Collaborative Learning Automata-Based Routing for Rescue Operations in Dense Urban Regions Using Vehicular Sensor Networks


In vehicular sensor networks (VSNs), a rise within the density of the vehicles on road and route jamming in the network causes delay in receiving the emergency alerts, that results in overall system performance degradation. So as to handle this issue in VSNs deployed in dense urban regions, in this paper, we tend to propose collaborative learning automata-primarily based routing algorithm for sending info to the meant destination with minimum delay and most throughput. The learning automata (LA) stationed at the nearest access points (APs) within the network learn from their past experience and build routing decisions quickly. The proposed strategy consists of dividing the whole region into completely different clusters, primarily based on which an optimized path is chosen using collaborative LA having input parameters as vehicle density, distance from the nearest service unit, and delay. A theoretical expression for density estimation comes, that is used for the selection of the “best” path by LA. The performance of the proposed theme is evaluated with respect to metrics like packet delivery delay (network delay), packet delivery ratio with varying node (vehicle) speed, transmission range, density of car, and variety of road side units/APs). The results obtained show that the proposed theme performs higher than the benchmark chosen during this study, as there is a thirtypercent reduction in network delay and a 20p.c increase in packet delivery ratio.

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