IoT is the main component in the development of Smart Cities. we have several things while developing smart cities in which IoT components are used.
Use Cases of IoT for Smart Cities
Smart cities provide that their citizens get from point A to point B as safely and efficiently as possible. To achieve this, municipalities turn to IoT development and implement smart traffic solutions. Smart traffic solutions use different types of sensors, as well as fetch GPS data from drivers’ smartphones to determine the number, location, and speed of vehicles.
At the same time, smart traffic lights connected to a cloud management platform allow monitoring of green light timings and automatically alter the lights based on the current traffic situation to prevent congestion. Additionally, using historical data, smart solutions for traffic management can predict where the traffic could go and take measures to prevent potential congestion.
With the help of GPS data from drivers’ smartphones smart parking solutions regulate that the parking spots are occupied or available and create a real-time parking map when the closest part spot is free with the help of GPS, drivers receive a notification and use the map on their phone to find a parking spot faster and easier instead of blindly driving around.
The data from IoT sensors can help to use the patterns of how citizens use transport. Public transportation operators can use this data to enhance the traveling experience and achieve a higher level of safety and punctuality, To carry out a more sophisticated analysis, smart public transport solutions can combine multiple sources, such as ticket sales and traffic information.
IoT-enabled smart cities allow citizens to save money by giving them more control over their home utilities. IoT enables different approaches to smart utilities:
Smart meters and billing
Smart meters provide municipalities can ofter cost-effective connectivity to utility companies’ IT systems to the citizens. Smart meters can send data directly to a public utility over a telecom network, providing it with reliable meter readings. Smart metering allows utility companies to bill accurately for the amount of water, energy, and gas consumed by each household.
Revealing consumption patterns
Smart meter networks facilitate utility companies to make outstanding visibility and see how their customers consume energy and water. With a network of smart meters, utility companies can monitor demand in real-time and redirect resources as necessary or encourage consumers to use less energy or water at times of shortage.
IoT smart cities can also facilitate citizens with utility management services. These services allow citizens to use their smart meters to track and control their usage remotely. For instance, a householder can turn off their home central heating using a mobile phone. Additionally, if a problem (e.g., a water leakage) occurs, utility companies can notify householders and send specialists to fix it.
IoT-enabled smart cities provide maintenance and control of street lamps more straightforwardly and cost-effective. If we equip streetlights with sensors and connect them to cloud management it greatly helps to modify the lighting schedule to the lighting zone.
Smart lighting solutions collect the data on illuminance, and movement of people and vehicles, and combine it with historical and contextual data (e.g., special events, public transport schedule, and analyze it to improve the lighting schedule. So smart lighting solution “tells” a streetlight to dim, brighten, switch on, or switch off the lights based on the outer conditions.
Similarly, when pedestrians cross the road, the lights around the crossings can switch to a brighter setting; when a bus is expected to arrive at a bus stop, the streetlights around it can be automatically set brighter than those further away, etc.
IoT-enabled smart city helps to improve waste-collecting schedules by tracking waste levels. Each waste container gets a sensor that gathers data about the level of the waste in a container. Once it is close to a certain threshold, the waste management solution receives a sensor record, processes it, and sends a notification to a truck driver’s mobile app. Thus, the truck driver empties a full container, avoiding emptying half-full ones.
For a Healthy Environment, IoT-enabled smart cities allow tracking parameters at the highest level. A city can extend a network of sensors across the water grid and connect them to a cloud management platform to monitor water quality. Sensors measure pH levels, the amount of dissolved oxygen, and dissolved ions.
The chemical composition of water changes and leakage occurred then the cloud platform triggers an output defined by the users. For example, if a Nitrate (NO3-) level exceeds 1 mg/L, a water quality management solution alerts maintenance teams of contamination and automatically creates a case for field workers, who then start fixing the issue.
Another use case is monitoring air quality. For that, a network of sensors is deployed along busy roads and around plants, Sensors gather data on the amount of CO, nitrogen, and sulfur oxides, while the central cloud platform analyzes and visualizes sensor reading so that platform users can view the map of air quality and use this data to point out an area where air pollution is critical and work out recommendations for citizens.
IoT-enabled smart city technologies offer real-time monitoring, analytics, and decision. making tools for enhancing public safety. By collecting the data from auditory sensors and CCTV cameras deployed throughout the city with the data from social media feeds and analyzing it, public safety solutions can predict potential crime scenes. This will allow the police to stop potential perpetrators or successfully track them.
For example, more than 90 cities across the United States use a gunshot detection solution. The solution uses connected microphones installed throughout a city. The data from microphones pass over to the cloud platform, which analyzes the sounds and detects a gunshot. The platform measures the time it took for the sound to reach the microphone and estimates the location of the gun. When the gunshot and its location are identified, cloud software alerts the police via a mobile app.
Iterative Approach to Implementing Smart City Solutions
The range of smart city applications is highly different. If we want to expand the range of smart city services in the future, it will be possible to upgrade the existing architecture with new tools and technologies without having to rebuild it.
We have a six-step implementation model to follow for creating an efficient and scalable IoT architecture for a smart city.
Stage 1: Basic IoT-based Smart City Platform
A basic IoT solution for smart cities includes four components:
The Network of Smart Things: A smart city – like any IoT system – uses smart things equipped with sensors and actuators. The main aim of sensors is to collect data and pass it to a central cloud management platform. Actuators allow devices to act – alter the lights, restrict the flow of water to the pipe with leakage, etc.
Gateways: Any IoT system has two parts – a “tangible” part of IoT devices and network nodes and a cloud part. Pass from one point to another point. So we used field gateway. Field gateways provide data gathering and compression by preprocessing and filtering data before moving it to the cloud. The cloud gateway ensures secure data transmission between field gateways and the cloud is part of a smart city solution.
Data lake: The work of a data lake is to store data. Data lakes preserve data in its raw states. When we need the data it’s extracted and passed over to the big data warehouse.
Big Data Warehouse: A big data warehouse is a single data repository. It contains only structured data. Once the value of data has been defined, it’s extracted, transformed, and loaded into the big data warehouse. Furthermore, it stores contextual information about connected things, e.g., when sensors were installed, as well as the commands sent to devices’ actuators by control applications.
Stage 2: Monitoring and Basic Analytics
With data analytics, it is possible to monitor devices’ environments and set rules for control applications to carry out a particular task. For example, by analyzing the data from soil moisture sensors deployed across a smart park, cities can set rules for the electronic valves to close or open based on the identified moisture level. The data collected with sensors can be visualized on a single platform dashboard, allowing users to know the current state of each park zone.
Stage 3: Deep Analytics
Processing IoT-generated data, city administrations beyond monitoring & basic analytics, and identifying patterns and hidden correlations in sensor data. Data analytics uses advanced techniques like machine learning (ML) and statistical analysis.
ML algorithms analyze historical sensor data stored in the big data warehouse to identify trends and create predictive models based on them. The models are used by control applications that send commands to IoT devices’ actuators. ML algorithms are applied to historical sensor data to reveal traffic patterns and adjust signal timings, helping to improve average vehicle speed and avoid congestion.
Stage 4 Smart Control
Control applications assure better automation of smart city objects by sending commands to their actuators They “tell” the actuators what to do to solve a particular task. There are rule-based and MI-based control applications. Rules for rule-based control applications are defined manually, while ML-based control applications use models created by ML algorithms These models are identified based on data analysis; they are tested, approved, and regularly updated.
Stage 5: Instant Interacting with Citizens via User Applications
User applications allow citizens to connect to the central smart city management platform to monitor and control IoT devices, as well as receive notifications and alerts, For example, using GPS data from drivers’ smartphones, a smart traffic management solution identifies a traffic jam.
To prevent even bigger congestion, the solution automatically sends a notification to the drivers in the area, encouraging them to take a different route.
Stage 6: Integrating Several Solutions
“Smartness” is a continuous process. IoT-enabled smart cities suggest not only increasing the number of sensors but, more importantly, the number of functions. To consider the functional scalability with the example of a smart city solution for traffic monitoring.
A city extends a traffic management solution to detect traffic jams in real time and manage traffic lights to reduce traffic in areas with intensive traffic. After some time, the city decides to ensure city traffic doesn’t harm the environment and integrates the traffic management solution with a smart air quality monitoring solution.
Cross-solution integration allows for controlling both traffic and air quality in the city dynamically. For that, traffic lights or street lights along the roads can be equipped with sensors that monitor air quality. Sensors measure the amount of CO, NO, and NO2, in the air and pass data records to a central air quality management platform for processing.
If the amount of harmful gases in the air is critical, control applications apply rules or use models to take an output action, It is possible due to the integration of the traffic management solution to the air quality management solution. The traffic management platform performs real-time analysis and identifies if it is possible to alter the traffic lights. If altering the lights is acceptable, control applications send a command to the traffic lights’ actuators, which execute the command.
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