Location Prediction Model Based on the Internet of Vehicles for Assistance to Medical Vehicles

Cheng, Jiujun; Yan, Huaichen; Zhou, Aiguo; Liu, Chunmei; Ding, Cheng; Gao, Shangce; Zang, Di; Cheng, Deli

doi:10.1109/access.2019.2957785

Cited by 7 publications

(5 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With this information, the simulation is performed to estimate false rate, accuracy, data utilisation, displacement error, and processing latency. Finally, these metrics are compared with the existing GMEPP [21], IVRN-DRL [26], and LPMV [33] methods discussed in the related works section.…”

Section: Resultsmentioning

confidence: 99%

“…Cheng et al [33] developed an IoT-based location prediction model for assistance to medical vehicles (LPMVs). The proposed model uses a combination of long short-term memory and deep belief nets to enhance the accuracy of medical vehicle processes.…”

Section: Related Workmentioning

confidence: 99%

“…Based on the literature analysis, there are several problems with existing methods in achieving less false rate, displacement error, processing latency and high accuracy, and data utilisation for varying displacement intervals such as GMEPP [21], IVRN-DRL [26], and LPMV [33] methods. Hence, this paper uses the NDP-AD method for analysing the opposite vehicle distance and traffic to provide accurate guidance independently.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Emergency Vehicle Driving Assistance System Using Recurrent Neural Network with Navigational Data Processing Method

Anjum

Shahab

2023

Sustainability

View full text Add to dashboard Cite

Emergency vehicle transportation is important for responding to and transporting individuals during emergencies. This type of transportation faces several issues, such as road safety, navigation and communication, time-critical operations, resource utilisation, traffic congestion, data processing and analysis, and individual safety. Vehicle navigation and coordination is a critical aspect of emergency response that involves guiding emergency vehicles, such as ambulances, to the location of an emergency or medical centre as quickly and safely as possible. Therefore, it requires additional effort to reduce driving risks. The roadside units support emergency vehicles and infrastructure to decrease collisions and enhance optimal navigation routes. However, during the emergency vehicle’s data communication and navigation process, communication is interrupted due to vehicle outages. Therefore, this study proposes the Navigation Data Processing for Assisted Driving (NDP-AD) method to address the problem. The proposed approach assimilates infrastructure and neighbouring location information during driving. The integrated information is processed for distance and traffic during the previous displacement interval. The NDP-AD method employs a recurrent neural network learning approach to analyse opposing vehicle distance and traffic to provide accurate, independent guidance. This effective learning-based guidance process minimises false navigations and deviation in displacement. System efficiency is evaluated based on processing latency, displacement error, data utilisation, false rate, and accuracy metrics.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Emergency Vehicle Driving Assistance System Using Recurrent Neural Network with Navigational Data Processing Method

Anjum

Shahab

2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…In addition, they also consider the relative order of location in the context to enhance the prediction accuracy. In [ 30 ], a location prediction model for assistance to medical vehicles was proposed, considering the best method for location prediction where the trajectory information is the only consideration. They take into account real-time traffic information and use deep belief networks and long short-term memory to achieve the goal of location prediction.…”

Section: Methodsmentioning

confidence: 99%

The Efficient Mobile Management Based on Metaheuristic Algorithm for Internet of Vehicle

Huang

Chen

et al. 2022

Sensors

View full text Add to dashboard Cite

With the low latency, high transmission rate, and high reliability provided by the fifth-generation mobile communication network (5G), many applications requiring ultra-low latency and high reliability (uRLLC) have become a hot research topic. Among these issues, the most important is the Internet of Vehicles (IoV). To maintain the safety of vehicle drivers and road conditions, the IoV can transmit through sensors or infrastructure to maintain communication quality and transmission. However, because 5G uses millimeter waves for transmission, a large number of base stations (BS) or lightweight infrastructure will be built in 5G, which will make the overall environment more complex than 4G. The lightweight infrastructure also has to be considered together. For these reasons, in 5G, there are two mechanisms for handover, horizontal, and vertical handover; hence, it must be discussed how to handle handover to obtain the best performance for the whole network. In this paper, to address handover selection, we consider delay time, energy efficiency, load balancing, and energy consumption and formulate it as a multi-objective optimization (MOO) problem. At the same time, we propose the handover of the mobile management mechanism based on location prediction combined with heuristic algorithms. The results show that our proposed mechanism is better than the distance-based one for energy efficiency, load, and latency. It optimizes by more than about 20% at most.

show abstract

“…Cheng et al [20] developed an urban-road connectivity model using possibility, data forwarding time, link forwarding capability, and packet error rate. Cheng et al [21] proposed a position prediction framework for IoV to be used by medical units. Their framework considered vehicle's attributes, road conditions, and driving load.…”

Section: Preliminaries a Fundamentals Of The Internet Of Vehiclesmentioning

confidence: 99%

A Network-Centric Analysis for the Internet of Vehicles and Simulation Tools

et al. 2020

View full text Add to dashboard Cite

The Internet of Vehicles (IoV) is an emerging research framework, with network and graph theories as two of the major fields. Researchers in these topics use a variety of tools and approaches to simulate and perform experimentation on their proposed methodologies. A comprehensive study to facilitate the selection of such simulation tools is lacking from the literature. In this work, we provide a systematic review of the different simulation platforms. More precisely, the contributions of this paper are fourfold: firstly, we propose a two-tier hierarchical taxonomy based on the trends in the literature; secondly, we investigate the strengths and limitations of different simulation platforms; and thirdly, we take a network theoretic approach to identify the patterns in IoV research. To this end, we create a network of the publications and populate the edges among them. Community detection is performed using Louvian and Clauset-Newman-Moore algorithms. To the best of our knowledge, this is a novel approach to reviewing the literature which provides a more in-depth analysis of the trends in the literature. Finally, we review the common datasets for IoV experimentation.

show abstract

Location Prediction Model Based on the Internet of Vehicles for Assistance to Medical Vehicles

Cited by 7 publications

References 27 publications

Emergency Vehicle Driving Assistance System Using Recurrent Neural Network with Navigational Data Processing Method

Emergency Vehicle Driving Assistance System Using Recurrent Neural Network with Navigational Data Processing Method

The Efficient Mobile Management Based on Metaheuristic Algorithm for Internet of Vehicle

A Network-Centric Analysis for the Internet of Vehicles and Simulation Tools

Contact Info

Product

Resources

About