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Статья
Ubiquitous vehicular ad-hoc network computing using deep neural network with iot-based bat agents for traffic management
2021
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.
Цитирование
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Версии
- 1. Version of Record от 2021-04-01
Метаданные
Название журнала
- Electronics (Switzerland)
Том
- 10
Выпуск
- 7
Финансирующая организация
- National Natural Science Foundation of China
Номер гранта
- 61950410603
Тип документа
- journal article
Тип лицензии Creative Commons
- CC BY
Правовой статус документа
- Свободная лицензия
Источник
- scopus
