Abstract
The Internet of Vehicles (IoV) is a developing technology attracting attention from the industry and the academia. Hundreds of millions of vehicles are projected to be connected within the IoV environments by 2035. Each vehicle in the environment is expected to generate massive amounts of data. Currently, surveys on leveraging deep learning (DL) in the IoV within the context of big data analytics (BDA) are scarce. In this paper, we present a survey and explore the theoretical perspective of the role of DL in the IoV within the context of BDA. The study has unveiled substantial research opportunities that cut across DL, IoV, and BDA. Exploring DL in the IoV within BDA is an infant research area requiring active attention from researchers to fully understand the emerging concept. The survey proposes a model of IoV environment integrated into the cloud equipped with a high-performance computing server, DL architecture, and Apache Spark for data analytics. The current developments, challenges, and opportunities for future research are presented. This study can guide expert and novice researchers on further development of the application of DL in the IoV within the context of BDA.
Highlights
By 2025, the massive ecosystem of the Internet of ings (IoT) is projected to pave a smooth way for 100 billion connections. us, the IoT can revolutionize future industries [1]. e IoT has been extended to the Internet of Vehicles (IoV) [2] due to incorporation of intelligent transportation systems for enhanced services [3]. e IoV allows vehicles to communicate with their internal and external environments. e communications of vehicles in sharing information can be in a different form
We present a survey and theoretical perspective leverage of deep learning (DL) in the IoV within the context of big data analytics (BDA). e intention is to stimulate the research community to focus on exploring DL in the IoV within the context of BDA. is approach can unveil valuable knowledge from the large-scale data expected to be generated from the IoV
Reference [83] offered a technique for cloud-based AzureML named Generalized Flow, which allows binary classification and multiclass datasets and processes them to maximize the overall classification accuracy. e performance of the technique is tested on datasets based on the optimized classification model. e authors used three public datasets and a local dataset to evaluate the proposed flow using the classification. e result of the public datasets has shown an accuracy of 97.5%
Summary
By 2025, the massive ecosystem of the Internet of ings (IoT) is projected to pave a smooth way for 100 billion connections. us, the IoT can revolutionize future industries [1]. e IoT has been extended to the Internet of Vehicles (IoV) [2] due to incorporation of intelligent transportation systems for enhanced services [3]. e IoV allows vehicles to communicate with their internal and external environments. e communications of vehicles in sharing information can be in a different form. Reference [7] argued that the autonomous vehicle market is presently growing and is expected to hit USD 131.9 billion in 2019. In USA, hundreds of autonomous vehicles are expected to start operating on public roads in the near future. Reference [10] estimated that in 2021, 380 million connected vehicles will be running on public roads, and each was projected to generate 25 GB of data every hour. Despite the success of DL in different domains and the unprecedented attention it currently receives from researchers, the empirical exploration of DL in the IoV within the context of BDA is highly limited in the literature. Erefore, a theoretic viewpoint is required to guide the effective empirical applications of DL in the IoV within the context of big data.
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