The core of making IOT work

How would you define a smart city? A smart city is characterized as one that can employ IOT devices in order to make intelligent decisions for its citizens on an autonomous basis. While that may seem simple, it is anything but.

The core of making IOT work is the ability of data collection through a simple sensor. By collecting data on a massive scale and correlating it with one another we can get a holistic view on how the city operates and we can use that data to make intelligent decisions. The most important point is that the technologies in the sensors. Taking that sensor data and then making use of it is where DXchain comes into play and truly solves a problem.

The idea of a centralized cloud server and taking that to push it out as far as possible and to create interoperability in the process through intricate web of networks is the future. As these networks grow and expand, they become more efficient in terms of economies to scale and they’re much faster latency wise when they are closer to one another, this is a concept we could never imagine was possible say a decade ago.

It’s also very important that not only does that one device learn in a linear rate, but that all devices in proximity to this device can operate in tandem in order to collect relevant information and afterwards that this information gets processed in meaningful ways to make intelligent decisions for the city.

A smart city is a huge blanket term. There are a lot of items underneath the notion of an IOT based city, and it’s best to leave it up to gradual progression instead of trying to implement everything in one go. One of the largest friction points in any city is the traffic congestion. Figuring out the areas of largest friction when it comes to traffic something that IOT can help with, we can outline an example used in a real megacity. For example, in Shanghai, they worked with the government transport agency to try rand figure out the transportation infrastructure. Typically speaking you need to start out very microscopic, so we looked at the general public transit such as the buses and the trains and over months you can begin to examine other types of transportation infrastructure.

Once you have a holistic view, then you can figure out best how to collate this information and use it to serve it’s citizens. Then the question becomes how to we use AI/ML and advanced analytics to process this data? DXChain offers a solution for both of these issues. For large cities it’s probably optimal to start with a specific sub section and build out from there. IN smaller municipalities perhaps it makes sense to take it wholly.

In other cities you can start with a small variable such as energy consumption and how to delegate electricity more effectively. The only bottle neck is government bureaucracy which is a topic discussion in it of itself, but at least the technology is there.

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