Min Loading Max Reusability Fusion Classifiers for Sensor Data Model

With the deployment of sensor network applications there is lot of breakthrough in digital or smart sensor designs. These smart sensors have a dedicated processor which allows interfacing to many sensors which measure ambient readings of temperature, pressure and humidity of the environment. These sensors once calibrated they function independently running using a self-powered battery which operates 220-mA hours of operation and smart sensor uses 2.45 uA to calibrate and measure the values giving a lifetime of 220 mAh/2.45 muA=10.25 years. A sensor network typically uses 100 to 1,000 of these sensor nodes to monitor the measured values in a given region of deployment. The modeled network use data forwarding, energy conservation and fault tolerance and some of the deployment use secure communication as these are wireless based sensors. This networking model is optimized to use very less central coordination as it is an infrastructure less protocol. Due to the nature of the sensor model we develop two categories of fusion techniques. One is purely based on distributed quantitative routing which is defined by min node loading and max node reusability fusion technique. This fusion technique initially classifies the robustness of the distributed algorithms during its lifetime by defining them as tamely faulty sensors and widely faulty sensors. This model is further qualitatively extended for decision making defined by min data redundancy and max sensor fault-tolerant using a measurement independent probability data model for each clustering algorithm.