Abstract
In this paper, Deep Neural Networks (DNN) with Bat Algorithms (BA) offer a dynamic form of traffic control in Vehicular Adhoc Networks (VANETs). The former is used to route vehicles across highly congested paths to enhance efficiency, with a lower average latency. The latter is combined with the Internet of Things (IoT) and it moves across the VANETs to analyze the traffic congestion status between the network nodes. The experimental analysis tests the effectiveness of DNN-IoT-BA in various machine or deep learning algorithms in VANETs. DNN-IoT-BA is validated through various network metrics, like packet delivery ratio, latency and packet error rate. The simulation results show that the proposed method provides lower energy consumption and latency than conventional methods to support real-time traffic conditions.
Highlights
Introduction iationsVehicular Ad-hoc Networks (VANETs) are an important class of ubiquitous computing, which operate as a key technology for enabling the Vehicular Adhoc Networks (VANETs) applications [1,2,3,4].VANETs have recently provided their users with the means for safety management and data management, where the control methods are designed to work under any circumstances based on network dynamics [5]
The roadside units (RSU) operates the cryptographic credentials to decode vehicle information and the Deep Neural Networks (DNN)-Internet of Things (IoT)-Bat Algorithm (BA) algorithm and stores them; Traffic Management Center (TMC): The IoT-BA calculates the traffic density using road segments
This paper shows that DNN-IoT-BA offers an efficient routing of vehicles on highThis paper shows that
Summary
The RSU operates the cryptographic credentials to decode vehicle information and the DNN-IoT-BA algorithm and stores them; Traffic Management Center (TMC): The IoT-BA calculates the traffic density using road segments. In order to obtain road traffic information, the directional connection of TMC to RSUs and other IoT-BAs are used. The path between any two crossings was considered to connect two or more street segments, such as the segment s(i,j) and intersection I(i) s(i,j), providing a collection of different characteristics, which include road widths and lengths, vehicle traffic density, and the number of roads between two intersections [78,79,80]. The DNN made appropriate decisions by forming the graph G = with V in the proposed method, as the intersections between the source, the destination vehicle, and E are considered the road segments connected to intersections in V.
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