BA-CNN: Bat Algorithm-Based Convolutional Neural Network Algorithm for Ambulance Vehicle Routing in Smart Cities

• Published in: Mobile information systems Vol. 2022; pp. 1 - 14
• Main Authors: Darwassh Hanawy Hussein, Taha, Frikha, Mondher, Ahmed, Sulayman, Rahebi, Javad
• Format: Journal Article
• Language: English
• Published: Amsterdam Hindawi 08.06.2022; John Wiley & Sons, Inc
• Subjects: Accidents; Algorithms; Artificial neural networks; Big Data; Communication; Data encryption; Data transmission; Genetic algorithms; Internet of Things; Light; Neural networks; Optimization; Performance evaluation; Position (location); Route planning; Sensors; Shortest-path problems; Smart cities; Traffic congestion; Traffic control; Traffic flow; Vehicle routing; Vehicles
• ISSN: 1574-017X; 1875-905X; 1875-905X
• DOI: 10.1155/2022/7339647

This article proposes an ambulance vehicle routing approach in smart cities. The approach is based on the bat algorithm and convolutional neural network (BA-CNN). It aims to take transfer the patients confidentially, accurately, and quickly. The type of CNN used in this research is a residual network (ResNet). The node method is responsible for creating the city map. In the beginning, information about the accident place is received by the control station and forwarded to both the hospital and the ambulance. The driver feeds the data that contain the ambulance vehicle’s node position and the accident location to the BA-CNN vehicle routing algorithm. The algorithm then obtains the shortest path to reach the location of the accident by the driver. When the vehicle arrives at the accident location, the driver updates the algorithm with hospital and accident positions. Then, the shortest path (which leads to the fast reach time) to the hospital is calculated. The bat algorithm provides offline data for a possible combination of different source and destination coordinates. The offline data are then trained by utilizing a neural network. The neural network is used for finding the shortest routes between source and destination. The performance evaluation of the BA-CNN algorithm is based on the following metrics: end-to-end delay (EED), throughput, and packet delivery fraction (PDF). This BA-CNN is compared with counterparts, including three different existing methods such as TBM, TVR, and SAODV. The experiments demonstrate that the PDF of our method is 0.90 for 10 malicious nodes, which is higher than in the TBM, TVR, and SAODV.