While   global   navigation   satellite   system   (GNSS)technologies have always been the go-to solution for localizationproblems, they may not be the best choice for some Internet-of-Things (IoT) applications due to the incurred power consumptionand cost. In this paper, we present an alternative satellite-basedlocalization method exploiting the signature of Doppler shifts andangle  of  arrival  measurements  as  seen  by  a  receiving  satellite.We first derive the joint likelihood function of the measurements,which  is  represented  as  a  combination  of  three  Gaussian  distri-butions.  Then,  we  show  that  the  maximum  likelihood  problemreduces  to  a  more-efficient  mean  squared  error  minimizationin  the  Gaussian  case  as  inferred  from  real  measurements  wecollected  from  low-earth-orbit  satellite  using  a  tracking  groundstation.  Thus,  we  propose  utilizing  a  stochastic  optimizer  tosearch for the global minimum of the mean squared error whichrepresents the location of the ground IoT device as estimated bythe  satellite  platform.  The  emulated  results  show  that  the  IoTdevice localization, under such realistic model, can be performedwith  sufficient  accuracy  for  IoT  applications.

Many Internet-of-Things (IoT)-over-satellite applications rely on affordable location-aware but energy-constrained IoT sensors. In this paper, we propose a novel method to estimate the satellite visibility window in IoT devices based on simple Doppler measurements. We present two scenarios where the orbital information of the serving satellite’s is initially unknown to the IoT device: (i) we assume that the geographic coordinates are known to the IoT device, and (ii) we assume that the coordinates are completely unknown. Accordingly, we  derive the Doppler measurement likelihood function, and simplify it to a root mean square error (RMSE) minimization problem. From a sequence of Doppler measurements, we estimate the orbital parameters of the serving satellite using a stochastic optimizer to minimize the RMSE. From the orbital estimation, we then predict the satellite visibility window (satellite pass). To gauge the accuracy of the window estimation, we apply the intersection-over-union metric to compute the overlapping visibility window between the ground truth and the estimation, and consequently present the results based on extensive Monte Carlo simulations.