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A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (28 February 2017) | Viewed by 28562

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Special Issue Editors

Prof. Dr. Laurence T. Yang

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Guest Editor

Department of Computer Science, St. Francis Xavier University, Canada
Interests: parallel and distributed computing; embedded and ubiquitous/pervasive computing; big data; sustainable computing

Dr. Qingchen Zhang

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Guest Editor

Department of Computer Science, St. Francis Xavier University, Antigonish, NS, Canada
Interests: big data; cloud computing; deep learning
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Prof. Dr. M. Jamal Deen

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Guest Editor

Department of Electrical & Computer Engineering, Faculty of Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada
Interests: microelectronics; nanoelectronics; opto-electronics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Steve Yau

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Guest Editor

Arizona State University, USA
Interests: cyber trust; cloud computing; software engineering; service-based systems; parallel and distributed computing systems

Special Issue Information

Dear Colleagues,

The rapid development of information technology and computer science takes human society toward smart environments. In the smart environments, anything that can assist to solve the current challenges in business, industry, science, daily life, etc., can refer to ‘Smart X’. Smart X includes smart city, smart home, smart grids, smart building, smart forest, smart geosciences, smart computing, etc. Smart X can help human in various dimensions, e.g., better living with better resources, faster and better decision making, more precise future predicting, quicker response making to challenges in surrounding environments, and so on. However, how to implement Smart X? Today, smart techniques, together with the advances in big data and high-performance computing power, offer us big opportunities and transformative potential for intelligent decisions and predictive services. The major challenges we are facing involves extracting valuable knowledge from big data, i.e. smart data for smart X, studying creative computing theories and techniques for Smart X, and designing dynamic and globally cooperative infrastructure built upon Smart X. Smart X also leads to a dramatic paradigm shift in our scientific research towards smart-driven computing.

While sustainability is now the key driver in achieving a transition to more sustainable planet, Smart X has a large role to play in sustainability. What are the ways Smart X are responding to challenges raised by the need for the human society sustainability? What are the implications for Smart X, big data and human behavior? Is Smart X an opportunity for sustainable innovations? How are big data and social sustainability related? New scientific, technological and policy insights, visions, principles and experiences addressing such questions and related issues form the essence of this Special Issue. Topics of interest include, but are not limited to:

Human sustainability
Smart information process for sustainability
Smart transportation for sustainability
Smart city and environment for sustainability
Social networks and computing for sustainability
Smart sensors and devices for sustainability
Smart learning and deep computation for sustainability
Big data and smart data mining for sustainability
Cloud computing and fog computing for sustainability
Security, privacy and trust for sustainability
Intelligent decision and semantic analytics for sustainability
Human behavior for sustainability
Internet of things for sustainability
Ecological citizen and environment for sustainability

Prof. Laurence T. Yang
Dr. Qingchen Zhang
Prof. M. Jamal Deen
Prof. Steve Yau
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (5 papers)

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Research

449 KiB

Open AccessArticle

A Smart MCDM Framework to Evaluate the Impact of Air Pollution on City Sustainability: A Case Study from China

by Qingyong Wang, Hong-Ning Dai and Hao Wang

Sustainability 2017, 9(6), 911; https://doi.org/10.3390/su9060911 - 29 May 2017

Cited by 52 | Viewed by 6262

Abstract

Air pollution has become one of the key environmental concerns in the urban sustainable development. It is important to evaluate the impact of air pollution on socioeconomic development since it is the prerequisite to enforce an effective prevention policy of air pollution. In this paper, we model the impact of air pollution on the urban economic development as a Multiple Criteria Decision Making (MCDM) problem. In particular, we propose a novel Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analysis framework to evaluate multiple factors of air pollutants and economic development. Our method can overcome the drawbacks of conventional TOPSIS methods by using Bayesian regularization and the Back-Propagation (BP) neural network to optimize the weight training process. We have conducted a case study to evaluate our proposed framework. Full article

(This article belongs to the Special Issue Smart X for Sustainability)

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3587 KiB

Open AccessArticle

An Optimal Rubrics-Based Approach to Real Estate Appraisal

by Zhangcheng Chen, Yueming Hu, Chen Jason Zhang and Yilun Liu

Sustainability 2017, 9(6), 909; https://doi.org/10.3390/su9060909 - 29 May 2017

Cited by 7 | Viewed by 4850

Abstract

Traditional real estate appraisal methods obtain estimates of real estate by using mathematical modeling to analyze the existing sample data. However, the information of sample data sometimes cannot fully reflect the real-time quotes. For example, in a thin real estate market, the correlated sample data for estimated object is lacking, which limits the estimates of these traditional methods. In this paper, an optimal rubrics-based approach to real estate appraisal is proposed, which brings in crowdsourcing. The valuation estimate can serve as a market indication for the potential real estate buyers or sellers. It is not only based on the information of the existing sample data (just like these traditional methods), but also on the extra real-time market information from online crowdsourcing feedback, which makes the estimated result close to that of the market. The proposed method constructs the rubrics model from sample data. Based on this, the cosine similarity function is used to calculate the similarity between each rubric for selecting the optimal rubrics. The selected optimal rubrics and the estimated point are posted on a crowdsourcing platform. After comparing the information of the estimated point with the optimal rubrics on the crowdsourcing platform, those users who are connected with the estimated object complete the appraisal with their knowledge of the real estate market. The experiment results show that the average accuracy of the proposed approach is over 70%; the maximum accuracy is 90%. This supports that the proposed method can easily provide a valuable market reference for the potential real estate buyers or sellers, and is an attempt to use the human-computer interaction in the real estate appraisal field. Full article

(This article belongs to the Special Issue Smart X for Sustainability)

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1162 KiB

Open AccessArticle

Towards Massive Data and Sparse Data in Adaptive Micro Open Educational Resource Recommendation: A Study on Semantic Knowledge Base Construction and Cold Start Problem

by Geng Sun, Tingru Cui, Ghassan Beydoun, Shiping Chen, Fang Dong, Dongming Xu and Jun Shen

Sustainability 2017, 9(6), 898; https://doi.org/10.3390/su9060898 - 26 May 2017

Cited by 30 | Viewed by 5741

Abstract

Micro Learning through open educational resources (OERs) is becoming increasingly popular. However, adaptive micro learning support remains inadequate by current OER platforms. To address this, our smart system, Micro Learning as a Service (MLaaS), aims to deliver personalized OER with micro learning to satisfy their real-time needs. In this paper, we focus on constructing a knowledge base to support the decision-making process of MLaaS. MLaas is built using a top-down approach. A conceptual graph-based ontology construction is first developed. An educational data mining and learning analytic strategy is then proposed for the data level. The learning resource adaptation still requires learners’ historical information. To compensate for the absence of this information initially (aka ‘cold start’), we set up a predictive ontology-based mechanism. As the first resource is delivered to the beginning of a learner’s learning journey, the micro OER recommendation is also optimized using a tailored heuristic. Full article

(This article belongs to the Special Issue Smart X for Sustainability)

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1042 KiB

Open AccessArticle

Food Image Recognition via Superpixel Based Low-Level and Mid-Level Distance Coding for Smart Home Applications

by Jiannan Zheng, Z. Jane Wang and Chunsheng Zhu

Sustainability 2017, 9(5), 856; https://doi.org/10.3390/su9050856 - 19 May 2017

Cited by 14 | Viewed by 5721

Abstract

Food image recognition is a key enabler for many smart home applications such as smart kitchen and smart personal nutrition log. In order to improve living experience and life quality, smart home systems collect valuable insights of users’ preferences, nutrition intake and health conditions via accurate and robust food image recognition. In addition, efficiency is also a major concern since many smart home applications are deployed on mobile devices where high-end GPUs are not available. In this paper, we investigate compact and efficient food image recognition methods, namely low-level and mid-level approaches. Considering the real application scenario where only limited and noisy data are available, we first proposed a superpixel based Linear Distance Coding (LDC) framework where distinctive low-level food image features are extracted to improve performance. On a challenging small food image dataset where only 12 training images are available per category, our framework has shown superior performance in both accuracy and robustness. In addition, to better model deformable food part distribution, we extend LDC’s feature-to-class distance idea and propose a mid-level superpixel food parts-to-class distance mining framework. The proposed framework show superior performance on a benchmark food image datasets compared to other low-level and mid-level approaches in the literature. Full article

(This article belongs to the Special Issue Smart X for Sustainability)

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6610 KiB

Open AccessArticle

Analysis of the Effectiveness of Urban Land-Use-Change Models Based on the Measurement of Spatio-Temporal, Dynamic Urban Growth: A Cellular Automata Case Study

by Yilun Liu, Yueming Hu, Shaoqiu Long, Luo Liu and Xiaoping Liu

Sustainability 2017, 9(5), 796; https://doi.org/10.3390/su9050796 - 10 May 2017

Cited by 30 | Viewed by 5039

Abstract

Developing countries have been undergoing dramatic urban growth over the past three decades. It is essential to understand and simulate the urban growth process for smart urban planning and sustainable development purposes. Cellular automata (CA) modeling is an efficient approach to simulating urban land use/cover change; however, the traditional CA method has limitations in simulating the various urban growth patterns and processes. This study aims to analyze the influences of different urban growth characteristics on the effectiveness of CA modeling by conducting a case study over the area in the Pearl River Delta of Southern China. We used the growth rate, landscape expansion index, and spatial dependency to quantify the urban growth characteristics. The effectiveness of CA modeling was measured through a comparison of the simulation results with the reference data. The simulation results and validation analyses reveal that the traditional CA is not applicable for the following three situations: (1) the urban growth pattern characterized by less growth area or a higher ratio of outlying expansion; (2) the urban region that includes several subregions with disparate growth characteristics; and (3) the existence of temporal differences in growth characteristics over a long period. Full article

(This article belongs to the Special Issue Smart X for Sustainability)

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