Digital Cities Roadmap. Группа авторов

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СКАЧАТЬ Not emphasized specifically on the SB domain and its application services. Tsai et al. [21] Data mining technologies for IoT applications data reviewed. SB applications not emphasized. Mahdavinejad et al. [22] Discussed and analyzed some ML methods applied to IoT data by studying smart cities as a use case scenario. Not concentrated on SB and its applications as a use case.

Chan et al. in 2008 [12] gave a description of intelligent home study. It even speaks about smart and friendly robotics. The article examines the nation and the continent’s smart home programs. Alam et al. [13] presented information on sensors, apps, algorithms and protocols of communication used in smart homes. The paper explores intelligent homes focused on their facilities and study aims: protection, fitness and comfort.

      Lobaccaro et al. [14] shared the notion of a smart house but smart grid technology and address obstacles, advantages and potential developments of intelligent home technology. Pan et al. [15] analyzed the research of SBs with microgrids on efficient energy usage. The study explores subjects for analysis and latest developments in SBs and microgrid vision.

      For multiple study articles research on making the autonomous lives of seniors for smart homes simpler has been checked. Ni et al. [16] have reported on sensing machine features including practices which can help elderly people reside peacefully in intelligent residences. Rashidi and Mihailidis provided a study on environmental assistance systems for elderly people [17]. Peetoom et al. [18] concentrated software tracking that understands householder existence, including reduced identification and changes of safety condition. Salih et al. [19] proposed a health-assisted urban knowledge report surveillance system identifying different methods included in current research literature, as well as connectivity and wireless sensor network technology.

1.3 Machine Learning

      A brief list of the different algorithms for machine learning [49] in sustainable and resilient building is obtained below.

       Decision Tree—Decision Tree is a supervised learning system used for classification or regression. A training model is built in Decision Tree Learning and the importance of the results is determined through the learning decision rules derived from the data attributes. In Big data there are many drawbacks to these decision tree algorithms. Firstly, if the data are very large, it is very time to build a decision tree. Secondly, there is no optimal solution to the distribution of data that contributes to higher communication cost.

       Support Vector Machine (SVM)—Support Vector Machine is a supervised learning approach that can be used for either regression or classification. When used on big data, due to its high machine complexity, the SVM technique is not successful. The demand for measurement and storage is increased considerably for enormous amount of data.

       K-Nearest Neighbor (KNN)—For regression and classification problems, K-Nearest Neighbor (KNN) algorithms are used. KNN approaches are using data and graded use similar steps to different data points. The information is reserved for the class with the closest neighbors. The value of k increases with the increase of the number of closest neighbors. KNN is not realistic on big data applications because of the high cost of calculation and memory.

       Naive Bayes Classifier—For classification function Naive Bayes Classifier is commonly used. For any class or data point that belongs to a certain class, they define membership probabilities. The most probable class is the one with the highest likelihood. The efficiency of Naive Bayes is not possible in text classification tasks due to text redundant features and rough parameter estimation.

       Neural Networks—A semi-supervised technique for classification and regression, the Neural networks. Neural Nets is a computing device consisting of highly interrelated processing elements that process data via their dynamic state response. Back Propagation is one of the best-known algorithms in the neural network. Neural networks have few challenges for big data with the growing scale of information. The huge quantity of information makes it difficult for the technique to maintain both reliability and efficiency and also increases the system operating load.

1.4 What is Resilience?

Over the last couple of decades, the concept of resilience has received increasing attention in several ways and is now viewed as a desirable feature of physical systems and communities. A popular feature in both meanings is that resilience “is the system’s capacity to tolerate external disturbance(s), adjust and rapidly return to the initial or a new stage,” and also offers a multi-disciplinary concept in resilience within the engineering sector [9–15].

      Resilience can be described as an ability to reach a desired level of reliability or provide a desired level of service or features in the physical systems, Q, immediately after a risk arises.

       1.4.1 Sustainability and Resiliency Conditions

      Most societies choose to be resilient and sustainable [50]. When priorities and plans are formulated separately in order to enhance resilience and sustainability, there are strong risks that the targets may overlap and may also clash. This chapter looks at the principles of safe and durable cities, how increasing environmental and constructed environments and stressors will need different approaches and resources to improve stability and longevity for the environment.

      When their resilience and sustainable strategies align themselves, the best results for communities occur. However, sustainable and resilient advancement must be accomplished before promising future generations are delivered. Challenges include reduction of impacts on environmental systems, management and the time it takes to change current practices and replace existing infrastructure with standard renewal rates. Nevertheless, while the governance potential and sustainability and adaptation strategies are open, intergenerational wealth is undermined by expectations that natural environments (our atmosphere, habitats, and climate) are secure and healthy [16–20]. Introduction to sustainability and the resilience of buildings, the dynamic nature of natural systems has not been fully understood through their intricate interrelationships across time and space and their preference for inclination points and threshold values. Many experts face the challenge of developing dangerous model infrastructure that does not involve potential improvements in risk magnitude or frequency, because scientific consensus is not yet formed on this topic. In fact, today’s construction methodology does not take into consideration the harm rates and related impacts on building operation recovery—a critical aspect of resilience.

       1.4.2 Paradigm and Challenges of Sustainability and Resilience

A basic yet strong definition is sustainable development to ensuring that society “combines the present need without compromising potential generations’ capacity to fulfill their needs” (UN 1987). The groundbreaking Bruntland Commission study on sustainable growth presented this Sustainable development concept for the first time. With the implementation of the Sustainable Development Goals in 2015, sustainable development remains an international initiative which has motivated policy and individuals worldwide to alleviate some of the more drastic consequences of mankind on the global operation of the environment.

The idea of cohesive societies emerged concurrently. Application and special concept of resilience to a variety of subjects and dimensions, include psychology, economics, public safety, protection, СКАЧАТЬ