A Dead-Reckoning Based Local Positioning System for Intelligent Vehicles

Zhang, Meng; Yang, Jian; Zhao, Jifu; Dai, Yanjie

doi:10.1109/icpics47731.2019.8942565

Cited by 4 publications

(6 citation statements)

References 3 publications

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“…As noted before, the methods in [15,16] are based on the dead reckoning principle. The method reported in [15] provides an accurate location estimation of the nodes in ad hoc networks, and the reported estimation error is around [1.5 × 10 −2 to 3.5 × 10 −2 ]; however, they consider the time and angle of arrival estimation at each node in the route.…”

Section: High Dense Network Highmentioning

confidence: 99%

“…The method reported in [15] provides an accurate location estimation of the nodes in ad hoc networks, and the reported estimation error is around [1.5 × 10 −2 to 3.5 × 10 −2 ]; however, they consider the time and angle of arrival estimation at each node in the route. Similarly, in [16], the authors propose a method based on previous measurements of the speed and acceleration of vehicles to calculate the current location using the dead reckoning approach; this implies that every vehicle in the network has measurement capabilities. The authors reported high location estimation errors.…”

Section: High Dense Network Highmentioning

confidence: 99%

“…The localization of a NoI is obtained through the addition of successive vectors corresponding to the link hops. This demands that the hop lengths and physical directions be known [15,16].…”

Section: Context-aware Statistical Dead Reckoning Approachmentioning

confidence: 99%

“…A dead reckoning approach usually depends on gyroscopic, accelerometer observations, the awareness of an initial reference location often called "a fix", and on the knowledge of the consecutive traveling direction finding [10] of the consecutive steps, as well as the range or path length in each travel direction, in order to determine the progress of a traveler at a given time. Thus, conventional dead reckoning approaches demand highly equipped nodes with accurate angle/distance measurement capabilities [15,16].…”

Section: Introductionmentioning

confidence: 99%

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Context-Aware Statistical Dead Reckoning for Localization in IoT Scenarios

Muñoz-Rodríguez

Villalpando-Hernández

Vargas-Rosales

2023

Sensors

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The current trends in 5G and 6G systems anticipate vast communication capabilities and the deployment of massive heterogeneous connectivity with more than a million internet of things (IoT) and other devices per square kilometer and up to ten million gadgets in 6G scenarios. In addition, the new generation of smart industries and the energy of things (EoT) context demand novel, reliable, energy-efficient network protocols involving massive sensor cooperation. Such scenarios impose new demands and opportunities to cope with the ever-growing cooperative dense ad hoc environments. Position location information (PLI) plays a crucial role as an enabler of several location-aware network protocols and applications. In this paper, we have proposed a novel context-aware statistical dead reckoning localization technique suitable for high dense cooperative sensor networks, where direct angle and distance estimations between peers are not required along the route, as in other dead reckoning-based localization approaches, but they are obtainable from the node’s context information. Validation of the proposed technique was assessed in several scenarios through simulations, achieving localization errors as low as 0.072 m for the worst case analyzed.

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Section: High Dense Network Highmentioning

confidence: 99%

Section: High Dense Network Highmentioning

confidence: 99%

Section: Context-aware Statistical Dead Reckoning Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Context-Aware Statistical Dead Reckoning for Localization in IoT Scenarios

Muñoz-Rodríguez

Villalpando-Hernández

Vargas-Rosales

2023

Sensors

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“…This technique, applied to pedestrian navigation, measures and analyzes a person’s way of walking in order to understand and measure displacement in relation to an initial position based on a set of sensors that allow the extraction of speed and altitude information. The calculated positioning is based entirely on the information collected through sensors [ 111 ], the error being accumulated because the current position is calculated based on the previous one, and so on. This method has been used in conjunction with GPS to improve the determination of a user’s position outdoors.…”

Section: Smart Mobility Services and Applicationsmentioning

confidence: 99%

Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges

Paiva

Ahad

Tripathi

et al. 2021

Sensors

205

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The increasing population across the globe makes it essential to link smart and sustainable city planning with the logistics of transporting people and goods, which will significantly contribute to how societies will face mobility in the coming years. The concept of smart mobility emerged with the popularity of smart cities and is aligned with the sustainable development goals defined by the United Nations. A reduction in traffic congestion and new route optimizations with reduced ecological footprint are some of the essential factors of smart mobility; however, other aspects must also be taken into account, such as the promotion of active mobility and inclusive mobility, encouraging the use of other types of environmentally friendly fuels and engagement with citizens. The Internet of Things (IoT), Artificial Intelligence (AI), Blockchain and Big Data technology will serve as the main entry points and fundamental pillars to promote the rise of new innovative solutions that will change the current paradigm for cities and their citizens. Mobility-as-a-service, traffic flow optimization, the optimization of logistics and autonomous vehicles are some of the services and applications that will encompass several changes in the coming years with the transition of existing cities into smart cities. This paper provides an extensive review of the current trends and solutions presented in the scope of smart mobility and enabling technologies that support it. An overview of how smart mobility fits into smart cities is provided by characterizing its main attributes and the key benefits of using smart mobility in a smart city ecosystem. Further, this paper highlights other various opportunities and challenges related to smart mobility. Lastly, the major services and applications that are expected to arise in the coming years within smart mobility are explored with the prospective future trends and scope.

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