SPOTLESS: Similarity patterns of trajectories in label-less sensor streams

The process of inversion, estimation and reconstruction of the sensor quality matrix, allows modeling the precision and accuracy, and in general the reliability of the model. When the sensor data ranges are not known a priori, current systems do not train on new data samples, rather they approximate based on the parameter's global average value, losing most of the spatial and temporal features. The proposed model, which we call SPOTLESS, checks the spatial integrity and temporal plausibility of streams generated by mobility patterns due to varying channel conditions. We define a minimum quality of the measured sensor data as local stream (QoD) requirements to give high precision by using distributed labeled training. In our SPOTLESS datacleaning steps, to account for packet errors due to varying channel conditions, a soft-phy based decoding is selected for various Bit Error Rates (BER), minimizing packet loss at the mobile receiver. Numerical experiments for Rayleigh fading channels and mobile BER model examples are compared with large deployment of ground sensor collecting static data streams and Data MULE collecting multi-hop temporal data from the sensor to provide hypothetical parameter accuracy. Our results were obtained in the context of provisioning a minimum precision and accuracy stream (QoD) required for 802.15.4 mobile services. SPOTLESS data-cleaning algorithm coding provides 90% precision for static streams, and increases the plausible relevance of multi-hop mobile streams by 85% for task-based learning.