Sense-Store-Send: Trajectory Optimization for a Buffer-aided Internet of UAVs
In this letter, we study a buffer-aided Internet of unmanned aerial vehicles (UAVs) in which a UAV performs data sensing, stores the data, and sends it to the base station (BS) in cellular networks. To minimize the overall completion time for all the sensing tasks, we formulate a joint trajectory, sensing location, and sensing time optimization problem. To solve this NP-hard problem efficiently, we propose an iterative trajectory, sensing location and sensing time optimization (ITLTO) algorithm, and discuss the trade-off between sensing time and flying time. Simulation results show that the proposed algorithm can effectively reduce the completion time for the sensing tasks.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Emergent Culture in Minimal LLM Systems
What happens when LLM agents operate with no context outside a turn, minimal prompting, and simple tools? Inspired by swarm engineering, we give collectives of three agents the ability to send messages and manipulate a s…
Energy Sharing for Multiple Sensor Nodes with Finite Buffers
We consider the problem of finding optimal energy sharing policies that maximize the network performance of a system comprising of multiple sensor nodes and a single energy harvesting (EH) source. Sensor nodes periodical…
Q-LearningEnhancing PPO with Trajectory-Aware Hybrid Policies
Proximal policy optimization (PPO) is one of the most popular state-of-the-art on-policy algorithms that has become a standard baseline in modern reinforcement learning with applications in numerous fields. Though it del…
continuous-controlContinuous ControlReason for Future, Act for Now: A Principled Framework for Autonomous LLM Agents with Provable Sample Efficiency
Large language models (LLMs) demonstrate impressive reasoning abilities, but translating reasoning into actions in the real world remains challenging. In particular, it remains unclear how to complete a given task provab…
Secure Aggregation for Buffered Asynchronous Federated Learning
Federated learning (FL) typically relies on synchronous training, which is slow due to stragglers. While asynchronous training handles stragglers efficiently, it does not ensure privacy due to the incompatibility with th…
Federated Learning