无线信号之间的干扰阻碍了信号的并发传输,降低了无线网络的吞吐量.链路调度是提高无线网络吞吐量、减少信号传输延迟的一种有效方法.因为SINR (signal to interference plus noise ratio)模型准确地描述了无线信号传播的固有特性,能够...无线信号之间的干扰阻碍了信号的并发传输,降低了无线网络的吞吐量.链路调度是提高无线网络吞吐量、减少信号传输延迟的一种有效方法.因为SINR (signal to interference plus noise ratio)模型准确地描述了无线信号传播的固有特性,能够真实反映无线信号之间的干扰,提出一种在动态无线网络中基于SINR模型的常数近似因子的在线分布式链路调度算法(OLD_LS).在线的意思是指,在算法执行的过程中任意节点可以随时加入网络,也可以随时离开网络.节点任意加入网络或者从网络中离开体现了无线网络的动态变化的特性. OLD_LS算法把网络区域划分为多个正六边形,局部化SINR模型的全局干扰.设计动态网络下的领导者选举算法(LE),只要网络节点的动态变化速率小于1/ε, LE就可以在O(log n+log R)ε≤5(1-21-α/2)/6,α表示路径损耗指数, n是网络节点的规模, R是最长链路的长度.根据文献调研,所提算法是第1个用于动态无线网络的在线分布式链路调度算法.展开更多
Two packet scheduling algorithms for rechargeable sensor networks are proposed based on the signal to interference plus noise ratio model.They allocate different transmission slots to conflicting packets and overcome ...Two packet scheduling algorithms for rechargeable sensor networks are proposed based on the signal to interference plus noise ratio model.They allocate different transmission slots to conflicting packets and overcome the challenges caused by the fact that the channel state changes quickly and is uncontrollable.The first algorithm proposes a prioritybased framework for packet scheduling in rechargeable sensor networks.Every packet is assigned a priority related to the transmission delay and the remaining energy of rechargeable batteries,and the packets with higher priority are scheduled first.The second algorithm mainly focuses on the energy efficiency of batteries.The priorities are related to the transmission distance of packets,and the packets with short transmission distance are scheduled first.The sensors are equipped with low-capacity rechargeable batteries,and the harvest-store-use model is used.We consider imperfect batteries.That is,the battery capacity is limited,and battery energy leaks over time.The energy harvesting rate,energy retention rate and transmission power are known.Extensive simulation results indicate that the battery capacity has little effect on the packet scheduling delay.Therefore,the algorithms proposed in this paper are very suitable for wireless sensor networks with low-capacity batteries.展开更多
The past decades have witnessed a wide application of federated learning in crowd sensing,to handle the numerous data collected by the sensors and provide the users with precise and customized services.Meanwhile,how t...The past decades have witnessed a wide application of federated learning in crowd sensing,to handle the numerous data collected by the sensors and provide the users with precise and customized services.Meanwhile,how to protect the private information of users in federated learning has become an important research topic.Compared with the differential privacy(DP)technique and secure multiparty computation(SMC)strategy,the covert communication mechanism in federated learning is more efficient and energy-saving in training the ma-chine learning models.In this paper,we study the covert communication problem for federated learning in crowd sensing Internet-of-Things networks.Different from the previous works about covert communication in federated learning,most of which are considered in a centralized framework and experimental-based,we firstly proposes a centralized covert communication mechanism for federated learning among n learning agents,the time complexity of which is O(log n),approximating to the optimal solution.Secondly,for the federated learning without parameter server,which is a harder case,we show that solving such a problem is NP-hard and prove the existence of a distributed covert communication mechanism with O(log logΔlog n)times,approximating to the optimal solution.Δis the maximum distance between any pair of learning agents.Theoretical analysis and nu-merical simulations are presented to show the performance of our covert communication mechanisms.We hope that our covert communication work can shed some light on how to protect the privacy of federated learning in crowd sensing from the view of communications.展开更多
基金supported by the National Natural Science Foundation of China under Grants 62272256,61832012,and 61771289Major Program of Shandong Provincial Natural Science Foundation for the Fundamental Research under Grant ZR2022ZD03+1 种基金the Pilot Project for Integrated Innovation of Science,Education and Industry of Qilu University of Technology(Shandong Academy of Sciences)under Grant 2022XD001Shandong Province Fundamental Research under Grant ZR201906140028。
文摘Two packet scheduling algorithms for rechargeable sensor networks are proposed based on the signal to interference plus noise ratio model.They allocate different transmission slots to conflicting packets and overcome the challenges caused by the fact that the channel state changes quickly and is uncontrollable.The first algorithm proposes a prioritybased framework for packet scheduling in rechargeable sensor networks.Every packet is assigned a priority related to the transmission delay and the remaining energy of rechargeable batteries,and the packets with higher priority are scheduled first.The second algorithm mainly focuses on the energy efficiency of batteries.The priorities are related to the transmission distance of packets,and the packets with short transmission distance are scheduled first.The sensors are equipped with low-capacity rechargeable batteries,and the harvest-store-use model is used.We consider imperfect batteries.That is,the battery capacity is limited,and battery energy leaks over time.The energy harvesting rate,energy retention rate and transmission power are known.Extensive simulation results indicate that the battery capacity has little effect on the packet scheduling delay.Therefore,the algorithms proposed in this paper are very suitable for wireless sensor networks with low-capacity batteries.
基金supported in part by the National Key Research and Development Program of China under Grant 2020YFB1005900the National Natural Science Foundation of China(NSFC)under Grant 62102232,62122042,61971269Natural Science Foundation of Shandong province under Grant ZR2021QF064.
文摘The past decades have witnessed a wide application of federated learning in crowd sensing,to handle the numerous data collected by the sensors and provide the users with precise and customized services.Meanwhile,how to protect the private information of users in federated learning has become an important research topic.Compared with the differential privacy(DP)technique and secure multiparty computation(SMC)strategy,the covert communication mechanism in federated learning is more efficient and energy-saving in training the ma-chine learning models.In this paper,we study the covert communication problem for federated learning in crowd sensing Internet-of-Things networks.Different from the previous works about covert communication in federated learning,most of which are considered in a centralized framework and experimental-based,we firstly proposes a centralized covert communication mechanism for federated learning among n learning agents,the time complexity of which is O(log n),approximating to the optimal solution.Secondly,for the federated learning without parameter server,which is a harder case,we show that solving such a problem is NP-hard and prove the existence of a distributed covert communication mechanism with O(log logΔlog n)times,approximating to the optimal solution.Δis the maximum distance between any pair of learning agents.Theoretical analysis and nu-merical simulations are presented to show the performance of our covert communication mechanisms.We hope that our covert communication work can shed some light on how to protect the privacy of federated learning in crowd sensing from the view of communications.
