Anti-theft vehicle tracking and regaining system with automatic police notifying using Haversine formula

Monawar, Tareq; Mahmud, Shafayat Bin; Hira, Avijit

doi:10.1109/icaee.2017.8255459

Cited by 24 publications

(10 citation statements)

References 4 publications

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“…Pertinently, Figs. 4 b- 7 b presents the conversion of the mercatorian SPP model into the corresponding spatial SPP model with the aid of the Haversine formula [46] , [47] , [48] , [49] , [50] . Precisely, these figures give the actual dimensions of SPP concentrated areas.…”

Section: Resultsmentioning

confidence: 99%

“…(10)

Haversine formula in Eq. (6) determines the geographical distance,

between any two locations [46] , [47] , [48] , [49] , [50] by converting the mercatorian model into the spatial form using Eq. (11)

22

where

is the radius of the Earth (a perfect sphere) equal to 6371 km [46] , [51] , [52] , [53] ,

(km) is the distance separating the two locations;

and

are the latitudes and longitudes of the locations, respectively, x (km) is the distance in the x-direction and y (km) is the distance in the y-direction.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Development of solar isodose lines: Mercatorian and spatial guides for mapping solar installation areas

Nnamchi¹,

Mundu²

2022

Heliyon

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 99%

“…(10)

Haversine formula in Eq. (6) determines the geographical distance,

between any two locations [46] , [47] , [48] , [49] , [50] by converting the mercatorian model into the spatial form using Eq. (11)

22

where

is the radius of the Earth (a perfect sphere) equal to 6371 km [46] , [51] , [52] , [53] ,

(km) is the distance separating the two locations;

and

are the latitudes and longitudes of the locations, respectively, x (km) is the distance in the x-direction and y (km) is the distance in the y-direction.…”

Section: Methodsmentioning

confidence: 99%

Development of solar isodose lines: Mercatorian and spatial guides for mapping solar installation areas

Nnamchi¹,

Mundu²

2022

Heliyon

View full text Add to dashboard Cite

“…They are used for tracking the location of the vehicle to prevent theft of the vehicle. There has been a lot of research on the problem of vehicle anti-theft systems [112,113,[115][116][117][118][119]. Some of the works used GPS to track vehicle location and a global system for mobile communications (GSM) system to notify the owner when vehicle location changes [115] (see Figure 12).…”

Section: Intelligent Safety Systemsmentioning

confidence: 99%

Prospects and challenges of Metaverse application in data‐driven intelligent transportation systems

Njoku

Nwakanma

Amaizu

et al. 2022

IET Intelligent Trans Sys

131

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The Metaverse is a concept used to refer to a virtual world that exists in parallel to the physical world. It has grown from a conceptual level to having real applications in virtual reality games. The applicability of the Metaverse in numerous sectors like marketing, education, social, and even advertising exists. However, there exists little or no work on Metaverse applicability to the transportation industry. Data‐driven intelligent transportation systems (DDITS) aim to provide more intelligent systems based on exploiting data. This paper reviews the concepts and features of the Metaverse. Also, the review goes over three dominant DDITS challenges: vehicle fault detection and repair, testing new technologies, and anti‐theft systems. In addition, it highlights prospective Metaverse solutions that apply to the DDITS. Buttressing the utility of Metaverse in DDITS, this paper presents two major case studies: the invisible to visible (I2V) and the Metaverse on Wheels (MoW) technologies. Finally, the influence, limitations, and open issues of Metaverse applications to DDITS are discussed.

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“…These coordinates can be referred to as GPS coordinate A for an IIoT device and GPS coordinate B for a fog server cluster. In order to compute the fog server clusters' distances to these IIoT devices we used the Haversine formula [24], [25]:…”

Section: Caapimentioning

confidence: 99%

Context-Aware Fog Computing Implementation for Industrial Internet of Things

Atieh

Nanda

Mohanty

2021

2021 International Wireless Communications and Mobile Computing (IWCMC)

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The connectivity of devices has increased in the last decade enabling multiple innovative applications and solutions to serve industries and societies. This has solved multiple challenges and facilitated the improvement of methodologies and techniques adapted by humanity. One of the newly created paradigms that changed industries and technology is the Industrial Internet of Things (IIoT). IIoT is currently being adapted by various industries creating interactive supply chain ecosystems through the use of cloud computing. The size and distributions of these ecosystems introduced latency and Quality of Service (QoS) issues for edge devices sending data to the cloud. This research paper explores a paradigm called "Fog Computing" which aims to reduce the latency between IIoT devices and the cloud by deploying a "cloud-like" computing layer closer to the IIoT devices. In addition, a Context-Aware implementation of fog computing is proposed in this paper to provide the most optimised service to edge devices. Furthermore, this paper includes various experiments that examine the different context-awareness perspectives this paper proposes for fog computing. The results and outcomes of these experiments show reduction in latency and automated resource scaling from the use of context-awareness with fog computing over cloud computing for IIoT.

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Anti-theft vehicle tracking and regaining system with automatic police notifying using Haversine formula

Cited by 24 publications

References 4 publications

Development of solar isodose lines: Mercatorian and spatial guides for mapping solar installation areas

Development of solar isodose lines: Mercatorian and spatial guides for mapping solar installation areas

Prospects and challenges of Metaverse application in data‐driven intelligent transportation systems

Context-Aware Fog Computing Implementation for Industrial Internet of Things

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