Solving Smart City Challenges In 2020

A smart city achieves these benefits and improves city operations using a network of IoT sensors, big data analytics, smart mobility services, and more. In this article, you will learn about crucial smart city challenges as well as possible solutions to overcome them.

1. The burden of sensor infrastructure

The challenge

Smart cities use sensors to collect information, analyzing it with big data and AI technologies to improve quality of life. The data can include live traffic information, air quality, health data from across the city, etc.

This sensor infrastructure represents a heavy investment and a major operational burden. Cities must consider how they will be powered – hard-wired, solar, or battery – and which city department and budget will be responsible for their installation and maintenance.

Major cities are already spending a large percentage of their budgets on legacy infrastructure such as underground cables, tunnels and internet connectivity. Funding for new infrastructure like sensors is limited and projects can take years to deploy.

What’s more, installing sensors is not an end to itself – it is a long term investment that will take time to affect the lives of city residents.

How to overcome

Smart city planners should consider the infrastructure challenge from the very beginning. Planning for smart city infrastructure and raising special funding from smart city organizations and government programs can be a great start.

Furthermore, in many cases cities started collecting data using existing infrastructure, such as bus system ticketing, existing CCTV and legacy traffic monitoring systems. This is an alternative for cities where funding or resources are limited.

2. The connectivity problem

The challenge

Smart cities need to provide strong connectivity for residents and visitors to support economic development and enable connected city services. However, even in large Western cities, achieving a good level of connectivity is far from trivial. There are three elements involved:

• Connectivity operators, who need to provide sufficient coverage and capacity for different regions of the city.
• Venue owners and businesses who host connectivity equipment or run their own private equipment.
• Municipal governments, who need to cooperate with operators and private businesses to ensure there is sufficient coverage across the city.

In a dense urban environment, Radio Frequency (RF) signals are often impeded by structures and certain building materials. Green buildings sometimes have specific requirements that can conflict with the need for RF coverage.

Cities need an energy-efficient, scalable and effective solution that supports the need of human mobile users and also connectivity requirements of IoT and Machine to Machine (M2M) data transfers.

How to overcome

One technological approach is intelligent digital distributed antenna systems (idDAS). This approach is a network topology that supports multiple connectivity requirements in smart cities.

Digital DAS is cost-effective, energy-efficient and can provide a good level of coverage in almost any urban environment. DAS is also able to support 5G technologies, for cities preparing for the next generation of mobile connectivity.

3. Safe use of cloud computing in a smart city

The challenge

Smart cities must rely on cloud computing technology to host data and operational services, share data with stakeholders, and provide broad access to residents. At the same time, city services manage large volumes of highly sensitive data, much of it subject to privacy concerns and regulations, which may not be appropriate for storage on a public cloud.

Most cities adopt a hybrid cloud strategy, with some systems hosted on-premises or in a private cloud architecture, and some on the public cloud. But this raises multiple operational and security challenges, which are difficult to handle given the limited IT resources available to cities.

How to overcome

In 2019, the Smart City and Community Challenge group released a blueprint that can help smart cities and communities adopt a secure cloud architecture that supports confidentiality and protect personally identifiable information (PII) based on hybrid cloud concepts. 

The blueprint can help smart cities adopt mechanisms to coordinate cloud services, including cloud backups for disaster recovery. Its recommendations are based on the National Institute of Standards and Technology (NIST) Cybersecurity Framework, and contain steps to help cities limit risks to the confidentiality, integrity and availability of data. Following the blueprint can help cities adopt a practical, secure cloud architecture without needing to “reinvent the wheel”.

The recommendations include a three-level data classification scheme for data risk, which can help build a hybrid cloud or multi-cloud architecture. The three tiers are:

• Red, for highly sensitive data like PII
• Yellow, for data that can be shared more widely
• Green, for low sensitivity data that can be shared openly

Based on data classification, smart city officials can determine legal and regulatory requirements, security policies, and data storage and collection practices to protect privacy and security.

4. Efficient data processing and analytics

The challenge

A smart city needs to be able to efficiently collect and analyze exponentially growing volumes of IoT data. The data comes in many formats, from log data to environmental sensor readings, to rich media like CCTV video recordings.

A smart city is only effective if it can process its data and extract insights from it on an ongoing basis, often in real time. This requires a robust infrastructure that can store the data and perform the required analysis in an automated manner.

One of the biggest challenges is prioritizing the data. How can a smart city determine, in real time, which of tens of thousands of hours of camera footage is critical for analysis? Or what part of its sensor data can yield actionable insights that can improve city services?

Without automated mechanisms for prioritizing data, it will be impossible to apply meaningful analysis and make operational use of the data.

How to overcome

Smart cities must build infrastructure based on machine learning and AI that can automatically prioritize data streams and focus on the data that matters, bringing the relevant insights to the attention of city officials.

For example, an AI system can identify parts of the city in which air quality is especially problematic at certain times of day. By drilling down on this data and identifying the sources of pollution, the city can act and take specific action to improve air quality, which will be felt throughout the city.

Conclusion

The purpose of creating a smart city is to apply digital transformation in a way that drives efficiency and centralize services and operations. However, these efforts could go wrong at any step of the way.

This is why planning is a crucial step for any smart city in the making. Proper planning can help cities avoid unnecessary overhead and delays.

A good smart city plan should not only employ new tech, but also leverage existing resources. New tech should be introduced with connectivity to legacy operations in mind. Cybersecurity should be a concern during every step, while ensuring the protection of data and privacy of users.

As technologies continually change and mature, smart cities should be planned with flexibility in mind, predicting and allowing for the future evolution of the smart city.