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
In recent years,due to the wide implementation of mobile agents,the Internet-of-Things(IoT) networks have been applied in several real-life scenarios,servicing applications in the areas of public safety,proximity-base...In recent years,due to the wide implementation of mobile agents,the Internet-of-Things(IoT) networks have been applied in several real-life scenarios,servicing applications in the areas of public safety,proximity-based services,and fog computing.Meanwhile,when more complex tasks are processed in IoT networks,demands on identity authentication,certifiable traceability,and privacy protection for services in IoT networks increase.Building a blockchain system in IoT networks can greatly satisfy such demands.However,the blockchain building in IoT brings about new challenges compared with that in the traditional full-blown Internet with reliable transmissions,especially in terms of achieving consensus on each block in complex wireless environments,which directly motivates our work.In this study,we fully considered the challenges of achieving a consensus in a blockchain system in IoT networks,including the negative impacts caused by contention and interference in wireless channel,and the lack of reliable transmissions and prior network organizations.By proposing a distributed consensus algorithm for blockchains on multi-hop IoT networks,we showed that it is possible to directly reach a consensus for blockchains in IoT networks,without relying on any additional network layers or protocols to provide reliable and ordered communications.In our theoretical analysis,we showed that our consensus algorithm is asymptotically optimal on time complexity and is energy saving.The extensive simulation results also validate our conclusions in the theoretical analysis.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.
基金supported by the National Key Research and Development Program of China (No. 2020YFB1005900)the National Natural Science Foundation of China (NSFC) (Nos. 6212200494,61971269,and 6210070740)。
文摘In recent years,due to the wide implementation of mobile agents,the Internet-of-Things(IoT) networks have been applied in several real-life scenarios,servicing applications in the areas of public safety,proximity-based services,and fog computing.Meanwhile,when more complex tasks are processed in IoT networks,demands on identity authentication,certifiable traceability,and privacy protection for services in IoT networks increase.Building a blockchain system in IoT networks can greatly satisfy such demands.However,the blockchain building in IoT brings about new challenges compared with that in the traditional full-blown Internet with reliable transmissions,especially in terms of achieving consensus on each block in complex wireless environments,which directly motivates our work.In this study,we fully considered the challenges of achieving a consensus in a blockchain system in IoT networks,including the negative impacts caused by contention and interference in wireless channel,and the lack of reliable transmissions and prior network organizations.By proposing a distributed consensus algorithm for blockchains on multi-hop IoT networks,we showed that it is possible to directly reach a consensus for blockchains in IoT networks,without relying on any additional network layers or protocols to provide reliable and ordered communications.In our theoretical analysis,we showed that our consensus algorithm is asymptotically optimal on time complexity and is energy saving.The extensive simulation results also validate our conclusions in the theoretical analysis.
