Performance Enhancement of Vehicular Ad hoc Network (VANET) for Real-life Applications in Smart Cities

Vehicular ad hoc network is one of the fastest-growing research areas within a decade in industry and academia. The performance of VANET is dependent on the communication between vehicles and RSUs. Nowadays, vehicles are not considered a traditional carrier. Still, the technological advancement in the electro-mechanical-humanity department, availability of various sensors at affordable cost, a communication unit, availability of vehicular standard for effective communication, availability of 5G for the quick transmission of data make the vehicle smart and intelligent. It is used for a wide range of autonomous applications. The demand for various autonomous applications in smart cities and the wide range of safety and non-safety applications of VANET motivates to enhance the performance of VANET for the real-life application of the smart cities. Selection of optimum path from source to destination, the quick transmission of data from the source and quick reception of data at the destination for low density of vehicles, the quick transmission of information for dynamic destination, and detection of faulty OBU and isolate the faulty vehicle from routing for the successful implementation of the application through VANET are the objectives of the thesis. Simulation through SUMO and OMNET++ and the testbed experiments are performed for the performance measurement of the proposed work. A position based routing protocol is proposed for the transmission of emergency messages. During message communication, the vehicles communicate with the neighboring vehicle in the same direction and take advantage of vehicles moving in the opposite directions in another lane. Also, the routing protocol considers the combination of parameters such as the shortest path, the density of vehicles, and the delay. To make the network scenario realistic, it assumes an important network impairment such as attenuation. The simulation result shows that the proposed work performs better than the existing protocol in terms of E2E delay, number of hops, number of vehicular gaps, and Total Service Time (TST). NIB based routing is proposed for the health parameters monitoring at the health observation center. During COVID-19, the transmission of health parameters from the quarantine center to the health observation center and analysis of the health parameters at the observation center through VANET is proposed. The transmission of the health parameters is presented using an automated routing through NIB, vehicles, and RSU. Analysis and classification of health parameters at the observation center are done using one against one (OAO) method of support vector machine. The performance of the proposed routing is compared with P-GEDIR, A_STAR, and GSR, and performs better in terms of end-to-end viii delay, number of network gap, packet delivery ratio, etc. Health parameters analysis is done using the parameters precision, recall, f1-score, and support. The testing accuracy for the health data analysis phase is determined as 96.8%. Location service based routing is proposed for location identification which is suitable for the mobile destination. The house location identification is considered based on the house owner’s name or house number, including the patient’s house for this purpose. House location information is stored in a distributed open-source document database called MongoDB in which the location of the house is searched either in terms of a house number or house owner name. The searching of the house location information is done using a tool node-red. The performance of the proposed work is compared with the location service GLS and HLS and performs better in terms of query success rate, location MAC bandwidth consumption, and request travel time. A fault based routing algorithm considering a complete fault set is proposed to diagnose the hard fault, three categories of the soft fault, i.e., permanent, intermittent, and transient, for the faulty OBU of the VANET. Along with soft fault detection, soft fault classification is done. The information regarding the faulty vehicle is transmitted from the RSU to the faulty vehicle with the help of an automated routing. The K-S test does the hard fault detection with a timeout mechanism. The soft fault detection is done by the chi-square test. Both the hard fault and the soft fault are evaluated by the parameters FDA and FAR. Soft fault classification is done using the ECOC method of the SVM multi-class classifier. The evaluation of the proposed routing method is carried out by E2E delay, the number of hops, and network gaps and performs better as compared to GSR, A_STAR, and Bhoi et al