Tracking Neighbors via Beaconing in Heterogeneous Vehicle Conditions: To Predict or to Relay

PROJECT TITLE :

To Predict or to Relay: Tracking Neighbors via Beaconing in Heterogeneous Vehicle Conditions

ABSTRACT:

Because of the widespread availability of capabilities for vehicular Communications, periodic beaconing is becoming an essential component in the process of tracking neighbors. To be more specific, a vehicle will periodically broadcast the kinematic data that it collects, and receivers will then estimate the position of the sender as it moves. When tracking neighbors through beaconing, it is necessary for both the position errors and the transmission delay to be as small as possible. Previous proposals have relied on the unrealistic assumption that all vehicles are equipped with the same kind of radio frequency (RF) devices in order to satisfy the stringent requirements. As a result, these proposals have utilized multiple RF devices. In point of fact, various RF devices can be found in vehicles ( heterogeneous vehicle conditions ). It is difficult to meet the requirements when the conditions are heterogeneous because there is a low level of network connectivity, and the multi-hop transmissions that are used to improve network connectivity contribute to an increase in network congestion. In order to overcome this difficulty, we have developed a novel strategy that makes use of model-based trajectory prediction and multi-hop transmissions in an adaptive manner. Each vehicle generates a model for predicting its own trajectory and distributes that model in order to ensure that the overall model is as accurate as possible. Our method uses probabilistic relay in conjunction with periodic scan for translator and disconnected neighbors (PSTN) in order to ensure the reliability of multihop transmissions (PR). To the best of our knowledge, this is the first study to take into account different vehicle conditions when attempting to track neighbors using beaconing. Evaluation demonstrates that our method is superior to other approaches in terms of accurately tracking neighbors.

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