Real Life Huawei Collaborative Smart City and Safe City Solution in Action
According to this U.N. report, by 2030 two-thirds of the world’s population will be living in cities, the urban population in developing countries will double, and the area covered by cities could triple. This rapid urbanization is increasing pressure on city infrastructure and services, forcing many cities to rethink how they operate.
One important way city managers are responding to these challenges is through the development of Smart Cities. I’ve been fascinated with Smart City and Safe City tech ever since I read about the concept a few years ago. There’s so much violence in the world today, and a solution to keep citizens safer through more collaborative technology seems like a no-brainer to me.
A Safe City is a Smart City that focuses on public safety, so the technology is all connected. As Ed Diender, Vice President of Huawei’s Government and Public Utility Sector, puts it:
“A Safe City is the foundation of a Smart City.”
Yesterday I attended the Huawei: Creating a Smart City Nervous System event on Facebook (the briefing was taking place live in Barcelona at the same time). I came away with an even deeper understanding about Smart City technology, and how it all works.
It was fascinating to learn how Huawei’s Smart City ecosystem enables partners and cities to undergo digital transformation. The briefing featured case studies from two of the world’s most famous Smart Cities — Yanbu, Saudi Arabia and Weifang, China.
These 2 paragraphs from the press release sum it up well –
“Like a living organism, a Smart City has a nervous system that comprises a ‘brain’ [the control center] and ‘peripheral nerves’ [the network and sensors] gathering real-time information about the health and status of the city, its environment and infrastructure. For example, sensors can provide data on the transportation system performance enabling the brain to manage congestion, smooth demand and safely reduce delays for citizens.
Leading global ICT company, Huawei has helped urban authorities across the world to create digitally-connected ecosystems that have transformed the way cities function — improving connectivity between people and things to generate innovation, economic growth and social progress. Through leading new ICT, Huawei provides the nervous system to deliver early warnings to potential city issues and drive unified coordination, cross-sector collaboration, and intelligent analysis for effective management of city services. In fact, Huawei’s Smart City solutions have been deployed in more than 200 cities across 40 countries.”
But if you’re like me, you want to understand exactly how a smart city would keep us safer. I wanted an example of how it would work in a real life situation.
I got the answer I was looking for at Huawei Connect 2017 in Shanghai, and it blew my mind. I’ll explain the real life scenario below — and I hope it gives you an aha moment also.
Let me back up a moment to give you some context. A few months ago, I interviewed Huawei’s Global Chief Public Safety Expert, Hong-Eng Koh. You can read that post at The Road to Collaborative Public Safety.
At Huawei Connect 2017, I got the pleasure of meeting Hong-Eng Koh in person. He’s a cheerful, friendly person who is passionate about his job. He worked in the public safety sector as a criminal investigator for 30 years before envisioning Huawei’s Collaborative Public Safety Solution.
Hong-Eng Koh explained to me that when he was a criminal investigator, it would take weeks or sometimes months to gather the same information that police can now get in a few minutes with Huawei’s safe city solution. This means crimes can get solved in a tiny fraction of the time it used to take.
Now I Want to Blow Your Mind
I’d like to give you an example of Huawei’s Collaborative Public Safety Solution in action — and show you how it helped to arrest a criminal.
This is not a hypothetical — This really happened
In real time, this whole scenario took 5 minutes. It may take you longer than that to read it. So even though I’m being detailed, imagine it happening quickly on a computer screen inside a police station.
Here’s the Situation
A good driver stopped at a stop sign. The person in the car behind him was impatient, got out of his car, attacked the good driver, walked back to his car and drove away. The good driver was able to snap a quick pic of the attacker (and his car) after the attack. The good driver reported the crime to the police.
What Happened Next
- The police are able to tell from the picture that the attacker is a big guy.
- During questioning, the good driver remembers that he saw the attacker make a phone call from his car. (this turns out to be important information)
- From the pic the driver took, police are able to work with the DMV to get the registration of the car and a profile pic of the registered owner.
- The computer tells the police that the registered owner is connected to 4 other people. Profile pics of those 4 people pop up on the screen.
- Police look into the registered owner of the car. His profile picture looks skinny. The attacker in the pic that the driver took looks large. That skinny person can’t be the attacker.
- Plus, the registered owner flew to another city and checked into a hotel during the time of the attack. He was in a city 4 hours away by plane, so it couldn’t be him. (In China, police have access to travel records.)
- Police also notice that the attacker’s vehicle has been involved many traffic violations. They also notice the registered owner didn’t pay for those violations. Someone else paid those fines.
- This led police to believe the registered owner sold the car without changing the registration. They now had enough information to determine that the registered owner was most likely not the attacker they were looking for.
- The police look deeper into the car and find that in 2015, 2016 and 2017 — the car was under the control of 3 different people.
- They use the “Timeline Analysis” feature to zoom in on 2017 to see who is currently in control of the car. They find that it’s a physically small lady. We’ve already established that the attacker is a large male. She is not big enough to be the attacker.
- The police look at the people that the computer says she’s connected to. Again, a few other profile pictures pop onto the computer screen.
- Keep in mind — there are hundreds of databases all linked. The safe city solution is all about collaboration!
- The computer says the lady driver is connected to 3 other people. One is her mother, one is her father and one is her husband.
- Her mother is too small to be the attacker. Her father looks too slim. Her husband is a large man.
- They begin to look at who and what the husband is connected with. They find out that the husband reported a crime where he was the victim.
- They also learn that according to his travel records, he’s related to 3 other people. (Remember, this happened in China where travel records are accessible.)
- The police see that he traveled to the same cities and checked into the same hotels as those other 3 people. That can’t be a coincidence. So now they know that the husband travels with those people.
- As the police look at the profile pics of these 3 people, they notice that 2 of them are hexagon shaped on screen. In China, if the profile pics are hexagon shaped, it means there are violent crimes associated with those people.
- So, the husband is connected to 2 people with violent criminal records. This could mean the husband is also a violent person, or maybe not. It’s worth investigating further.
- The police look at the phone number associated with the husband and see that it’s an old record, so they aren’t sure if the number is still active.
- Since they already saw that the husband recently reported a crime, they checked the phone number he left for the police at that time. It was the same phone number, so the number is active.
- Now the police look at who he’s been calling and texting (not the content, just the meta data). From the call detail record, they can see the whole list of recent phone calls and texts.
- The police see that on the day of the attack, he made a phone call from the location of the attack. They remember that the good driver said he saw the attacker make a phone call from his car.
- At this point, they had enough evidence to determine he was involved in the attack. Remember, the police figured all this out in 5 minutes without leaving the station.
- They put a warrant out for his arrest. When this happens in China, they put a Tripwire on the offender. 2 hours later when the police activated the Tripwire, they saw that he was at the airport about to fly out. (the image below is an example of Tripwire)
They immediately went to the airport and arrested him.
Is your mind blown? Mine was.
Some people think Smart/Safe Cities are just cities with a lot of video cameras and surveillance, but obviously that’s not the case. I hope this real life example gave you a small taste of what’s possible and how intelligent cities can keep us safer.
The scope of Smart City and Safe City technology is huge, and it hinges on collaboration — collaboration between governmental agencies and citizens. Aside from the scenario I explained, collaborative public safety also includes crisis and disaster management and even predictive policing.
Smart city development is emerging as a major solution to tackle challenges relating to rapid urbanization in cities across the world. With 13 OpenLabs around the globe, Huawei and its partners conduct joint research to build sustainable ecosystems and offer localized Smart City solutions. Through leading new ICT infrastructure, together with collaboration among its ecosystem of public and private sector partners, Huawei provides a Smart City nervous system that creates an intelligent and interconnected society to improve citizens’ quality of life.
If you’d like to learn more about Huawei’s Smart City / Safe City solutions, please click over to the links.
Additionally, you can reach me at @adamsconsulting, and you can reach Mr. Hong-Eng Koh at @he_koh. I invite your comments below if you’d like to add to the conversation. Most of all, thank you for reading my post!
