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ForrestApp

A comprehensive IoT solution for real-time detection and localization of environmental emissions across large, remote areas

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  • 99%+
    Achieved minute-scale detection of air quality anomalies with over 99% accuracy
  • ~1 km²
    Each IoT sensor covers ~1 km², ensuring broad coverage with no blind spots even in areas without network infrastructure
Client
ForrestApp (Environmental IoT Startup)
Industry
Environmental Monitoring / IoT
Client overview

ForrestApp is an early-stage climate technology startup specializing in air quality and emissions monitoring. Their flagship product (the ForrestApp system) helps enterprises like utilities, mines, landfills, and oil & gas operations monitor greenhouse gas emissions and air pollutants in real time.

Their "digital nose" sensors combine gas detection with AI to pinpoint where an emission is coming from. Deployed as a network, they're meant to catch leaks and pollution events early enough to do something about them.

We built the entire software side from scratch, from the IoT backbone to the apps.

forrest app screenshot
Problem statement

Monitoring air quality over vast and remote areas is extremely challenging with traditional methods. The client faced the need to detect gas leaks, pollution, or wildfire smoke quickly and precisely across locations like forests and industrial sites.

Existing solutions (fixed stations or manual inspections) were either too slow, lacked coverage, or provided insufficient data analysis. They wanted a system that could identify anomalies in real time and localize the emission source to within a small area, enabling rapid response before minor issues became major disasters.

forrest app screenshot
Context

ForrestApp needed to deploy sensors in remote regions (e.g. large ranches, vineyards, mountain forests) often without reliable internet or power. This meant the solution must work in offline conditions and still gather data continuously.

Pain points
  • Connectivity Gaps

    Many target sites have limited cellular coverage. The system had to transmit data via alternative means (e.g. satellite communication) to ensure no data loss. Likewise, field personnel required offline access to maps and sensor data when working on-site.

  • Real-Time Alerts

    Detecting an emission event (such as a methane leak or fire) even a few hours late could lead to regulatory fines or safety hazards. The client required instant alerts and up-to-the-minute status updates from every device.

  • Scalability & Coverage

    To cover large geographic areas, multiple sensors would be deployed (each sensor can monitor ~1 square kilometer). The platform needed to manage dozens or hundreds of devices and aggregate their data into one dashboard.

  • Accuracy & Reliability

    False alarms or missed detections had to be minimized. The solution's analytics had to be robust and AI-driven to ensure high precision in identifying true emission events, as the client promised >99% detection accuracy to their end-users. Additionally, devices needed to be low-maintenance (battery-powered with long life) and the software architecture had to be fault-tolerant given the critical nature of the data.

Approach

Our development team engineered a full-stack IoT platform that met ForrestApp's challenges by combining a cross-platform app, IoT connectivity, and cloud infrastructure.

We chose Flutter for the client application to deliver a uniform experience on Android, iOS, and web from a single codebase. On the backend, we integrated the ResIOT IoT platform and AWS cloud services to handle device communication, data processing, and scaling.

The solution was designed with an offline-first mindset and real-time capabilities from day one.

forrest app screenshot
Technologies used

Flutter · ResIOT IoT Platform · AWS (Cloud & IoT Services) · Satellite & LoRaWAN communication · Map APIs (with offline tiles) · MQTT/WebSockets for live updates

forrest app screenshot
  • Key solution features
    IoT Sensor Network Integration

    We connected a distributed network of smart environmental sensors ("digital noses") to the cloud. Each sensor device was equipped with LoRaWAN radio and satellite uplink capabilities for transmitting data. Utilizing ResIOT's platform, the sensors communicate their readings in real time to a central server, even from remote sites with no ground internet. This IoT backbone ensured continuous monitoring 24/7, as sensors could send telemetry via satellite if 4G/LTE was unavailable.

  • Key solution features
    Offline Mode & Maps

    The ForrestApp mobile app (built in Flutter) was designed to function reliably offline. We implemented local data caching so that if a user goes out of network range, they can still view the last synced sensor readings and status. Interactive maps (based on OpenStreetMap data) are available in offline mode by downloading map tiles in advance, allowing field technicians to navigate to sensor locations and view coverage areas without connectivity. This offline-first approach was critical for usage in the field and in rural deployments.

  • Key solution features
    Real-Time Monitoring & Alerts

    The system delivers real-time device status updates and instant anomaly alerts. We leveraged MQTT and WebSocket channels (via ResIOT and AWS IoT services) to push live data to the app's dashboard. The moment a sensor detects an abnormal reading (e.g., a spike in methane levels or smoke particles), an alert is generated on the platform. Users receive push notifications and can see the event on the map with the affected sensor highlighted. The backend analytics, powered by ForrestApp's AI algorithms, filter out noise and false positives, so only credible threats trigger alerts. Operators see the event while it's still happening, not in next week's report.

  • Key solution features
    Unified Dashboard & Platform

    We developed a user-friendly dashboard accessible via both the mobile app and a web portal. All sensor data streams into this centralized platform. The interface allows the client and their customers to view current readings, historical trends, and analytics insights for each sensor or group of sensors. Key metrics (like gas concentration, battery status, signal strength) update live. We incorporated ResIOT's widget capabilities to include charts, device controls, and map views of sensor locations on the dashboard. Everything lands in one place, across every site.

  • Key solution features
    Robust Cloud Infrastructure

    The backend runs on AWS. Sensor data is ingested in real time and triggers serverless functions that run anomaly detection and send notifications. The infrastructure scales from 10 sensors to 10,000 without manual intervention, and telemetry is stored across multiple availability zones with failover, because a monitoring system that goes down is worse than no monitoring at all.

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  1. Implementation details
    Phase 1. Initial assessment & Design
    • In this phase, we worked closely with the client to gather requirements and understand the environmental monitoring workflow. We defined the system architecture, choosing Flutter for the app and identifying ResIOT as a suitable IoT platform for device management and communication. The team planned for key features like offline operation, real-time alerts, and integration with the client's AI anomaly detection model. We created wireframes for the user interface (dashboard and mobile screens) and designed the database schema on AWS to store sensor data and event logs.
  2. Implementation details
    Phase 2. Development & Integration
    • Our engineers developed the Flutter application and the cloud backend in tandem. On the frontend, we implemented an intuitive UI with dashboards, maps, and device controls, ensuring a smooth UX on both mobile and desktop. We integrated map functionality with offline caching so users could download maps of their sites. Meanwhile, the IoT integration team configured the ResIOT platform to register ForrestApp sensors and set up data connectors. We wrote backend services on AWS (using AWS Lambda and API Gateway) to interface with ResIOT's MQTT data stream, processing incoming sensor data in real time. During this phase, we also embedded the client's AI logic: sensor data would be fed into an anomaly detection algorithm (either at edge devices or in the cloud) to decide if an emission event is occurring. The development was iterative, with weekly demos to the client, allowing us to incorporate feedback on features like alert thresholds and reporting analytics.
  3. Implementation details
    Phase 3. Testing, Deployment & Launch
    • We conducted extensive testing of the complete system. This included simulation of sensor data to test real-time alerting, offline scenario tests (running the app with no internet to ensure data persistence), and field tests with actual hardware devices. We fine-tuned the satellite communication workflows to handle intermittent connectivity gracefully (e.g., buffering sensor readings if a satellite pass was delayed). Once validated, we deployed the cloud components to the client's AWS environment and published the Flutter app to the App Store/Play Store for controlled release. In its first field deployment, the system detected a test gas release in under two minutes.

Results and Impact

Real-Time Incident Response
detection and action within minutes, minimizing risks of accidents, environmental damage, and penalties
Broad Coverage with Fewer Gaps
~1 km² per sensor, continuous monitoring without blind spots, including remote areas
High Precision & Fewer False Alarms
99%+ accuracy through AI-driven analytics, with dramatically fewer false alarms
Improved Operational Efficiency
centralized dashboard boosts operational efficiency by ~30%, replacing manual checks with automated insights
Client Impact
launched with pilot customers and became the core of the client's commercial offering

Tech Stack

before forrest app implementation
Before implementation

Environmental monitoring was reactive and fragmented. The client's customers relied on a patchwork of local sensors, periodic manual inspections, and after-the-fact reporting. Coverage was limited, and detecting an emission source could take hours or even days.

after forrest app implementation
After implementation

ForrestApp provides a proactive, unified monitoring system. Automated IoT sensors continuously sniff out issues, and the moment an anomaly arises, an alert with the exact location is sent out. What once was a slow, manual process is now an instantaneous, data-driven operation. For instance, a landfill operator using ForrestApp can locate a gas leak immediately on the digital map, instead of searching acres of land – drastically reducing response time and improving safety.

Key takeaways
Early detection works at field scale

Pairing sensor networks with AI anomaly detection moved leak and pollution detection from hours to minutes, at a scale where manual inspection was never realistic. For the industries involved, that's the difference between an incident log and a fine.

Key takeaways
Design for Offline and Remote Use

Offline-first was the right call. Satellite backhaul and local caching keep the system working in places with no connectivity at all, which is exactly where the sensors are needed most.

Key takeaways
Cross-Platform Efficiency with Flutter

One Flutter codebase covered iOS, Android, and web, which mattered on a startup budget. The identical UI across platforms also made user training simpler.

Key takeaways
Scalability and Sustainability

The AWS and ResIOT foundation absorbs growth in sensors and regions without rework. Long battery life and over-the-air updates keep the operating cost low enough for a startup to actually run the fleet long-term.

Key takeaways
Built with the client, weekly

Weekly demos and iterative testing with the startup's team tuned alert thresholds and dashboard features against real expectations. For a novel product, that feedback loop was the difference between shipping their idea and shipping ours.

Need a similar solution?

If you're putting sensors in the field and need the software side to be as reliable as the hardware, talk to us. We'll design the whole pipeline, from device to dashboard.

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