with Avnet, Edge Impulse, Microsoft, Nordic Semiconductor, Taoglas, u-blox, Vulcan EarthRanger, Western Digital and Smart Parks
Win up to $5,000 in prizes!
In response to the growing crisis facing Africa’s diminishing elephant population, Hackster.io and Smart Parks are coming together with leading technology and conservation partners to protect the gentle giants in their natural habitats.
Elephant deaths and abuse like those pictured below are much too common, but they can be stamped out with stronger legislation, law enforcement, and conservation. In order to make that a reality, pro-conservation teams need to better data on what is happening, something our collaboration and technology are perfectly positioned to provide.
We're inviting all Hacksters to join us as we develop the world's most advanced elephant tracking collar, which can help park rangers reduce animal loss from illegal ivory poaching, trophy hunting, human conflict, and environmental degradation. With #ElephantEdge, we're calling on the community to build ML models using the Edge Impulse Studio and tracking dashboards using Avnet's IoTConnect, which will be deployed onto 10 production-grade collars manufactured by our engineering partner, Institute IRNAS, and deployed by Smart Parks.
Target design for the ElephantEdge tracker
Build ML models with Edge Impulse that will be used for collar deployments
These new models will create a new Human to Elephant Language, powered by TinyML, that will help conservation efforts:
Poaching Risk Monitoring: Build models that can identify an increased risk for poaching. Example: Know when an elephant is moving into a high-risk area and send real-time notifications to park rangers.
Human Conflict Monitoring: Build models and dashboards that can prevent conflict between humans and elephants. Example: Sense and alert when an elephant is heading into an area where farmers live. This collar could detect human presence by scanning if any mobile phones or WiFi hotspots are near, by tapping the available radio modules (Nordic Semiconductor nRF52840, NINA-B30x BLE, Semtech LR1110).
Elephant Musth Monitoring: Build models and dashboards that can recognize when an elephant bull is in musth (a periodic condition in male elephants characterized by highly aggressive behavior and accompanied by a large rise in reproductive hormones). Example: Utilize the motion and acoustic sensors to discern this state of erratic, loud, and aggressive behavior.
Elephant Activity Monitoring: Build models and dashboards that can classify the general behavior of the elephant, such as when it is drinking, eating, sleeping, etc. Example: Detect and report the elephant activity by using accelerometer data. Or go more advanced and use a water detection sensor to see when the elephant goes swimming, drinking, or digging for water.
Elephant Communication Monitoring: Build models and dashboards that can listen for vocal communications between elephants via the onboard microphone. Example: Use sound-recording technology to listen to their vocalizations. Here's how.
This is an urgent problem that no one has totally solved. Do you have completely out-of-the-box ideas that have never considered?
Build IoT tracking dashboards
Build an IoTConnect dashboard that will be used for collar deployments and help park rangers, track, monitor, and get on-demand alerts that are critical to conservation efforts:
- Simulate dashboards that track location and leaving protected areas
- Build dashboards that report the frequency of entering high-risk areas
- Monitor and infer active period vs resting period for the elephants
- Simulate alerts when activity deviates from the expected range
- Alerts to replace batteries or when a collar malfunctions, goes offline
- Design and ideate any other telemetry data and inference
The new collar will use the following hardware and software:
- Nordic Semiconductor nRF52840 Bluetooth 5, Thread and Zigbee multi-protocol SoC, powered by the
- u-blox NINA-B30x BLE module and ZOE-M8G GPS/GNSS module
- Taoglas low-power wide-area (LPWA) antennas
- Western Digital Edge SDSDQAB-016G microSD storage
- Semtech LR1110 ultra-low-power transceiver
- STMicroelectronics LIS2DW12 ultra-low-power high-performance three-axis MEMS accelerometer, with configurable single/double-tap recognition, free-fall, wakeup, portrait/landscape, and 6D/4D orientation detections
- STMicroelectronics LSM303AGR ultra-compact high-performance eCompass module, with a 3D digital linear acceleration sensor and a 3D digital magnetic sensor
- STMicroelectronics MP23ABS1 high-performance MEMS audio sensor with single-ended analog bottom-port microphone
- IoTConnect Platform for dashboard creation of asset tracking
- Edge Impulse Studio TinyML modeling software
You do not need any hardware to build the ML models. Use datasets to sample, analyze, and build your TinyML models. You can also use your mobile phone to run simulated data collections and deployment.
You do not need specific hardware to build the dashboards. Use any hardware you already have, from Arduino to Microchip, Seeed, Adafruit to STMicroelectronics, to send data to the IoTConnect platform.
How to Enter
Step 1: Register for the Contest
- Start by creating a free account on Hackster.io (or sign-in if already a member).
- Register for the contest by clicking “Register as a participant”.
Step 2: Build and Document Your Project
- Build your project according to the submission requirements
- Document your project build using Hackster’s project template. To create a new project, click “create new project” on the contest page or the “+” symbol in the top right corner of your Hackster page.
Step 3: Review and submit your project
- Review your project and make sure it meets all the submission requirements
- .Upload a previously created project by clicking “add existing project” on the contest page.
- Submit your project by Oct 16, 2020 at 11:59 PM PT by clicking “review and submit project” on the contest page.