Towards Smart City

Dutta, Joy; Chowdhury, Chandreyee; Roy, Sarbani; Middya, Asif Iqbal; Gazi, Firoj

doi:10.1145/3007748.3018286

Cited by 73 publications

(17 citation statements)

References 9 publications

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“…where E j t is the estimate for the missing information at time t, E j t−1 ∈ C j is the last information perceived by ACA i , w t the weight obtained through a cooperation behavior between ACA i and the other available agents. The weight w t is calculated by (2). Equation (3) considers the last information acquired because we assume that environmental information perceived in consecutive temporal instants do not present relevant changes.…”

Section: ) Missing Information Calculationmentioning

confidence: 99%

“…In this way, the ACA i provides to each ACA in Γ (t) an indication of which are the temporal instants associated with ACWs that are similar to C t , that is, the ACW containing the information to estimate at time t. Each ACA in Γ (t) , therefore, evaluates the distance between the ACW observed at the time instant t and the ACWs whose indices are indicated by the set Σ (t) . Each ACA calculates a weight by using the (2).…”

Section: ) Cooperative Evaluation Of the Set Of Weightsmentioning

confidence: 99%

“…The increasing diffusion and accessibility of the Internet Of Things (IoT) sensors enabled cities to become urban sensing platforms [1]. Data acquisition, through these platforms, enables cities to become "Smart", using environmental information collected in a participatory way to improve services and reduce their ecological footprint [2]. Environmental information includes information about air, water, soil, land, flora and fauna, energy, noise, waste and emissions, but also information about decisions, policies and activities that affect the environment [3].…”

Section: Introductionmentioning

confidence: 99%

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A Cooperative Multi-Agent System for Crowd Sensing Based Estimation in Smart Cities

2020

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The concept of Smart City has spread as a solution to ensure better access to information and services to citizens, but also as a means to reduce the environmental footprint of cities. To this end, a continuous and wide observation of the environment is necessary to analyze information that enables government bodies to act on the environment appropriately. Moreover, a diffused acquisition of information requires adequate infrastructure and proper devices, which results in relevant installation and maintenance costs. Our proposal enables reducing the number of necessary sensors to be deployed while ensuring that information is available at any time and anywhere. We present the HybridIoT system to cope with the lack of environmental information in the urban context through an estimation technique that integrates heterogeneous data acquired from some different sensors. HybridIoT can be deployed in large-scale contexts and ensures data accessibility even if devices enter or leave the system at any time and everywhere. We compare the results to those obtained by state-of-the-art techniques to assess the validity of our proposal, in particular concerning the properties of openness, large-scale, and heterogeneity, of primary importance in the context of the development of systems to be deployed in the smart city context.

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Section: ) Missing Information Calculationmentioning

confidence: 99%

Section: ) Cooperative Evaluation Of the Set Of Weightsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Cooperative Multi-Agent System for Crowd Sensing Based Estimation in Smart Cities

2020

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“…In [56], Joy Dutta et al proposed an Air Quality Monitoring System AQMD for tracking urban air pollution. They used two MQ135 and MQ7 air quality sensors, connected to an Arduino board that interacted with the Bluetooth module HC-05.…”

Section: Related Workmentioning

confidence: 99%

Review on Smart Electronic Nose Coupled with Artificial Intelligence for Air Quality Monitoring

Faleh¹,

Bedoui²,

Kachouri³

2020

Adv. sci. technol. eng. syst. j.

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With the advent of the Internet of Things Technologies (IOT), smart homes, and smart city applications, E-Nose was created. Almost of gas sensors consisting the electronic nose system suffer from cross sensitivity and lack of selectivity. Coupling smart gas sensors with artificial intelligence algorithms can thus empower conventional gas sensing technologies and increase accuracy in gas detection. This study describes the overall types of smart gas sensors used in air quality control, signal pre-processing and extraction features. Also it presents pattern recognition methods used in E-nose applications including linear methods such as Linear Discriminant Analysis LDA, K-Nearest Neighbors KNN and Non-linear such as algorithms Support vector Machine SVM and Artificial Neural Network ANN and their impact on improving accuracy rate for gas detection. Finally, this paper summarizes by providing directions for about how to leverage the benefits of combining these classifiers which is known as Data fusion approach and ensemble classifiers.

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“…Ensuring measurement credibility becomes a key scienti c challenge in this context. To this end, we carried out research on the example of air pollution health symptoms, an emerging trend particularly related to odour (Arias et al, 2018) and green pollutant (Bastl et al, 2015) crowdsensing (Dutta et al, 2017, Feng et al, 2018. As crowdsensing (or, more generally, crowdsourcing) methods for health symptom mapping are subject to data bias (Zupančič and Žalik 2019), we developed and tested the quality assurance mechanism (QAm) framework (section 2.2), which can be transferred to similar health symptom-based studies.…”

Section: Introductionmentioning

confidence: 99%

Crowdsourcing Without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping Toward an SDG Indicator

Samulowska

Chmielewski

Raczko

et al. 2020

Preprint

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The United Nations (UN) sustainable development goals (SDGs), a strategy to guide the world’s social and economic transformation, highlight the issue of urban air pollution in SDG 11. Open data, as an output of citizen science (CS), are needed to supply and improve the SDG indicator system. Therefore, we propose a CS framework to extend the paradigm of urban air pollution monitoring from particulate matter concentration levels to air quality-related health symptom load, and foster the development of a tier-3 SDG indicator (which we call indicator 11.6.3). Building this new perspective for CS contributions to the achievement of SDGs, we address the problem of crowdsourced data bias as a prerequisite for better quality open data output. The aim of this study is to propose an air pollution symptom mapping framework for citizen-driven research and to find the most robust data quality assurance system (QAs) in this field. The method includes a GeoWeb application as well as data quality assurance mechanisms based on conditional statements, in order to reduce crowdsourced data bias. A four-month crowdsourcing campaign, released in Lubelskie voivodship (Poland), resulted in 1823 outdoor reports with a rejection rate of up to 28%, depending on the applied QA system (QAs). Testing the QAs variants, we find the most robust data bias solving method in survey-based symptom mapping. The framework output is shared via GeoWeb dashboards, including the 11.6.3 indicator evaluation. By familiarizing the public with citizen science, a city can track the progress of its SDG achievements and increase the transparency of the process through the use of GeoWeb.

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Towards Smart City

Cited by 73 publications

References 9 publications

A Cooperative Multi-Agent System for Crowd Sensing Based Estimation in Smart Cities

A Cooperative Multi-Agent System for Crowd Sensing Based Estimation in Smart Cities

Review on Smart Electronic Nose Coupled with Artificial Intelligence for Air Quality Monitoring

Crowdsourcing Without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping Toward an SDG Indicator

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