Kavli Affiliate: Feng Wang
| First 5 Authors: Lijun He, Ziye Jia, Juncheng Wang, Feng Wang, Erick Lansard
| Summary:
In Earth Observation Satellite Networks (EOSNs) with a large number of
battery-carrying satellites, proper power allocation and task scheduling are
crucial to improving the data offloading efficiency. As such, we jointly
optimize power allocation and task scheduling to achieve energy-efficient data
offloading in EOSNs, aiming to balance the objectives of reducing the total
energy consumption and increasing the sum weights of tasks. First, we derive
the optimal power allocation solution to the joint optimization problem when
the task scheduling policy is given. Second, leveraging the conflict graph
model, we transform the original joint optimization problem into a maximum
weight independent set problem when the power allocation strategy is given.
Finally, we utilize the genetic framework to combine the above special
solutions as a two-layer solution for the joint optimization problem.
Simulation results demonstrate that our proposed solution can properly balance
the sum weights of tasks and the total energy consumption, achieving superior
system performance over the current best alternatives.
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