随着无人机(UAV,unmanned aerial vehicle)与物联网(IoT,Internet of things)技术的深度融合,低空物联网中传输了大量包含敏感信息的数据,存在严重的隐私泄露风险。联邦学习(FL,federated learning)允许多个参与者共同训练模型而无须共...随着无人机(UAV,unmanned aerial vehicle)与物联网(IoT,Internet of things)技术的深度融合,低空物联网中传输了大量包含敏感信息的数据,存在严重的隐私泄露风险。联邦学习(FL,federated learning)允许多个参与者共同训练模型而无须共享敏感数据,为低空物联网安全应用提供了隐私保护的方案。但是,随着应用场景越来越丰富,节点异构性、网络动态性等特点导致低空物联网下的联邦学习非常不稳定。提出了一种结合Raft选举算法和权重计算的新型联邦学习方法(FedPRE-W,federated fearning based on proxy Raft election and weight calculation),提高了联邦学习的稳定性和效率。针对遮挡、网络动态变化以及节点能量耗尽等导致的代理设备中断问题,通过Raft选举算法选举新的代理设备,保障联邦学习的稳定性。结合节点异构性,通过计算节点权重,选举性能强的节点当选代理,提升了联邦学习的效率。最后,在公开数据集上对所提方法进行验证,结果显示,FedPRE-W算法在减少通信轮数、加速模型收敛以及提高系统稳定性等方面有显著优势。该方法为低空物联网进行安全、稳定、高效的联邦学习提供了一种可行的解决方案。展开更多
Wireless sensor networks（WSNs） are emerging as essential and popular ways of providing pervasive computing environments for various applications. Unbalanced energy consumption is an inherent problem in WSNs, charact...Wireless sensor networks（WSNs） are emerging as essential and popular ways of providing pervasive computing environments for various applications. Unbalanced energy consumption is an inherent problem in WSNs, characterized by multi-hop routing and a many-to-one traffic pattern. This uneven energy dissipation can significantly reduce network lifetime. In multi-hop sensor networks, information obtained by the monitoring nodes need to be routed to the sinks, the energy consumption rate per unit information transmission depends on the choice of the next hop node. In an energy-aware routing approach, most proposed algorithms aim at minimizing the total energy consumption or maximizing network lifetime. In this paper, we propose a novel energy aware hierarchical cluster-based（NEAHC） routing protocol with two goals： minimizing the total energy consumption and ensuring fairness of energy consumption between nodes. We model the relay node choosing problem as a nonlinear programming problem and use the property of convex function to find the optimal solution. We also evaluate the proposed algorithm via simulations at the end of this paper.展开更多
文摘随着无人机(UAV,unmanned aerial vehicle)与物联网(IoT,Internet of things)技术的深度融合,低空物联网中传输了大量包含敏感信息的数据,存在严重的隐私泄露风险。联邦学习(FL,federated learning)允许多个参与者共同训练模型而无须共享敏感数据,为低空物联网安全应用提供了隐私保护的方案。但是,随着应用场景越来越丰富,节点异构性、网络动态性等特点导致低空物联网下的联邦学习非常不稳定。提出了一种结合Raft选举算法和权重计算的新型联邦学习方法(FedPRE-W,federated fearning based on proxy Raft election and weight calculation),提高了联邦学习的稳定性和效率。针对遮挡、网络动态变化以及节点能量耗尽等导致的代理设备中断问题,通过Raft选举算法选举新的代理设备,保障联邦学习的稳定性。结合节点异构性,通过计算节点权重,选举性能强的节点当选代理,提升了联邦学习的效率。最后,在公开数据集上对所提方法进行验证,结果显示,FedPRE-W算法在减少通信轮数、加速模型收敛以及提高系统稳定性等方面有显著优势。该方法为低空物联网进行安全、稳定、高效的联邦学习提供了一种可行的解决方案。
基金supported by the National Youth Science Fund Project(61501052,61501047)the Fundamental Research Funds for the Central Universities of China(2015RC05)
文摘Wireless sensor networks（WSNs） are emerging as essential and popular ways of providing pervasive computing environments for various applications. Unbalanced energy consumption is an inherent problem in WSNs, characterized by multi-hop routing and a many-to-one traffic pattern. This uneven energy dissipation can significantly reduce network lifetime. In multi-hop sensor networks, information obtained by the monitoring nodes need to be routed to the sinks, the energy consumption rate per unit information transmission depends on the choice of the next hop node. In an energy-aware routing approach, most proposed algorithms aim at minimizing the total energy consumption or maximizing network lifetime. In this paper, we propose a novel energy aware hierarchical cluster-based（NEAHC） routing protocol with two goals： minimizing the total energy consumption and ensuring fairness of energy consumption between nodes. We model the relay node choosing problem as a nonlinear programming problem and use the property of convex function to find the optimal solution. We also evaluate the proposed algorithm via simulations at the end of this paper.
