A massive increase in the number of mobile devices and data hungry vehicular network applications creates a great challenge for Mobile Network Operators (MNOs) to handle huge data in cellular infrastructure. However, due to fluctuating wireless channels and high mobility of vehicular users, it is even more challenging for MNOs to deal with vehicular users within a licensed cellular spectrum. Data offloading in vehicular environment plays a significant role in offloading the vehicle's data traffic from congested cellular network's licensed spectrum to the free unlicensed WiFi spectrum with the help of Road Side Units (RSUs). In this paper, an Intelligent Reward based Data Offloading in Next Generation Vehicular Networks (IR-DON) architecture is proposed for dynamic optimization of data traffic and selection of intelligent RSU. Within IR-DON architecture, an Intelligent Access Network Discovery and Selection Function (I-ANDSF) module with Q-Learning, a reinforcement learning algorithm is designed. I-ANDSF is modeled under Software-Defined Network (SDN) controller to solve the dynamic optimization problem by performing an efficient offloading. This increases the overall system throughput by choosing an optimal and intelligent RSU in the network selection process. Simulation results have shown the accurate network traffic classification, optimal network selection, guaranteed QoS, reduced delay and higher throughput achieved by the I-ANDSF module.

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Unmanned Aerial Vehicle (UAV) or drone, is an evolving technology in today's market with an enormous number of applications. Mini UAVs are developed in order to compensate the performance constraints imposed by larger UAVs during emergency situations. Multiple mini autonomous UAVs require communication and coordination for ubiquitous coverage and relaying during deployment. Multi-UAV coordination or swarm optimization is required for reliable connectivity among UAVs, due to its high mobility and dynamic topology. In this paper, a Secured UAV (S-UAV) model is proposed which takes the location of the UAVs as inputs to form a Wireless Mesh Network (WMN) among multiple drones with the help of a centralized controller. After WMN formation, efficient communication takes place using A* search, an intelligent algorithm that finds the shortest communication path among UAVs. Further, the S-UAV model utilizes cryptographic techniques such as Advanced Encryption Standard (AES) and Blowfish to overcome the security attacks efficiently. Simulation results show that the S-UAV model offers higher throughput, reduced power consumption and guaranteed message transmission with reduced encryption and decryption time.

Intelligent Transportation System (ITS) are helping to enhance road safety and traffic management applications. Internet of Vehicles (IoV) plays a promising role in this field, which turns each vehicle into a smart object with its own compute, storage, and networking capabilities. Nowadays, accidents have been increased mainly due to un-notified alerts about other accidents, work-in-progress, and excessive motorized vehicles at peak times. This non-line of sight information can be efficiently delivered using vehicular communication. IoV network, however has its own challenges like high mobility and dynamic network topology. The above mentioned challenges are addressed with the assistance of a centralized Software Defined Network (SDN), which isolates the control plane from the data plane. In IoV, SDN provides logically centralized traffic management and improves the vehicular communication. In this paper, the Software Defined-Internet of Vehicles (SD-IoV) system is designed to manage heavy traffic and avoids broadcast storm problem with high packet delivery ratio. The proposed broadcast routing mechanism uses selective forwarding and neighbor awareness of the vehicle to efficiently broadcast emergency alert messages, thereby avoiding traffic jams and reducing travel time. On-Board Unit (OBU) in vehicles detects the accident and initializes the broadcast algorithm in SD-IoV system. The accident detection by OBU in vehicles is simulated using machine learning technique with an accuracy of 90%. Simulation performed in SUMO and OMNeT++ shows that with the help of the SDN controller, the IoV network achieves a high packet delivery ratio with minimal delay.

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