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 Study and Research on IoT and Big Data Analysis for Smart City Development

2021 ◽  
Vol 22 (2) ◽  
Author(s):  
Haixia Yu ◽  
Ion Cosmin Mihai ◽  
Anand Srivastava
Keyword(s):  
Big Data ◽  
Smart City ◽  
Smart Cities ◽  
Data Generation ◽  
City Development ◽  
Smart Meters ◽  
Smart Systems ◽  
Smart Parking ◽  
Iot Devices ◽  
Time Required

With the development of smart meters, like Internet of Things (IoT), various kinds of electronic devices are equipped with each smart city. The several aspects of smart cities are accessible and these technologies enable us to be smarter. The utilization of the smart systems is very quick and valuable source to fulfill the requirement of city development. There are interconnection between various IoT devices and huge amount of data is generated when they communicate each other over the internet. It is very challenging task to effectively integrate the IoT services and processing big data. Therefore, a system for smart city development is proposed in this paper which is based on the IoT utilizing the analytics of big data. A complete system is proposed which includes various types of IoT-based smart systems like smart home, vehicular networking, and smart parking etc., for data generation. The Hadoop ecosystem is utilized for the implementation of the proposed system. The evaluation of the system is done in terms of throughput and processing time. The proposed technique is 20% to 65% better than the existing techniques in terms of time required for processing. In terms of obtained throughput, the proposed technique outperforms the existing technique by 20% to 60%.

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 Neighbor Aware Protocols for IoT Devices in Smart Cities—Overview, Challenges and Solutions

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 902
Author(s):  
Sungwon Lee ◽  
Muhammad Azfar Azfar Yaqub ◽  
Dongkyun Kim
Keyword(s):  
Internet Of Things ◽  
Duty Cycle ◽  
Smart City ◽  
Mac Protocol ◽  
Smart Cities ◽  
Iot Applications ◽  
Simulation Results ◽  
Iot Devices ◽  
The Impact

The principle of Smart Cities is the interconnection of services, based on a network of Internet of Things (IoT) devices. As the number of IoT devices continue to grow, the demand to organize and maintain the IoT applications is increased. Therefore, the solutions for smart city should have the ability to efficiently utilize the resources and their associated challenges. Neighbor aware solutions can enhance the capabilities of the smart city. In this article, we briefly overview the neighbor aware solutions and challenges in smart cities. We then categorize the neighbor aware solutions and discuss the possibilities using the collaboration among neighbors to extend the lifetime of IoT devices. We also propose a new duty cycle MAC protocol with assistance from the neighbors to extend the lifetime of the nodes. Simulation results further coagulate the impact of neighbor assistance on the performance of IoT devices in smart cities.

scholarly journals Sustainable Virtual Reality Patient Rehabilitation Systems with IoT Sensors Using Virtual Smart Cities

2021 ◽  
Vol 13 (9) ◽  
pp. 4716
Author(s):  
Moustafa M. Nasralla
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Virtual Reality ◽  
Time Series Analysis ◽  
Smart City ◽  
Smart Cities ◽  
Rehabilitation Services ◽  
Simulation Scenario ◽  
Common Problems ◽  
Iot Devices

To develop sustainable rehabilitation systems, these should consider common problems on IoT devices such as low battery, connection issues and hardware damages. These should be able to rapidly detect any kind of problem incorporating the capacity of warning users about failures without interrupting rehabilitation services. A novel methodology is presented to guide the design and development of sustainable rehabilitation systems focusing on communication and networking among IoT devices in rehabilitation systems with virtual smart cities by using time series analysis for identifying malfunctioning IoT devices. This work is illustrated in a realistic rehabilitation simulation scenario in a virtual smart city using machine learning on time series for identifying and anticipating failures for supporting sustainability.

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 Smart City as A Tools to Achieve Sustainability City

2019 ◽  
Vol 5 (2) ◽  
pp. 1-13
Author(s):  
Helen Dian Fridayani ◽  
Rifaid Rifaid
Keyword(s):  
Smart City ◽  
Open Space ◽  
Smart Cities ◽  
Smart Environment ◽  
Sustainable City ◽  
Co2 Gas ◽  
The Government ◽  
And Behavior ◽  
The Impact ◽  
Smart Government

Sustainable city is a city that designed by considering the impact on the environment, inhabited by population with a number and behavior that requires minimal support for energy, water and food from the outside, and produces less CO2, gas, air and water pollution. Moreover the national government envisions Indonesia2030which shallimplement the smart city towards sustainable development.Especially in Sleman Regency, the government is committed to make Sleman Regency as a Smart Regency in 2021. It could be shown in the vision of Sleman Regency which is The realization of a more prosperous Sleman community, Independent, Cultured and Integratede-governmentsystem to the Smart Regency in 2021”. This paper would like to analyze how the Sleman Regency implement the Smart city concept, and does the smart city concept can achive the sustainability city. The research uses the qualitative approach with in-deepth interview in examining the data, also the literature review. The result in this study reveals the following: firstly, from 2016-2019 Sleman regency has several applications to support the smart city implementation such as One Data of UMKM, Home Creative Sleman, Lapor Sleman app, Sleman Smart app, online tax app, e-patient, sleman emergency service, and Sleman smart room. Second, there are many elements in smart cities that are very important for smart government, smart life, smart economy, smart society, and smart environment. However, in supporting to support the realization of smart cities, not all aspects must be implemented properly to achieve a managed city, components related to smart environment cannot be implemented properly in Sleman Regency. There are still many problems regarding environmental problems such as the development of the construction of hotels and apartments that do not heed the environment, incrasing the populations, the limitations of green open space.

scholarly journals IMPROVING THE ENERGY EFFICIENCY OF SMART CITIES DURING THE COVID-19 PANDEMIC

2021 ◽  
Vol 229 (3) ◽  
pp. 354-364
Author(s):  
MAKSIM D. PUSHKAREV ◽  
◽  
DMITRY A. PROKOFIEV ◽  
Keyword(s):  
Energy Efficiency ◽  
Smart City ◽  
Smart Cities ◽  
Urban Infrastructure ◽  
High Tech ◽  
The World ◽  
The Impact

Smart city technologies make the functioning of urban infrastructure more efficient, and the lives of citizens more comfortable and safe. During the COVID-19 pandemic, they were very popular, and this could not but affect the energy efficiency of high-tech megacities around the world. This article examines the impact of the COVID-19 pandemic on smart cities, and also offers a solution to the problem of energy efficiency of smart cities.

scholarly journals A Parallel Meta-Heuristic Approach to Reduce Vehicle Travel Time in Smart Cities

2020 ◽  
Author(s):  
Hector Rico-Garcia ◽  
Jose-Luis Sanchez-Romero ◽  
Antonio Jimeno-Morenilla ◽  
Hector Migallon-Gomis
Keyword(s):  
Travel Time ◽  
Smart City ◽  
Smart Cities ◽  
Travelling Salesman Problem ◽  
Urban Environments ◽  
Departure Point ◽  
Classic Problem ◽  
Iot Devices ◽  
Vehicle Travel Time ◽  
Gpu Architecture

The development of the smart city concept and the inhabitants’ need to reduce travel time, as well as society’s awareness of the reduction of fuel consumption and respect for the environment, lead to a new approach to the classic problem of the Travelling Salesman Problem (TSP) applied to urban environments. This problem can be formulated as “Given a list of geographic points and the distances between each pair of points, what is the shortest possible route that visits each point and returns to the departure point?” Nowadays, with the development of IoT devices and the high sensoring capabilities, a large amount of data and measurements are available, allowing researchers to model accurately the routes to choose. In this work, the purpose is to give solution to the TSP in smart city environments using a modified version of the metaheuristic optimization algorithm TLBO (Teacher Learner Based Optimization). In addition, to improve performance, the solution is implemented using a parallel GPU architecture, specifically a CUDA implementation.

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.

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