In recent years,intensified environmental pollution and climate change have increasingly exposed the world to natural disasters such as earthquakes and floods,resulting in substantial economic losses[1].These disaster...In recent years,intensified environmental pollution and climate change have increasingly exposed the world to natural disasters such as earthquakes and floods,resulting in substantial economic losses[1].These disasters frequently damage terrestrial communication infrastructures,making the rapid deployment of emergency communication networks in affected areas critical in increasing rescue efficiency[2].展开更多
Meteor Burst Communication(MBC),a niche yet revolutionary wireless communication paradigm,exploits the transient ionized trails generated by meteors ablating in Earth’s atmosphere to enable sporadic yet resilient lon...Meteor Burst Communication(MBC),a niche yet revolutionary wireless communication paradigm,exploits the transient ionized trails generated by meteors ablating in Earth’s atmosphere to enable sporadic yet resilient long-distance radio links.Known for its exceptional resilience,robustness,and sustained connectivity,MBC holds significant promise for applications in emergency communications,remote area connectivity,military/defense systems,and environmental monitoring.However,the scientific exploration and application of MBC have long been highly challenging.In particular,under the combined influence of multiple physical field factors,the channel experiences superimposed multiple random fading effects,exhibiting bursty,highly time-varying,and strongly random characteristics.This persistent technical challenge has resulted in the absence of a practical statistical channel model for MBC to date.展开更多
This paper studies the use of unmannedaerial vehicles (UAV) equipped with radio frequency(RF) energy harvesting (EH) technology to quickly establishtemporary communication networks in disasterareas to provide artifici...This paper studies the use of unmannedaerial vehicles (UAV) equipped with radio frequency(RF) energy harvesting (EH) technology to quickly establishtemporary communication networks in disasterareas to provide artificial intelligence (AI) basedservices to users with limited resources. In particular,to ensure the quality of AI-based services and improvethe lifetime of emergency communication networks,we study how to reduce the service latency andenergy consumption when fine-tuning models of AIbasedservices in the resource-constrained emergencysystem. A joint optimization problem of model trainingand RF EH for UAV-based emergency communicationnetwork is formulated. Due to the nonlinear RFEH circuit characteristics, the optimization problemis non-convex. We transform the non-convex probleminto solvable subproblems and propose an energyefficientand low-latency federated learning algorithm(EL-FL) to solve these subproblems. Theoretical analysisof the convergence and computational complexityof EL-FL is provided. Simulation results show thatthe proposed scheme significantly outperforms otherbaseline methods in various network environments.展开更多
Public communication infrastructures are susceptible to disasters. Thus, the Emergency Communication Networks(ECNs) of small groups are necessary to maintain real-time communication during disasters. Given that ECNs a...Public communication infrastructures are susceptible to disasters. Thus, the Emergency Communication Networks(ECNs) of small groups are necessary to maintain real-time communication during disasters. Given that ECNs are self-built by users, the unavailability of infrastructures and the openness of wireless channels render them insecure. ECN security, however, is a rarely studied issue despite of its importance. Here, we propose a security scheme for the ECNs of small groups. Our scheme is based on the optimized Byzantine Generals’ Problem combined with the analysis of trusted security problems in ECNs. Applying the Byzantine Generals’ Problem to ECNs is a novel approach to realize two new functions, debugging and error correction, for ensuring system consistency and accuracy. Given the limitation of terminal devices, the lightweight fast ECDSA algorithm is introduced to guarantee the integrity and security of communication and the efficiency of the network. We implement a simulation to verify the feasibility of the algorithm after theoretical optimization.展开更多
基金supported in part by the National Natural Science Foundation of China(U2441226).
文摘In recent years,intensified environmental pollution and climate change have increasingly exposed the world to natural disasters such as earthquakes and floods,resulting in substantial economic losses[1].These disasters frequently damage terrestrial communication infrastructures,making the rapid deployment of emergency communication networks in affected areas critical in increasing rescue efficiency[2].
文摘Meteor Burst Communication(MBC),a niche yet revolutionary wireless communication paradigm,exploits the transient ionized trails generated by meteors ablating in Earth’s atmosphere to enable sporadic yet resilient long-distance radio links.Known for its exceptional resilience,robustness,and sustained connectivity,MBC holds significant promise for applications in emergency communications,remote area connectivity,military/defense systems,and environmental monitoring.However,the scientific exploration and application of MBC have long been highly challenging.In particular,under the combined influence of multiple physical field factors,the channel experiences superimposed multiple random fading effects,exhibiting bursty,highly time-varying,and strongly random characteristics.This persistent technical challenge has resulted in the absence of a practical statistical channel model for MBC to date.
基金supported in part by the Key Program of the National Natural Science Foundation of China under Grant 62436004in part by the National Key Research and Development Program of China under Grant 2022YFB3104903.
文摘This paper studies the use of unmannedaerial vehicles (UAV) equipped with radio frequency(RF) energy harvesting (EH) technology to quickly establishtemporary communication networks in disasterareas to provide artificial intelligence (AI) basedservices to users with limited resources. In particular,to ensure the quality of AI-based services and improvethe lifetime of emergency communication networks,we study how to reduce the service latency andenergy consumption when fine-tuning models of AIbasedservices in the resource-constrained emergencysystem. A joint optimization problem of model trainingand RF EH for UAV-based emergency communicationnetwork is formulated. Due to the nonlinear RFEH circuit characteristics, the optimization problemis non-convex. We transform the non-convex probleminto solvable subproblems and propose an energyefficientand low-latency federated learning algorithm(EL-FL) to solve these subproblems. Theoretical analysisof the convergence and computational complexityof EL-FL is provided. Simulation results show thatthe proposed scheme significantly outperforms otherbaseline methods in various network environments.
文摘Public communication infrastructures are susceptible to disasters. Thus, the Emergency Communication Networks(ECNs) of small groups are necessary to maintain real-time communication during disasters. Given that ECNs are self-built by users, the unavailability of infrastructures and the openness of wireless channels render them insecure. ECN security, however, is a rarely studied issue despite of its importance. Here, we propose a security scheme for the ECNs of small groups. Our scheme is based on the optimized Byzantine Generals’ Problem combined with the analysis of trusted security problems in ECNs. Applying the Byzantine Generals’ Problem to ECNs is a novel approach to realize two new functions, debugging and error correction, for ensuring system consistency and accuracy. Given the limitation of terminal devices, the lightweight fast ECDSA algorithm is introduced to guarantee the integrity and security of communication and the efficiency of the network. We implement a simulation to verify the feasibility of the algorithm after theoretical optimization.
