We can already determine if and when sensitive equipment was dropped, if perishable goods were sufficiently refrigerated during transit, if your asset was in fact shipped via air instead of by sea or ground, or the exact XYZ location a package is stored within a warehouse down to the individual pallet. Now imagine we can greatly extend the battery of life of this system by selecting the right ultra-low power SoC and building our architecture carefully around it.

The sensors themselves are inexpensive and for the most part low power when used with the correct duty cycle and averaged over long periods of time. Radios are available for Bluetooth, 5G, or any number of low-power wide area networks (LP-WAN) and can also be considered low power over long periods of time when used only when needed. Until recently, the hard part was finding an AI-based edge processor that itself operated at ultra-low power while bringing more intelligence and reducing inference latency.

From tracking to understanding

The opportunity is to combine location, movement and environmental data with on-device intelligence so the system can understand what is happening to an asset—not simply where it is.

Ultra-Low Power AI at the Edge

This is where our ECS-DoT AI system-on-chip (SoC) comes in. ECS-DoT SoCs are ultra low-power and feature real-time AI inference engines to support always-on multimodal sensor fusion at the milli-Watt scale. There are many companies developing edge AI chips and solutions, but the ECS-DoT is designed to deliver exceptional performance while consuming dramatically less power than conventional approaches.

The ECS-DoT and its sensors sit in low power modes most of the time, until they are needed to determine if other system resources (additional sensors, higher-level processing, radio) need to be activated. The lowest power sensors that can make a decision make a decision, while “expensive” resources wake only when the context warrants.

Let’s take cold storage of produce for example. The ECS-DoT and temperature/gas sensors are considered higher power than a barometer or accelerometer, so we use these two sensors to send an interrupt to the ECS-DoT to wake up and measure temperature / ambient gases when we have detected an impact or anomalous pressure change. This allows us to keep relatively low duty cycle on the high current consumers and lower average system power.

Low Power, Fast Decisions

Not only does ECS-DoT management of system resources lead to low battery usage, but the extraordinary efficiency of our AI algorithms also translates into ultra-low wake-to-decision latency. This approach means we can keep the entire system in lower power states longer instead of staying awake and processing more data for longer.

AI capabilities allow users to go beyond simple motion thresholds, for example, to characterize specific activity such as impacts, tampering, or changes in environmental conditions like temperature and humidity. This leads to context-aware edge intelligence through multimodal sensor fusion and is instrumental in making event-based reporting possible.

Since a radio or apps processor is the hungriest component in the system, a smart system lean on the intelligence of the embedded controller and lower power sensors to only report events that are necessary.

Optimizing Battery Life and Contextual Awareness

Depending on variables that will change from user to user – such as the configuration of sensors and requirements for reporting – can increase the battery life of the system OR increase the contextual awareness of the asset tracker.

A real-world implementation developed with Bosch integrates ECS-DoT with an accelerometer, an inertial measurement unit (IMU, which combines an accelerometer and a gyroscope), a barometer, a magnetometer, and a gas meter.

A Tiered Approach to Sensor Monitoring

The inclusion of both a standalone accelerometer and an IMU may seem redundant at first, but there’s a good reason for it.

Standalone accelerometers are inexpensive, and they operate with ~100x less power than a more complex IMU for typical use cases. Our edge AI processor can rely on the standalone accelerometer to determine if an event is significant.

If not, the processor returns the entire system to deep sleep mode. If the data from the accelerometer indicates that more data would be useful, however, the processor then activates the IMU to get a richer data set, and more degrees of freedom for measurement.

The potential savings in power consumption derived from this tiered approach to waking and sensing easily justifies the use of both accelerometer and IMU.

Why the tiered approach matters

The potential savings in power consumption derived from waking and sensing only when necessary can justify the use of both a low-power standalone accelerometer and a richer IMU.

Flexible Sensor Configurations for Different Applications

This combination of sensors that EMASS and Bosch put together can be used for many different purposes, and can also be optimized for each use case by selecting different sensor configuration profiles and different models on the ECS-DoT.

One profile is better for monitoring fragile systems, another is more suitable for perishables, while a third is more appropriate for tracking assets within busy warehouses.

Of course, this initial combination with Bosch is just one possible configuration. Engineers can create sensor modules that best fit their specific applications.

Microphones and sensors that detect sound, light, flow, temperature, pressure, and other phenomena create endless opportunities for always on mW intelligent systems.

Beyond Tracking: Intelligent Asset Monitoring

Our ECS-DoT SoCs open new possibilities for intelligent asset monitoring, helping organizations move beyond simply knowing where their assets are to understand their condition, context, and movement in real time in the always on milliwatt domain.