### 计算机代写|机器学习代写machine learning代考|A Cross-Domain Landscape of ICT Services in Smart Cities

statistics-lab™ 为您的留学生涯保驾护航 在代写机器学习 machine learning方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写机器学习 machine learning代写方面经验极为丰富，各种代写机器学习 machine learning相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 计算机代写|机器学习代写machine learning代考|Layered View of Smart Services

To unify the structure of smart city services and to build a basis for their interconnected holistic description, we introduce the concept of a layered view of the smart city [1]. The approach is based on service value proposition where the structure emerges automatically from the ordering of services according to their purpose.

In this model, five layers of services are identified where the layer one is proposing the value to the final user such as city citizen. Services from lower layers are providing their functionality to services from the upper level. These five layers are: (1) smart features – complex services, offering high perceived value to the city citizens. The value proposition depends on a particular configuration of services from the lower levels (e.g., mobility); (2) smart services-complex services that are using other (more simple) services. Their value proposition is aimed at smart features, even the possibility to use them directly is not excluded, but very limited (e.g., traffic control); (3) support services-simple services with predefined API that you can use to obtain particular information (e.g., vehicle of public transport position check); (4) software-layer that contains all basic software systems that are used to collect, store, process, or control the data; (5) hardware-layer of basic devices to get the data, e.g., sensors, activators, servers, and networks. This approach allows us to model the structure of the services across different domains and it identifies the smart service system in which the value can be perceived, diffused, and co-created on different layers of the service structure.

## 计算机代写|机器学习代写machine learning代考|Urban Planning

Building a smart city needs to take many factors into consideration. To create a city that can adapt citizens needs and demands as well as changing technologies, it is necessary to approach the smart city design in a holistic way. Urban planning aims at connecting all the other parts of the city into one interconnected functional entity. Therefore, urban planning can be defined as “a technical and political process concerned with the control of land use and design of urban environments, which can benefit from trace data, analysis and mining” $[30,31]$. We consider that urban planning creates prerequisites for all other services within the smart city and is responsible for deciding how many infrastructures the city needs, how they should be distributed and whether the infrastructure is sufficient for the needs of citizens. We can therefore state that “an efficient urban planning can, without any doubts, improve the quality of the life of all the citizens” [24].

Important tools for efficient urban planning are data tracing, data mining and analysis. There are open datasets as well as preserved datasets from the enterprises. Currently, due to crowdsourcing initiatives, when citizens play a role of sensor (usually by means of their smart phones), open data contain a wide range of data about location of citizens. On the other hand, preserved data from telecommunication companies also provide valuable insights into mobility across the city. Additionally, there are plenty of datasets regarding demographic and geographic information provided by the city. These datasets represent a valuable source for data mining and analysis and can support efficient urban planning [31]. As plotted in Fig. 1, urban planning includes a wide range of services. One of the most significant groups of services is smart buildings. Smart buildings are defined as the buildings with intelligent features such as ability to measure, store, and analyze data from the environment, namely for the purpose of household automation $[13,8]$, energy savings $[23]$, or building safety $[32]$.

## 计算机代写|机器学习代写machine learning代考|Smart Energy

One of the fundamental elements in smart cities is the optimization of energy use within the entire community, which aims at achieving eco-friendly lifestyle with high quality of living $[27,34,7]$. The crucial role in this process is operated by ICT empowered electricity grid, which is known as the smart grid. In the smart grid, the ICT infrastructure improves efficient use of the physical infrastructure, providing the capacity to safely integrate more renewable energy sources and smart devices, delivering power more efficiently in secure and reliable way through new control and monitoring capabilities. By using automatic grid reconfiguration, the city can prevent or restore outages and enable consumers to have greater control over their consumption of electricity $[35,36,37]$.

Several concepts of smart grid development and implementation have been introduced [17]. However, a complete picture of the smart grid ICT architecture and its existing design alternatives is still missing. When building the view in Fig. 2, we studied smart grid implementation from EU and USA. A valuable input for our study was the DISCERN project [18], which resulted into the generally accepted smart grid Reference Architecture (SGAM) model, as well as a usecase approach to smart grid development, where the use cases help to determine the key performance indicator (KPI) for the smart grid architecture [10]. Another project, called Grid4EU, presents six demo architectures. All of them were created with regard to SGAM. One of the demo architectures was proposed by a Czech energy distribution company [18]. This proposed solution focuses on automatic operation within the grid. It is supported by remote-control devices and connections that enable fast communication via regional dispatcher system. Another useful information source about smart grid architecture development in the USA was provided by a study of the California State University of Sacramento [38]. Although its main focus was cyber security and vulnerability of smart grid architecture, this study also brings information about the architecture itself and its stage of development in the USA. Finally, the description of the hardware part of the smart grid infrastructure can be found in [21], where the purpose of all hardware components in Fig. 2 is described.

## 有限元方法代写

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## MATLAB代写

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