Anti-exploitation system
A Tom-and-Jerry type of thing.

  • Use different sources of sensor data to validate a movement. For example, the GPS path needs to be reasonable based on pedometers.
  • Depending on the confidence level of the path, penalize the earning accordingly.
  • Detect fake generated data and auto correct the real path to improve the accuracy.
  • Validate human movement patterns from other types like pets.

  • Max speed of a normal human.
  • Max step length of a normal human.
  • Sensors like: Gyroscope, Pedometer, Accelerometer.
  • Potential App data from other health apps.
  • Real User Monitoring (RUM): app interaction, donation and reputation.

  • Detect multiple accounts from the same person in one trip.
  • Tell the difference between walking pals and single people with multiple devices.
  • Detect data replay from emulators. A potentially hard to detect cheating method is using an emulator to replay real paths with some randomization, which may pass the movement validation. Our approach is to build the fingerprints of each previous route and detect if the new routes match any of the previous routes.

  • GPS paths.
  • Sensors like: Gyroscope, Pedometer, Accelerometer.
  • Real User Monitoring (RUM): app interaction, donation and reputation.

  • Initial purchase of headphone NFT as game enablement along with strict penalty policy
  • Capped daily income with modelled payback period

  • Registration by invite only
  • Invite code allowance based on user's reputation score and level

  • Malicious activities/cheating attempts will cause user's reputation breach, which will result in a significant scale down in each session's token outcome
  • When maximum number of reputation breaches reach, user's account and associated NFT assets will be locked indefinitely
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Cross Movement Validation
Path Similarity Analysis
In-app governance control