دانلود رایگان مقاله انگلیسی تحلیل و ارزیابی معماری شهر هوشمند بر پایه ی دانش با ارئه خدمات API - الزویر 2017

abstract

The main technical issues regarding smart city solutions are related to data gathering, aggregation, reasoning, data analytics, access, and service delivering via Smart City APIs (Application Program Interfaces). Different kinds of Smart City APIs enable smart city services and applications, while their effectiveness depends on the architectural solutions to pass from data to services for city users and operators, exploiting data analytics, and presenting services via APIs. Therefore, there is a strong activity on defining smart city architectures to cope with this complexity, putting in place a significant range of different kinds of services and processes. In this paper, the work performed in the context of Sii-Mobility smart city project on defining a smart city architecture addressing a wide range of processes and data is presented. To this end, comparisons of the state of the art solutions of smart city architectures for data aggregation and for Smart City API are presented by putting in evidence the usage semantic ontologies and knowledge base in the data aggregation in the production of smart services. The solution proposed aggregate and re-conciliate data (open and private, static and real time) by using reasoning/smart algorithms for enabling sophisticated service delivering via Smart City API. The work presented has been developed in the context of the Sii-Mobility national smart city project on mobility and transport integrated with smart city services with the aim of reaching a more sustainable mobility and transport systems. Sii-Mobility is grounded on Km4City ontology and tools for smart city data aggregation, analytics support and service production exploiting smart city API. To this end, Sii-Mobility/Km4City APIs have been compared to the state of the art solutions. Moreover, the proposed architecture has been assessed in terms of performance, computational and network costs in terms of measures that can be easily performed on private cloud on premise. The computational costs and workloads of the data ingestion and data analytics processes have been assessed to identify suitable measures to estimate needed resources. Finally, the API consumption related data in the recent period are presented.

7. Conclusions

Most of the smart city solutions are transforming data to services for city users and operators, exploiting data and data analytics solutions. Smart City services usually integrate open and private data, static and real time data coming from the administrations and from private operators. Smart City APIs may offer different functionalities depending on the chosen architectural solutions to pass from data to services, limiting or enabling the possibility of exploiting aggregated and reconciliated data by reasoning algorithms enabling the production of sophisticated services, such as those for implementing personal assistants, connected drive, smart services, etc. The effective exploitation of data and semantic relationships to provide smart services stimulated us on creating a smart city architecture with a data aggregation based on semantic computing at the ground. Therefore, the proposed architecture included: (i) a data aggregation layer focused on bringing data into a knowledge base for the city (a sort of expert system with inference capability), (ii) a solution for executing a large range of different data analytics with the support of process management tools, (iii) the formalization of Smart City APIs by which all the web and mobile Apps, and dashboards may access to the data and knowledge base with spatial and temporal reasoning and inference. To this aim, the paper presented a comparison among a range of state of the art architectural solutions for data aggregation for smart cities and for providing Smart City API, offering the comparison in term of requirements coverage and flexibility. The comparisons have been performed to analyze pros and cons of the introduction of semantic computing into the smart city architecture with the aim of providing smart services.