Pub Date : 2022-11-01DOI: 10.1109/candar57322.2022.00005
J. Bordim, M. Koibuchi, S. Fujita, K. Nakano
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Pub Date : 2019-11-01DOI: 10.1109/CANDAR.2019.00023
Paulo V. G. Farias, J. Bordim, Marcos F. Caetano
Accident prevention in roads and highways is one of the most important aspects of vehicular networks. In a Vehicular Ad Hoc Network (VANET), vehicles are equipped with a device that enables wireless communication between them. In VANETs, vehicles usually build network topology by periodic exchange of messages known as beacons. These messages should include the vehicle's position, speed and heading. Safety applications in VANETs define strict requirements to guarantee their proper functionality, among them, beacon transmission rates that can vary between 10 to 50 Hz. However, high beacon transmission rates can lead to a problem known as broadcast storm, which may impair correct operation of safety applications in high vehicle density scenarios. During a broadcast storm, numerous messages are transmitted simultaneously, which increases collisions and decreases packet delivery ratio. In this work, we propose a Density-based Congestion Avoidance Protocol (DCAP) to adapt beacon transmission rate in VANETs and minimize broadcast storm occurrence. Experimental results show that even in high vehicle density scenarios, DCAP improves on other protocols by 8% and 50% regarding packet delivery ratio, while still assuring coverage of approximately 100%.
{"title":"A Density-Based Congestion Avoidance Protocol for Strict Beaconing Requirements in VANETs","authors":"Paulo V. G. Farias, J. Bordim, Marcos F. Caetano","doi":"10.1109/CANDAR.2019.00023","DOIUrl":"https://doi.org/10.1109/CANDAR.2019.00023","url":null,"abstract":"Accident prevention in roads and highways is one of the most important aspects of vehicular networks. In a Vehicular Ad Hoc Network (VANET), vehicles are equipped with a device that enables wireless communication between them. In VANETs, vehicles usually build network topology by periodic exchange of messages known as beacons. These messages should include the vehicle's position, speed and heading. Safety applications in VANETs define strict requirements to guarantee their proper functionality, among them, beacon transmission rates that can vary between 10 to 50 Hz. However, high beacon transmission rates can lead to a problem known as broadcast storm, which may impair correct operation of safety applications in high vehicle density scenarios. During a broadcast storm, numerous messages are transmitted simultaneously, which increases collisions and decreases packet delivery ratio. In this work, we propose a Density-based Congestion Avoidance Protocol (DCAP) to adapt beacon transmission rate in VANETs and minimize broadcast storm occurrence. Experimental results show that even in high vehicle density scenarios, DCAP improves on other protocols by 8% and 50% regarding packet delivery ratio, while still assuring coverage of approximately 100%.","PeriodicalId":127873,"journal":{"name":"International Symposium on Computing and Networking - Across Practical Development and Theoretical Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128416477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/CANDARW51189.2020.00018
Taiki Iwao, S. Fujita
In this paper, we propose a system which forecasts the degree of congestions in a given space without actually visiting there. The proposed system is based on an assumption such that the arrival and departure of users concerned with the target space follows a specific probability distribution such as Gaussian mixture distribution and Poisson distribution. The system estimates parameters of the underlying probability distribution from time-series data reflecting the movement of users, and forecasts the degree of congestions at a certain time in the near future by using estimated parameters. The experimental results based on actual data acquired in a classroom of university show that the accuracy of parameter estimation could be comparable to that for complete data by filling missing future part with dummy data generated according to an appropriate normal distribution.
{"title":"Realtime Congestion Forecasting of Remote Space Through BLE Beacons","authors":"Taiki Iwao, S. Fujita","doi":"10.1109/CANDARW51189.2020.00018","DOIUrl":"https://doi.org/10.1109/CANDARW51189.2020.00018","url":null,"abstract":"In this paper, we propose a system which forecasts the degree of congestions in a given space without actually visiting there. The proposed system is based on an assumption such that the arrival and departure of users concerned with the target space follows a specific probability distribution such as Gaussian mixture distribution and Poisson distribution. The system estimates parameters of the underlying probability distribution from time-series data reflecting the movement of users, and forecasts the degree of congestions at a certain time in the near future by using estimated parameters. The experimental results based on actual data acquired in a classroom of university show that the accuracy of parameter estimation could be comparable to that for complete data by filling missing future part with dummy data generated according to an appropriate normal distribution.","PeriodicalId":127873,"journal":{"name":"International Symposium on Computing and Networking - Across Practical Development and Theoretical Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125869009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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