In the past years, various routing methods have been advanced for VANETs. Routing protocols that utilize various parameters have been defined to be most suitable for vehicle networks due to their efficiency in production with DE modifies due to vehicular network mobility. Parameters are linked-stable, network speed, and environment situations, etc. This research article presents a traffic-based management system-based protocols for VANETs satisfactory for urban city background. The novel method is an improved description of the dynamic source routing (DSR) energy-based protocol. The developed protocol, termed effective DSR, uses an ant-based method to search a path that has optimized network connectivity. It is supposed that every vehicle node has a vehicle id of paths such as road and streets, etc. Utilizing data included of small control network packets called ANTLIONs, the vehicle nodes evaluate a distance, energy for roadsides or streets to the network connections. ANTLION data packets are defined by the vehicle in particular areas. To search the valuable and the perfect route among source to sink node, the src vehicle defines the route on roads with minimum total distance and energy for the complete path. The fitness function of the planned routing protocol has been identified, and its presentation has been calculated in reproduction parameters or inputs. The experiment outcomes define that the PDR rate 10% improved as compared with the existing protocol (VACO: vehicle ant colony optimization) that also utilized and vehicle ant lion optimization (VALO) method. In the calculation, the control the E2D and network overhead (NO) is also mitigated.


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    Title :

    Vehicular Ant Lion Optimization Algorithm (VALOA) for Urban Traffic Management


    Additional title:

    Lect. Notes in Networks, Syst.


    Contributors:


    Publication date :

    2021-04-24


    Size :

    16 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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