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Artificial intelligence (AI) is driving the next wave of tech innovation, and data is its fuel. As such, data processing within your AI implementation is arguably one of the most important parts to get right, especially in the distributed and often-disconnected environments so common to Internet of Things (IoT) applications.
The trick is you need a database that can handle the demands of IoT and AI.
In a recent post on The New Stack about cloud-to-edge AI with a mobile database, I explored how a mobile database platform with built-in data synchronization and support for AI can accelerate the development of AI-based features and capabilities in edge applications.
By leveraging such a database, AI-powered apps can realize the benefits of edge computing: They run faster because data is located physically closer to the point of interaction, and they become more reliable by eliminating dependencies on an inherently unreliable internet.
In IoT, edge computing becomes especially important because IoT devices live literally at the edge of the network in the form of sensors, actuators, cameras and the like. These devices capture high volumes of data, absorbing it like sponges and streaming it at high velocity. Applications using this data must be able to react to it as quickly as possible, but it comes so fast and at such high volumes that using it effectively becomes extremely difficult. AI holds the key.
For example, with high-speed and often repetitive time-series sensor readings, trained machine learning models can quickly evaluate the data in real time to find issues and anomalies, sifting out the noise and immediately zeroing in on areas in need of attention.
Achieving this is particularly challenging when internet connectivity is not available; where you process data and locate AI models can make a huge difference. If it’s all in the cloud, you have the potential for significant latency because you have to send data over the internet and then wait for results to come back down the wire. Worse, apps can stall if the connection is interrupted.
A mobile database platform solves this by enabling an edge AI database architecture that brings data and AI processing to the edge, including on devices, eliminating internet dependencies. Data synchronization happens in the background when connectivity is available, keeping the whole ecosystem consistent.
This architecture enables you to process data and AI in the cloud, at the edge and on the device, bringing the scale to handle the massive amounts of data inherent in IoT apps and the edge capabilities to take immediate advantage of it.
Some examples of AI in IoT applications include:
Smart lighting solutions in major metropolitan areas use IoT sensors deployed to municipal lighting grids. The sensors detect traffic, pedestrians, weather and ambient natural lighting, and they evaluate those conditions to autonomously adjust or turn off lights according to real-time needs anywhere in the city. This can save more than 75% in lighting costs while increasing safety for citizens. These solutions leverage trained machine-learning models as they assess their environment, so they can do things like tell the difference between a walking pedestrian and a wind-blown object, and then act accordingly. AI also makes recommendations for improvements based on trends, suggesting expansions and alternative deployment locations to optimize the grid.
Autonomous machines can perform repetitive and/or hazardous tasks in a warehouse, such as picking, sorting, packaging and transporting materials. With these solutions, fleets of robots autonomously perform the tasks in large-scale warehouse operations — even in areas with no network connectivity — faster, more accurately and more tirelessly than human workers can. AI makes these robots smart enough to detect, get around or even move obstacles as they do their tasks. The system’s AI also analyzes data patterns over time to recommend warehouse layout and traffic optimizations.
Many cruise lines, amusement parks and resort hotels offer wearable IoT devices to guests that act as access keys to guest rooms and attractions, as well as touchless payment for goods and amenities. The systems also track the devices as guests move around the environment, providing insights that the hospitality provider can use to personalize the guest experience. AI in the system uses data such as guest profiles, location and history to find and present compelling offers in real time. It can also assess conditions, such as guest movement and concentrations, offering recommendations to optimize pedestrian traffic flow and crowd control. These types of apps must work regardless of internet connectivity — you don’t want a guest to be stranded outside their room or unable to make a purchase — so they benefit from the edge AI database architecture’s ability to provide maximum uptime.
By leveraging an edge AI database architecture, organizations can enable faster, more reliable AI-augmented IoT applications that provide the highest guarantees of speed, accuracy and uptime.
Couchbase Mobile is a mobile database platform that natively supports edge-computing architectures. It synchronizes data between the cloud, the edge and individual devices as connectivity allows, and during network disruptions, apps continue to operate using local data processing. Couchbase Mobile can integrate machine learning models in both the cloud database and the embedded database, enabling AI processing from cloud to edge.
With Couchbase Mobile, you can develop and deploy AI-powered IoT apps at the edge to meet any speed, availability or security requirements.
Learn more and sign up for a free trial of Couchbase Capella.
