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With the evolution of wireless technologies such as 5G, cloud technologies, and the Internet of Things (IoT), the opportunity to better the quality of life of citizens is greater than ever before. Major cities across the world are already innovating on smart city technologies for urbanization, government operations, energy efficiency, citizen security, and more.

Building a smart city could be a mammoth project given that the project intersects complex bodies including the government, technology, processes, private citizens, policy advocates, publicly available data, network, and the city’s nature-imposed limitations such as its geographical location, population, and climate. Cities that have successfully implemented smart city technologies have shared their experience and methodologies for use by others. However, there is no silver bullet for the successful planning and implementation of smart city projects. Nevertheless, certain protocols aid in planning and building smart cities. This article will focus on such an approach through a six-step strategy to building smart cities.

Build A Strong Business Case And Identify Use Cases For Smart City Implementation

Any digital initiative without a strong quantifiable business case has the weakness of being abandoned at some point. This is true for Smart City projects too. As there can be complex steps involved in implementing a highly connected city and challenges posed by local laws, security requirements, government workforce, and civilian response to disrupting changes, a smart city project always poses threats of abandonment if the project was not laid on top of a strong business case.

Hence the first step toward building the smart city should be to build a strong and quantifiable business case. The business case should address the resource limitations, or the challenges faced by the administration of the city – for example, a city that is short on water resources might want to manage the resources effectively, a city that is highly urbanized with a significant population density might want to effectively manage the parking facilities, a city that hosts events frequently might want to address the security of the event places and the civilians.

Review And Create Policies Towards Data Sharing

The next step toward building a smart city should be to review the data-sharing policies that correspond to the geographical location of the city. This step involves legal activities toward creating and reviewing policies around data sharing by different entities in the city. Data is at the front and center of any digitalization service including building smart cities. Several entities in a city generate sufficient data that critically informs the planning and implementation of smart cities. Entities such as energy services providers, commutation service providers, security service providers can share non-PII data with government bodies. However, such data sharing should be legally approved and directed in the policies. Without the data sharing policies in place, it can lead to confusion when the city plans to implement smart city projects. The policies should clearly define the roles of entities such as data creator, data provider, data processor, data consumer, and more as applicable. The policies should also define the purpose of each entity in the data-sharing network.

Data sharing policies should be elaborate and specific in defining several aspects including what data is being collected from civilians or enterprises, for what purposes the collected data is being used, where is the data stored and for how long, what happens to the data when it expires, what choices do civilians have in electing to share their data, is the data anonymized not exposing the identity of the civilians, how is the data secured and more.

Data sharing policies should also address privacy-preserving techniques so that the data cannot be traced back to its origin. This may include approaches like homomorphic encryption, adding white noise to the datasets that mask the source from which the data was obtained, and more.

Define Data Exchange Standards And API’fy Data Request Processing

The next critical step in smart city projects is to define standards for data exchange between different entities. Defining data exchange standards aids the automation of data integration practices. For example, when a ridesharing services provider shares their trip data, standards can define a consistent way of formatting the date and location information. When data exchange standards are established, entities participating in building the smart cities can immensely benefit from common software libraries that can be used to ingest and transform data.

While there are data protocols established for low-power devices in the IoT such as MQTT (Message Queuing Telemetry Protocol), CoAP (Constrained Application Protocol), DDS (Data Distribution Service), the standards being referred to here are the ones for large-scale data sharing between different entities in the smart city ecosystem that help integrate data and analyze them for insights into the design of the smart city itself.

Integrate Data From Multiple Sources

Data such as hotspots for energy consumption, commutation routes with high frequency, high-definition video footage from traffic monitoring systems, sensors relaying information around real-time availability of parking spots, water consumption statistics by time of the day can be of immense value feeding into the smart city planning. Once the policies have been created to share data across the providers, then the primary focus in planning a smart city should be centered around data integration. The Big Data analytics and IoT technologies can be leveraged to integrate data from various sources including government agencies, corporate service providers, residential complex owners, commutation service providers such as ridesharing and bike-sharing, security services providers, and private citizen smart homes.

Major Cloud Service Providers such as AWS, Azure, GCP, and Snowflake have revolutionized the approaches for data sharing across different entities. Depending on the use-case, the volume of data, frequency of sharing, efficient technologies such as AWS Athena, Google BigQuery, Azure Synapse, Snowflake sharing enables no-copy data sharing approaches. Integrating data from IoT devices to the cloud can be accomplished using various services provided by the platform including AWS Kinesis Data Streams from Amazon, Azure Stream Analytics from Azure, Pubsub from Google Cloud.

Build The Infrastructure

While Data is the foundation of any digital initiative, network infrastructure is the backbone of smart city initiatives. When it comes to IoT Infrastructure, there is a set of physical requirements which is laid through telecommunications network and digital requirements such as latency of the devices (Things in IoT) to send data to the central hub. For the telecommunications network, few choices are available among LoRaWAN (Low Range Wide Area Network), NB-IoT (Narrowband IoT Network), 5G, Wi-Fi, BLE, Zigbee. While choosing the right network technology, considerations must be around the type of sensors and their installment, maintenance of the devices, the required lifespan of the devices without a battery change, and more. Also focus should be on the frequency of the data transmission from the sensors to the IoT hub. While almost everything on the field can be captured by sensors including weather, road surface temperature, high-definition traffic video footage, water leakage, air quality, water quality, soil moisture, civilian movement, automobile movement, the requirements for the sensors to transmit data to the IoT hub might vary significantly. For

Sensors, Data Capture, And Automation

In the most common architecture, an IoT platform can have three layers – The perception layer, Network layer, and Processing layer. The Perception layer represents the hardware devices or “things”. These “things” are usually electronic sensors and Actuators. Sensors capture signals from the real world, convert them into machine understandable digital formats, feed them into the IoT control center. If Sensors are the entry point of an IoT process, Actuators can be considered as the result or outcome of the IoT process. Actuators receive electrical input and convert that into a physical action on the target device. The Network layer comprises of the data transmission networks and the gateways.

Conclusion

Smart City Architectures are indeed local, and hence there is no global reference architecture to build upon. A city that successfully implemented water resource monitoring might have used technologies and approaches that custom fit the use case. Hence the choice of architecture, standards, and protocols for another city that wants to implement an intelligent traffic routing system that involves transmitting high-definition video footage for analytics and real-time response might be significantly different. While the architecture heavily depends on the use case, the recipe for planning and building the smart cities can be dealt with using common approaches as mentioned in the article. The most important factor in implementing a smart city is that the program should address the real problem in the city faced by its civilians or authorities that manage the city’s physical infrastructure. The technology will enable the transformation that the city aspires through the recipe outlined in this article.

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Autor(en)/Author(s): Kumarasenthil Muthuvel

Quelle/Source: Field Technologies Online, 04.04.2022

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