Science has given humans the best way to understand and make decision about our world, life and our place in the cosmos. SenseMaking is established when a MNN, a Mapping Network Method has reseached a computation consenus across the network.
Human body temperature example: Lets assume we are at a point in time where the core temperature of a human being is not known. An individual will put forward an MNM, that is a human readable statement that states their belief the core body temperature is 40 Degrees Celsius. The MNM is then Rolled out to the network and other individual peers start contributing sensor data. All the data is fed into the Mapping Protocol and this will provide a rolling consensus until a value is found, 36.5C to 37.5C. These consensus numbers are then available to be used in further MNM.
Hyper-Local weather forecasting: From home weather stations to solar panel installations, the volume of data recording the local climate is exploding. The Mapping Protocol allows all these data points to be combined with local weather forecasting computational models contributed via MNM. The value gain from these collective modeling being used to deliver hyper-local weather forecasting to farmers, food manufacturers and day to day, life, work, sport and lifestyle activities.
Crop Farm Insurance: A CMC is constructed between a farmer and an Insurance company. The CMC includes a list of MNM that state when a crop has failed based on sensor analysis of the crop e.g. using satellite or in field webcam, or laser chemical analysis of the crop. The commercial details include degrees of certainty and percentage of the crops the needs to fail to trigger payments to the farmer on event of a e.g. a drought.
Quantified Self Peer to Peer
Over the last few years wearable sensors have taken off e.g. fitbits etc. Today the sensor data is captured by an app and synced back to the cloud for analysis. The Dsensor Protocol would give individuals the option to save the data back to their own private blockchain and then share the data securely to discover patterns in data found by the Quantified Self individuals contributing MNM to model their activities.