article · IEEE Transactions on Network Science and Engineering
The surge in Internet of Things devices requires expanded network capacity, particularly across remote areas. Combining low Earth orbit satellites equipped with mobile edge computing and energy-harvesting unmanned aerial vehicles offers a potential solution for 6G coverage. However, managing computational task offloading remains difficult due to the limited onboard energy of aerial vehicles and the brief communication windows created by fast-moving satellites. A proposed network architecture addresses these constraints by coordinating satellite server selection, transmission power, and partial task offloading. This approach aims to maximise service satisfaction while minimising energy consumption under deadlines and energy limits. A mixed discrete-continuous control deep reinforcement learning algorithm with an action shaping function was developed to resolve the dynamic decision-making problem. In simulations, this method converged effectively and surpassed existing alternatives in managing network performance and resources.
Providing reliable wireless services to remote sensors is critical for modern connectivity, yet remote regions often lack terrestrial infrastructure. Integrating energy-harvesting aerial vehicles with orbital edge computing provides coverage, but orchestrating communication windows and power is complex. Intelligent self-optimising algorithms ensure task processing remains continuous, power-efficient, and dependable across challenging, remote environments.
This research could benefit satellite telecommunication providers and operators of remote environmental or industrial monitoring networks using unmanned aerial vehicles. The approach provides an automated method for managing compute loads between airborne drones and satellites. Evaluated only via simulations, the work is at an early research stage and requires validation on physical satellite and aerial testbeds before practical commercial deployment.
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Given the notable surge in Internet of Things (IoT) devices, low Earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs) have emerged as promising networking components to supplement the network capacity and ensure seamless coverage in 6G, especially over remote areas. However, task offloading and resource management are challenging to realize because of the limited connectivity duration of LEO satellites attributable to their high mobility and UAVs limited resources. Thus, this paper proposes a network model in which mobile edge computing (MEC)-enabled multiple LEO satellites in-orbit provide computational services for a resource-constrained energy harvesting UAV (EH-UAV). The EH-UAV collects data from remote IoT/sensor devices and periodically generates a computational task. To optimize the system model, we formulate a joint LEO-MEC server selection, transmission power allocation, and partial task offloading decision-making problem to maximize the service satisfaction and alleviate energy dissipation under the constraints of connectivity duration, task deadline, and available energy. To circumvent the non-convexity and dynamicity of the problem, it is reformulated as a reinforcement learning problem and solved using a novel mixed discrete-continuous control deep reinforcement learning ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$MDC^{2}-DRL$</tex-math></inline-formula> ) based algorithm with an action shaping function. Simulation results demonstrate that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$MDC^{2}-DRL$</tex-math></inline-formula> effectively converges and outperforms the existing methods.
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DOI: 10.1109/tnse.2023.3349321
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