  The advancements of remote sensing (RS) pose increasingly high demands on
computation and transmission resources. Conventional ground-offloading
techniques, which transmit large amounts of raw data to the ground, suffer from
poor satellite-to-ground link quality. In addition, existing
satellite-offloading techniques, which offload computational tasks to low earth
orbit (LEO) satellites located within the visible range of RS satellites for
processing, cannot leverage the full computing capability of the network
because the computational resources of visible LEO satellites are limited. This
situation is even worse in hotspot areas.
  In this paper, for efficient offloading via LEO satellite networks, we
propose a novel computing-aware routing scheme. It fuses the transmission and
computation processes and optimizes the overall delay of both. Specifically, we
first model the LEO satellite network as a snapshot-free dynamic network, whose
nodes and edges both have time-varying weights. By utilizing time-varying
network parameters to characterize the network dynamics, the proposed method
establishes a continuous-time model which scales well on large networks and
improves the accuracy. Next, we propose a computing-aware routing scheme
following the model. It processes tasks during the routing process instead of
offloading raw data to ground stations, reducing the overall delay and avoiding
network congestion consequently. Finally, we formulate the computing-aware
routing problem in the dynamic network as a combination of multiple dynamic
single source shortest path (DSSSP) problems and propose a genetic algorithm
(GA) based method to approximate the results in a reasonable time. Simulation
results show that the computing-aware routing scheme decreases the overall
delay by up to 78.31% compared with offloading raw data to the ground to
process.
