A cornerstone of the transition from traditional urban models to smart cities is environmental sustainability. Smart cities built for the future cannot survive without eliminating pollution, lowering carbon emissions, increasing their efforts in recycling, and investing in renewable green energy.

 

This blog is a part of a series that explores key trends in smart infrastructure by leveraging Artificial Intelligence and IoT technologies. The write-up highlights smart applications in reducing pollution, streamlining waste management, improving road safety, and reducing traffic congestion.

 

AI & IoT applications for pollution reduction

Factories are the principal source of air, water, and soil pollution. Harmful greenhouse gases are constantly emitted at alarming rates in countries with heavy R&D, such as India and China. Toxic waste is often dumped into nearby water sources or land. This heavily damages the surrounding arable land and increases the levels of toxicity in fishing grounds.

 

IoT sensors can be placed at key pollution points in factories to collect samples from water, soil, or air. This data can then be sent to information processing facilities where Big Data processes cross-reference these levels against permitted values and benchmarks. Artificial Intelligence applications can map predictive models of environmental degradation based on values from these IoT sensors.

 

Artificial Intelligence & IoT in waste management

Advanced ‘Smart Sorting’ technologies utilize IoT image sensors that scan specified areas and send the information to an advanced AI-powered image recognition software that differentiates between recyclable material and waste products. This sequence is currently applied in “conveyer belt style” sorting facilities. AI-powered sensors are significantly better at detecting and recognizing materials from different angles than traditional optical sensors.

 

Intelligent Bins are another application of AI & IoT in smart infrastructure. Intelligent sensors placed in public garbage bins across the city can actively monitor the ‘fullness’ of trash cans. This information can be sent in real-time to an Artificial Intelligence-powered software that optimizes and determines the best garbage collection route and frequency. This provides municipalities with an efficient option to collect trash and reduce fuel consumption.

 

 

Artificial Intelligence & IoT applications in streetlights

Traditional Streetlights have always been expensive to maintain and extremely energy-consuming. Municipalities in smart cities have since switched to intelligent LED streetlights and within a year have reported an 80% decrease in overall costs, including electricity usage, maintenance, and replacements.

 

Taipei, among other cities, has made the move to intelligent LED streetlights. This new technology utilizes various IoT sensors to measure key data trends in its immediate environment, such as weather, light intensity, humidity, and visibility. Aggregate datasets are sent over the internet to information processing facilities where data from streetlights all over the city are processed by a central Artificial Intelligence-powered software.

 

Artificial Intelligence streetlight control systems can adjust light intensity according to IoT sensors data. Streetlights can sense and activate only when cars pass by during low traffic instances. Intelligent streetlights can self-report on faults when they require maintenance. Real-time information availability creates safer & more vibrant cities during low visibility conditions and contributes to maintaining efficient energy consumption levels.

 

Artificial Intelligence & IoT applications in roads & traffic

The World Health Organization (WHO) reported 1.3 million annual road accident deaths globally in 2021. Additionally, between 20 and 50 million people suffered non-fatal injuries that may lead to permanent disability. Such accidents heavily contribute to unsafe cities, lower productivity, an increase in traffic congestion, and a decrease in the overall quality of life.

 

Although most of these accidents occur in low to mid-income regions due to poor road infrastructure, unsafe road systems, and a lack of road laws adherence, developed urban cities still account for a significant amount of fatal & non-fatal road accidents. As such, smart cities must tackle the challenges of road surveillance, traffic control, and safety systems by Artificial Intelligence, Big Data analytics, and IoT technologies.

 

Road Congestion

IoT-powered traffic lights contain smart sensors placed strategically across different areas. They monitor and measure traffic congestion levels. This data can be sent and processed in real time by AI-Powered Big Data Analytical routing algorithms, which re-route vehicles across the city simultaneously while displaying road congestion levels in different areas, accidents, and obstacles on roads and highways.

 

Centralized AI-powered Big Data software can measure traffic flow data collected from CCTV and AI video analytics software to generate predictive traffic flow patterns. This allows municipalities to recognize areas that need infrastructure expansion and additional routes.

 

Smart Parking

IoT beacons and smart sensors placed on parking spots can detect and light up when parking spaces become available. This simple mechanism can save time wasted in finding parking spaces in hospitals, shopping malls, and other public infrastructure.

 

Smart anti-theft systems

In the event of car theft, AI-powered Big Data analytics software can tap into a connected network of IoT sensors, beacons, and CCTVs with video recognition software to track and follow criminals throughout the city while relaying that information in real-time to the authorities.

 

 

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