| Full text | |
| Author(s): |
Akabane, Ademar T.
[1]
;
Immich, Roger
[1]
;
Bittencourt, Luiz F.
[1]
;
Madeira, Edmundo R. M.
[1]
;
Villas, Leandro A.
[1]
Total Authors: 5
|
| Affiliation: | [1] Univ Estadual Campinas, Inst Comp, Campinas, SP - Brazil
Total Affiliations: 1
|
| Document type: | Journal article |
| Source: | COMPUTER COMMUNICATIONS; v. 151, p. 306-319, FEB 1 2020. |
| Web of Science Citations: | 0 |
| Abstract | |
In the past few years, several systems have been proposed to deal with issues related to the vehicular traffic management. Usually, their solutions include the integration of computational technologies such as vehicular networks, central servers, and roadside units. Most systems use a hybrid approach, which means they still need a central entity (central server or roadside unit) and Internet connection to find out an en-route event as well as alternative routes for vehicles. It is easy to understand the need for a central entity because selecting the most appropriate vehicle to perform aforementioned procedures is a difficult task. This is especially true in a highly dynamic network. In addition to that, as far as we know, there are very few systems that apply the altruistic approach (not selfish behavior) to routing decisions. Because of that, the issue addressed in this work is how to perform the vehicular traffic management, when an en-route event is detected, in a distributed, scalable, and cost-effective fashion. To deal with these issues, we proposed a distributed vehicle traffic management system, named as dEASY (distributed vEhicle trAffic management SYstem). The dEASY system was designed and implemented on a three-layer architecture, namely environment sensing and vehicle ranking, knowledge generation and distribution, and knowledge consumption. Each layer of the dEASY architecture is responsible for dealing with the main issues that were not addressed in related works or could be improved. The three-layer architecture is arranged as follows: the first layer deals with the task of selecting the most appropriate vehicle to perform data forwarding and/or knowledge generation, the second one addresses the knowledge generation and distribution, and the third layer applies an altruistic approach to choose an alternative route. Simulation results have shown that, compared with other systems from the literature, our proposed system has lower network overhead due to applied vehicle selection and broadcast suppression mechanisms. On average, dEASY also outperformed all other competitors in what regards to the travel time and time lost metrics. Through the analysis of results, it is possible to conclude that our infrastructure-less system is scalable and cost-effective. (AU) | |
| FAPESP's process: | 15/25588-6 - Distributed information management in vehicular social networks |
| Grantee: | Ademar Takeo Akabane |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 14/50937-1 - INCT 2014: on the Internet of the Future |
| Grantee: | Fabio Kon |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 16/24454-9 - In-network Data Aggregation in VANETs |
| Grantee: | Ademar Takeo Akabane |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate |
| FAPESP's process: | 18/02204-6 - Efficient and resilient video delivery in smart cities |
| Grantee: | Roger Kreutz Immich |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |