The rol of technology in Smart Cities

5 key concepts to grasp climate change

Learning Objectives

Understand how specific technologies impact smart city development.

Learn about the use of IoT, AI, Big Data and Blockchain technologies in Smart Cities.

10 min read

Course Overview

1. INTRODUCTION
2. HOW TECHNOLOGY IS IMPLEMENTED IN SMART CITIES.
3. STEPS OF TECHNOLOGY VALUE CHAINS IN THE IMPLEMENTATION OF SMART CITIES
      3.1 Technologies for data collection
      3.2 Technologies for data transmission
      3.3 Technologies for data storage and analysis
      3.4 Service provision platform
      3.5 Smart Cities end services
4. TECHNOLOGIES APPLIED IN SMART CITIES
      4.1 Internet of Things (IoT)
      4.2 Big Data
      4.3 Artificial Intelligence
      4.4 Blockchain
5. CONCLUSIONS
6. QUIZES
7. BIBLIOGRAPHY

INTRODUCTION

In the contemporary era, cities have begun to transform their structure and functioning through technological innovation, giving rise to the concept of “smart cities”; These cities of the future not only seek to improve the quality of life of their inhabitants, but also to optimize the management of resources and services by integrating various technologies. In this index, we will address in detail the fundamental components that facilitate the implementation of technology in this emerging urban context.

First, we will explore how technological implementation is carried out in smart cities, analyzing the approaches and strategies that allow municipalities to adopt innovative solutions. Next, we will examine the steps that make up technological value chains, highlighting the key phases from the conception of an idea to its materialization in sustainable and efficient projects. Finally, we will present an overview of the technologies that are applied in these urban environments, including everything from traffic management systems to citizen participation platforms and environmental sustainability.

This index will serve as a guide to understanding how technology not only redefines urban infrastructure, but also promotes social and economic development within a framework of innovation and sustainability.

2. HOW TECHNOLOGY IS IMPLEMENTED IN SMART CITIES

In this module you will learn which different technologies are used in the implementation of Smart Cities and how they are connected to each other to provide valuable information and services for citizens that really improve their quality of life.

To begin with, you need to understand that a Smart City is a complex ecosystem involving numerous technologies and multiple actors that implement, operate and use them. These technologies also face challenges such as scalability, capacity, mobility, and information security and privacy management. Therefore, to fully understand the value chain of the proposed smart city services, it is also necessary to understand what the technology can offer.

The creation of a smart city is much more than the provision of certain services individually (Medina et al. 2021). Deploying a Smart City is associated with the creation of a series of infrastructures, as well as having information management mechanisms and different platforms, all integrated under a global perspective.

In summary, five steps can be defined in what could be called the ‘technological value chain’ of the Smart City (Preukschat, 2017):

● First, there is the stage of collecting data from the city. This is done using sensors, actuators and different devices, including people’s mobile phones, different devices in the home environment, vehicles, as well as measuring devices located in fixed infrastructures such as street furniture, buildings, canal and pipe systems, weather stations and so on.

● Secondly, the data collected from the city is transmitted via communication networks. This is done through a combination of wireless, mobile and fixed infrastructure depending on the mobility, bandwidth and latency requirements of the particular application.

● A third phase comprises data storage and analysis: data collected in the city environment is stored on a central platform while facilitating further processing by different analytical systems. To this end, the information repository must be non-volatile, and the data can be used by applications and services at a later stage.

● Fourthly, the data feeds a Service Delivery Platform. This platform facilitates the provision of services in the Smart City environment and is in turn made up of modules that allow, for example, price management, billing, customer relationship management, etc. In addition, it has interfaces that will be used to implement the services that will be delivered to the end customers.

● Finally, there are the Smart City Services, which may be developed by the same agents involved in the rest of the technological value chain or by other agents, in many cases, the agents already involved in the provision of each specific service in the scope of the city belonging to different sectors and economic spheres.

In the following section of this module, we will go into more detail on each of the different steps of the ‘Technology Value Chain’ and then, in the next section of this module, we will analyse in detail the main technologies used in these value chains.

Ecologists study these relationships among organisms and habitats of many different sizes, ranging from the sudy of microscopic bacteria growing in a fish tank, to the complex interactions between the thousands of plant, animal, and other communities found in a desert.

3. STEPS OF TECHNOLOGY VALUE CHAINS IN THE IMPLEMENTATION OF SMART CITIES

3.1 DATA COLLECTION TECHNOLOGIES

For a Smart City to be able to take the pulse of the city, it needs, first of all, the massive deployment of instrumentation, such as sensors and other data capture devices that allow the collection of information, which will usually be of a very diverse and unstructured nature.

But what are sensors?

Sensors are devices capable of converting physical quantities such as temperature, luminosity, atmospheric pressure, etc. into numerical values that can be processed as appropriate. They come in different types (Bouskela, 2016):

● Resources (electricity, water, gas): in this case they can be divided into two groups according to their function. The first is dedicated to measuring consumption (they act as meters), and on the other hand, those that allow us to know at all times the available reserves of a given resource (leveL sensors).

● Safety: this group includes smoke detectors that produce a certain signal when smoke is present in the air. Gas sensors, on the other hand, generally consist of a physical element that reacts by changing its physical or chemical properties in the presence of a certain gas. This group would also include pollution detection systems that group together a set of sensors dedicated to registering parameters in this sense.

● Illumination: this group of sensors is composed of a photoelectric transducer that is capable of transforming the light it receives into an electrical signal.

● Presence sensors: in this case, there are different types depending on how they detect changes around them: infrared, vibration, photoelectric, ultrasonic or acoustic.

● Climatic conditions: this group includes sensors such as temperature sensors. Other important sensors in this field are humidity and atmospheric pressure sensors.

● Transport infrastructure: this group includes sensors designed to collect information on as many aspects of roads, railways, interchanges, etc. as possible. These include presence sensors (cameras, infrared, etc.), pollution sensors, speed radars and vehicle identification systems, among many others.

● Motion: in this case the sensor is the accelerometer, which measures the forces exerted on it and, together with a gyroscope, provides information on the movement of an object.

● Positioning: this is the electronic compass that provides the direction of the horizontal component of the natural magnetic field, and global positioning systems or GPS.

Although these are the most important sensors, the range is wider and covers most physical quantities. In addition to the above, there are those that monitor water pressure, noise level, turbidity, solar radiation and ultraviolet radiation, among others. On the other hand, to this group we should also add the group of actuators and controllers that facilitate the performance of different actions, the cameras, the sensors, etc.

Most of these sensors have already existed for many years, although nowadays what has taken place is a technological evolution that consists of their digitalisation and subsequent connection to the Internet. Thanks to this, it is possible to make a large amount of information available to the public in real time on different physical variables and thus propose new services within the framework of the Smart City (Tarazona, 2020). In most cases, these sensors have adopted the adjective of smart, as they use information from the surrounding environment as well as information on their own functioning.

The keys to this structure of sensor networks that make up what has come to be known as ‘smart environments’ are the capacity to carry out processing thanks to the microprocessor they have, the capacity to store information in the built-in memory and the ease of sending data thanks to a wireless transmission module.

Currently, there are a multitude of sensor networks whose data can be consulted over the Internet, but the problem is that each network uses its own standards, protocols and data representation formats. It is therefore important to have a platform that helps to manage this

heterogeneity, as discussed in a later section.

It should be noted that in a Smart City project it is especially important that the sensors have the following characteristics:

➢ are easy to install
➢ self-identify,
➢ self-diagnose themselves,
➢ are reliable,
➢ coordinate with other nodes,
➢ incorporate software that enables them to digitally process the signal,
➢ use standard network and control protocols,
➢ have a low power consumption that allows them to be active for a long time and are easy to maintain.

In addition, they must be visually integrated with the environment in which they will be placed, as the urban landscape is an environmental concept that must be protected in the legal system. It is also important that these sensor nodes can be re-programmed wirelessly without the need for an operator to travel. In this respect, the over the air programming (OTA) methodology is often used for maintenance.

Measurers connected to Internet

Another set of technologies that are grouped together at this point in the technology value chain are identification technologies, including RFID (Radio Frequency IDentification) tags. An RFID tag is a small device, like a sticker, that can be attached to or embedded in a product, animal or person. RFID tags contain antennas to enable them to receive and respond to radio frequency requests from an RFID transceiver. The information it contains can be received by a user for interpretation or be interpreted by the endpoint in a way that leads to some kind of action.

This technology is very useful in inventory management, in the secure identification of assets (documentation, equipment, etc.), etc.

RFID Label

BiDi and QR codes should also be mentioned here as elements that contain encoded information and allow extended information on multiple objects and elements to be consulted. These are squares similar to barcodes that contain information that can be accessed by a mobile phone capable of reading them.

Using a BiDi code to consult information

This group also includes smartphones, which act as devices that help in this data capture in the urban environment. In short, these technologies make it possible to ‘feel’ the city’s infrastructures, its vehicles and its inhabitants.

Increasingly, these devices are equipped with more and more sensors, sound, light, acceleration, cameras, etc. that allow information to be collected and sent to the Internet. As users become part of the platform and generate more data, more applications will be developed. Data is already being collected in many areas and is in fact being acted upon in real time. An example of this idea is the WideNoise app (Kyriazopoulou, 2015) that allows measuring noise pollution with a smartphone and sharing it through the network with other users in real time. Another example in this line is its use to get an idea of the concentration of people in each area of the city, as well as the movement that they follow throughout it, is the case of the iPhone application Citizen’s connect in the city of Boston in the United States, which allows citizens to report different types of incidents in the city using the smartphone’s camera.

In this way the resolution of the same can be carried out in a much more agile way. Smartphone and citizen in this case are the city’s sensors. That is to say, any daily activity is susceptible to allowing interaction through one of these devices.

3.2 DATA TRANSMISSION TECHNOLOGIES

Once the data has been collected, it is necessary to facilitate communication, allowing the transmission of the information to central services and storage platforms, or facilitating communication between the smart devices themselves.

Communication networks play a fundamental role in the development and deployment of services associated with Smart Cities as they are the fundamental infrastructures that enable communication between devices, between people and between people and devices. The networks involved in such deployments are very heterogeneous, so interoperability and transparency will be essential (Daneva, M, 2018).

This element of the technological value chain facilitates the rest of the links that make up the Smart City, unified communications regardless of the network standards and communication protocols used. The biggest challenge of these technologies is precisely that of managing the growing, dispersed and heterogeneous number of machines, sensors and actuators distributed throughout the city. In this context, fixed networks will be necessary, which, with their capillarity, will help to offload wireless networks. However, in the field of Smart Cities, wireless networks are the ones that truly help to complete the concept from the point of view of ubiquity. That is why this section focuses especially on them (Daneva, M, 2018).

Currently, there is a multitude of wireless technologies that seek, in each case, to meet the premises of offering sufficient bandwidth, within the necessary radius of action, and with the lowest possible power consumption that allows, given the mobile nature of many devices, to make reasonable use of them.

In any case, communications in the Smart City are usually considered at different levels. In a first proximity network, data is collected from sensors in elements that are usually called repeaters. These, in addition, can sometimes encrypt the data. At a second level, the repeaters send the data to other elements that route the data through the higher level transport network. These elements are called gateways. To communicate these levels, for example, mesh networks (with Zigbee wireless technology, for example) can be used, and then, to connect to the upper transport network, cellular technologies such as GPRS or 3G or, in the case that these gateways are connected to fixed networks, technologies such as ADSL or fibre optics (Monzon, 2015) are usually used (Monzon, 2015).

Practical Example :

All this information can be better understood by using a practical example:

In the case of applications that manage car parks in cities, it is necessary to distribute sensors placed inside a plastic capsule inserted in the asphalt in each parking space, which forms a mesh network of wireless communications. This mesh network is connected through a series of repeaters to a gateway, which sends the data to a central server via the Internet. As can be seen, this example involves several technologies for collecting information and also for transmitting the information.

Communications between devices, also known as machine-to-machine (M2M) communications, which are very common in the smart city environment, are also having a major impact on the development of new wireless networks. For this reason, most standardisation bodies are taking this fact and the specific needs of M2M services into account as a fundamental aspect in the standardisation process for new versions of the technologies.

3.3 TECHNOLOGIES FOR DATA STORAGE AND ANALYSIS

This group includes technologies that facilitate the processing of data, as well as their subsequent homogenisation for storage in large databases. It also includes technologies for data analysis and visualisation. This layer makes it possible, on the one hand, to have all the information necessary to provide services within the framework of the Smart City and, on the other, to be able to improve decision-making processes by analysing data from different parts of the city. It is also about building a unified ‘city’ model that can be used by different Smart City applications and services. Information management also requires certain levels of protection, security and privacy assurance, and this is the layer in which they will have to be provided (Telefónica, 2011).

Data is the fundamental raw material of any service in the Smart City framework. The management of this data is a task that is quite complex as it is usually consumed in real time, it tends to be very varied, it has different formats, it often needs to incorporate geolocation information and it needs to be integrated into a complex data model that ideally represents the entire city. In this context, it is therefore necessary to have tools that facilitate their processing: extraction, homogenisation and storage in easily accessible structures

In this sense, data warehouses are widely known tools in all sectors where large amounts of information need to be stored and processed. In these warehouses, data that are necessary or useful for an organisation are written as an intermediate step to later transform them into useful information for the user. The use of different decision support systems, executive information tools and information visualisation systems will help the subsequent analysis task.

In the case of smart cities, data warehouses must take into account two fundamental characteristics in their design: the handling of large amounts of data in real time and the need for the information to be geolocated. For the latter type of cases, what is called the ‘spatial data warehouse’ is used, which adds precisely this geolocation information to the data. In this case, the geographic component is not an aggregate data, but an additional dimension, in such a way that the entire complexity of the city can be modelled, and through online analytical processing tools, not only a high performance in multidimensional queries but also the results can be spatially visualised: as mentioned, visualisation techniques are especially relevant in the context of the Smart City (Telefónica, 2011).

A layer of analysis and control is therefore necessary to make the most of the data and even to carry out activities to forecast behaviour and situations that help to plan different public policies at the local level. In this sense, data mining techniques are essential. This layer would also include tools that facilitate the monitoring of the most important events that are happening in the city and that help, for example, to detect alarms in real time through notifications. Furthermore, the information will be presented aggregated in different ways and at different levels according to the target audience, trying to make the presentation as intuitive as possible. The aim is to present different visions of the city, depending on the objective of the consultation and the different thematic areas. This module will therefore be fundamental for the definition and monitoring of the objectives and policies that will govern the functioning of the smart city and that will help the city both in its day-to-day management and in its medium and long-term evolution (Ospina, 2013).

3.4 SERVICE PROVISION PLATFORM

The Smart City service provision platform offers a set of modules that are common to the multiple services offered in the smart city framework. It is therefore a horizontal and scalable platform, which allows services to be offered in a secure manner and with privacy guarantees.

This platform will perform the tasks of authenticating users, obtaining permissions to access private data, establishing prices in real time, transaction capabilities for the payment of services, secure data storage, facilities for the analysis of service use, etc. It is therefore the technologies involved that are responsible for providing these capabilities to the other services. This type of platform is called SDP (Service Delivery Platform) and in an urban environment they have come to be known as Urban Operating Systems (Urban OS). They are essential for the construction of a Smart City as they are the ones that integrate the vision of the city, facilitating common and already largely solved tasks to the rest of the services that are the ones that have to provide the added value to the smart city.

3.5 END SERVICES OF SMART CITIES

The final services of the Smart City rely on all the technologies, infrastructures and platforms mentioned above to offer their final value to citizens. There are numerous examples of possible end services, as many as there are public services to be provided by the City Council, but not only. There are also other services that can be provided within the framework of the Smart City platform by other agents that do not necessarily have to be public services but that will become indispensable to ensure both quality of life and sustainability in cities. In this sense, many business opportunities are opening up.

Therefore, talking about technologies in the field of end services becomes a very broad topic because the technologies will be as many and as varied as those used by the sectors that use the Smart City Platform to offer their added value service. Thus, in areas such as the provision of health services, the technologies involved will have to do with systems in the field of health, for example, with sensors that facilitate the monitoring of vital signs, with medical standards such as DICOM for medical images or IHE for communication between information systems, with telemedicine, tele-assistance, etc.

In short, this set of services is part of the Internet of the future in which the use of information and communication technologies is present in all sectors and areas of human activity, making the world more accessible and sustainable.

In the Smart City model, the city is seen as a set of systems that consumes resources to offer a series of services, and in which a suitable technological platform can optimise all processes, providing these services with greater quality and more efficient consumption of these resources.

4. TECHNOLOGIES APPLIED IN SMART CITIES

4.1 INTERNET OF THINGS

4.1.1 What is Internet of Things?

The Internet of Things (IoT) refers to the interconnection of physical devices over the internet, allowing them to collect, exchange and analyse data without direct human intervention. In the context of smart cities, IoT becomes a fundamental component to optimise resource management and improve the quality of life of citizens. This is achieved through the implementation of sensors, monitoring devices and data analysis techniques that enable more informed and efficient decision-making.

Smart cities use IoT to transform urban infrastructure, facilitating connectivity between transport systems, public services, safety, security and the environment. For example, smart traffic lights can adapt in real time to traffic flow, while waste management systems use sensors to optimise collection routes. This constant collection of data not only helps solve immediate problems, but also provides valuable information for the long-term planning and sustainable development of cities.

In addition to optimising urban operations, IoT also empowers citizen participation, allowing inhabitants to interact with the technology around them. Applications that monitor air quality or noise levels empower citizens by providing them with relevant information about their surroundings. Thus, the Internet of Things stands as a fundamental pillar in the construction of smarter, more sustainable and resilient cities, where technology and the community collaborate in search of a better urban future.

4.1.2. Applications of IoT in Smart Cities

This chapter gives a short list of IoT-based applications and services. However, it is only a limited description to understand all the possible new applications and services that IoT could provide:

Smart connected buildings: Improvements in efficiency (energy management and savings) and security (sensors and alarms). Domotic applications including smart sensors and actuators to control household appliances. Health and education services in the home. Remote control of treatments for patients. Cable/satellite services. Energy storage/generation systems. Automatic shutdown of electronics when not in use. Smart thermostats. Smoke detectors and alarms. Access control applications. Smart locks. Sensors embedded in building infrastructure to guide first aiders and assistants. Security for all family members.

Smart cities and transport: Integration of security services. Optimisation of public and private transport. Parking sensors. Intelligent management of parking services and traffic in real time. Intelligent management of traffic lights based on traffic queues. Location of cars that have exceeded their parking time. Smart energy grids. Security (cameras, smart sensors, citizen information). Water management. Park and garden irrigation. Smart waste bins. Pollution and mobility controls. Get immediate feedback and know the opinions of citizens. Smart governance. Voting systems. Accident monitoring, emergency action coordination.

● Education: Linking virtual and physical classrooms for learning, more efficient and accessible e-learning. Access services to virtual libraries and educational portals. Real-time sharing of reports and results. Lifelong learning. Foreign language learning. Attendance management.

● Consumer electronics: Smartphones. Smart TV. Laptops, computers and tablets. Smart refrigerators, washing machines and dryers. Smart home theatre systems. Smart appliances. Sensors for pet collars. Personalisation of the user experience. Stand-alone product operation. Personal locators. Smart glasses.

● Health: Monitoring of chronic diseases. Improving the quality of care and quality of life of patients. Activity Trackers. Remote diagnostics. Connected wristbands. Interactive belts. Sport and fitness activity monitoring. Smart drug tags. Drug use tracking. Biochips. Brain-computer interfaces. Eating habits monitoring.

● Automotive: Smart Cars. Traffic control. Advance information on what’s broken. Wireless monitoring of car tyre pressure. Smart energy management and control. Self-diagnostics. Accelerometers. Position, presence and proximity sensors. Analysis of the best way to go in real time to a site. GPS location. Vehicle speed control. Autonomous vehicles using IoT services.

● Agriculture and environment: Measurement and control of environmental pollution (CO2, noise, pollutants present in the environment). Forecasting climatic changes based on the monitoring of smart sensors. Passive RFID tags associated with agricultural products. Sensors on product pallets. Waste management. Nutrition calculations.

● Energy services: Accurate data on energy consumption. Smart metering. Smart grids. Analysis and prediction of energy consumption behaviours and patterns. Forecasting future energy trends and needs. Wireless sensor networks. Energy production and recycling.

● Smart connectivity: Data management and service delivery. The use of social media and social networks. Access to e-mail, voice and video services. Interactive group communication. Real-time streaming. Interactive games. Augmented reality. Network security monitoring. Available user interfaces. Affective computing. Biometric authentication methods. Consumer telematics. M2M communication services. Big data analytics. Virtual reality. Cloud computing services. Ubiquitous computing. Computer vision. Smart antennas.

● Manufacturing: Gas and flow sensors. Intelligent sensors for humidity, temperature, motion, force, load, leakage and levels. Machine vision. Acoustic and vibration detection. Composite applications. Intelligent robot control. Control and optimisation of manufacturing processes. Pattern recognition. Automatic learning. Predictive analytics. Mobile logistics. Warehouse management. Preventing overproduction. Efficient logistics.

● Shopping: Smart shopping. RFID and other electronic tags and readers. Barcodes in retail. Inventories. Control of the geographical origin of food and products. Food quality and safety control.

4.2 BIG DATA

4.2.1. What is Big Data?

It is a technology that works with a large volume of data. Through this technology, various information can be analysed in order to, for example, improve the services offered in a city or to help decision-makers make better decisions and better strategic moves for the city. This data has three main characteristics:

➢ Extreme volume of data. Data can come from a variety of sources, from e.g. sales records to sensors used in IoT technologies, and can be raw or pre-processed.

➢ Variety of data types: there can be a wide variety of data file types. They can be structured, e.g. SQL databases; unstructured data, e.g. information received from sensors; or unstructured.

➢ Data processing speed: measures the time it takes to enter all the data from the different sources. In the process, the data is analysed, correlated with each other and sorted in a specific way (depending on the business of the application to be implemented).

However, the most important part in the development of this technology is not the data storage or the data itself, but what is done with all of it and what is achieved through its processing. There is no point in having a large amount of well-structured, high-quality data if you don’t have human operators to understand it and make the right queries to manage a Big Data project.

4.2.2. The dimension of Big Data

IDC defines Big Data as a new generation of technologies and architectures designed to extract economic value from large volumes of a wide variety of data, through the ability to capture, discover and/or analyse it at high velocity.

This definition encompasses hardware, software and services for data integration, orchestration, management, analysis and presentation characterised by the four Vs: Volume, Variety, Velocity and Value.

According to IBM, Big Data solutions are distinguished from traditional ICT solutions by four dimensions:

● Volume: Big Data solutions must manage and process much larger amounts of data.

● Velocity: Big Data solutions must process data that arrives at a faster rate.

● Variety: Big Data solutions must handle more types of data, both structured and unstructured.

● Veracity: Big Data solutions must validate the correctness of the large amount of data arriving at high velocity.

As a result, Big Data solutions are characterised by complex real-time processing and data correlation, and advanced analytics and search capabilities. These solutions emphasise the flow of data, and move analytics from research centres to the key processes and functions of organisations.

But let’s look at these dimensions of Big Data from the perspective of what this technology can bring to Smart Cities:

● Volume: In the context of Smart Cities, the volume of data is colossal, as a wide range of devices and sensors are continuously generating information. From monitoring traffic and energy consumption to air quality and public services, smart cities generate terabytes of data on a daily basis. This vast amount of information allows local governments to analyse patterns and trends on a large scale, facilitating informed decision-making and improving urban planning.

● Velocity: The speed at which data is generated and processed in Smart Cities is crucial. Data from sensors and IoT devices is collected in real time, enabling cities to respond quickly to unexpected situations, such as traffic accidents or environmental emergencies. The ability to process and analyse this data at high speed is critical to maintain the flow of services in optimal conditions, thereby optimising the operation of critical infrastructure and improving the user experience.

● Variety: The variety of data in Smart Cities encompasses not only structured data from conventional databases, but also unstructured data from social networks, surveillance videos, weather information, and more. This diversity of data sources enriches the analysis and provides a more complete view of urban dynamics. By integrating different types of data, cities can create more innovative and effective solutions, such as smart transport systems that adapt to traffic and weather conditions.

● Veracity: Data veracity in Smart Cities management is essential to ensure that decisions made are based on accurate and reliable information. Data validation involves implementing quality control mechanisms to ensure that the information collected from various sources is accurate and relevant. This is particularly important for critical decision-making and can influence public policy, as erroneous data could lead to ineffective or even harmful solutions being implemented for the community.

4.2.3 Big Data: Beyond technology, transformation

As we have seen, having the right contextual information for decision-making is essential to improve the management of the city and the quality of life of its citizens. However, the implementation of Big Data solutions goes beyond technology and its dimensions of Volume, Velocity, Variety and Veracity; it involves a major transformation that requires operational and organisational changes; and all aligned with the strategic objectives of creating value for the city.

Every city is different and their strategic objectives vary from city to city. However, most share similar problems and challenges, with the main focuses being traffic and public transport, public safety and crime reduction, energy management, the integrated water cycle and urban waste management. At the same time, in addition to managing the daily activity of the city, they pursue economic development objectives by creating or attracting economic activity to expand and improve their business fabric, which is currently very much oriented towards job creation. And many face these great challenges with old and obsolete technological infrastructures, information silos and very bureaucratic processes in which there is no collaboration between the different departments and bodies, lack of common objectives aligned for the whole city, which greatly complicates undertaking new initiatives of global value for the city.

The Smart City concept can help to structure a comprehensive approach to respond to these major challenges. In the definitions we saw earlier, the Smart City is a solution that, with the support of technology, embraces city transformation in a sustainable and scalable way, with openness to citizens and businesses and with transparency in management.

One of the main levers of sustainable growth is to implement a culture of innovation and encourage the collaboration and involvement of citizens and businesses in the daily problems of the city and in the search for and implementation of solutions. Citizens, with society’s current level of access to technology, are the main ‘sensors’ that city managers currently have at their disposal.

IBM has conducted a very interesting and revealing study, which concludes that the most successful companies systematically apply data analytics initiatives throughout their organisation to make more informed and intelligent decisions, act faster and optimise results.

But beyond the technology, there is a fundamental question to answer: how can organisations monetise their analytics investments by leveraging the existing and rapidly growing amount of data? The IBM study concludes that there is a need for proper co-ordination between strategy, technology and organisational structure.

Analytics implementation strategies must help deliver key business objectives; existing technology must support the analytics strategy; and the culture of the organisation must evolve for people to embrace the technology. The right coordination between these three key dimensions is necessary to generate tangible results.

IBM identifies nine levers that enable organisations to generate value from an ever-growing volume of data from a variety of sources; value that results from the knowledge that has been generated and the actions taken at all levels of the organisation.

These nine levers represent the skill sets that most differentiated leaders from other respondents:

● Culture: availability and use of data and analytics in the organisation 3 Data: structure and formality of the organisation’s data governance processes and the security of its data

● Knowledge: development of, and access to, data management and analytics competencies and capabilities

● Funding: financial rigour of the analytics funding process

● Measurement: assessment of impact on business outcomes

● Platform: integrated capabilities provided by hardware and software

● Value source: actions and decisions that drive results

● Sponsorship: support and involvement of management

● Trust: management confidence

The conclusions of this study are fully applicable to municipal corporations and their entire ecosystem of companies and organisations, which must align their strategic objectives, technology and organisational structure; beyond electoral cycles that would not make the implementation and adoption of a true culture of innovation viable.

4.3 ARTIFICIAL INTELLIGENCE

Artificial Intelligence (AI) offers a wide range of possibilities and is widely used to improve the lives of citizens in so-called Smart Cities. We can say that AI makes it possible to automate and optimize the different processes and services offered to citizens in cities.

But before going into the different applications that AI has within cities, let’s explain in more detail what Artificial Intelligence is.

4.3.1 What is artificial intelligence?

Artificial Intelligence refers to the development of computer solutions that are capable of carrying out activities and tasks that, traditionally, have been solely attributable to humans; and that therefore require human intelligence.

The European Commission defines it as software systems (and possibly also hardware) designed by humans that, faced with a complex objective, act in the physical or digital dimension:

● Perceiving their environment, through the acquisition and interpretation of structured or unstructured data.

● Reasoning about knowledge, processing the information derived from this data and deciding on the best actions to achieve the given objective.

AI systems can use symbolic rules or learn a numerical model. They can also adapt their behavior by analyzing how the environment is affected by their previous actions.

Among the actions that have traditionally been considered as only feasible by human intelligence, and that Artificial Intelligence is now capable of performing, we find the following:

Machine learning: This is the use of AI that is dedicated to self-learning to improve predictions based on the information provided, without the need to program the software to learn each specific task.

Generative AI: Generative AI, rather than being focused on learning and prediction, is based on the creation of new text, image, audio or video content that is relevant and useful based on the premises provided by the user. Generative AI can devise new solutions to conflicts or generate artistic creations autonomously.

Natural Language Processing: Natural language processing refers to the ability of a computer system to acquire verbal and everyday language skills from people, communicating with the user through this language. An example of this could be the most advanced voice assistants.

Computer vision: It is an AI that simulates the human eye, being able to interpret the content of images and videos.

Cognitive computing: It imitates human reasoning in complex scenarios where there are no precise and concrete answers.

Robotics and Autonomous Systems: combination of the use of software based on Artificial Intelligence with hardware that allows it to be located in physical space and allows it to perform manual or movement tasks, identifying its environment and acting on it autonomously.

If you want to learn more about artificial intelligence, understand how it works and what are the different elements that make up artificial intelligence, we recommend taking the following free course, designed by the University of Helsinki: Elements of AI

4.3.2. Applications of Artificial Intelligence in the development and deepening of Smart Cities

The main function of AI in smart cities is based on its ability to collect, process and make sense of a huge amount of data that is collected through different devices such as sensors, cameras, location devices, etc.

AI can improve cities in different ways:

● Simulate complex urban systems.

A simulation of urban systems is of great value because it allows different public policies to be tested and experimented with. For example, if a city is considering introducing new regulations to reduce pollution, artificial intelligence can model how this measure would affect air quality, traffic behaviour and the health of citizens. This allows decision-makers to evaluate different scenarios and choose the most effective option. Artificial intelligence applied to Smart Cities allows simulations of complex urban systems to be carried out, improving the understanding, planning and management of cities.

● Making cities safer.

The applications of AI in improving the security of our cities are multiple and very effective. To start with, AI can be used in video surveillance cameras. This technology is capable of automatically detecting potential threats, as well as monitoring movements and social behavior. At the same time, precise identification systems make it possible to identify people through biometric systems that, for example, can perform facial recognition. This is especially useful when authorities are looking for a person with criminal behavior.

● More efficient use of city resources.

AI has a fundamental role to play in optimising the management of different resources in cities, such as energy, water and waste. AI can make highly optimised decisions based on the analysis of large amounts of information in the elaboration of predictions that allow adjusting supply to demand in a very precise and constant way. In addition, AI is able to control energy consumption patterns in smart buildings in cities, anticipating their consumption, and thus ensuring a stable, efficient and low fossil energy consuming electricity grid.

With regard to drinking water, a basic but increasingly scarce commodity, AI, in conjunction with different sensors, is able, while predicting atmospheric phenomena (episodes of heavy rainfall, droughts, etc.), to optimise water flow and storage capacity in order to provide greater robustness and reliability to cities’ drinking water supply. AI is also capable of detecting water leaks, preventing waste and optimising water resources. Machine learning and robotics and autonomous systems are specific AI technologies capable of performing these actions.

● Traffic and public transport management.

Directing traffic based on street congestion, accurately predicting the departure and arrival times of buses at different urban stops, identifying the most polluting cars in episodes of high air pollution, helping in the prevention of traffic accidents and, in the future, driverless vehicles, are just some of the functionalities that artificial intelligence can have in the field of urban mobility.

● Intelligent Street lighting.

Increasing the energy efficiency of cities through smart street lighting management is one of the most illustrative AI applications. Adapting the light intensity of street lighting based on natural daylight, switching off lights when there are no pedestrians or vehicles passing by specific locations, and even using street lighting as a warning tool or to increase safety, can be done through the use of Artificial Intelligence, more specifically with technologies such as computer vision and cognitive computing.

● Personalised and advanced services for citizens.

Integrating Artificial Intelligence (AI) into citizen engagement helps make government more inclusive. AI and Large Language Models (LLM) allow more people to access government services and make it easier to analyse citizens opinions efficiently. This shows how AI can help to better understand community priorities using social network data.

By establishing clear methods for obtaining information and using AI to analyse the data, governments can ensure that their actions are aligned with what the community really needs. This approach highlights the importance of AI in improving communication between citizens and government, ensuring that policies and services reflect the diverse views of the people they are intended to serve.

● More efficient public administrations.

Generative Artificial Intelligence, together with natural language processing, can be of great use among municipal administrators because it streamlines workflows and improves efficiency. A multitude of routine tasks can be automated. AI is also capable of transforming raw, messy data into high-value information with cross-referenced and analysed data. For example, it can analyse emails and summarise them; it can incorporate interactive graphs and maps that extract information from dozens of emails into a document at a glance.

4.3.4 Ethical considerations when using AI in cities.

The use of AI in cities allows the accumulation and analysis of large amounts of citizen data and information, which can be used for purposes of questionable ethics, such as greater control of the population. In addition, strict security protocols will be required to avoid possible information theft, as Artificial Intelligence will have a record of very sensitive data on citizens, such as biometric data, continuous geolocation, etc.

At the same time, artificial intelligence is not free from possible biases, the accumulation of which can lead to unfair decisions in areas such as public safety, resource distribution or urban planning. It should be borne in mind that Artificial Intelligence is fed by all the content on the Internet; and we all know that all kinds of positions exist on the Internet, including those that are discriminatory or that criminalise vulnerable groups, regions, etc. While it is true that all this misinformation can influence AI, there are more and more firewalls and protections against these contributions that contaminate the content generated through these systems.

At the heart of the ethics of the use of Artificial Intelligence, authorities must place privacy, security, fairness and transparency; this is the only way to ensure that this technology generates sufficient trust so that all citizens can benefit from it.

To conclude this section, we recommend that you take a look at the following video, produced by the BBC, where, in a very illustrative way, it speculates what the city of the future could be like with the deep implementation of Artificial Intelligence:

● How will artificial intelligence change the cities we live in? | BBC Ideas

In addition, in the following video you can learn about different examples of different cities around the world that have made use of Artificial Intelligence with specific and different objectives between them:

● How Are Cities Around the World Utilizing Artificial Intelligence? – YouTube

4.4 BLOCKCHAIN

Today, more than half of the worlD’s population lives in cities and many cities face major challenges in managing rapid urbanisation. These challenges include helping the growing population overcome the environmental impact of urban sprawl and reducing vulnerability to natural, man-made or epidemiological disasters, such as the COVID-19 pandemic.

But before we continue, let’s explain in a simple way what the Blockchain is:

4.4.1. What is Blockchain?

Imagine the blockchain as a giant ledger that many people can see and use, but no one can erase or change. In this ledger, every time someone makes a transaction, such as buying something or lending money, a new page is written.

Now, imagine that each page is connected to the previous and the next. So, if someone wanted to cheat and change a page, they would have to change all the previous pages and that would be very difficult. Also, this book is not kept in one place, but many people have copies of the same book. This means that everyone can see that what is written is correct and no one can easily cheat.

In short, the blockchain is a secure and transparent system that helps people keep track of what happens without needing an intermediary, such as a bank. It is like a ledger that everyone can see, but no one can change, ensuring that everything is fair and true.

In addition, cities face challenges that include economic inequality, poverty, unemployment, more environmental conditions, high levels of greenhouse gas emissions.

As a result of population growth coupled with the expansion of production and manufacturing, cities will consume significant resources and require more efficient and sustainable services. Without the provision of such services to cities in a more controlled manner, urban areas and surrounding environments will suffer, hindering the potential for cities to drive growth, innovation and prosperity for themselves and the country as a whole.

The advancement of technology is a key part of addressing these challenges for cities. Its integration into the city will help to make it more efficient, greener and more socially inclusive.

If you need more information about what is a Blockchain, you can watch this YouTube video:

4.4.2. Blockchain’s contribution to smart cities

Blockchain technology is seen as a tool to drive data transparency and traceability in smart cities.

As a distributed infrastructure, blokchain technology can serve as a suitable means to manage the growing networks emanating from smart cities in terms of monitoring supply chains, executing and validating data trails, as well as ensuring data authenticity and integrity.

The blokchain technology through a secure and transparent infrastructure promises an immutable and traceable exchange ofconfidential data and proprietary values, not only between people but also between machines. As a result, blokchain technology is increasingly capturing the attention of businesses as well as public institutions.

Cities can use blokchain to create a secure, shared registry to manage real-time data in transport, energy and utilities.

Implementing the technology can help cities optimise the way they interact with citizens, reduce resource consumption and share public data with authorised third parties.

In addition, the infrastructure of the future will demand high security standards to reliably guarantee the required degree of networking, automation, decentralisation and participation. These requirements are aligned with Sustainable Development Goal (SDG) 11: Make cities and communities inclusive, safe, resilient and sustainable.

The advantages of blokchain technology compared to the objectives that define smart cities, it could be confirmed that blokchain technology presents the following common objectives: transparency, immutability, traceability, savings, efficiency, security and privacy, distributed network and technology.

The ever-increasing demand for transparency by citizens can find support in blockchain technology given that, as discussed in previous chapters, transparency is an intrinsic feature of this technology, in addition to the immutability of data once it has been validated.

Likewise, the distributed nature of this technology makes the network secure as it does not depend on central nodes and, together with the possibility of anonymity in the network, satisfies citizens’ needs for data security and privacy, which are increasingly in demand in this information society.

This distributed network and equal privilege model reinforces citizen participation and promotes the idea of a transversal system, as opposed to the traditional verticality of city services.

On the other hand, the Peer to Peer (P2P) system that characterises blokchain technology, by dispensing with intermediaries, means greater efficiency in processes, as well as cost and time savings. Efficiency is a key factor in achieving a smart city.

Furthermore, there is a link between blokchain and sustainable development goals. The implementation of this disruptive technology can contribute to achieving sustainable development objectives within the framework of the smart city, as we will see in the next section with the case studies.

In short, the advance of technologies such as blokchain, studied in detail and with precision, is a key element in achieving resilient cities, prepared to face the changes and situations that may arise in the best possible way.

The following are some possible uses of the blokchain in different areas of the city:

● Renewable energy: streamlines transactions between agents certifies renewable origin and allows the user to produce and transfer renewable energy.

● Government institutions: real-time information provision and transparency.

● Electronic voting: secures signatures and prevents hacking.

● Internet of things: household appliances can make purchases on their own.

● Food; ensures traceability.

● Financial transactions: more agile and cheaper.

● Privacy of databases: in healthcare, security and tourism, etc.

● Smart contracts or smart contracts: self-executing when both parties comply with what they have signed and automatic reimbursement is guaranteed in the event of non-compliance.

5. CONCLUSIONS

This module has been designed to provide a comprehensive understanding of the key technologies that make up the Smart Cities ecosystem. Throughout the content, the importance of an advanced technological infrastructure for data collection, transmission, storage and analysis has been highlighted. Technologies such as the Internet of Things (IoT), Big Data, Artificial Intelligence and Blockchain are not only innovative tools in urban development, but also act as drivers of transformation that allow cities to address contemporary challenges such as sustainability, efficiency in the use of resources and improvement in the quality of life of citizens.

The effective use of these technologies allows for a more informed and dynamic management of cities. Through the interconnection of devices, the capture and analysis of vast volumes of data and the applicability of artificial intelligence algorithms, Smart Cities can optimise public services, manage traffic efficiently, ensure safety and promote citizen participation. Moreover, the interdependence of these technologies reinforces the idea that a smart city is not just a set of technological solutions, but a complex system that requires a collaborative approach between various actors, including governments, the private sector and the community.

Finally, it is crucial to recognise that the implementation of these technologies also comes with significant challenges, such as privacy management, data security and equity of access to technology. The key to the success of Smart Cities lies in not only adopting advanced technologies, but doing so from an ethical and sustainable perspective, including citizens in the decision-making process and ensuring that the benefits are distributed equitably. As cities continue to evolve, a commitment to collaboration and the use of accurate data will be critical to building a more resilient urban future that is tailored to the needs of its inhabitants.

KEY TERMS - GLOSSARY

#ArtificialIntelligente #AI #SmartCities #Technologies #BigData #BlockChain #IoT

QUIZES

Quiz 1

Quiz 2

Quiz 3

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