An efficient destination-based data management policy for vehicular networks

Abstract

The Vehicular Ad hoc Network (VANET) takes the advantage of the relative mobility of the vehicles to opportunistically share dynamic information when they meet, aiming to avoid accidents and traffic jams, get local information of the nearby places, enjoy entertainment applications among others. However, the shared information must be handled properly, saving bandwidth and tackling the unnecessary duplication of data, preventing the scalability problem and congestion. The current solutions of these problems incorporate the concept of Relevance to share the information in a clever way but they do not exploit the advantage of knowing the destination of the vehicles and context-aware services to follow behavior patterns. Therefore, we propose an efficient destination-based data management policy, which follows the vehicular behavior and context to determine the relevance or importance of the information using a publisher/subscriber model and clusterbased dissemination. In order to demonstrate the feasibility of the proposal we implemented sets of simulation in which the results reported an outstanding performance in terms of congestion and delivery ratio.

An Adaptable Destination-Based Dissemination Algorithm Using a Publish/Subscribe Model in Vehicular Networks

“…The equation shows two cases. When the time that the message is traveling in the network (i.e., current time (t) minus creation time of the message (CT), (t − CT)) is less than or equal to the TTL, the value of ∆a is between 0 and 1; otherwise the value of ∆a is greater than the value of TTL [15].…”

“…• Distance to the current location ∆CL: It is the difference in distance between the current location of the vehicle (CL) and the target location (TL) in terms of (x, y) coordinates [15], calculated using Equation (4).…”

“…• Distance to the relative destination ∆D: It is the difference in distance between the relative destination of the vehicle (RD) and TL in terms of (x, y) coordinates [15]. This difference can be calculated with the information from maps as shown in Equation ( 5).…”

“…However, in some remote areas, the availability of maps is limited. In this case ∆D is calculated by employing the relative destination [15].…”

“…This calculation of ∆d emphasizes the relevance of the messages when the vehicles are within the radius R of the target zone for the message [15] as shown in Equation ( 7).…”