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

As the capabilities for vehicular communications have become widespread, periodic beaconing is becoming fundamental to tracking neighbors. Specifically, a vehicle periodically broadcasts its kinematic data and receivers estimate the sender’s evolving position. To ensure safety, tracking neighbors via beaconing requires position errors and transmission delay to be small. To satisfy stringent requirements, previous proposals have employed multiple RF devices based on the unrealistic assumption that vehicles have the same type of RF devices. In reality, vehicles possess different RF devices (
heterogeneous vehicle conditions
). Satisfying the requirements under heterogeneous conditions is challenging because network connectivity is low and multi-hop transmissions to improve the connectivity aggravate network congestion. To address this challenge, we propose a novel scheme using a model-based trajectory prediction and multi-hop transmissions adaptively. To maintain accuracy of the model, each vehicle creates a model for predicting its own trajectory and distributes the model. For reliable multihop transmissions, our scheme employs periodic scan for translator and disconnected neighbors (PSTN) and probabilistic relay (PR). To our knowledge, this is the first to consider heterogeneous vehicle conditions for tracking neighbors via beaconing. Evaluation confirms that our scheme tracks neighbors more accurately than previous work.