In an autonomous vehicle (AV), in order to efficiently exploit the acquired resources, big data analyses will be a reliable source for extracting valuable information from various sensors and actuators. The data extracted with the combined ability of telematics and real-time investigation forms the vibrant asset for self-driving cars. To demonstrate the significances of big data analysis, this study proposes a competent architecture for real-time big data analysis for an AV, which indeed keeps pace with the latest trends and advancement concerning an emerging paradigm. There are a massive amount of sensors and independent systems needed to be realised for better competence in an AV, and the proposed model focuses on independent sensors that distinguish objects and handles visual information to decide the path. In order to attain the objective as mentioned above, a sensor fusion mechanism is proposed, which combines 3D camera sensor data and Lidar sensor information to provide an optimised solution for path selection. Furthermore, three algorithms, namely overlapping algorithm, sequential adding algorithm, the distance-focused algorithm is designed for higher efficiency in sensor fusion mechanism. The proposed methodology is for the best exploitation of the enormous dataset, meant for real-time processing for an AV.
MANET is commonly known as Mobile Ad Hoc Network in which cluster of mobile nodes can communicate with each other without having any basic infrastructure. The basic characteristic of MANET is dynamic topology. Due to the dynamic behavior nature, the topology of the network changes very frequently, and this will lead to the failure of the valid route repeatedly. Thus, the process of finding the valid route leads to notable drop in the throughput of the network. To identify a new valid path to the targeted mobile node, available proactive routing protocols use simple broadcasting method known as simple flooding. The simple flooding method broadcasts the RREQ packet from the source to the rest of the nodes in mobile network. But the problem with this method is disproportionate repetitive retransmission of RREQ packet which could result in high contention on the available channel and packet collision due to extreme traffic in the network. A reasonable number of routing algorithms have been suggested for reducing the lethal impact of flooding the RREQ packets. However, most of the algorithms have resulted in considerable amount of complexity and deduce the throughput by depending on special hardware components and maintaining complex information which will be less frequently used. By considering routing complexity with the goal of increasing the throughput of the network, in this paper, we have introduced a new approach called Dynamic Probabilistic Route (DPR) discovery. The Node's Forwarding Probability (NFP) is dynamically calculated by the DPR mobile nodes using Probability Function (PF) which depends on density of local neighbor nodes and the cumulative number of its broadcast covered neighbors.
Summary
With the advancement of Internet of Things (IoT), the devices are allowed to interact with other networks like mobile ad hoc network (MANET). The MANET‐IoT systems often undergo energy balancing problem between the sensor nodes, whereas the MANETs operate on mobile sensor nodes. Hence, proper utilization of battery power is required to maintain the network connectivity during a multi‐hop transmission. In this paper, we propose a DeepSense IoT‐MANET framework that effectively routes the packets from the IoT nodes via mobile sensor nodes in MANETs. The routings between the MANETs are organized by DeepSense interconnected with deep neural network (DNN) learning methods. The performance of the DeepSense DNN method is evaluated against various network metrics to evaluate the efficacy of the model.
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