The NTT Smart Cities Solution for the City of Las Vegas
| The NTT Smart Cities Solution for the City of Las Vegas | |
|---|---|
| |
 Smart Cities Solution Las Vegas | |
| Team Organizations | NTT Data Services |
| Team Leaders | William Baver |
| Participating Municipalities | Las Vegas NV |
| Status | Implemented |
| Initiative | {{{initiative}}}Property "Has initiative" (as page type) with input value "{{{initiative}}}" contains invalid characters or is incomplete and therefore can cause unexpected results during a query or annotation process. |
| Document | None |
Description
NTT Smart Cities Solution leverages IoT edge analytics for public safety. High definition video cameras, sound and motion sensors, and an array of IoT devices are integrated and deployed to monitor a location or venue to create a multi-channel solution. The solution, in turn, provides situational awareness, warnings and alerts to city agencies and venue security teams of incidents as they develop.
The system proactively provides early notification of potential and active public safety incidents to command and control authorities. If the city deems it appropriate they then notify the appropriate first responders. By leveraging advanced analytics including machine learning technologies, the system “learns” normal patterns and detects patterns that appear abnormal. The initial use cases are limited to specific types of incidents that can be detected through video and sound sensor analytics running on edge compute devices as well as social media monitoring. As the system evolves, we expect to expand detection capabilities to detect and alert to a wider variety of incident types.
Our current solution includes technology for “lost person” identification and “vehicle identification”, to assist should an Amber Alert be issued. It also includes wrong way vehicle detection. Future plans could also include facial recognition modules or other advanced detection components and may also include more automated incident response component for greater awareness of the developing situation.
Challenges
Since this was a proof of concept, there were no challenges that could not be overcome, but some that can be highlighted for consideration in future projects:
- Procurement and installation of sensors and related technology needed to be managed in accordance with typical municipality challenges
- Development of use cases and go forward planning on data usage and ownership was important to setup from the beginning
- Evaluation criteria against success factors was hampered since existing data or tracking was non-existent in the use cases we used for the project.
Solutions
The Smart City solution leverages multiple technology components. We are deploying a 3-tiered architecture; a cognitive platform consisting of a cognitive foundation (Host / Integrate – IaaS), cognitive engine (build / assemble - PaaS) and then the applications (consume – SaaS). The exact configuration will depend on the size of the implementation but at a high level the solution includes:
- NTT DATA’s Cognitive Platform
- Dimension Data Network and IoT Deployment Services
- Raging Wire Data Center for hosting
- Dell IoT gateways
- Edge Compute components (Dell Servers); VMware Components (vSphere, NSX, others); Data Center Components (Dell Servers, HC, HCI, Storage); Cloud Offerings (Dell EMC systems infrastructure located in NTT data centers). Given the solution design, the edge compute components are deployed close to the monitored location. The data center components could be implemented in the customer’s environment or hosted in an NTT’s cloud.
Major Requirements
For the Las Vegas Innovation District, we began the “smart” safety pilot by creating an agile, robust cognitive foundation of information and communications technology (ICT) systems. The systems “think” and can assess multiple data sources, perceive current conditions, plan, decide, and act on those conditions. It is possible for a cognitive solution to even learn from the consequences of its actions, while using past knowledge and hone current and future decisions. In Las Vegas, NTT is deploying a “thinking solution” by enabling situational awareness and insight. Our complete solution involves many technologies and partners all seamlessly integrated to facilitate safety. Here’s how:
- Sensors and edge computing at work: Using a secure, distributed platform with micro data centers located near sensors around the Innovation District allows for rapid deployment of ICT resources and faster analysis of sensor inputs, enabling the safety professionals to detect safety incidents and take actions quickly.
- Predictive and diagnostic analytics at play: Micro data centers use advanced analytics to deliver real-time data to the locations where the data can provide maximum value. Diagnostic analytics at the edge analyze large volumes of data, but only send data indicating an incident has occurred or needs investigation back to the core datacenter. This approach minimizes data transport volumes and response times to reduce the demands on the ICT infrastructure.
- Applying Artificial Intelligence (AI): Cognitive analytics correlate and apply AI and machine learning techniques to the multiple edge data inputs and sources, including historical data, crime information, weather data and social media updates. This information provides deeper insights for responsive and preventive services to combat crime and improve public safety.
- Deep learning at work: The cognitive foundation allows the ICT infrastructure to monitor workloads and adjust or move workloads dynamically, based on the specific situation. For example, in future versions if an AMBER alert is issued, the cognitive foundation could automatically scale up the network and compute power for the portions of the system that perform enhanced recognition and license plate recognition functions or capture higher resolution images than normal.
Performance Targets
| Key Performance Indicators (KPIs) | Measurement Methods |
|---|---|
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We targeted 5 areas for tracking during the POC
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Baselined what the City had in-place prior to NTT DATA’s Smart City POC going Live. KPI performance metrics exceeded the City’s expectations. |
Standards, Replicability, Scalability, and Sustainability
- By creating a repeatable infrastructure, the safe city is transferrable to other municipalities
- The cognitive data engine allows for advanced thinking and processing of data, the input could be from anything sensors, social media, cameras or IoT devices—the engine does not care the inputs, it is how data is processed and handled that makes it innovative and unique
- The solution is developed on an open platform that used commercially available appliances to establish the data collection, normalization of the data and movement / tracking of the appliances to allow further analytics using proprietary technology and then the display of information back to the users.
- The open platform allows for scalability and replicability within an existing municipality and beyond to potential regional uses of the information captured and processed.
Cybersecurity and Privacy
- The project uses both IoT and data already collected by the city to provide advanced analytics and advance safety. Privacy is protected as data is not actually stored together in one place – a portion of the data is stored on the edge and part on the city data center. Data is only displayed to authorized personnel on combining the information.
- The solution also takes away the risk of any unknown or unwanted devices getting onto a community network because of the ability to use PKI to perform mutual authentication of an IoT device to the correct corresponding community IoT network.
Impacts
A few miles off the Las Vegas strip, in an area of Las Vegas known as the Innovation District, a safe city proof of concept (POC) is currently underway. The initiative is part of the city’s larger initiative called—Innovate. Vegas. Las Vegas created an Innovation District to provide a proving ground for emerging technologies like smart city. Vegas is all in on becoming a smart city, even translating that commitment to a charter, “to provide safe, reliable and efficient civic technology that stimulates economic growth.” Las Vegas has invested significantly in smart infrastructure to make the charter a reality, from installing 123 miles of fiber optic cable to creating other smart POC’s around the city. NTT and Dell Technologies have joined Las Vegas to help implement the city’s vision starting with a smart safety solution. It is the hope of the city and their technology partners that the safe city solutions will help to decrease response times and assist first responders in multiple ways—ultimately making Las Vegas safer in the following ways:
- Improve Response Times and Incident Accuracy. Detecting aggressive crowd activity or wrong way vehicles allows responders to be notified sooner to impending incidents. One of the problems with current video surveillance systems is that someone needs to be watching at all times to generate alerts. Most video surveillance systems are currently used to find out what happened and not for real time situational awareness.
- Determine Response Type. The solution allows agencies to better determine the type of response needed so the best first response team can be sent. Is a fire engine needed? An ambulance? More than one police car?
- Streamlined Critical Information for First Response. High definition video surveillance systems generate a large load on networks. Many cities do not have the network bandwidth to deploy these types of cameras and the cost for implementing high speed fiber networks to support these systems is often too costly to be feasible. Our solution processes the feeds from cameras and sensors in micro-datacenters located in close proximity to the monitored location. If an incident is detected, only relevant data is shared, for example, the alert and related video clip is sent back to public safety agencies for evaluation. Individual cities will determine what information is shared according to their specific governance policies.
- Alleviates Privacy Concerns. In some communities there is public resistance to government video surveillance. With this solution, government agencies can accurately state they are not recording or keeping recorded video, except the video of a triggered public safety incident. This is often more palatable to some segments of the community. We work closely with the city to follow the individual data policies of the city; all data is owned by the city.
Demonstration/Deployment
The first phase of the solution is a proof of concept and then a commercialization and deployment of the solution to a broader set of prospects. Details of our efforts are as follows:
- Establish partnership between Las Vegas and NTT / Dell team – May 2018
- Develop POC and Deploy Equipment – May – September 2018
- Deploy POC – October 2018
- Enhance and evaluate POC – October – December 2018
- Advance V1.0 of product within City – Target 1st Quarter 2019
- Make commercially available product available early 2019
- Target additional releases every 3 months and deployment to additional target clients
The pilot concepts will be demonstrated at the GCTC Expo through an augmented reality model diorama (a version of which is shown in the picture below) and a slide show of the system dashboard.