An Analysis of Context-Aware Data Models for Smart Cities: Towards
Fiware and Etsi Cim Emerging Data Model

Jara, Antonio J.; Bocchi, Yann; Fernández, David; Molina, G. Arturo; Gómez, A López

doi:10.5194/isprs-archives-xlii-4-w3-43-2017

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…[9] instead highlights the great capacity of job sorting within a network made up of cloud and edge devices, which can be managed and reconfigured through JSON messages and with a micro-service oriented architecture. In [10] it is highlighted how necessary a data model that offers advanced functionality for the description of the context can exploit different data sources. However, the data can be used in various contexts for different solutions.…”

Section: Related Workmentioning

confidence: 99%

Virtual Device Model extending NGSI-LD for FaaS at the Edge

Martella

Parrino

Ciulla

et al. 2021

2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid)

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Smart environments are composed of an everincreasing number of heterogeneous resources and devices for collecting and processing of a large amount of context data. These activities can be performed at the edge of the smart area, over a distributed and heterogeneous infrastructure, so to be close to the end-user and optimize response time. However, it is hard to define a data model able to support data exchange among different systems and between systems and users. This paper presents the key features of a smart environment and introduces the concept of virtual device, that is an abstracted component characterized by specific high-level functionalities. Then, the paper proposes a data model useful to represent and optimize the adoption of virtual device in smart environments. To better explain the data model features and benefits, we refer to a video surveillance use case, where a smart camera is able to provide the solid angle detection as a service.

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Section: Related Workmentioning

confidence: 99%

Virtual Device Model extending NGSI-LD for FaaS at the Edge

Martella

Parrino

Ciulla

et al. 2021

2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid)

View full text Add to dashboard Cite

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“…Unlike model updates, model mappings do not change the model itself, but rather define the syntax and structure details of the transformation from one model to another through meta-model instances [22][23][24].…”

Section: Formal Definitions and Rulesmentioning

confidence: 99%

Formal verification of ontology transformation for distribution network information model based on meta‐model theory

Qin

Dong

2020

IET cyber-phys. syst.

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This study presents a formal description and verification method of the ontology transformation process of the distribution network information model based on the meta-model theory. By defining the dynamic change structures and model mapping constraints of the ontology transformation process of distribution network, a formalised specification for describing the key attributes of model updating and model mapping processes was constructed. The model updating and model mapping instances were specified using model checking verification tool SPIN. The method fundamentally guarantees the standardisation and reliability of the model ontology transformation process achieved by the meta-model.

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“…The mqtt protocol is a text-based protocol, which consumes very little bandwidth and is used extensively in the Internet of Things and smart cities research works. For instance, in [55], mqtt was used to build a context-aware data model for smart cities, such as crowd monitoring (counting people in an area) or environmental information (pollution, air quality, temperature, humidity, noise, etc.). The protocol is built for low-consumption devices and allows encryption and different levels of quality of service.…”

Section: Data Presentationmentioning

confidence: 99%

On-the-Fly Olive Tree Counting Using a UAS and Cloud Services

et al. 2019

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Unmanned aerial systems (UAS) are becoming a common tool for aerial sensing applications. Nevertheless, sensed data need further processing before becoming useful information. This processing requires large computing power and time before delivery. In this paper, we present a parallel architecture that includes an unmanned aerial vehicle (UAV), a small embedded computer on board, a communication link to the Internet, and a cloud service with the aim to provide useful real-time information directly to the end-users. The potential of parallelism as a solution in remote sensing has not been addressed for a distributed architecture that includes the UAV processors. The architecture is demonstrated for a specific problem: the counting of olive trees in a crop field where the trees are regularly spaced from each other. During the flight, the embedded computer is able to process individual images on board the UAV and provide the total count. The tree counting algorithm obtains an F 1 score of 99 . 09 % for a sequence of ten images with 332 olive trees. The detected trees are geolocated and can be visualized on the Internet seconds after the take-off of the flight, with no further processing required. This is a use case to demonstrate near real-time results obtained from UAS usage. Other more complex UAS applications, such as tree inventories, search and rescue, fire detection, or stock breeding, can potentially benefit from this architecture and obtain faster outcomes, accessible while the UAV is still on flight.

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An Analysis of Context-Aware Data Models for Smart Cities: Towards Fiware and Etsi Cim Emerging Data Model

Cited by 7 publications

References 12 publications

Virtual Device Model extending NGSI-LD for FaaS at the Edge

Virtual Device Model extending NGSI-LD for FaaS at the Edge

Formal verification of ontology transformation for distribution network information model based on meta‐model theory

On-the-Fly Olive Tree Counting Using a UAS and Cloud Services

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