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Wireless Sensor Networks for Vehicle Navigation
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Wireless Sensor Networks for Vehicle Navigation

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (15 July 2021) | Viewed by 6080

Special Issue Editors

Prof. Dr. Kyoung Ho Choi

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Guest Editor
Dept. of EE, Mokpo National University, South Korea
Interests: sensor fusion; deep learning-based visual recognition
Dr. Do-Hyun Kim

E-Mail
Guest Editor
Artificial Interlligence Research Laboratory / Intelligent Robotics Research Division / Intelligent Positioning and Navigation Research Section, ETRI
Interests: automotive engineering; database management systems; ubiquitous sensor networks; intelligent transportation systems (ITS)

Special Issue Information

Dear Colleagues,

Vehicle navigation is one of the essential functions for self-driving cars and unmanned aerial vehicles (UAVs). To solve localization and navigation problems of self-driving cars and UAVs, wireless sensor network technologies can be exploited. Wireless sensor networks can be used to detect obstacles, pedestrians in the environment, and provide navigation information to unmanned or self-driving vehicles. In this Special Issue, we are aiming to bring together recent technical advances in wireless sensor networks and deep-learning techniques to solve vehicle navigation problems for self-driving cars and UAVs in the topics as follows, among others:

  • ­Wireless sensor networks for vehicle navigation;
  • ­Wireless sensor networks for vehicle localization;
  • Wireless sensor networks for road accident detection/warnings;
  • ­Wireless sensor networks for traffic monitoring;
  • Deep learning-based approaches for traffic monitoring/driver monitoring/vehicle navigation;
  • Sensor data fusion for video navigation;
  • Sensor data fusion for traffic monitoring;
  • Sensor data fusion for driver monitoring;
  • Applications of vehicle navigation.

The Special Issue aims to solve vehicle navigation problems using sensor networks.

Prof. Dr. Kyoung Ho Choi
Dr. Do-Hyun Kim
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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Published Papers (2 papers)

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Research

18 pages, 2262 KiB  
Article
A Proposal of a Troposphere Model in a GNSS Simulator for VANET Applications
by Mauro Tropea, Angelo Arieta, Floriano De Rango and Francesco Pupo
Sensors 2021, 21(7), 2491; https://doi.org/10.3390/s21072491 - 3 Apr 2021
Cited by 1 | Viewed by 2166
Abstract
Vehicle positioning is becoming an important issue related to Intelligent Transportation Systems (ITSs). Novel vehicles and autonomous vehicles need to be localized under different weather conditions and it is important to have a reliable positioning system to track vehicles. Satellite navigation systems can [...] Read more.
Vehicle positioning is becoming an important issue related to Intelligent Transportation Systems (ITSs). Novel vehicles and autonomous vehicles need to be localized under different weather conditions and it is important to have a reliable positioning system to track vehicles. Satellite navigation systems can be a key technology in providing global coverage and providing localization services through many satellite constellations such as GPS, GLONASS, Galileo and so forth. However, the modeling of positioning and localization systems under different weather conditions is not a trivial objective especially considering different factors such as receiver sensitivity, dynamic weather conditions, propagation delay and so forth. This paper focuses on the use of simulators for performing different kinds of tests on Global Navigation Satellite System (GNSS) systems in order to reduce the cost of the positioning testing under different techniques or models. Simulation driven approach, combined with some specific hardware equipment such as receivers and transmitters can characterize a more realistic scenario and the simulation can consider other aspects that could be complex to really test. In this work, the main contribution is the introduction of the Troposphere Collins model in a GNSS simulator for VANET applications, the GPS-SDR-SIM software. The use of the Collins model in the simulator allows to improve the accuracy of the simulation experiments throughout the reduction of the receiver errors. Full article
(This article belongs to the Special Issue Wireless Sensor Networks for Vehicle Navigation)
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27 pages, 2092 KiB  
Article
Gathering Big Data in Wireless Sensor Networks by Drone
by Josiane da Costa Vieira Rezende, Rone Ilídio da Silva and Marcone Jamilson Freitas Souza
Sensors 2020, 20(23), 6954; https://doi.org/10.3390/s20236954 - 5 Dec 2020
Cited by 9 | Viewed by 3206
Abstract
The benefits of using mobile sinks or data mules for data collection in Wireless Sensor Network (WSN) have been studied in several works. However, most of them consider only the WSN limitations and sensor nodes having no more than one data packet to [...] Read more.
The benefits of using mobile sinks or data mules for data collection in Wireless Sensor Network (WSN) have been studied in several works. However, most of them consider only the WSN limitations and sensor nodes having no more than one data packet to transmit. This paper considers each sensor node having a relatively larger volume of data stored in its memory. That is, they have several data packets to send to sink. We also consider a drone with hovering capability, such as a quad-copter, as a mobile sink to gather this data. Hence, the mobile collector eventually has to hover to guarantee that all data will be received. Drones, however, have a limited power supply that restricts their flying time. Hence, the drone’s energy cost must also be considered to increase the amount of collected data from the WSN. This work investigates the problem of determining the best drone tour for big data gathering in a WSN. We focus on minimizing the overall drone flight time needed to collect all data from the WSN. We propose an algorithm to create a subset of sensor nodes to send data to the drone during its movement and, consequently, reduce its hovering time. The proposed algorithm guarantees that the drone will stay a minimum time inside every sensor node’s radio range. Our experimental results showed that the proposed algorithm surpasses, by up to 30%, the state-of-the-art heuristics’ performance in finding drone tours in this type of scenario. Full article
(This article belongs to the Special Issue Wireless Sensor Networks for Vehicle Navigation)
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