A vehicular network with Road Side Units (RSUs) provides an efficient way to connect vehicles even on the move. However, due to high deployment and maintenance cost of RSUs, it is necessary to use lesser number of RSUs such that total cost is minimized. It is suggested that cellular networks such as LTE are capable of fulfilling the demands posed in vehicular network scenarios. Availability of high bandwidth, large coverage area, and low latency are some of the advantages of cellular networks which help in overcoming the challenges of high speed vehicular communication. In this paper, we propose a maiden approach to analyse the performance of a vehicular network with cellular infrastructure as a backbone. For this, we use mobile femto access points as relays in place of RSUs. We model the network using M/M/m queue and compare the delay and throughput performance with traditional IEEE 802.11p vehicular networks. We also formulate an optimization problem and propose a subchannel power control algorithm to handle increased co-channel interference which emerges due to high mobility of vehicles in the network. Our suggested approach shows improvement in terms of delay, throughput, and energy efficiency. The results are verified using extensive simulations.Index Terms-Vehicular network, cellular network, mobile femto access points, delay, energy efficiency.
Intelligent Automation (IA) in automobiles combines robotic process automation and artificial intelligence, allowing digital transformation in autonomous vehicles. IA can completely replace humans with automation with better safety and intelligent movement of vehicles. This work surveys those recent methodologies and their comparative analysis, which use artificial intelligence, machine learning, and IoT in autonomous vehicles. With the shift from manual to automation, there is a need to understand risk mitigation technologies. Thus, this work surveys the safety standards and challenges associated with autonomous vehicles in context of object detection, cybersecurity, and V2X privacy. Additionally, the conceptual autonomous technology risks and benefits are listed to study the consideration of artificial intelligence as an essential factor in handling futuristic vehicles. Researchers and organizations are innovating efficient tools and frameworks for autonomous vehicles. In this survey, in-depth analysis of design techniques of intelligent tools and frameworks for AI and IoT-based autonomous vehicles was conducted. Furthermore, autonomous electric vehicle functionality is also covered with its applications. The real-life applications of autonomous truck, bus, car, shuttle, helicopter, rover, and underground vehicles in various countries and organizations are elaborated. Furthermore, the applications of autonomous vehicles in the supply chain management and manufacturing industry are included in this survey. The advancements in autonomous vehicles technology using machine learning, deep learning, reinforcement learning, statistical techniques, and IoT are presented with comparative analysis. The important future directions are offered in order to indicate areas of potential study that may be carried out in order to enhance autonomous cars in the future.
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