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The advancements of remote sensing (RS) pose increasingly high demands oncomputation and transmission resources. Conventional ground-offloadingtechniques, which transmit large amounts of raw data to the ground, suffer frompoor satellite-to-ground link quality. In addition, existingsatellite-offloading techniques, which offload computational tasks to low earthorbit (LEO) satellites located within the visible range of RS satellites forprocessing, cannot leverage the full computing capability of the networkbecause the computational resources of visible LEO satellites are limited. Thissituation is even worse in hotspot areas.  In this paper, for efficient offloading via LEO satellite networks, wepropose a novel computing-aware routing scheme. It fuses the transmission andcomputation processes and optimizes the overall delay of both. Specifically, wefirst model the LEO satellite network as a snapshot-free dynamic network, whosenodes and edges both have time-varying weights. By utilizing time-varyingnetwork parameters to characterize the network dynamics, the proposed methodestablishes a continuous-time model which scales well on large networks andimproves the accuracy. Next, we propose a computing-aware routing schemefollowing the model. It processes tasks during the routing process instead ofoffloading raw data to ground stations, reducing the overall delay and avoidingnetwork congestion consequently. Finally, we formulate the computing-awarerouting problem in the dynamic network as a combination of multiple dynamicsingle source shortest path (DSSSP) problems and propose a genetic algorithm(GA) based method to approximate the results in a reasonable time. Simulationresults show that the computing-aware routing scheme decreases the overalldelay by up to 78.31% compared with offloading raw data to the ground toprocess.

Computing-Aware Routing for LEO Satellite Networks: A Transmission and Computation Integration Approach