |
| ids.openalex | https://openalex.org/W4308358342 |
| fwci | 0.0 |
| type | article |
| title | A Machine Intelligent Cloud Based Framework to Monitor Driving Behavior of Vehicles Using Internet of Things |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T12406 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.7702000141143799 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2210 |
| topics[0].subfield.display_name | Mechanical Engineering |
| topics[0].display_name | IoT and GPS-based Vehicle Safety Systems |
| topics[1].id | https://openalex.org/T12707 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.7644000053405762 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2214 |
| topics[1].subfield.display_name | Media Technology |
| topics[1].display_name | Vehicle License Plate Recognition |
| topics[2].id | https://openalex.org/T11344 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.7337999939918518 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2215 |
| topics[2].subfield.display_name | Building and Construction |
| topics[2].display_name | Traffic Prediction and Management Techniques |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C81860439 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8615436553955078 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q251212 |
| concepts[0].display_name | Internet of Things |
| concepts[1].id | https://openalex.org/C79974875 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7879445552825928 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q483639 |
| concepts[1].display_name | Cloud computing |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.5768795013427734 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C149635348 |
| concepts[3].level | 1 |
| concepts[3].score | 0.4739247262477875 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q193040 |
| concepts[3].display_name | Embedded system |
| concepts[4].id | https://openalex.org/C38652104 |
| concepts[4].level | 1 |
| concepts[4].score | 0.3593981862068176 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q3510521 |
| concepts[4].display_name | Computer security |
| concepts[5].id | https://openalex.org/C111919701 |
| concepts[5].level | 1 |
| concepts[5].score | 0.30897200107574463 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[5].display_name | Operating system |
| keywords[0].id | https://openalex.org/keywords/internet-of-things |
| keywords[0].score | 0.8615436553955078 |
| keywords[0].display_name | Internet of Things |
| keywords[1].id | https://openalex.org/keywords/cloud-computing |
| keywords[1].score | 0.7879445552825928 |
| keywords[1].display_name | Cloud computing |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.5768795013427734 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/embedded-system |
| keywords[3].score | 0.4739247262477875 |
| keywords[3].display_name | Embedded system |
| keywords[4].id | https://openalex.org/keywords/computer-security |
| keywords[4].score | 0.3593981862068176 |
| keywords[4].display_name | Computer security |
| keywords[5].id | https://openalex.org/keywords/operating-system |
| keywords[5].score | 0.30897200107574463 |
| keywords[5].display_name | Operating system |
| language | en |
| locations[0].id | pmh:oai:zenodo.org:7298089 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400562 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Zenodo (CERN European Organization for Nuclear Research) |
| locations[0].source.host_organization | https://openalex.org/I67311998 |
| locations[0].source.host_organization_name | European Organization for Nuclear Research |
| locations[0].source.host_organization_lineage | https://openalex.org/I67311998 |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | submittedVersion |
| locations[0].raw_type | info:eu-repo/semantics/article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | International Journal of Innovative Research in Engineering & Management (IJIREM), 9(5), 30-34, (2022-08-02) |
| locations[0].landing_page_url | https://zenodo.org/record/7298089 |
| locations[1].id | doi:10.5281/zenodo.7298089 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400562 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | Zenodo (CERN European Organization for Nuclear Research) |
| locations[1].source.host_organization | https://openalex.org/I67311998 |
| locations[1].source.host_organization_name | European Organization for Nuclear Research |
| locations[1].source.host_organization_lineage | https://openalex.org/I67311998 |
| locations[1].license | cc-by |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article-journal |
| locations[1].license_id | https://openalex.org/licenses/cc-by |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.5281/zenodo.7298089 |
| indexed_in | datacite |
| authorships[0].author.id | https://openalex.org/A5044453219 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-5173-3453 |
| authorships[0].author.display_name | Sourav Kumar Bhoi |
| authorships[0].countries | IN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I19008122 |
| authorships[0].affiliations[0].raw_affiliation_string | Post Doctoral Fellow, Research Center Department: Computer Science and Information Science, Institute of Computer Science and Information Science, Srinivas University, Mangaluru, Karnataka, India |
| authorships[0].institutions[0].id | https://openalex.org/I19008122 |
| authorships[0].institutions[0].ror | https://ror.org/05fep3933 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I19008122 |
| authorships[0].institutions[0].country_code | IN |
| authorships[0].institutions[0].display_name | Mangalore University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Sourav Kumar Bhoi |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Post Doctoral Fellow, Research Center Department: Computer Science and Information Science, Institute of Computer Science and Information Science, Srinivas University, Mangaluru, Karnataka, India |
| authorships[1].author.id | https://openalex.org/A5038904775 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Krishna Prasad K |
| authorships[1].affiliations[0].raw_affiliation_string | Associate Professor, Institute of Computer Science and Information Science, Srinivas University, Pandeshwar, Mangaluru, Karnataka, India |
| authorships[1].author_position | last |
| authorships[1].raw_author_name | Krishna Prasad K |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Associate Professor, Institute of Computer Science and Information Science, Srinivas University, Pandeshwar, Mangaluru, Karnataka, India |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://zenodo.org/record/7298089 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | A Machine Intelligent Cloud Based Framework to Monitor Driving Behavior of Vehicles Using Internet of Things |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T12406 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.7702000141143799 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2210 |
| primary_topic.subfield.display_name | Mechanical Engineering |
| primary_topic.display_name | IoT and GPS-based Vehicle Safety Systems |
| related_works | https://openalex.org/W2383532021, https://openalex.org/W4282591514, https://openalex.org/W2924169909, https://openalex.org/W2586486898, https://openalex.org/W2751166006, https://openalex.org/W4247531025, https://openalex.org/W2994888226, https://openalex.org/W2561426078, https://openalex.org/W3177145444, https://openalex.org/W2964514946 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:zenodo.org:7298089 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400562 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Zenodo (CERN European Organization for Nuclear Research) |
| best_oa_location.source.host_organization | https://openalex.org/I67311998 |
| best_oa_location.source.host_organization_name | European Organization for Nuclear Research |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I67311998 |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | info:eu-repo/semantics/article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | International Journal of Innovative Research in Engineering & Management (IJIREM), 9(5), 30-34, (2022-08-02) |
| best_oa_location.landing_page_url | https://zenodo.org/record/7298089 |
| primary_location.id | pmh:oai:zenodo.org:7298089 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400562 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Zenodo (CERN European Organization for Nuclear Research) |
| primary_location.source.host_organization | https://openalex.org/I67311998 |
| primary_location.source.host_organization_name | European Organization for Nuclear Research |
| primary_location.source.host_organization_lineage | https://openalex.org/I67311998 |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | submittedVersion |
| primary_location.raw_type | info:eu-repo/semantics/article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | International Journal of Innovative Research in Engineering & Management (IJIREM), 9(5), 30-34, (2022-08-02) |
| primary_location.landing_page_url | https://zenodo.org/record/7298089 |
| publication_date | 2022-08-02 |
| publication_year | 2022 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 6, 41, 60, 90, 149, 167, 179 |
| abstract_inverted_index.As | 20 |
| abstract_inverted_index.In | 87 |
| abstract_inverted_index.an | 117 |
| abstract_inverted_index.be | 83 |
| abstract_inverted_index.by | 173 |
| abstract_inverted_index.if | 59 |
| abstract_inverted_index.in | 12, 47, 104, 205 |
| abstract_inverted_index.is | 5, 31, 62, 95, 121, 146, 155, 171, 187, 196 |
| abstract_inverted_index.it | 30, 195 |
| abstract_inverted_index.of | 3, 10, 15, 28, 40, 79, 102, 109, 140, 184 |
| abstract_inverted_index.on | 25 |
| abstract_inverted_index.to | 35, 43, 82, 97, 134 |
| abstract_inverted_index.IoT | 137 |
| abstract_inverted_index.The | 50, 112, 169, 182 |
| abstract_inverted_index.all | 125 |
| abstract_inverted_index.and | 85, 130, 176 |
| abstract_inverted_index.are | 22, 53, 114 |
| abstract_inverted_index.for | 57, 123, 138, 157 |
| abstract_inverted_index.not | 54 |
| abstract_inverted_index.the | 13, 26, 37, 45, 48, 99, 105, 128, 144, 159, 164, 185, 193, 207 |
| abstract_inverted_index.who | 52 |
| abstract_inverted_index.(RF) | 201 |
| abstract_inverted_index.From | 192 |
| abstract_inverted_index.area | 9 |
| abstract_inverted_index.city | 106 |
| abstract_inverted_index.etc. | 75 |
| abstract_inverted_index.from | 127, 166 |
| abstract_inverted_index.left | 70 |
| abstract_inverted_index.much | 33 |
| abstract_inverted_index.need | 81 |
| abstract_inverted_index.over | 178 |
| abstract_inverted_index.that | 154, 198 |
| abstract_inverted_index.then | 76 |
| abstract_inverted_index.this | 88 |
| abstract_inverted_index.unit | 119 |
| abstract_inverted_index.very | 7, 32 |
| abstract_inverted_index.with | 116, 148, 211 |
| abstract_inverted_index.(CA). | 215 |
| abstract_inverted_index.Here, | 143 |
| abstract_inverted_index.after | 162 |
| abstract_inverted_index.cloud | 135, 145 |
| abstract_inverted_index.field | 14 |
| abstract_inverted_index.found | 197 |
| abstract_inverted_index.model | 153, 170, 202 |
| abstract_inverted_index.right | 73 |
| abstract_inverted_index.roads | 27 |
| abstract_inverted_index.these | 77, 132 |
| abstract_inverted_index.tool. | 191 |
| abstract_inverted_index.track | 36 |
| abstract_inverted_index.turn, | 71, 74 |
| abstract_inverted_index.types | 78 |
| abstract_inverted_index.using | 107, 136, 189 |
| abstract_inverted_index.which | 120 |
| abstract_inverted_index.work, | 89 |
| abstract_inverted_index.Forest | 200 |
| abstract_inverted_index.Python | 190 |
| abstract_inverted_index.Random | 199 |
| abstract_inverted_index.better | 204 |
| abstract_inverted_index.driver | 61 |
| abstract_inverted_index.reduce | 44 |
| abstract_inverted_index.rising | 23 |
| abstract_inverted_index.sudden | 65, 67, 69, 72 |
| abstract_inverted_index.tested | 188 |
| abstract_inverted_index.things | 110 |
| abstract_inverted_index.(ITSs). | 19 |
| abstract_inverted_index.(IoTs). | 111 |
| abstract_inverted_index.Systems | 18 |
| abstract_inverted_index.cities, | 29 |
| abstract_inverted_index.cities. | 49 |
| abstract_inverted_index.drivers | 51 |
| abstract_inverted_index.driving | 1, 38, 55, 100, 141, 160 |
| abstract_inverted_index.example | 58 |
| abstract_inverted_index.highest | 212 |
| abstract_inverted_index.machine | 91, 151 |
| abstract_inverted_index.monitor | 98 |
| abstract_inverted_index.pattern | 161 |
| abstract_inverted_index.sending | 131 |
| abstract_inverted_index.sensors | 129 |
| abstract_inverted_index.testing | 177 |
| abstract_inverted_index.vehicle | 42, 209 |
| abstract_inverted_index.accuracy | 214 |
| abstract_inverted_index.accurate | 208 |
| abstract_inverted_index.behavior | 2, 39, 101, 210 |
| abstract_inverted_index.dataset. | 181 |
| abstract_inverted_index.deployed | 147 |
| abstract_inverted_index.internet | 108 |
| abstract_inverted_index.on-board | 118 |
| abstract_inverted_index.performs | 203 |
| abstract_inverted_index.proposed | 96 |
| abstract_inverted_index.readings | 126, 133, 165 |
| abstract_inverted_index.research | 11 |
| abstract_inverted_index.results, | 194 |
| abstract_inverted_index.selected | 172 |
| abstract_inverted_index.standard | 180 |
| abstract_inverted_index.training | 175 |
| abstract_inverted_index.vehicle. | 168 |
| abstract_inverted_index.vehicles | 4, 21, 80, 103, 113 |
| abstract_inverted_index.accidents | 46 |
| abstract_inverted_index.detected. | 86 |
| abstract_inverted_index.detection | 139 |
| abstract_inverted_index.essential | 34 |
| abstract_inverted_index.framework | 94, 186 |
| abstract_inverted_index.important | 8 |
| abstract_inverted_index.installed | 115 |
| abstract_inverted_index.monitored | 84 |
| abstract_inverted_index.patterns. | 142 |
| abstract_inverted_index.properly, | 56 |
| abstract_inverted_index.receiving | 163 |
| abstract_inverted_index.Monitoring | 0 |
| abstract_inverted_index.collecting | 124 |
| abstract_inverted_index.conducting | 174 |
| abstract_inverted_index.performing | 64 |
| abstract_inverted_index.supervised | 150 |
| abstract_inverted_index.Intelligent | 16 |
| abstract_inverted_index.cloud-based | 93 |
| abstract_inverted_index.identifying | 158, 206 |
| abstract_inverted_index.intelligent | 92, 152 |
| abstract_inverted_index.performance | 183 |
| abstract_inverted_index.responsible | 122, 156 |
| abstract_inverted_index.continuously | 63 |
| abstract_inverted_index.tremendously | 24 |
| abstract_inverted_index.acceleration, | 66 |
| abstract_inverted_index.deceleration, | 68 |
| abstract_inverted_index.Transportation | 17 |
| abstract_inverted_index.classification | 213 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.13649394 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |