Geospatial data preprocessing and visualization for the logistics industry

Gupta, Kamal Kumar; Sadana, Sanjay Kumar; Gupta, Bhoomi

doi:10.1080/09720529.2020.1721859

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…Around all types of files could be included in the mainframe image Steganography, but images were proven to be the most complete aspect of the embedding due to their high level of joblessness. When we analyze, discuss, and extend steganography techniques, three considerations [13][14][15][16] must be considered: ➢ Capability: switch to the sum of data that can interleave to the swathe reflection, which was called payload for a moment [17]. ➢ Resilience: conflicts with various compressed image processing.…”

Section: Visual Cryptography and Methods Challengesmentioning

confidence: 99%

A Hybrid Visual Cryptography Method using Sigmoid Function for Security Enhancement in Gray Scale Images

Kumar¹,

Patidar²,

Saxena³

et al. 2021

IJAINN

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Visual encryption technology becomes the latest research area in which a lot of scopes persist. Presently such a particular cryptosystem procedure is now used by numerous other countries around the world for the private transmission of formal records, financial documents, content visuals, digital voting, and so on. Visualization Cryptographic algorithms one of the protected methods of transferring pictures online. The main benefit of image encryption has been that it disguises peripheral vision with encrypt data secret data with no computation usually needed. In this work a hybrid visual cryptography method using a sigmoid function (HVMSF) for enhancing the security in gray images. HVMSF strategy utilizes a chaos framework to scramble pixel values as well as blocks while using the Modified Arnold Cat Map method (MACM) as well as the Henon Map method (HMM). The methodology includes a confusion procedure wherein the location of each image pixel is shuffled by utilizing MACM. The shuffling of image pixel leads to the creation of a subset pixel which will be protected for transmitting. This proposed HVMSF mainly tries to overcome the limitation of the previous approaches by applying sigmoid function in image feature space for contrast enhancement throughout the consequent source images. The experimental outcomes precisely show that the suggested strategy can further give additional effectiveness to ensure the protection of transmitting information out over previous techniques.

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Section: Visual Cryptography and Methods Challengesmentioning

confidence: 99%

A Hybrid Visual Cryptography Method using Sigmoid Function for Security Enhancement in Gray Scale Images

Kumar¹,

Patidar²,

Saxena³

et al. 2021

IJAINN

View full text Add to dashboard Cite

show abstract

“…On the shop floor, Big Data could be useful to visualize the logistics trajectory and evaluate the performance and efficiency of logistics operators (52). In the transportation sector, Big Data helps companies to understand and explore patterns in freight activities (57), especially when it is combined with tracking and geolocation services (58). To achieve the benefits of Big Data in logistics, it is necessary to gather heterogeneous, homogeneous, and dynamic SC data in the shortest possible time (59).…”

Section: Big Data Analyticsmentioning

confidence: 99%

Challenges and Trends of the logistics 4.0

Malagon-Suarez

Castro

2023

Ing.

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Context: Owing to the technological breakthrough in the worldwide productive systems, generated by the 4.0 revolution, it is compulsory to make sweeping changes to logistics both nationally and internationally to allow supply chains to enhance their performance and their response time. Hence, the concept of Logistics 4.0 was born. Although many developed countries have implemented the principles of Logistics 4.0, there is still a breach in its study and application worldwide. This article explores the challenges and tendencies in the implementation of Logistics 4.0. Method: Articles published from 2015 to 2021 in the databases of Scopus, Science Direct, Taylor and Francis and Google Scholar, were analyzed by a systematic literature review. The explored publications were found using the search terms: (("logistics 4.0") OR ("supply chain 4.0") OR ("industry 4.0" AND ("logistics" OR "supply chain"))) and ((“blockchain” OR “IoT” OR “Cyber Physical Systems” OR “Big Data”) AND “logistics”) in title, abstract and keywords. Results: It was proposed a conceptualization of Logistics 4.0 including definition, objectives, characteristics, and most representative technologies in its implementation. Likewise, the main challenges and trends that industries could face in the implementation of Logistics 4.0 within logistics systems, and supply chains were identified. Conclusions: Logistics 4.0 is a novelty term that has aroused the interest of researchers, governments, and companies worldwide, due to its promising benefits in reducing response times and increasing flexibility and collaboration in supply chains. However, the lack of a common framework for its study and adoption has hindered its integration in companies and supply chains, specially in those located in developing countries, which must face technical, social, economic, and legal barriers for the implementation of logistics 4.0.

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“…Furthermore, there is a significant presence of geospatial big data in the smart environment domain, in disaster monitoring (Fang et al, 2015), air quality management (Chinnaswamy et al, 2019;Xuyao, Hui, Kexin, Yijin, & Jinhang, 2013;Zou et al, 2021), and water and sewage management (Howell, Rezgui, & Beach, 2018). Other domains were also mentioned, such as logistics (Fernández et al, 2017;Fernández, Suárez, Trujillo, Domínguez, & Santana, 2018;Finogeev et al, 2019;Gupta, Sadana, & Gupta, 2020;Kang et al, 2016;Li et al, 2015;Suárez, Trujillo, Domínguez, & José Miguel Santana, 2015), culture and tourism (Benedusi, Chianese, Marulli, & Piccialli, 2015;Chianese, Marulli, Piccialli, Benedusi, & Jung, 2017;Li, Liao, & Huang, 2020;Mello et al, 2019), and smart water (Howell et al, 2018). Some works explained the type of geospatial data analyzed but did not specify the application domain.…”

Section: Decisionmentioning

confidence: 99%

“…The OGC (2000) proposed Geography Markup Language (GML; see also Luan et al, 2015;Cai et al, 2016). There is also applicability for JavaScript Object Notation (JSON; Benedusi et al, 2015;Chen et al, 2015;Chianese et al, 2017;Crockford, 2000;Gupta et al, 2020;Leung, Braun, & Cuzzocrea, 2019;Qader & Hristidis, 2017;Suárez et al, 2015), a format for storage and exchange of data; GeoJSON (Butler et al, 2016;Işikdağ, 2020;Kraft et al, 2019), a JSON variation oriented to the codification of geographic structures; and…”

Section: Rq3 What Are the Main Data Formats Stored?mentioning

confidence: 99%

Applications of geospatial big data in the Internet of Things

Silva

Holanda

2021

Transactions in GIS

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The Internet of Things (IoT) has emerged as a communication paradigm that allows the interconnection of devices that interact with the environment through sensors and actuators, collect data from it, and transmit these data over a network without requiring human interaction (Armstrong, Wang, & Zhang, 2019). IoT architectures can demand database management systems capable of handling high data generation and transmission rates, bringing the research on the IoT closer to the studies on big data (Ge,

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Geospatial data preprocessing and visualization for the logistics industry

Cited by 7 publications

References 5 publications

A Hybrid Visual Cryptography Method using Sigmoid Function for Security Enhancement in Gray Scale Images

A Hybrid Visual Cryptography Method using Sigmoid Function for Security Enhancement in Gray Scale Images

Challenges and Trends of the logistics 4.0

Applications of geospatial big data in the Internet of Things

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