We have been discussing the Internet of Things (IoT) for nearly three decades. Adding a bit of intelligence to a standard sensor, camera, or device and enabling it to communicate via the Internet would allow remote monitoring and management of devices, systems, and processes while collecting valuable data to better understand performance and improve productivity. After overcoming latency issues by locating compute power at the edge of the network, the IoT data spigot opened, and enterprises began capturing trillions of pieces of data.
Monetizing IoT requires a precarious network of fully automated off-the-shelf devices and industry- or enterprise-specific applications that deliver unique functionality. Artificial Intelligence (AI) and Decision Intelligence (DI) tools enable enterprises to finally take full advantage of both the real-time data captured from IoT devices and the context derived from relevant data that has been captured and archived. Rather than initiating a fixed process, AI/DI models are designed to deliver actionable intelligence based on aggregated knowledge to execute specific actions rapidly.
Time is of the essence
The value of IoT rests in the ability to react and respond quickly to alarms and metrics. While AI/DI models can rapidly process all these inputs, those models require access to all relevant data sources, from the device at the edge of the network to the models and archives stored in private or public clouds and data warehouses. Centralizing the data in a single warehouse affects performance by increasing latency and network costs. The reality is, for most enterprises, that relevant data is scattered across the enterprise and often around the globe.
To take full advantage of IoT solutions, AI/DI tools need reliable, fast connections to multiple sources that ensure timely response and action. For all the promise of AI/DI applied to IoT, several factors could derail the opportunity:
- Data management and interoperability – In addition to the volume of data, integration between storage, IoT devices, and AI/DI applications is challenging for enterprises with distributed data stores.
- Latency and bandwidth – Processing closer to the IoT device at the network edge improves latency but requires stout, secure connectivity to AI/DI applications and data in multiple locations.
- Security – Transmitting data via the Internet from many IoT devices introduces risk and creates vulnerability when connecting to AI/DI applications.
One of the biggest challenges when implementing AI/DI solutions for IoT is scalability and performance. IoT data can come from numerous locations, devices and sources – sensors, cameras, phones, homes – at any time. AI/DI workloads must be rapidly scalable to accommodate spikes in data volume and variable demand. One example is a natural disaster. Insurance companies converge on the affected area, and all connect to their systems, customers, and local services.
For example, insurance companies are quickly inundated with calls and contacts for help in the event of a hurricane. Individuals submit claims, photos, or videos of flooding, damage to homes and vehicles, or other personal property. To prioritize its response to those most affected, an insurance company might use AI/DI to evaluate the claims and score them against a model validated using previous disaster responses. The real-time claim is quickly assessed, and the right help is dispatched. To prevent further losses, dispatching a crew to place a temporary cover over a large hole in a roof would be prioritized over the response to a car dented by a tree limb.
Vultr hybrid cloud architecture for IoT, edge computing, and AI
Using AI/DI to make rapid data-driven decisions and initiate action fulfills the promise of IoT. The AI/DI infrastructure must be distributed so that processing can occur at the network's edge and actions are determined using data from numerous locations and sources.
A fully integrated AI/DI infrastructure stack from Vultr using Console Connect combined with proven network services results in a reliable and secure AI/DI infrastructure configuration tuned to individual enterprises' needs.
Vultr's composable cloud, with bare metal and powerful GPUs, offers the flexibility and performance essential for IoT and AI/DI innovation. With 32 cloud data center locations worldwide, Vultr enables enterprises to deploy and manage latency-sensitive solutions at the edge, ensuring global reach and fast local performance. This infrastructure facilitates seamless integration between cloud and IoT devices, making efficient data processing and analysis crucial for real-time decisions and enhancing operational efficiency.
Instead of a closed architecture with complex interdependencies, Vultr and its partners have implemented an open architecture that enables access to multiple cloud platforms and is customizable across all levels of the cloud stack, from the infrastructure layer to the application layer. AMD and NVIDIA GPUs power the Vultr solution and enable AI/DI, IoT, and wireless connections at the edge to improve response time, reduce latency, and accelerate real-time decision-making. Vultr’s cloud infrastructure solution is optimized by AMD and NVIDIA GPUs, like the AMD MI300X Accelerator, the NVIDIA GH200 Grace Hopper™ Superchip, and the NVIDIA H100 Tensor Core GPU, to facilitate advanced AI/DI training and complex simulations.