Vehicle ad hoc networks (VANETs) is an emerging ad hoc network technology with a promising future but significant limitations, particularly in communication networks. An effective routing system can significantly enhance the performance of VANETs. However, creating an effective routing protocol in an urban context to transport the data packet to the destination is still challenging due to complex road conditions, sporadic connectivity among vehicles, frequent disconnections, and quick changes in network topology. To enhance the accuracy of routing decisions in an urban setting, we propose a novel “multiple attributes decision-making junction selection routing (MADMJSR)” routing protocol based on vehicle position for VANETs. Formerly, most of the research was focused on static weight-based attribute selection for communication rather than optimal values, which led to a drop in junction decision-making accuracy. Our proposed scheme considers the multiple attribute selection and the optimal weight value assigned to each attribute, which significantly improves the routing decision accuracy. The results show a high packet delivery ratio (PDR) gain within minimum latency and less network load. Furthermore, a thorough discussion of the functioning of our proposed protocol is presented along with a detailed performance evaluation in comparison to other protocols.

SummaryAiming to the applications from security surveillance, military operational capabilities to the content and package delivery, unmanned aerial vehicles (UAVs) has a successfully created his space in the available technologies. The compact sized powerful flying robots are wirelessly controlled and are capable to complete tasks with and without direct human intervention. UAVs however still face serious challenges that limit the dream of complete autonomous unmanned flying machines. The key challenges include path planning and obstacle avoidance of these unmanned flying robots that are unavoidable while performing the application‐specific functionalities both in indoor and outdoor environments. In this manuscript with a survey, we investigate the state‐of‐the‐art UAV path planning algorithms and obstacle avoidance techniques. We have also summarized and compared the schemes in tabular form. In addition, current and future research directions and challenges are also discussed, showing the prospective research directions.

Intelligent transportation system (ITS) provides an efficient solution to road safety traffic. Enabling ITS requires connectivity among vehicles. However, dynamic vehicular networks cause link disruption among vehicles. To overcome this, unmanned aerial vehicles (UAVs) are deployed to provide connectivity between vehicles that are beyond the communication range. UAVs act as a relay node in providing connectivity among vehicles. We propose multi-agent reinforcement learning to control the trajectory for the deployment of UAVs. Each UAV acts as an agent to find the optimal position, from where it can provide coverage to the maximum number of vehicles. The proposed scheme is expected to increase the packet delivery ratio (PDR) and throughput while reducing the end-to-end delay.

Recently the Internet of Things (IoT) have gain much attention and, therefore, the demands for various streaming applications based on the IoT have increased. In this regard, the Constrained Application Protocol (CoAP) is a lightweight transmission protocol widely used in the IoT environment for providing streaming services. Generally, the CoAP protocol defines two message types i.e. confirmable message and non-confirmable message. However, since CoAP is not originally a transmission protocol design for streaming applications. Therefore, it cannot guarantee the reliability and real-time performance when streaming media is transmitted over it. In this paper, we propose a method to improve the QoS of streaming media transmission by dynamically changing the proportion of confirmable messages and non-confirmable messages. As the network conditions gradually gets better, the proposed method appropriately reduces the proportion of confirmable messages to meet the real-time requirements of streaming media. Finally, the simulation results show that the proposed scheme significantly enhances the QoS of the CoAP protocol for streaming service.