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What issues do we need to consider before planning a smart city?

  Smart cities are intended to use highly intelligent applications to change people ’s daily lives, simplify life from cumbersome processes, improve traffic management and other city services, optimize electricity and water supply, and generally make them more efficient and sustainable Environmental transition. Smart cities realize these benefits through networks such as IoT sensors, big data analysis, and smart mobile services, and improve city operations. The following are the key smart city challenges and possible solutions.

  01. Challenges faced by the deployment cost burden of sensor infrastructure

  Smart cities use sensors to collect information, use big data and artificial intelligence technology to analyze information, and improve the quality of life. These data include real-time traffic information, air quality, and urban health data. The sensor infrastructure is a heavy investment and a major burden. For example, cities must consider how to supply power (wire, solar or batteries) and which city department and budget are responsible for installation and maintenance. Many cities have spent a large portion of their budgets on traditional infrastructure such as underground cables, tunnels, and Internet connections. Funding for new infrastructure such as sensors is limited, and projects may take years to deploy. More importantly, installing the sensor itself is not an end. It is a long-term investment that takes time to affect the lives of urban residents.

  How to overcome:

  Smart city planners should consider the challenges posed by infrastructure from the beginning. Planning smart city infrastructure and raising special funds from smart city organizations and government projects may be a good start.

  In addition, in many cases, cities began to use existing infrastructure to collect data, such as bus ticketing systems, existing closed-circuit television and the original traffic monitoring system. For cities with limited funds or resources, this is a viable alternative.

  02. Challenges of strong interconnection requirements


  Smart cities need to provide residents and tourists with strong interconnected resources to support economic development and interconnected city services. However, even in large Western cities, achieving good interconnection is no easy task. It includes three elements:

  ① Operators need to provide adequate coverage and capacity for different areas of the city.

  ② The site owner and the enterprise need the host to connect the equipment or run their own equipment.

  ③ The municipal government needs to cooperate with operators and private enterprises to ensure coverage of the entire city.

  In dense urban environments, RF signals are often hindered by buildings and certain construction materials. Some specific requirements for environmentally friendly buildings sometimes conflict with RF coverage requirements. Cities need energy-efficient, scalable and effective solutions that support the needs of mobile users and the connectivity requirements of the Internet of Things and machine-to-machine (M2M) data transmission.

  How to overcome:

  Intelligent Digital Distributed Antenna System (idDAS) provides a technical approach. This method is a network topology that supports multiple connection requirements in a smart city. Digital DAS has the characteristics of high cost performance and energy saving and high efficiency, and can provide good coverage in almost any urban environment. DAS can also support 5G technology to prepare for the next generation of mobile connectivity in cities.

  03. Cloud computing security issues in smart cities


  Smart cities must rely on cloud computing technology to carry data and operational services, share data with stakeholders, and provide residents with extensive access. At the same time, city services manage a large amount of highly sensitive data, many of which involve privacy and regulatory issues and are not suitable for storage on public clouds. Most cities adopt a hybrid cloud strategy, some systems are hosted on local or private cloud architectures, and some are hosted on public clouds. But this also brings operational and security challenges. Given the limited IT resources available in the city, these challenges are difficult to deal with.

  How to overcome:

  In 2019, the US "Smart City and Community Challenge" organization released a blueprint that can help smart cities and communities adopt a secure cloud architecture, protect personal identity information (PII) and support confidentiality based on the hybrid cloud concept. This blueprint can help smart cities adopt coordinated cloud service mechanisms, including recovery and cloud backup in the event of a disaster. It is based on the National Institute of Standards and Technology (NIST) cybersecurity framework and includes steps to help cities limit data confidentiality, integrity, and availability risks. Following this blueprint can help cities adopt a more practical and secure cloud architecture. The proposal includes a three-level classification scheme for data risks to help build a hybrid cloud or multi-cloud architecture. The three levels are: red, indicating highly sensitive data such as PII; yellow, indicating data that can be shared more widely; green, indicating low-sensitive data that can be shared publicly.

  Based on data classification, the person in charge of the smart city can protect privacy and security according to legal and regulatory requirements, security policies, data storage and collection practices and other indicators.

  04, efficient data processing and analysis


  Smart cities need to effectively collect and analyze the growing data of the Internet of Things. The data comes in many formats, from log data to environmental sensor readings to surveillance video recording. Smart cities are only effective if they process their data and continuously extract insights from the data (usually in real time). This requires a strong infrastructure that can store data and perform the required analysis in an automated manner. Therefore, one of the biggest challenges is to prioritize data. For example, in tens of thousands of hours of camera data, how can a smart city decide which analysis and insights are important in real time? Or which part of sensor data can generate actionable insights to improve city services, etc. If there is no automatic data prioritization mechanism , There is no way to implement meaningful analysis and to use and manipulate data reasonably.

  How to overcome:

  Smart cities must build infrastructure based on machine learning and artificial intelligence, which can automatically prioritize data streams and focus on important data so that city officials can notice relevant insights and analysis. For example, an artificial intelligence system can identify the air quality of a city during certain special times of the day. By studying these data in depth to determine the source of pollution, the city can take action to improve air quality.

  The purpose of creating a smart city is to improve efficiency through digital transformation, and to centralize services and operations. However, these efforts are not easy. Proper planning can help cities avoid unnecessary expenses and delays. A good smart city plan should not only use new technologies, but also use existing resources, and should take into account the connectivity of legacy operations when introducing new technologies. While ensuring the protection of data and user privacy, network security should be a concern at all times. With the continuous development and maturity of technology, flexibility should be considered when planning smart cities, and the future development of smart cities should be predicted and considered.