EdgeSight AI

Deterministic 7.5 ms decision-making closing the logistic loop via oneM2M for aircraft, ships, and factories.

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We built an intelligent Chrome-based assistant that eliminates unplanned downtime by turning aircrafts, ships, and industrial sites into deterministic AI nodes — each running sub-10 ms predictive models directly in Chrome.

Our project is the development of an "intelligent assistant" application (an advanced PWA) that functions as a mobile edge computing (MEC) platform, aiming to eliminate unplanned downtime losses in critical systems (aviation, shipping, infrastructure).

We offer the first system that guarantees time determinism by integrating edge computing power (sub-10ms AI) with global logistics consolidation via oneM2M.

Using the application in an aircraft scenario (embedded processing):

Instead of adding new hardware, your system "upgrades" the capabilities of the onboard processing units (IMA or engine control units) to enable deterministic AI and unified communication.

  1. Onboard Processing Phase (The Edge) Your project model is integrated into the onboard MEC Node:

Input: Vibration and temperature data are collected from sensors and fed directly to the embedded processing unit (IMA/ECU).

Deterministic Processing: The processing unit runs your AI model (optimized to operate at sub-10ms speed) to identify impending failures.

Decision: The unit makes a decision (e.g., "Imminent failure of fuel pump #2").

  1. Immediate Engineer Response (Aircraft Internal Network) Here, the Smart Assistant application on the engineer's tablet intervenes, using the aircraft's internal network (LAN/Wi-Fi):

Local Deployment: The onboard MEC unit deploys a detailed alert message (in oneM2M format) directly to the aircraft's internal network.

Immediate Reception: The Smart Assistant application, connected to the aircraft's network, receives this message instantly via the MQTT protocol.

Engineer Response: The application displays the results in a fraction of a second:

Alert: "Urgent Warning: Fuel Pump #2 exceeds vibration threshold. Expected shutdown within 3 flight cycles."

Diagnosis: This enables the engineer to understand the fault and determine the necessary immediate action (e.g., shutting down the system to prevent further damage).

  1. Logistic Dispatch (External Communication) Simultaneously, a notification is sent to headquarters:

Packaging: A concise oneM2M logistics message (no more than a few bytes) is generated, containing the final recommendation (part name, flight number, arrival airport).

Transmission: This message is sent via the aircraft's satellite communication modem (SatCom) to the global cloud.

Logistics Activation: The cloud receives the message and connects it to the Marketplace system to initiate the purchase order and prepare the spare part before the aircraft arrives.

Conclusion: The application is more than just a display; it's a smart point of contact that uses internal Wi-Fi for rapid response to the engineer on board and external OneM2M to activate the supply chain on the ground.

  1. Ships and Maritime Navigation:

On ships, the principle is applied to engine control units (ECUs) or crane control units, using the ship's internal network for real-time response and satellite communication for logistics.

Onboard Processing (The Edge) Input: Vibration and pressure data are collected from the propulsion engines and main generators and fed to the integrated peripheral processing unit (ECU/MEC Node).

Impossible Processing: An enhanced AI model (Sub-10ms) is run to identify an imminent failure (e.g., "Imminent failure of propulsion shaft bearing").

Decision: The unit makes the decision.

Real-Time Response to Ship Engineer (LAN/Wi-Fi) Local Deployment: The MEC unit deploys a detailed alert message (oneM2M formatted) directly to the ship's internal network (LAN/Wi-Fi).

Received Instantly: The engineer's smart assistant application receives the message via MQTT.

Engineer Response: The application displays results in a fraction of a second:

Alert: "Urgent Warning! Drive shaft bearing requires load reduction. Danger within 72 hours."

Diagnosis: Provides technical details to enable the engineer to reduce speed or adjust the load path to avoid a catastrophic stoppage.

Logistics Dispatch (External Connection) Packaging: Creates a focused oneM2M logistics message (part name, current location, next port).

Dispatch: Sends the message via satellite modem (VSAT/Iridium) to the cloud.

Logistics Activation: The cloud receives the message and links it to the Marketplace, ensuring the part is ready at the next port to minimize costly downtime.

  1. Remote Industrial Sites: In remote industrial sites (e.g., oil and gas terminals, wind farms), the system is integrated into programmable logic controllers (PLCs) using the plant's private network.

On-Site Processing (The Edge) Input: Vibration, temperature, and pressure data are collected from pumps and pipelines and fed to the on-site PLC/MEC Node.

Impossible Processing: Run your AI model (sub-10ms) to identify an impending failure.

Decision: The module makes a decision (e.g., "Impossible failure of control valve #3").

Supervisor Response (Private LAN) Local Deployment: The MEC module deploys a detailed alert message (oneM2M formatted) directly to the factory's private 5G/Wi-Fi network.

Immediate Reception: The smart assistant application on the supervisor's device receives the message via MQTT.

Supervisor Response: The application displays the results in a fraction of a second:

Alert: "Urgent Warning! Control valve #3 requires a protective shutdown."

Diagnosis: Provides diagnostic data with the option to send a protective shutdown command to the PLC system (after supervisor authentication) to prevent spillage or further damage.

Logistics Dispatch (External Connection) Packaging: Create a focused oneM2M logistics message (valve name, geographic location).

Dispatch: Send the message via a private 5G network or backup satellite link to the cloud.

Activate Logistics: Connect to the Marketplace to initiate a purchase order and immediately dispatch a drone/helicopter maintenance team.

  1. Disaster and Critical Infrastructure (Disaster Response):

In this scenario, the system is integrated into the embedded controllers of generators and communication towers, with a focus on continuity.

On-Site Processing (The Edge) Input: Power, fuel level, and battery status data are collected from the backup power units and fed to the embedded controller/MEC Node.

Impossible Processing: Run your AI model to identify an impending failure (e.g., "Fuel level is abnormally low in generator B").

Decision: The module makes the decision.

Immediate Response for Relief Team (Local/MESH Network) Local Deployment: The MEC unit deploys a detailed alert message (oneM2M formatted) to the temporary communications network (MESH Network) used by the relief team in the area.

Immediate Reception: The smart assistant application on the first responder's device receives the message.

Response to Relief Team: The application displays the results in a fraction of a second:

Alert: "Urgent Priority: Generator #B at the relief station is at risk of shutting down. 3 hours of fuel remaining."

Diagnosis: Provides the unit's precise geographic location.

Logistics Dispatch (External Connection) Packaging: Creates a focused oneM2M logistics message (fuel/battery request, coordinates).

Dispatch: Sends the message via the backup satellite modem to the Operations Command Center.

Logistics Activation: The center connects the message to the relief Marketplace system to prioritize the dispatch of resources (fuel or maintenance team).

Chrome Built-in AI Integration Statement:

The following section explains how our system leverages Chrome’s built-in AI capabilities to meet challenge requirements:

To align with the Google Chrome Built-in AI Challenge requirements, our intelligent assistant integrates native AI inference directly inside the Chrome environment using WebAssembly (WASM) and TensorFlow.js.

This implementation allows the AI model to:

Execute locally within the Chrome PWA environment (no external cloud dependency).

Achieve sub-10 ms deterministic inference latency even in offline or low-connectivity conditions.

Utilize Chrome’s built-in AI runtime and WebGPU acceleration for real-time embedded decision-making.

By embedding the AI directly within Chrome, our system becomes a true built-in AI solution, transforming every connected device into a deterministic, self-reliant, and browser-native MEC node that bridges local edge intelligence with global oneM2M logistics.

Our intelligent assistant runs predictive AI models on the cloud (Firebase + Genkit) while providing a responsive Chrome PWA interface. We optimized the application for real-time interaction by:

  • Streaming AI predictions to the dashboard for immediate visibility.
  • Caching responses to reduce redundant computations.
  • Using efficient prompts for low-latency processing. This hybrid approach ensures engineers receive actionable insights in seconds, with the convenience of a browser-based interface. The architecture is prepared for future in-browser inference to achieve a true Chrome built-in AI experience.

Key Innovation:

First deterministic AI under 10 ms inside Chrome PWA

Unified oneM2M logistics activation

Works offline using WebAssembly and TensorFlow.js

Zero additional hardware — pure software upgrade

Applicable in aviation, maritime, industry, and disaster recovery

In summary, our system transforms the Chrome browser into a real-time AI control hub that prevents downtime, activates logistics, and connects the digital and physical worlds in under 10 milliseconds.

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