scholarly journals Towards Establishing Cross-Platform Interoperability for Sensors in Smart Cities

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 562 ◽  
Author(s):  
Kanishk Chaturvedi ◽  
Thomas Kolbe
Keyword(s):  
Data Storage ◽  
Smart City ◽  
Smart Cities ◽  
Heterogeneous Data ◽  
International Standards ◽  
Integration Strategy ◽  
Heterogeneous Data Sources ◽  
Cross Platform ◽  
Iot Platforms ◽  
Different Sources

Typically, smart city projects involve complex distributed systems having multiple stakeholders and diverse applications. These applications involve a multitude of sensor and IoT platforms for managing different types of timeseries observations. In many scenarios, timeseries data is the result of specific simulations and is stored in databases and even simple files. To make well-informed decisions, it is essential to have a proper data integration strategy, which must allow working with heterogeneous data sources and platforms in interoperable ways. In this paper, we present a new lightweight web service called InterSensor Service allowing to simply connect to multiple IoT platforms, simulation specific data, databases, and simple files and retrieving their observations without worrying about data storage and the multitude of different APIs. The service encodes these observations “on-the-fly” according to the standardized external interfaces such as the OGC Sensor Observation Service and OGC SensorThings API. In this way, the heterogeneous observations can be analyzed and visualized in a unified way. The service can be deployed not only by the users to connect to different sources but also by providers and stakeholders to simply add further interfaces to their platforms realizing interoperability according to international standards. We have developed a Java-based implementation of the InterSensor Service, which is being offered free as open source software. The service is already being used in smart city projects and one application for the district Queen Elizabeth Olympic Park in London is shown in this paper.

scholarly journals Improving Sustainability of Smart Cities through Visualization Techniques for Big Data from IoT Devices

2020 ◽  
Vol 12 (14) ◽  
pp. 5595 ◽  
Author(s):  
Ana Lavalle ◽  
Miguel A. Teruel ◽  
Alejandro Maté ◽  
Juan Trujillo
Keyword(s):  
Big Data ◽  
Smart City ◽  
Smart Cities ◽  
Fire Department ◽  
Heterogeneous Data ◽  
Fire Extinguishing ◽  
Heterogeneous Data Sources ◽  
Iot Devices ◽  
Visualization Techniques ◽  
The Impact

Fostering sustainability is paramount for Smart Cities development. Lately, Smart Cities are benefiting from the rising of Big Data coming from IoT devices, leading to improvements on monitoring and prevention. However, monitoring and prevention processes require visualization techniques as a key component. Indeed, in order to prevent possible hazards (such as fires, leaks, etc.) and optimize their resources, Smart Cities require adequate visualizations that provide insights to decision makers. Nevertheless, visualization of Big Data has always been a challenging issue, especially when such data are originated in real-time. This problem becomes even bigger in Smart City environments since we have to deal with many different groups of users and multiple heterogeneous data sources. Without a proper visualization methodology, complex dashboards including data from different nature are difficult to understand. In order to tackle this issue, we propose a methodology based on visualization techniques for Big Data, aimed at improving the evidence-gathering process by assisting users in the decision making in the context of Smart Cities. Moreover, in order to assess the impact of our proposal, a case study based on service calls for a fire department is presented. In this sense, our findings will be applied to data coming from citizen calls. Thus, the results of this work will contribute to the optimization of resources, namely fire extinguishing battalions, helping to improve their effectiveness and, as a result, the sustainability of a Smart City, operating better with less resources. Finally, in order to evaluate the impact of our proposal, we have performed an experiment, with non-expert users in data visualization.

scholarly journals A Framework for Visualizing Heterogeneous Construction Data Using Semantic Web Standards

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Mostafa Ali ◽  
Yasser Mohamed
Keyword(s):  
Ad Hoc ◽  
3D Visualization ◽  
Heterogeneous Data ◽  
Data Merging ◽  
Levels Of Detail ◽  
Heterogeneous Data Sources ◽  
Web Standards ◽  
Visualization Process ◽  
Merging Process ◽  
Different Sources

3D Visualization provides a mean for communicating different construction activities to diverse audiences. The scope, level of detail, and time resolution of the 3D visualization process are determined based on the targeted audiences. Developing the 3D visualization requires obtaining and merging heterogeneous data from different sources (such as BIM model and CPM schedule). The data merging process is usually carried out on ad hoc basis for a specific visualization case which limits the reusability of the process. This paper discusses a framework for automatic merging of heterogeneous data to create a visualization. The paper describes developing an ontology which captures concepts related to the visualization process. Then, heterogeneous data sources that are commonly used in construction are fed into the ontology which can be queried to produce different visualization scenarios. The potential of this approach has been demonstrated by providing multiple visualization scenarios that cover different audiences, levels of detail, and time resolutions.

Using a distributed deep learning algorithm for analyzing big data in smart cities

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammed Anouar Naoui ◽  
Brahim Lejdel ◽  
Mouloud Ayad ◽  
Abdelfattah Amamra ◽  
Okba kazar
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analysis ◽  
Data Storage ◽  
Smart City ◽  
Smart Cities ◽  
Smart Environment ◽  
Data Systems ◽  
Content Type ◽  
Big Data Systems

PurposeThe purpose of this paper is to propose a distributed deep learning architecture for smart cities in big data systems.Design/methodology/approachWe have proposed an architectural multilayer to describe the distributed deep learning for smart cities in big data systems. The components of our system are Smart city layer, big data layer, and deep learning layer. The Smart city layer responsible for the question of Smart city components, its Internet of things, sensors and effectors, and its integration in the system, big data layer concerns data characteristics 10, and its distribution over the system. The deep learning layer is the model of our system. It is responsible for data analysis.FindingsWe apply our proposed architecture in a Smart environment and Smart energy. 10; In a Smart environment, we study the Toluene forecasting in Madrid Smart city. For Smart energy, we study wind energy foresting in Australia. Our proposed architecture can reduce the time of execution and improve the deep learning model, such as Long Term Short Memory10;.Research limitations/implicationsThis research needs the application of other deep learning models, such as convolution neuronal network and autoencoder.Practical implicationsFindings of the research will be helpful in Smart city architecture. It can provide a clear view into a Smart city, data storage, and data analysis. The 10; Toluene forecasting in a Smart environment can help the decision-maker to ensure environmental safety. The Smart energy of our proposed model can give a clear prediction of power generation.Originality/valueThe findings of this study are expected to contribute valuable information to decision-makers for a better understanding of the key to Smart city architecture. Its relation with data storage, processing, and data analysis.

scholarly journals Integração, Relacionamento e Representação de Dados em Cidades Inteligentes: Uma Revisão de Literatura

2018 ◽  
Author(s):  
Larysse Silva ◽  
José Alex Lima ◽  
Nélio Cacho ◽  
Eiji Adachi ◽  
Frederico Lopes ◽  
...  
Keyword(s):  
Decision Making ◽  
Literature Review ◽  
Data Integration ◽  
Smart Cities ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Application Development ◽  
Continuous Integration ◽  
Heterogeneous Data Sources ◽  
Computational Systems

A notable characteristic of smart cities is the increase in the amount of available data generated by several devices and computational systems, thus augmenting the challenges related to the development of software that involves the integration of larges volumes of data. In this context, this paper presents a literature review aimed to identify the main strategies used in the development of solutions for data integration, relationship, and representation in smart cities. This study systematically selected and analyzed eleven studies published from 2015 to 2017. The achieved results reveal gaps regarding solutions for the continuous integration of heterogeneous data sources towards supporting application development and decision-making.

scholarly journals A Description of Clinical Decision Support Framework Prediction in Heterogeneous Data Sources

2019 ◽  
Vol 8 (6S) ◽  
pp. 425-427
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Heterogeneous Data ◽  
Scientific Discipline ◽  
Work Environments ◽  
Data Sources ◽  
Heterogeneous Data Sources ◽  
Decision Support Framework ◽  
Different Sources

To keep pace with the updates in obliging scientific discipline, thriving recuperating knowledge is being assembled incessantly. Regardless, inferable from the not too appalling gathering of its categories and sources, therapeutic knowledge has over up being significantly hugger-mugger in numerous specialist's work environments that it currently wants Clinical call Support (CDS) system for its affiliation. To reasonably utilize the party flourishing knowledge, we tend to propose a CDS structure which will distort mixed thriving knowledge from totally different sources, for example, take a goose at workplace check works out as planned, important info of patients and action records into a joined depiction of options everything thought-about. Victimization the electronic roaring healing knowledge therefore created, multi-name delineation was accustomed endorse a layout of afflictions and so facilitate consultants in diagnosis or treating their patients' therapeutic problems a lot of competently. Once the ace sees the contamination of a patient, the running with organize is to contemplate the conceivable complexities of that disarray, which may impel a lot of infections

Global sustainable development: Smart cities and global implications of IT data risk governance, storage and control

2020 ◽  
Vol 1 (2) ◽  
pp. 33-43
Author(s):  
Beniamino Milioto
Keyword(s):  
Sustainable Development ◽  
Data Storage ◽  
Smart City ◽  
Social Evolution ◽  
Business Models ◽  
Smart Cities ◽  
Social Model ◽  
Green Economy ◽  
Risk Governance ◽  
Global Agenda

The present research paper focuses on the major economic and social evolution regarding the concept of “sustainable development and co-related smart green economy initiatives” in the current 3rd millennium global agenda. The main purpose of the article is to integrate the successful business and economic smart city business and social model with highly sensitive risk governance relating to data collection, data monitoring, data storage, data control, and data governance currently considered as an economic modern drive of development for future global societies and IT corporate businesses and, primarily, as a pivotal government’s asset for strategic political and economic global governance. The analysis will cover the 11 goals of the UN 2030 Global Agenda regarding the creation of “smart cities” as the economic/social concept for inclusive human and urban agglomeration. The paper methodology, supported with the current literature review, reports which technologies, applications, and parameters will define a smart city and how new innovative business models might influence the new economic global order in full respect of the environment and human life.

Integrating Heterogeneous Data Sources in the Web

2009 ◽  
pp. 2472-2488
Author(s):  
Angelo Brayner ◽  
Marcelo Meirelles ◽  
José de Aguiar Moraes Filho
Keyword(s):  
Query Language ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Distributed Data ◽  
Local Data ◽  
Multidatabase System ◽  
Integration Strategy ◽  
Heterogeneous Data Sources ◽  
Integration Problems ◽  
The Web

Integrating data sources published on the Web requires an integration strategy that guarantees the local data sources’ autonomy. A multidatabase system (MDBS) has been consolidated as an approach to integrate multiple heterogeneous and distributed data sources in flexible and dynamic environments such as the Web. A key property of MDBSs is to guarantee a higher degree of local autonomy. In order to adopt the MDBS strategy, it is necessary to use a query language, called the MultiDatabase Language (MDL), which provides the necessary constructs for jointly manipulating and accessing data in heterogeneous data sources. In other words, the MDL is responsible for solving integration conflicts. This chapter describes an extension to the XQuery Language, called MXQuery, which supports queries over several data sources and solves such integration problems as semantic heterogeneity and incomplete information.

scholarly journals MAKING CITIES INTEROPERABLE IN TURKEY

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 91-96
Author(s):  
H. Bayraktar ◽  
D. Y. Bayar ◽  
G. Bilgin
Keyword(s):  
Population Growth ◽  
Environmental Sustainability ◽  
Smart City ◽  
Smart Cities ◽  
Heterogeneous Data ◽  
Environmental Benefits ◽  
Full Potential ◽  
Energy And Environment ◽  
Secure Interoperability ◽  
Lock In

Abstract. The population of cities is increasing rapidly day by day, and it is predicted that this increase will continue in the following years. Accordingly, population growth creates a significant pressure in many different domains of cities such as infrastructure, traffic, energy, and environment. Smart cities come forward as a useful option to struggle with the pressure on cities caused by overwhelming population growth and to make cities liveable and sustainable. Smart city approach creates gains in the fields of sustainable development, competitiveness and environmental sustainability with its ability to transform information into economic, social and environmental benefits. However, smart city services and applications are mostly designed as independent and unrelated units so this approach causes isolated and heterogeneous data and technology islands. As the result, data flow problem occurs between vertical applications and service suppliers, and this interoperability problem causes emergence of independent silos in smart cities. Such silos hinders data integration, prevent citizens and public administrations benefit fully from smart cities, and cause vendor lock-in. In order to use the full potential of smart city approach, it’s vital to secure interoperability systems and applications of smart cities. In this study, interoperability terms and their necessity for smart city ecosystem will be addressed. Afterwards, Smart City Interoperability Model’s (SCIM) contributions to semantic, technical and operational interoperability will be discussed.

scholarly journals Semantic Interoperability for Context-Aware Autonomous Control using IoT and Edge Computing

2020 ◽  
Author(s):  
Kaleem Razzaq Malik ◽  
Iqra Iqbal Khan ◽  
Khalid S. Aloufi ◽  
Naima Chouikhi ◽  
Amir Hussain
Keyword(s):  
Smart Cities ◽  
Heterogeneous Data ◽  
Semantic Interoperability ◽  
Edge Computing ◽  
Context Model ◽  
Effective Utilization ◽  
Utilization Of Services ◽  
Heterogeneous Data Sources ◽  
Proposed Model ◽  
Heterogeneous Datasets

Abstract The exponential development of Internet of things (IoT) services over edge computing and cloud networks has increased the utilities of remote monitoring, control systems, continuous maintenance and effective utilization of services for applications, such as smart cities. However, data modelling is required to manage such heterogeneous data sources. IoT applications gather data from diverse sources. These applications sometimes obtain data in the form of datasets. Heterogeneous datasets are used for various purposes, and the issue of semantic interoperability arises. Therefore, this paper presents an empirical study of IoT-based semantic interoperability. This study aims at combining portable and fixed sensors with an intermediate microcontroller module and annotating data semantically for the smart autonomous environment, smart home. A context model is devised for developing a mechanism over an ontology schema for managing and passing controlling and monitoring messages to home appliances effectively. The proposed model integrates the environment with the context of a person in a smart autonomous environment for efficient energy consumption and enhanced living context model experience.

scholarly journals CDI/THREDDS interoperability in the SeaDataNet framework

2010 ◽  
Vol 28 ◽  
pp. 17-27 ◽  
Author(s):  
S. Nativi ◽  
P. Mazzetti ◽  
M. Santoro ◽  
E. Boldrini ◽  
G. M. R. Manzella ◽  
...  
Keyword(s):  
Biological Properties ◽  
Heterogeneous Data ◽  
International Standards ◽  
Information Discovery ◽  
Geospatial Information ◽  
Data Infrastructure ◽  
Metadata Model ◽  
Heterogeneous Data Sources ◽  
Discovery Service ◽  
Funded Project

Abstract. SeaDataNet is an EU funded project aiming to create and operate a pan-European, marine data infrastructure for managing the large and diverse datasets (i.e. temperature, salinity current, sea level, chemical, physical and biological properties) collected by the oceanographic fleets and the new automatic observation systems. In order to make the SeaDataNet system compliant with the INSPIRE Implementing Rules for discovery service, an ISO 19139 encoding of the SeaDataNet Common Data Index (CDI) metadata model was defined. Moreover, the problem of heterogeneous data sources has been addressed. In fact, a widely used system of SeaDataNet partners and oceanographic-marine community is THREDDS/OPeNDAP; this raises up the problem of federating into SeaDataNet framework THREDDS/OPeNDAP systems as well. In this paper we describe an interoperability framework to access resources (i.e. data and services) that are available through CDI and THREDDS/OPeNDAP services. The proposed solution implements a common catalog interface to discover and access the two heterogeneous resources in a common way. This catalog service is fully distributed and implements international standards as far as geospatial information discovery and query are concerned. The developed solution is called GI-cat and was experimented in the framework of the SeaDataNet European project.

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