Explanations & Tutorials

How To Use Google Cloud IoT in Smart City Waste Management

Smart waste management, powered by the Internet of Things adds intelligence to trash receptacles by sensing the fill level or attaching solar powered compactors.

James Schaefer

Every year, New York City spends $2.3 billion on waste management. Of that $2.3 billion, roughly $750 million is spent on curbside collection and street cleaning. While these numbers are already very high, they encompass only the payments from the New York City government for waste management. Much more is spent each year on waste management and garbage collection by private entities throughout New York City and the rest of the country.

But the numbers don’t have to be that high. Trash collection is fraught with inefficiencies, the largest of which is collection that is typically scheduled and does not occur based on how full the garbage receptacle is. This results in needless pickups that drive up waste management costs for private entities, and inefficient waste truck usage to empty unfilled dumpsters.

 

Enter smart waste management, powered by the Internet of Things. Adding intelligence to trash receptacles by sensing the fill level or attaching solar powered compactors can reduce operational costs by as much as 80%.

In this article, we will go over how to build a waste management system using Google Cloud Products, namely Cloud IoT Core, PubSub, BigQuery, and FireStore. To get started, let's learn more about the components and how we used them to build this smart city waste management solution.

Cloud IoT Core: A relatively new offering from Google, this product acts as the data ingestion layer for the system. Fill level and air quality sensors send data over the MQTT protocol through IoT Core, which manages the transmission security and devices.

Cloud Pub/Sub: Google Pub/Sub is designed around the concept of a few very high bandwidth topics, which is perfect for handling huge amounts of data flowing through a system at a time.

BigQuery: Google BigQuery is designed for massive data scale, and is capable of efficiently searching through large time series datasets in seconds. This makes it one of the cheaper options for historical data storage.

Cloud Firestore: Firestore is like Firebase’s more mature and stable brother, capable of storing real time data and ensuring that the data stays synchronized with any local applications.

Google Data Studio: Data Studio is a powerful analytics dashboard software for creating analytics views of historical data sets. Data studio gives further insight into the long-term behavior of a waste management solution.

The best part about all of these offerings is that they are fully managed services, meaning there is no need to worry about the reliability or uptime of your particular deployment. Everything is built to scale, so you can focus on building a product that has value to customers.

The demo we created is relatively simple in both architecture and data, but if your solution requires a more complex system, check out Google’s products and other architecture suggestions on their solutions page.

James Schaefer

Director, Product

As the Director of Product at Leverege, James's responsibilities include identifying patterns of problems that can be solved by new Leverege Platform features, designing and building those features, and helping his fellow engineers use them in applications. When he isn't heads down in a new database software or learning a new templating language, James enjoys rock climbing, hiking, and brewing beer.

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