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The pervasive use of technology in the urban environment requires evolved algorithms able to deliver messages across vehicular ad-hoc networks (VANETs). These networks could be exploited to run a vast plethora of applications, including critical ones such as emergency message distribution. The state-of-the-art broadcasting solutions are based on providing vehicles with different probabilities of forwarding the message in order to reduce message collisions and the number of hops to cover a certain area of interest. In this article, we examine a class of these propagation algorithms, discussing their model analysis and performance evaluation. Furthermore, we propose a possible algorithm's extension introducing a dynamic setting of parameters according to the vehicular network's conditions.

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Automation plays an important role in modern transportation and handling systems, e.g., to control the routes of aircraft and ground service equipment in airport aprons, automated guided vehicles in port terminals or in public transportation, handling robots in automated factories, drones in warehouse picking operations, etc. Information technology provides hardware and software (e.g., collision detection sensors, routing and collision avoidance logic) that contribute to safe and efficient operations, with relevant social benefits in terms of improved system performance and reduced accident rates. In this context, we address the design of efficient collision-free routes in a minimum-size routing network. We consider a grid and a set of vehicles, each moving from the bottom of the origin column to the top of the destination column. Smooth nonstop paths are required, without collisions nor deviations from shortest paths, and we investigate the minimum number of horizontal lanes allowing for such routing. The problem is known as fleet quickest routing problem on grids. We propose a mathematical formulation solved, for small instances, through standard solvers. For larger instances, we devise heuristics that, based on known combinatorial properties, define priorities, and design collision-free routes. Experiments on random instances show that our algorithms are able to quickly provide good quality solutions.