https://github.com/wirelesslife/mousetrap

By instrumenting trap’s with IoT sensors, cataloging the specific positions, and capturing trap & clear events in the cloud, we can apply machine learning algorithms to understand patterns and provide predictive analytics to optimally schedule trap visits and ideal location placement for trap & field technician efficiency.
https://github.com/wirelesslife/mousetrap

iot iot-device iot-framework iot-gateway iothub nodejs windowsiot10core windowsiotcore

Last synced: about 1 month ago
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By instrumenting trap’s with IoT sensors, cataloging the specific positions, and capturing trap & clear events in the cloud, we can apply machine learning algorithms to understand patterns and provide predictive analytics to optimally schedule trap visits and ideal location placement for trap & field technician efficiency.

• Host: GitHub
• URL: https://github.com/wirelesslife/mousetrap
• Owner: WirelessLife
• License: mit
• Created: 2019-10-02T18:15:57.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2022-09-08T16:53:20.000Z (over 2 years ago)
• Last Synced: 2025-04-24T00:05:10.883Z (about 1 month ago)
• Topics: iot, iot-device, iot-framework, iot-gateway, iothub, nodejs, windowsiot10core, windowsiotcore
• Language: HTML
• Homepage:
• Size: 6.67 MB
• Stars: 4
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Purpose:

To digitally transform pest control through operational efficiency driven by intelligent information. 

# Hypothesis: 

By instrumenting trap’s with IoT sensors, cataloging the specific positions, and capturing trap & clear events in the cloud, we can apply machine learning algorithms to understand patterns and provide predictive analytics to optimally schedule trap visits and ideal location placement for trap & field technician efficiency.

# Technology Overview:

PaaS Services used for easy Scale-out , Reliable “Always On” SLA, and low cost of maintenance.

Services loosely coupled together so the architecture is flexible to future enhancements & maintenance. 

# Trap Sensor: Windows IoT Core

- A closed circuit on GPIO is applied to a traditional trap, which is connected to an IP cloud-connected gateway device to collect and transmit data.

- Raspberry Pi 2 running Windows IOT Core

- UWP App monitors circuit and sends events to Azure IoT Hub

In production scenarios, it would be more cost efficient to use multiple sensors connected to a single gateway device, ideally through a wireless low power device (i.e. zigbee, zwave or Bluetooth)