Role of IoT and ML In Smart Cities


The world’s population is growing at an unprecedented rate today and not only does half of the population lives in urban areas, but it is also estimated to rise by 50% by the year 2050. The amount of population living in these megapolises thus puts an enormous strain on the environment which needs to be managed smartly and thankfully, smart technologies like Internet of things (IoT) combined with machine learning (ML) have the potential to tame the pressures of urbanization by creating new and smarter experiences for making day-to-day living more comfortable.

The concept of IoT has always been considered the key infrastructure in smart cities since its introduction and in this article, we aim to explain the role that IoT and ML play in smart cities. But, before moving forward, let us understand the concept of smart cities briefly.

What Is a Smart City?

A smart city can be understood as a framework composed of information and communication technologies to develop, deploy, and ultimately promote sustainable development practices to effectively manage cities and to address their growing urbanization challenges smartly.

A big part of this framework is an intelligent network of interconnected objects that transmit data using wireless technologies like gadgets and the cloud.

The IoT applications receive, manage, and analyze real-time data using machine learning to help municipalities, enterprises, citizens, and cities at large to make better decisions that can improve the quality of life.

Let us now move forward to discussing various use cases of IoT and ML in smart cities to understand how these technologies transform cities and life.

Applications of IoT and ML in Smart Cities

Smart Traffic Management

Smart cities ensure that their citizens travel as safely and efficiently as possible without a waste of time and fuel and, to achieve this, they turn to implement smart traffic solutions.

These traffic solutions use different types of sensors to determine the number, location, speed and other relevant information regarding the vehicles.

The working of these systems is as follows: the road-surface sensors and CCTV cameras send real-time traffic updates to a central traffic management platform. This platform then analyses the data using machine learning and updates the user about congestion and other related information. Additionally, these platforms also analyse the historical data using machine learning to predict the rush hours well in advance.

In addition to providing updates regarding congestion, these systems also help to adjust traffic lights by reacting to changing traffic conditions in real-time.

An example of a city that has successfully implemented this solution is Los Angeles.

Street Lighting

IoT based smart cities try to make the maintenance and control of street lamps cost-effective. They achieve this by equipping the streetlights with sensors and connecting them to a cloud management system.

The sensors help to gather relevant data on illuminance, movement of people and vehicles and public transport schedule, time of the day, year, and so on. The real-time data then combined with historical data is fed to the machine learning algorithms which help to analyze different situations and the amount of light required in each case. As a result, the smart lighting solution commands the streetlight to brighten, dim, turn off or on based on the physical environmental conditions thereby improving the overall lighting schedules.

Now, for instance, when pedestrians or vehicles cross a particular location, the lights around that crossing switch to a brighter setting.

Various cities like Miami, Paris, Madrid, etc. have successfully implemented smart streetlighting solutions with Miami being at the top with over 500,000 connected streetlights.

Public Safety Management

Public safety is one of the major concerns for urban cities and for enhancing security in these cities, the use of IoT based technologies that offer real-time monitoring, analytics and decision- making tools is made.

The working of these systems is as follows: these devices collect data from acoustic sensors and CCTV cameras deployed throughout the city and combine this data to predict potential crime scenes and deploy public safety solutions, if required, quickly. These systems thus, allow the security personnel to stop potential perpetrators or successfully track them.

One of the most important use cases of safety management is the use of gunshot detection solution. This solution potentially makes use of connected microphones that are installed throughout the city. These microphones constantly collect sounds and pass them over to the cloud platforms which then analyze these sounds by making use of machine learning algorithms to detect a gunshot. These platforms can even estimate the location of the gun by measuring the time taken by the sound to reach the microphone.

The United States has successfully implemented this solution in more than 90 cities.

Water Management Systems

Urban cities put an enormous strain on water supply and it has therefore become important to conserve water. But thankfully, IoT can also be used for water management, and the devices that allow this implementation are called smart meters.

These meters allow water utility companies to track water consumption and then analyze the average water consumption per household, industry, etc. using machine learning and also compare it with their current usage.

Machine learning algorithms can further be used to predict the future water consumption of these households and industries by analyzing the past data. These meters can also analyse water pressure, temperature and quality and help to improve them if required.

In addition to this, these meters also can detect potential leaks well in advance by monitoring the underground pipelines and understanding the data thereby helping to prevent serious water damages.

An example of a country that has successfully implemented smart water management solutions is United Nations.

Smart Parking Systems

IoT lies at the core of vehicle tracking platforms and combined with ML, it can perform more enhanced operations. Smart parking systems are used to find vacant locations for a vehicle at different public places.

Smart parking’s In-Ground vehicle detection sensor is the technology that plays a key role in the smart parking systems. Wireless tools like sensors are embedded into the pavement of individual parking spaces and these sensors collect data regarding the timing and duration of the space used by vehicles. This data is then transferred to the cloud gateways where it is processed and finally presented to drivers in the form of clear, understandable insights.

Further, the peak hours can also be detected in advance with the help of machine learning algorithms that make predictions by analyzing the past trends or by analyzing the historical data combined with real-time data.

These systems thus, help to reduce the unnecessary congestion, decrease vehicle emissions, save fuel and time of people and lower the enforcement costs.

Summing Up

From the use cases that we have discussed in this article and the current situation of IoT in the world, we can easily say that we are going to see many more smart cities in the near future because of the benefits they provide. Applying a diverse range of IoT and ML not only make city management much easier and efficient but also help to improve the quality of life of its inhabitants.

However, to enjoy these benefits, municipalities must take a consistent and well-planned approach to design functional smart city architectures that allow hastening the implementation of smart city solutions still leaving space for expansion.

This UrIoTNews article is syndicated fromDzone