Deep Learning For Smart Cities

1 mainIn recent years, technological advancements in hardware, software, and embedded systems are enabling billions of smart devices to be connected to the internet. This ecosystem is collectively referred to as Internet of Things. A lot of people are actively migrating to cities, which means the essential resources are going to get scarcer. Cities will have to manage infrastructure like water, power, transport, and so on very effectively if they want to support everybody. But how do we do that? The data that is being collected varies so much quality and format that it becomes very difficult to use it effectively. How can we effectively use the data being collected by connected sensors?  

What exactly are smart cities?

In order to address the issues mentioned in the previous paragraph, city governments and businesses are undertaking an initiative called Smart Cities. The goal here is to make the infrastructure smarter so that we can use the shrinking resources efficiently. They want to use modern technology to address many pressing problems including water wastage, energy consumption, traffic congestion, and so on.

Building smarter cities will help them solve all these problems, which will lead to positive economic outcomes. This will create a more efficient and sustainable living environment for people. This, in turn, will attract more citizens and businesses to come to the cities. As we can see here, this cycle is very useful for the economic growth of cities.

What are the challenges?

2 problemsThe premise of smart cities looks exciting, but how feasible is it? In order to be efficient, we need to collect data in every sector. Implementing solutions at this scale is difficult because every city is unique, and hence poses a new set of problems every time. If we want to build a scalable model, we need to find patterns that can be used over and over again to apply the smart city model to many different cities.

Some of most important things that need to addressed are centered on smart data analysis. They have to build robust layers for data collection, communication protocols, interoperability between devices, data storage systems, intelligence layer, and so on. If they want companies to freely build applications for smart cities, they should standardize a lot of these things so that companies can quickly build smart applications to address a lot of these issues. Lowering these barriers would definitely be beneficial in the long run.

How can Deep Learning be used to address these challenges?

3 solutionIn recent years, Deep Learning has made a lot of progress in the world of Artificial Intelligence. These techniques have been effectively used for analysis of data including images, speech, text, videos, and so on. In the case of smart cities, we are dealing with a lot of time series data uploaded from connected sensors. The good thing about Deep Learning is that it lends itself very nicely to sequential data analysis. Time series data is a particular form of sequential data. Recurrent Neural Networks have shown a lot of promise in this particular type of analysis.

In the figure above, the Deep Learning platform enables applications to be built quickly. These applications use sequence learning models to solve various problems including optimizing water distribution, detecting water leakage, minimizing energy needs, and so on. It’s very important to understand the long term temporal dependencies between various channels of data. This is where Deep Learning algorithms really shine. Once we have the trained engine ready, it can be used to control the actuators that can automatically take actions.


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