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Rapid increase in the private and public vehicles fleet causes urban centers heavily populated with limited transport road infrastructure. To overcome this, in real-time scenarios, queue length-based traffic light controllers are being designed utilizing light-weighted S-Edge devices. This system suffers from starvation problems if a road lane at the intersection continuously receives vehicles during peak hours. With this, higher green phase duration can be allocated to the same-lane multiple times despite vehicles on the other lanes' longer waiting time. To tackle this problem, an efficient and smart edge computing (S-Edge)-driven traffic light controller is proposed by accounting the real-time heterogeneous vehicular dynamics at the fog computing node. The fog node executes the proposed fuzzy inference system to generate phase-cycle duration. Further, to allocate the phase duration effectively, a method for estimating the lane pressure is proposed for the edge controller utilizing average queue length and waiting time. S-Edge is a light-weighted actuated traffic light controller that generates traffic light control cycle duration and phase (red/yellow/green) duration. To validate the S-Edge controller, a prototype is developed on an Indian city OpenStreetMap utilizing the low-computing power IoT devices, i.e., Raspberry Pi, and a well-known open-source simulator, i.e., Simulation of Urban MObility. [ABSTRACT FROM AUTHOR]

As vehicular networking has recently been developed and commercialized, vehicular cloud computing has received much attention in various research areas, such as intelligent transportation systems and vehicular ad hoc networks. An efficient intersection traffic control using vehicular cloud computing is one of the key research topics in intelligent transportation systems. To efficiently deal with intersection traffic control via vehicle-to-vehicle communications, we design a distributed mutual exclusion algorithm that does not rely on broadcast, which introduces communication overheads; instead, our algorithm use point-to-point messages sent between the vehicles to keep network traffic load lower. In our algorithmic design, to pass an intersection, the lead vehicle on a lane must get permissions from a subset of other vehicles and its following vehicles on the same lane can follow the lead vehicle without permissions unlike the previous research. To evaluate the performance of our distributed mutual exclusion algorithm, we conduct extensive experiments. The results show that our algorithmic design is both effective and efficient. [ABSTRACT FROM AUTHOR]