Edge computing plays a significant role in enabling and enhancing various aspects of smart city projects, which the VizioSense example shows. A smart city leverages technology and data to improve the quality of life for residents, enhance urban services, and make urban operations more efficient. Edge computing complements these goals by providing local data processing, real-time analytics, and reduced latency.
VizioSense redefines computer vision sensing with artificial intelligence and unparalleled privacy. Their plug-and-play optical sensor embeds an edge-AI chip enabling on-device computer vision to count and detect people or items. Respecting privacy by design, it sends only the counting or status information to the cloud and has no capability to stream videos. The generated data provides insights to businesses to improve their operations and generate savings or new revenues.
VizioSense is using Edge Signal to manage sensors equipped with on-board AI and computer vision for their smart city projects. Their VizioPark solution monitors parking space vacancies and detects availabilities using AI software on sensors, and VizioCount enables counting people or objects quantitatively and qualitatively. But to perform these tasks effectively, VizioSense needed the ability to process a massive amount of data collected at the edge. And they required a robust edge infrastructure platform to manage the sheer number of devices necessary.
VizioSense uses bulk registration capabilities of Edge Signal which results in smart cameras to be automatically registered to Edge Signal and AWS IoT Greengrass. This enables VizioSense to onboard thousands of devices with a zero-touch deployment. Once the devices are registered, they are also assigned to device groups in Edge Signal and automatically receive the pre-configured deployments. Once the edge applications are deployed to the remote units, they automatically receive configuration and start inferring AI models.
VizioSense started to onboard more customers and they are now ready to manage thousands of smart cameras. Maxime Schacht, CEO at VizioSense, explains:
"We have been using Edge Signal to manage our sensors equipped with on-board AI and computer vision for our smart city projects, and the results have been nothing short of impressive. The platform has allowed us to process real-time data from our smart cameras, making it possible to quickly analyze and respond to critical events. The ability to bring AI and computer vision capabilities to the edge has significantly improved the accuracy and efficiency of our systems, and has allowed us to create truly smart and responsive buildings and cities. We are thrilled with the results and highly recommend Edge Signal to anyone looking to take their smart city or building initiatives to the next level."
Powered by Wesley Clover International, Edge Signal provides a simple solution to integrating edge devices and data into the cloud as well as managing all aspects of edge applications. Customers choose Edge Signal to reduce time-to-market and to optimize their edge applications. Edge Signal also enables devices and gateways to be controlled, made more secure, and updated without costly on-site technicians or remote hands. Application developers can create solutions, including edge Artificial Intelligence (AI), Internet of Things (IoT), data aggregation, etc. from variety of sources without having in-depth knowledge of edge computing and its complexities.
Burak Cakmak, Chief Technology Officer at Edge Signal, explains what role AWS plays when it comes to the Edge Signal platform:
“When creating the Edge Signal platform, we decided on a pure serverless architecture. AWS Lambda gives us flexibility to run our workloads. First, we deploy AWS Lambda for our CRUD operations. We use Amazon API GW to trigger our functions which consume REST requests. Second, AWS enables our microservice-to-microservice event-driven architecture. We never invoke one function from another. Everything is event-driven, and with Amazon Simple Notification Service (SNS) and Amazon Simple Queue Service (SQS) we can trigger our functions in an async way. Third, AWS helps us with ingesting data from our device agents. Thanks to AWS IoT’s rules, you can run SQL-like queries on streams, and trigger the Lambda functions depending on your needs.
AWS IoT has a broad set of sub-services we also use in the Edge Signal platform. We employ AWS IoT Greengrass for containerized and native application deployment. And AWS IoT MQTT is integral to creating a bi-directional communication channel between devices and cloud services.
Amazon Timestream database helps us store and query timeseries data in an efficient way.
There are other AWS services Edge Signal employs, but AWS Cloud Development Kit (CDK) deserves special mention. Our system is immutable and easily deployed, and AWS CDK helps us spin up a new instance of frontend and backend easily. Since we develop our IaaC with Typescript, it’s aligned with our main programing language, and testable.”
As the VizioSense example shows, edge computing serves as a critical enabler for the diverse and complex ecosystem of smart city applications. It empowers cities and organizations to harness the potential of data and technology while addressing challenges related to latency, privacy, scalability, and reliability.