The emergence of new connectivity services for automated transportation marks a paradigm shift for the operation of wireless networks. Furthermore, the advent of blockchain technology promises to enable a plethora of smart mobility services, which are not contingent on any central authorities. Concepts such as distributed ledger require efficient and reliable data dissemination between vehicles. Traditional techniques based on Automatic Repeat Request (ARQ) are well known to scale poorly in all-cast networks due to the feedback implosion problem. Fountain and network coding techniques are arguably the most promising alternative solutions. In this paper we derive new analytical bounds on transmit message lengths and quantify bandwidth delay trade-offs for fountain coding based data dissemination for CAVs.
Numerous applications require the sharing of data from each node on a network with every other node. In the case of Connected and Autonomous Vehicles (CAVs), it will be necessary for vehicles to update each other with their positions, manoeuvring intentions, and other telemetry data, despite shadowing caused by other vehicles. These applications require scalable, reliable, low latency communications, over challenging broadcast channels. In this article, we consider the allcast problem, of achieving multiple simultaneous network broadcasts, over a broadcast medium. We model slow fading using random graphs, and show that an allcast method based on sparse random linear network coding can achieve reliable allcast in a constant number of transmission rounds. We compare this with an uncoded baseline, which we show requires O(log(n)) transmission rounds. We justify and compare our analysis with extensive simulations.
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