scholarly journals Combining Fog and Cloud Computing to Support Spatial Analytics in Smart Cities

2021 ◽  
Vol 12 (4) ◽  
Author(s):  
João Paulo Clarindo ◽  
João Pedro C. Castro ◽  
Cristina D. Aguiar
Keyword(s):  
Data Warehouse ◽  
Spatial Data ◽  
Smart Cities ◽  
Fog Computing ◽  
Real Data ◽  
Distributed Data ◽  
Spatial Analytics ◽  
Spatial Data Warehouse ◽  
Iot Devices

Spatial data generated by an Internet of Things (IoT) network is important to assist the spatial analytics process in issues related to smart cities. In these cities, IoT devices generate spatial data constantly. Thus, data can get increasingly voluminous very fast. In this paper, we investigate the challenge of managing these data through the use of a spatial data warehouse designed over a parallel and distributed data processing framework extended with a spatial analytics system. We propose an architecture aimed to assist a smart cities manager in the decision-making process. This architecture integrates a cloud layer where these technologies are located with a fog computing layer for extracting, transforming and loading the data into the spatial data warehouse. Furthermore, we introduce a set of guidelines to aid smart cities managers to implement the proposed architecture. These guidelines describe and discuss important issues that should be faced by the managers. We validate our architecture with a case study that uses real data collected by IoT devices in a smart city. This case study encompasses the execution of three different categories of spatial queries, demonstrating the architecture's efficacy and effectiveness to support spatial analytics in the context of smart cities.

Distributed Data Warehouse for Geo-spatial Services

2003 ◽  
pp. 132-145 ◽  
Author(s):  
Iftikhar U. Sikder ◽  
Aryya Gangopadhyay
Keyword(s):  
Decision Making ◽  
Data Warehouse ◽  
Spatial Data ◽  
System Integration ◽  
Prototype System ◽  
Distributed Data ◽  
Distributed Environment ◽  
Spatial Decision Making ◽  
Spatial Data Warehouse ◽  
Access To Data

This chapter introduces the research issues on spatial decision-making in the context of distributed geo-spatial data warehouse. Spatial decision-making in a distributed environment involves access to data and models from heterogeneous sources and composing disparate services into a meaningful integration. The chapter reviews system integration and interoperability issues of spatial data and models in a distributed computing environment. We present a prototype system to illustrate the collaborative access to data and as a model for supporting spatial decision-making.

scholarly journals Exploring Geostatistical Modeling and VisualizationTechniques of Uncertainties for Categorical Spatial Data

2021 ◽  
Vol 12 (4) ◽  
Author(s):  
Carlos A. Felgueiras ◽  
Jussara O. Ortiz ◽  
Eduardo C. G. Camargo ◽  
Laércio M. Namikawa ◽  
Thales S. Körting
Keyword(s):  
Spatial Data ◽  
Three Dimensional ◽  
Real Data ◽  
Geographic Region ◽  
Geostatistical Modeling ◽  
Probability Maps ◽  
Uncertainty Model ◽  
Probability Criterion ◽  
Sample Points

This article presents and analyzes the indicator geostatistical modeling and some visualization techniques of uncertainty models for categorical spatial attributes. A set of sample points of some categorical attribute is used as input information. The indicator approach requires a transformation of sample points on fields of indicator samples according to the classes of interest. Experimental and theoretical semivariograms of the indicator fields are defined representing the spatial variation of the indicator information. The indicator fields, along with their semivariograms, are used to determine the uncertainty model, the conditioned probability distribution function, of the attribute at any location inside the geographic region delimited by the samples. The probability functions are considered for producing prediction and probability maps based on the maximum class probability criterion. These maps can be visualized using different techniques. In this work, it is considered individual visualization of the predicted and probability maps and a combination of them. The predicted maps can also be visualized with or without constraints related to the uncertainty probabilities. The combined visualizations are based on three-dimensional (3D) planar projection and on the Red-Green-Blue to Intensity-Hue-Saturation (RGB-IHS) fusion transformation techniques. The methodology of this article is illustrated by a case study with real data, a sample set of soil textures observed in an experimental farm located in the region of São Carlos city in São Paulo State, Brazil. The resulting maps of this case study are presented and the advantages and the drawbacks of the visualization options are analyzed and discussed.

scholarly journals Spatial Data Lake for Smart Cities: From Design to Implementation

2020 ◽  
Vol 1 ◽  
pp. 1-15
Author(s):  
Rodrique Kafando ◽  
Rémy Decoupes ◽  
Lucile Sautot ◽  
Maguelonne Teisseire
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Smart Cities ◽  
Real Data ◽  
Heterogeneous Data ◽  
Geographic Information ◽  
Sensor Data ◽  
Remote Sensing Images ◽  
Thematic Information ◽  
General Data

Abstract. In this paper, we propose a methodology for designing data lake dedicated to Spatial Data and an implementation of this specific framework. Inspired from previous proposals on general data lake Design and based on the Geographic information – Metadata normalization (ISO 19115), the contribution presented in this paper integrates, with the same philosophy, the spatial and thematic dimensions of heterogeneous data (remote sensing images, textual documents and sensor data, etc). To support our proposal, the process has been implemented in a real data project in collaboration with Montpellier Métropole Méditerranée (3M), a metropolis in the South of France. This framework offers a uniform management of the spatial and thematic information embedded in the elements of the data lake.

scholarly journals IoT Security Configurability with Security-by-Contract

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4121 ◽  
Author(s):  
Alberto Giaretta ◽  
Nicola Dragoni ◽  
Fabio Massacci
Keyword(s):  
Internet Of Things ◽  
Fog Computing ◽  
Holistic Approach ◽  
The Internet ◽  
Iot Security ◽  
Iot Devices ◽  
Work First ◽  
The Internet Of Things

Cybersecurity is one of the biggest challenges in the Internet of Things (IoT) domain, as well as one of its most embarrassing failures. As a matter of fact, nowadays IoT devices still exhibit various shortcomings. For example, they lack secure default configurations and sufficient security configurability. They also lack rich behavioural descriptions, failing to list provided and required services. To answer this problem, we envision a future where IoT devices carry behavioural contracts and Fog nodes store network policies. One requirement is that contract consistency must be easy to prove. Moreover, contracts must be easy to verify against network policies. In this paper, we propose to combine the security-by-contract (S × C) paradigm with Fog computing to secure IoT devices. Following our previous work, first we formally define the pillars of our proposal. Then, by means of a running case study, we show that we can model communication flows and prevent information leaks. Last, we show that our contribution enables a holistic approach to IoT security, and that it can also prevent unexpected chains of events.

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