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Top1. Introduction
Smart traffic monitoring (Manivannan et al., 2020) enables the sharing of warning information between device implementations through communication channels and vehicle infrastructure. The VANET nodes are distinguished by their increased mobility and varied mobility patterns. Vehicle-to-vehicle communication was used by the drivers to exchange and communicate information in order to organise themselves for serious situation avoidance (Alabbas 2020). For example, avoiding unseen obstacles, allowing emergency vehicles free passage, assisting in speed control, preventing roadside accidents and traffic jams, and so on.
VANET is a major network communication topic in intelligent transportation systems (ITSs). There is a communication among roadside and vehicle equipment, as well as vehicles that are close by. The two types of communications in VANETs are vehicle-to-infrastructure communication and vehicle-to-vehicle communication (Fatemidokht et al., 2020).
Different hardware and software components, such as the On-Board Unit (OBU), Application Unit (AU), Application Unit (AU), and RoadSide Unit, are used to communicate between vehicles (RSU). Many applications, such as pedestrian crossing warnings, speed limits for curves, merging lanes, weather-related hazards, collisions, and road construction, are widely deployed in VANETs (Regin et al., 2020). VANETs also aid in paying attention to real-time traffic conditions, updating inbuilt vehicle navigation systems, finding parking spots and tolls, software updates, and downloading music and video.
The cars were recently outfitted with sophisticated sensors that identified a number of assistive features (Mallikarjun Maratha and Virupakshappa, 2016) such as suggestive lane changing, partially autonomous driving, automatic lane tracking, front collision avoidance (Sharanabasappa and Nandyal, 2021), and so on (Rashid et al., 2020). Nonetheless, secure message dissemination, authentication, and privacy are the most pressing concerns (Vinisha et al., 2014) (Rajkumar K et al., 2015). The rapid technological change and high mobility characterization of VANET makes it vulnerable to various malicious attacks. Researchers in VANET have used a variety of privacy-preserving and secure authentication methods over the years. The VANETs provided several useful services to vehicle passengers and drivers, making driving more comfortable and safe (Talib et al., 2021). Existing methods for cluster-head selection took into account protocols with various QoS metrics such as energy, end-to-end delay, bandwidth, delay, and throughput. Different types of routing protocols are used to consider different QoS constraints such as delay and bandwidth during optimal route selection (Thakur et al., 2020). However, the high mobility of vehicles is not taken into account, resulting in inefficient VANET protocols. To overcome these issues, an evolutionary Black Widow Optimization (BWO) based Neuro-Fuzzy Optimization (EBW-NFO) algorithm is used for creating thecluster routing protocol. The main contributions of this paper is summarized as follows: