Editorial

4 pages, 210 KiB

Open AccessEditorial

by Tania Cerquitelli, Sara Migliorini and Silvia Chiusano

Electronics 2021, 10(12), 1439; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10121439 - 15 Jun 2021

Cited by 5 | Viewed by 1725

In the last few years, cities have become engines of wealth creation thanks to the advent of the new information and communication [...] Full article

(This article belongs to the Special Issue Big Data Analytics for Smart Cities)

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Research

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26 pages, 6643 KiB

Open AccessArticle

A Data-Driven Energy Platform: From Energy Performance Certificates to Human-Readable Knowledge through Dynamic High-Resolution Geospatial Maps

by Tania Cerquitelli, Evelina Di Corso, Stefano Proto, Paolo Bethaz, Daniele Mazzarelli, Alfonso Capozzoli, Elena Baralis, Marco Mellia, Silvia Casagrande and Martina Tamburini

Electronics 2020, 9(12), 2132; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9122132 - 12 Dec 2020

Cited by 4 | Viewed by 2305

Abstract

The energy performance certificate (EPC) is a document that certifies the average annual energy consumption of a building in standard conditions and allows it to be classified within a so-called energy class. In a period such as this, when greenhouse gas emissions are [...] Read more.

The energy performance certificate (EPC) is a document that certifies the average annual energy consumption of a building in standard conditions and allows it to be classified within a so-called energy class. In a period such as this, when greenhouse gas emissions are of considerable importance and where the objective is to improve energy security and reduce energy costs in our cities, energy certification has a key role to play. The proposed work aims to model and characterize residential buildings’ energy efficiency by exploring heterogeneous, geo-referenced data with different spatial and temporal granularity. The paper presents TUCANA (TUrin Certificates ANAlysis), an innovative data mining engine able to cover the whole analytics workflow for the analysis of the energy performance certificates, including cluster analysis and a model generalization step based on a novel spatial constrained K-NN, able to automatically characterize a broad set of buildings distributed across a major city and predict different energy-related features for new unseen buildings. The energy certificates analyzed in this work have been issued by the Piedmont Region (a northwest region of Italy) through open data. The results obtained on a large dataset are displayed in novel, dynamic, and interactive geospatial maps that can be consulted on a web application integrated into the system. The visualization tool provides transparent and human-readable knowledge to various stakeholders, thus supporting the decision-making process. Full article

(This article belongs to the Special Issue Big Data Analytics for Smart Cities)

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11 pages, 379 KiB

Open AccessArticle

Big-But-Biased Data Analytics for Air Quality

by Laura Borrajo and Ricardo Cao

Electronics 2020, 9(9), 1551; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9091551 - 22 Sep 2020

Cited by 4 | Viewed by 2577

Abstract

Air pollution is one of the big concerns for smart cities. The problem of applying big data analytics to sampling bias in the context of urban air quality is studied in this paper. A nonparametric estimator that incorporates kernel density estimation is used. [...] Read more.

Air pollution is one of the big concerns for smart cities. The problem of applying big data analytics to sampling bias in the context of urban air quality is studied in this paper. A nonparametric estimator that incorporates kernel density estimation is used. When ignoring the biasing weight function, a small-sized simple random sample of the real population is assumed to be additionally observed. The general parameter considered is the mean of a transformation of the random variable of interest. A new bootstrap algorithm is used to approximate the mean squared error of the new estimator. Its minimization leads to an automatic bandwidth selector. The method is applied to a real data set concerning the levels of different pollutants in the urban air of the city of A Coruña (Galicia, NW Spain). Estimations for the mean and the cumulative distribution function of the level of ozone and nitrogen dioxide when the temperature is greater than or equal to 30

^{\circ}

C based on 15 years of biased data are obtained. Full article

(This article belongs to the Special Issue Big Data Analytics for Smart Cities)

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14 pages, 374 KiB

Open AccessFeature PaperArticle

Task-Agnostic Object Recognition for Mobile Robots through Few-Shot Image Matching

by Agnese Chiatti, Gianluca Bardaro, Emanuele Bastianelli, Ilaria Tiddi, Prasenjit Mitra and Enrico Motta

Electronics 2020, 9(3), 380; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9030380 - 25 Feb 2020

Cited by 6 | Viewed by 3299

Abstract

To assist humans with their daily tasks, mobile robots are expected to navigate complex and dynamic environments, presenting unpredictable combinations of known and unknown objects. Most state-of-the-art object recognition methods are unsuitable for this scenario because they require that: (i) all target object [...] Read more.

To assist humans with their daily tasks, mobile robots are expected to navigate complex and dynamic environments, presenting unpredictable combinations of known and unknown objects. Most state-of-the-art object recognition methods are unsuitable for this scenario because they require that: (i) all target object classes are known beforehand, and (ii) a vast number of training examples is provided for each class. This evidence calls for novel methods to handle unknown object classes, for which fewer images are initially available (few-shot recognition). One way of tackling the problem is learning how to match novel objects to their most similar supporting example. Here, we compare different (shallow and deep) approaches to few-shot image matching on a novel data set, consisting of 2D views of common object types drawn from a combination of ShapeNet and Google. First, we assess if the similarity of objects learned from a combination of ShapeNet and Google can scale up to new object classes, i.e., categories unseen at training time. Furthermore, we show how normalising the learned embeddings can impact the generalisation abilities of the tested methods, in the context of two novel configurations: (i) where the weights of a Convolutional two-branch Network are imprinted and (ii) where the embeddings of a Convolutional Siamese Network are L2-normalised. Full article

(This article belongs to the Special Issue Big Data Analytics for Smart Cities)

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16 pages, 2330 KiB

Open AccessFeature PaperArticle

From Hotel Reviews to City Similarities: A Unified Latent-Space Model

by Luca Cagliero, Moreno La Quatra and Daniele Apiletti

Electronics 2020, 9(1), 197; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9010197 - 20 Jan 2020

Cited by 11 | Viewed by 3095

Abstract

A large portion of user-generated content published on the Web consists of opinions and reviews on products, services, and places in textual form. Many travellers and tourists routinely rely on such content to drive their choices, shaping trips and visits to any place [...] Read more.

A large portion of user-generated content published on the Web consists of opinions and reviews on products, services, and places in textual form. Many travellers and tourists routinely rely on such content to drive their choices, shaping trips and visits to any place on earth, and specifically to select hotels in large cities. In the context of hospitality management, a challenging research problem is to identify effective strategies to explain hotel reviews and ratings and their correlation with the urban context. Under this umbrella, the paper investigates the use of sentence-based embedding models to deeply explore the similarities and dissimilarities between cities in terms of the corresponding hotel reviews and the surrounding points of interests. Reviews and point of interest (POI) descriptions are jointly modelled in a unified latent space, allowing us to deeply investigate the dependencies between guest feedbacks and the hotel neighborhood at different aggregation levels. The experiments performed on public TripAdvisor hotel-review datasets confirm the applicability and effectiveness of the proposed approach. Full article

(This article belongs to the Special Issue Big Data Analytics for Smart Cities)

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19 pages, 318 KiB

Open AccessFeature PaperArticle

Correlating Espresso Quality with Coffee-Machine Parameters by Means of Association Rule Mining

by Daniele Apiletti and Eliana Pastor

Electronics 2020, 9(1), 100; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9010100 - 04 Jan 2020

Cited by 9 | Viewed by 6524

Abstract

Coffee is among the most popular beverages in many cities all over the world, being both at the core of the busiest shops and a long-standing tradition of recreational and social value for many people. Among the many coffee variants, espresso attracts the [...] Read more.

Coffee is among the most popular beverages in many cities all over the world, being both at the core of the busiest shops and a long-standing tradition of recreational and social value for many people. Among the many coffee variants, espresso attracts the interest of different stakeholders: from citizens consuming espresso around the city, to local business activities, coffee-machine vendors and international coffee industries. The quality of espresso is one of the most discussed and investigated issues. So far, it has been addressed by means of human experts, electronic noses, and chemical approaches. The current work, instead, proposes a data-driven approach exploiting association rule mining. We analyze a real-world dataset of espresso brewing by professional coffee-making machines, and extract all correlations among external quality-influencing variables and actual metrics determining the quality of the espresso. Thanks to the application of association rule mining, a powerful data-driven exhaustive and explainable approach, results are expressed in the form of human-readable rules combining the variables of interest, such as the grinder settings, the extraction time, and the dose amount. Novel insights from real-world coffee extractions collected on the field are presented, together with a data-driven approach, able to uncover insights into the espresso quality and its impact on both the life of consumers and the choices of coffee-making industries. Full article

(This article belongs to the Special Issue Big Data Analytics for Smart Cities)

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20 pages, 1549 KiB

Open AccessFeature PaperArticle

On Car-Sharing Usage Prediction with Open Socio-Demographic Data

by Michele Cocca, Douglas Teixeira, Luca Vassio, Marco Mellia, Jussara M. Almeida and Ana Paula Couto da Silva

Electronics 2020, 9(1), 72; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9010072 - 01 Jan 2020

Cited by 18 | Viewed by 3352

Abstract

Free-Floating Car-Sharing (FFCS) services are a flexible alternative to car ownership. These transportation services show highly dynamic usage both over different hours of the day, and across different city areas. In this work, we study the problem of predicting FFCS demand patterns—a problem [...] Read more.

Free-Floating Car-Sharing (FFCS) services are a flexible alternative to car ownership. These transportation services show highly dynamic usage both over different hours of the day, and across different city areas. In this work, we study the problem of predicting FFCS demand patterns—a problem of great importance to the adequate provisioning of the service. We tackle both the prediction of the demand (i) over time and (ii) over space. We rely on months of real FFCS rides in Vancouver, which constitute our ground truth. We enrich this data with detailed socio-demographic information obtained from large open-data repositories to predict usage patterns. Our aim is to offer a thorough comparison of several machine-learning algorithms in terms of accuracy and ease of training, and to assess the effectiveness of current state-of-the-art approaches to address the prediction problem. Our results show that it is possible to predict the future usage with relative errors down to 10%, while the spatial prediction can be estimated with relative errors of about 40%. Our study also uncovers the socio-demographic features that most strongly correlate with FFCS usage, providing interesting insights for providers interested in offering services in new regions. Full article

(This article belongs to the Special Issue Big Data Analytics for Smart Cities)

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17 pages, 802 KiB

Open AccessArticle

A Comparison Analysis of BLE-Based Algorithms for Localization in Industrial Environments

by Davide Cannizzaro, Marina Zafiri, Daniele Jahier Pagliari, Edoardo Patti, Enrico Macii, Massimo Poncino and Andrea Acquaviva

Electronics 2020, 9(1), 44; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9010044 - 28 Dec 2019

Cited by 22 | Viewed by 4114

Abstract

Proximity beacons are small, low-power devices capable of transmitting information at a limited distance via Bluetooth low energy protocol. These beacons are typically used to broadcast small amounts of location-dependent data (e.g., advertisements) or to detect nearby objects. However, researchers have shown that [...] Read more.

Proximity beacons are small, low-power devices capable of transmitting information at a limited distance via Bluetooth low energy protocol. These beacons are typically used to broadcast small amounts of location-dependent data (e.g., advertisements) or to detect nearby objects. However, researchers have shown that beacons can also be used for indoor localization converting the received signal strength indication (RSSI) to distance information. In this work, we study the effectiveness of proximity beacons for accurately locating objects within a manufacturing plant by performing extensive experiments in a real industrial environment. To this purpose, we compare localization algorithms based either on trilateration or environment fingerprinting combined with a machine-learning based regressor (k-nearest neighbors, support-vector machines, or multi-layer perceptron). Each algorithm is analyzed in two different types of industrial environments. For each environment, various configurations are explored, where a configuration is characterized by the number of beacons per square meter and the density of fingerprint points. In addition, the fingerprinting approach is based on a preliminary site characterization; it may lead to location errors in the presence of environment variations (e.g., movements of large objects). For this reason, the robustness of fingerprinting algorithms against such variations is also assessed. Our results show that fingerprint solutions outperform trilateration, showing also a good resilience to environmental variations. Given the similar error obtained by all three fingerprint approaches, we conclude that k-NN is the preferable algorithm due to its simple deployment and low number of hyper-parameters. Full article

(This article belongs to the Special Issue Big Data Analytics for Smart Cities)

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