Algorithm 2 Data fusion operation

INPUT: The output data stream of algorithm_1 $FD=(f{d}^1,f{d}^2,...,f{d}^n)$ Covariance matrix calculated by Kalman filter in data filtering layer $R=(R^1,R^2,...,R^n)$ Identity Matrix Calculated by Kalman filter in data filtering layer $H=(H^1,H^2,...,H^n)$   if $f{d}^1=NONEANDf{d}^2=NONEAND...ANDf{d}^n=NONE$ then   2:  Exit    else   4:  $R\left(t\right)\leftarrow {\left[{\displaystyle \sum _{i=1}^N}{R}_i^{-1}\left(t\right)\right]}^{-1}$   5:  $z\left(t\right)\leftarrow {\left[{\displaystyle \sum _{i=1}^N}{R}_i^{-1}\left(t\right)\right]}^{-1}{\displaystyle \sum _{i=1}^N}{R}_i^{-1}\left(t\right)fd\left(i\right)$   6:  $H\left(t\right)\leftarrow {\left[{\displaystyle \sum _{i=1}^N}{R}_i^{-1}\left(t\right)\right]}^{-1}{\displaystyle \sum _{i=1}^N}{R}_i^{-1}\left(t\right)H_i\left(t\right)$     Calculate total estimated value using Kalman filter based on total $z\left(t\right)$, $R\left(t\right)$ and $H\left(t\right)$     send z(t) to cloud.   7:  if there is a missing data then   8:   send the estimated value calculated by Kalman filter is sent the cloud.   9:  end if    end if