In vehicular communication, the unmanned aerial vehicles (UAVs) are incorporated to overcome the drawbacks in the vehicle such as unpredictable mobility and rapid topological changes. The aerial vehicles are highly flexible and cost effective so that it is able to control the vehicles in a better manner. Currently the aerial vehicles are used to perform highly confidential data transmission in a collaborative way. Several challenges occurred in search works in terms of limited battery power and environmental condition. To overcome this in this article latency aware optimization for collaborative aerial vehicles (ELAOC-UAVs) are developed. The core modules of this process are traffic model and trajectory design creation and optimization among the UAVs using glowworm swarm optimization (GSO) algorithm. With the presence of this process the delay occurrences among the aerial vehicles are greatly reduced and that helps to improve the overall performance of the network. The ELAOC-UAVs model is constructed in the software NS3 and the perimeters which are used to measure the performance of the network are data accuracy, data loss, routing overhead, network throughput and average delay. From the final result it is proven that the ELAOC-UAVs obtained better results in terms of throughput and data accuracy when compared with the earlier baseline methodology.
<div style="text-align:justify">The Vehicular Ad hoc Network (VANETs) is a subclass of mobile communication which occupies maximum of the promising approaches of future intelligent communication systems (ITS). Currently the vehicles which are present in this technology are tremendously increased and that creates several challenges in providing efficient communication. Due to its high speed and random mobility several routing issues and optimization problems are created which increase is the delay and overhead during data transmission. To come these drawbacks in this article an improved VANETs routing with particles swarm optimization to improve the quality-of-service parameters (IVRPSO) are developed. The core modules which are present in the article are RPL protocol-based routing and particles swarm optimization. Through these processes that routing of the high-speed vehicles are standardized and the quality of communication is improvised. The implementation of this network is done in NS3 software and the parameters which are concentrated to analyses the performance are data success rate, network throughput, routing overhead, data loss date and average delay. The simulation output states that from the execution of the varying number of vehicles the performance of the IVRPSO is better than the earlier works concerned with efficiency and data success rate.</div>
A prototype for an IoT based smart home is developed to control and monitor various systems such as light, lamp, socket and fan using ESP32 controller. Later the appliances are switched ON/OFF through virtual key using mobile APP. Firebase cloud is used to transfer data from the controller.
