Weight

Sensor
Weight is the degree to which a particular sensor's data will impact an aggregator's computation.

Overview
Basic properties, assumptions, recommendations, and general statements about weight include:


 * 1. A weight may be hardwired or modified on-the-fly.
 * 2. A weight may be based on a sensor’s perceived trustworthiness, e.g., based on who is the sensor’s owner, manufacturer, geographic location where the sensor is operating, sensor age or version, previous failures or partial failures of sensor, sensor tampering, sensor delays in returning data, etc. A weight may also be based on the worth of the data, uniqueness, relation to mission goals, etc.
 * 3. Different NoTs may leverage the same sensor data and re-calibrate the weights per the purpose of a specific NoT.
 * 4. It is not implied that an aggregator is necessarily a functionally linear combination of sensor outputs. Weights could be based on other logical insights, such as the following: if sensor A output is greater than 1 use Sensor B output else use Sensor C’s output.
 * 5. Aggregators may employ artificial intelligence techniques to modify their clusters and weights on-the-fly.
 * 6. Weights will affect the degree of information loss during the creation of intermediate data.

￼:7. Redundant sensors may increase a sensor’s weight if a grouping of redundant sensor data is in agreement and produces the same result. Repeated sampling of the same sensor might also affect a sensor’s weighting, either positively or negatively, depending on the continuity of a particular output value during a fixed time interval.
 * 8. Security concerns for weights is related to possible tampering of the weights.
 * 9. The appropriateness (or correctness) of the weights is crucial for the purpose of a NoT.