Pub Date : 2024-12-30DOI: 10.26599/TST.2024.9010068
Ya Kang;Qingyang Song;Jing Song;Lei Guo;Abbas Jamalipour
In the realm of autonomous driving, cooperative perception serves as a crucial technology for mitigating the inherent constraints of individual vehicle's perception. To enable cooperative perception, vehicle-to-vehicle (V2V) communication plays an indispensable role. Unfortunately, owing to weak virus protection in V2V networks, the emergence and widespread adoption of V2V communications have also created fertile soil for the breeding and rapid spreading of worms. To stimulate vehicles to participate in cooperative perception while blocking the spreading of worms through V2V communications, we design an incentive mechanism, in which the utility of each sensory data requester and that of each sensory data provider are defined, respectively, to maximize the total utility of all the vehicles. To deal with the highly non-convex problem, we propose a pairing and resource allocation (PRA) scheme based on the Stackelberg game theory. Specifically, we decompose the problem into two subproblems. The subproblem of maximizing the utility of the requester is solved via a two-stage iterative algorithm, while the subproblem of maximizing the utility of the provider is addressed using the linear search method. The results demonstrate that our proposed PRA approach addresses the challenges of cooperative perception and worm spreading while efficiently converging to the Stackelberg equilibrium point, jointly maximizing the utilities for both the requester and the provider.
{"title":"Joint Resource Optimization for Secure Cooperative Perception in Vehicular Networks","authors":"Ya Kang;Qingyang Song;Jing Song;Lei Guo;Abbas Jamalipour","doi":"10.26599/TST.2024.9010068","DOIUrl":"https://doi.org/10.26599/TST.2024.9010068","url":null,"abstract":"In the realm of autonomous driving, cooperative perception serves as a crucial technology for mitigating the inherent constraints of individual vehicle's perception. To enable cooperative perception, vehicle-to-vehicle (V2V) communication plays an indispensable role. Unfortunately, owing to weak virus protection in V2V networks, the emergence and widespread adoption of V2V communications have also created fertile soil for the breeding and rapid spreading of worms. To stimulate vehicles to participate in cooperative perception while blocking the spreading of worms through V2V communications, we design an incentive mechanism, in which the utility of each sensory data requester and that of each sensory data provider are defined, respectively, to maximize the total utility of all the vehicles. To deal with the highly non-convex problem, we propose a pairing and resource allocation (PRA) scheme based on the Stackelberg game theory. Specifically, we decompose the problem into two subproblems. The subproblem of maximizing the utility of the requester is solved via a two-stage iterative algorithm, while the subproblem of maximizing the utility of the provider is addressed using the linear search method. The results demonstrate that our proposed PRA approach addresses the challenges of cooperative perception and worm spreading while efficiently converging to the Stackelberg equilibrium point, jointly maximizing the utilities for both the requester and the provider.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 3","pages":"1044-1059"},"PeriodicalIF":6.6,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817771","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-30DOI: 10.26599/TST.2024.9010106
Binbin Bao;Chuanwen Luo;Yi Hong;Zhibo Chen;Xin Fan
Unmanned Aerial Vehicles (UAVs) are promising for their agile flight capabilities, allowing them to carry out tasks in various complex scenarios. The efficiency and accuracy of UAV operations significantly depend on high-precision positioning technology. However, the existing positioning techniques often struggle to achieve accurate position estimates in conditions of Non-line-Of-Sight (NLOS). To address this challenge, we propose a novel high-precision UAV positioning method based on Multilayer Perceptron (MLP) integrating Ultra-WideBand (UWB) and Inertial Measurement Unit (IMU) technologies, which can acquire centimeter-level high-precision location estimation. In the method, we simultaneously extract key features from channel impulse responses and state space of UAV for training an MLP model, which can not only reduce error of UWB signals from dynamically flying UAV to anchor in NLOS environments, but also adapt to the diverse environment settings. Specifically, we respectively apply the anchor node assisted position calibration method and cooperative positioning techniques to the dynamic flying UAVs for solving the issues of UWB signal being blocked and lost. We conduct extensive real-world experiments to demonstrate the effectiveness of our approach. The results show that the median positioning errors of UAV in hovering and flight are 6.3 cm and within 20 cm, respectively.
{"title":"High-Precision UAV Positioning Method Based on MLP Integrating UWB and IMU","authors":"Binbin Bao;Chuanwen Luo;Yi Hong;Zhibo Chen;Xin Fan","doi":"10.26599/TST.2024.9010106","DOIUrl":"https://doi.org/10.26599/TST.2024.9010106","url":null,"abstract":"Unmanned Aerial Vehicles (UAVs) are promising for their agile flight capabilities, allowing them to carry out tasks in various complex scenarios. The efficiency and accuracy of UAV operations significantly depend on high-precision positioning technology. However, the existing positioning techniques often struggle to achieve accurate position estimates in conditions of Non-line-Of-Sight (NLOS). To address this challenge, we propose a novel high-precision UAV positioning method based on Multilayer Perceptron (MLP) integrating Ultra-WideBand (UWB) and Inertial Measurement Unit (IMU) technologies, which can acquire centimeter-level high-precision location estimation. In the method, we simultaneously extract key features from channel impulse responses and state space of UAV for training an MLP model, which can not only reduce error of UWB signals from dynamically flying UAV to anchor in NLOS environments, but also adapt to the diverse environment settings. Specifically, we respectively apply the anchor node assisted position calibration method and cooperative positioning techniques to the dynamic flying UAVs for solving the issues of UWB signal being blocked and lost. We conduct extensive real-world experiments to demonstrate the effectiveness of our approach. The results show that the median positioning errors of UAV in hovering and flight are 6.3 cm and within 20 cm, respectively.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 3","pages":"1315-1328"},"PeriodicalIF":6.6,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817766","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-30DOI: 10.26599/TST.2024.9010076
Jiayu Cao;Supeng Leng;Kai Xiong;Xiaosha Chen
Platoon-based autonomous driving is indispensable for traffic automation, but it confronts substantial constraints in rugged terrains with unreliable links and scarce communication resources. This paper proposes a novel hierarchical Digital Twin (DT) and consensus empowered cooperative control framework for safe driving in harsh areas. Specifically, leveraging intra-platoon information exchange, one platoon-level DT is constructed on the leader and multiple vehicle-level DTs are distributed among platoon members. The leader first makes critical platoon-driving decisions based on the platoon-level DT. Then, considering the impact of unreliable links on the platoon-level DT accuracy and the consequent risk of unsafe decision-making, a distributed consensus scheme is proposed to negotiate critical decisions efficiently. Upon successful negotiation, vehicles proceed to execute critical decisions, relying on their vehicle-level DTs. Otherwise, a Space-Air-Ground-Integrated-Network (SAGIN) enabled information exchange is utilized to update the platoon-level DT for subsequent safe decision-making in scenarios with unreliable links, no roadside units, and obstructed platoons. Furthermore, based on this framework, an adaptive platooning scheme is designed to minimize total delay and ensure driving safety. Simulation results indicate that our proposed scheme improves driving safety by 21.1% and reduces total delay by 24.2% in harsh areas compared with existing approaches.
{"title":"A Digital Twin and Consensus Empowered Cooperative Control Framework for Platoon-Based Autonomous Driving","authors":"Jiayu Cao;Supeng Leng;Kai Xiong;Xiaosha Chen","doi":"10.26599/TST.2024.9010076","DOIUrl":"https://doi.org/10.26599/TST.2024.9010076","url":null,"abstract":"Platoon-based autonomous driving is indispensable for traffic automation, but it confronts substantial constraints in rugged terrains with unreliable links and scarce communication resources. This paper proposes a novel hierarchical Digital Twin (DT) and consensus empowered cooperative control framework for safe driving in harsh areas. Specifically, leveraging intra-platoon information exchange, one platoon-level DT is constructed on the leader and multiple vehicle-level DTs are distributed among platoon members. The leader first makes critical platoon-driving decisions based on the platoon-level DT. Then, considering the impact of unreliable links on the platoon-level DT accuracy and the consequent risk of unsafe decision-making, a distributed consensus scheme is proposed to negotiate critical decisions efficiently. Upon successful negotiation, vehicles proceed to execute critical decisions, relying on their vehicle-level DTs. Otherwise, a Space-Air-Ground-Integrated-Network (SAGIN) enabled information exchange is utilized to update the platoon-level DT for subsequent safe decision-making in scenarios with unreliable links, no roadside units, and obstructed platoons. Furthermore, based on this framework, an adaptive platooning scheme is designed to minimize total delay and ensure driving safety. Simulation results indicate that our proposed scheme improves driving safety by 21.1% and reduces total delay by 24.2% in harsh areas compared with existing approaches.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 3","pages":"1096-1111"},"PeriodicalIF":6.6,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817716","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-30DOI: 10.26599/TST.2024.9010027
Yong Cheng;Weihao Cao;Hao Fang;Shaobo Zang
The rapid growth of online services has led to the emergence of many with similar functionalities, making it necessary to predict their non-functional attributes, namely quality of service (QoS). Traditional QoS prediction approaches require users to upload their QoS data to the cloud for centralized training, leading to high user data upload latency. With the help of edge computing, users can upload data to edge servers (ESs) adjacent to them for training, reducing the upload latency. However, shallow models like matrix factorization (MF) are still used, which cannot sufficiently extract context features, resulting in low prediction accuracy. In this paper, we propose a context-aware edge-cloud collaboration framework for QoS prediction, named CQEC. Specially, to reduce the users upload latency, a distributed model training algorithm is designed with the collaboration of ESs and cloud. Furthermore, a context-aware prediction model based on convolutional neural network (CNN) and integrating attention mechanism is proposed to improve the performance. Experiments based on real-world dataset demonstrate that COEC outperforms the baselines.
{"title":"A Context-Aware Edge-Cloud Collaboration Framework for QoS Prediction","authors":"Yong Cheng;Weihao Cao;Hao Fang;Shaobo Zang","doi":"10.26599/TST.2024.9010027","DOIUrl":"https://doi.org/10.26599/TST.2024.9010027","url":null,"abstract":"The rapid growth of online services has led to the emergence of many with similar functionalities, making it necessary to predict their non-functional attributes, namely quality of service (QoS). Traditional QoS prediction approaches require users to upload their QoS data to the cloud for centralized training, leading to high user data upload latency. With the help of edge computing, users can upload data to edge servers (ESs) adjacent to them for training, reducing the upload latency. However, shallow models like matrix factorization (MF) are still used, which cannot sufficiently extract context features, resulting in low prediction accuracy. In this paper, we propose a context-aware edge-cloud collaboration framework for QoS prediction, named CQEC. Specially, to reduce the users upload latency, a distributed model training algorithm is designed with the collaboration of ESs and cloud. Furthermore, a context-aware prediction model based on convolutional neural network (CNN) and integrating attention mechanism is proposed to improve the performance. Experiments based on real-world dataset demonstrate that COEC outperforms the baselines.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 3","pages":"1201-1214"},"PeriodicalIF":6.6,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817722","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-30DOI: 10.26599/TST.2024.9010046
Yanhua Pei;Yunzhi Zhao;Fen Hou
With the advance of 5G technologies and the development of space-air-ground-sea applications, the fast and efficient collection and processing of the explosive growth of sensing data have become significant and challenging. In this paper, considering the Age of Information (AoI), the limited coverage of Base Stations (BS), and the constrained computation capability of Unmanned Aerial Vehicle (UAV), we propose a hybrid communication framework that utilizes UAVs as relays to optimize the collection of sensing data. We aim to minimize the average AoI of the data among all sensor nodes while considering the energy consumption constraints of sensor nodes, which is formulated as a Mixed Integer NonLinear Programming (MINLP). To address this problem, we decompose it into communication resource allocation and computation resource allocation. Finally, the average AoI of the whole system is minimized and the average energy consumption constraint of sensor nodes is satisfied. The simulation results show that our proposed method can achieve significant performance improvement. In specific, our proposed method can reduce the average AoI by 20%, 11%, and 43% compared to the three counterparts, Data Transmission Directly Algorithm (DTDA), Max Weight Algorithm (MWA), and matching algorithm, respectively.
{"title":"Minimizing Age of Information in UAV-Assisted Edge Computing System with Multiple Transmission Modes","authors":"Yanhua Pei;Yunzhi Zhao;Fen Hou","doi":"10.26599/TST.2024.9010046","DOIUrl":"https://doi.org/10.26599/TST.2024.9010046","url":null,"abstract":"With the advance of 5G technologies and the development of space-air-ground-sea applications, the fast and efficient collection and processing of the explosive growth of sensing data have become significant and challenging. In this paper, considering the Age of Information (AoI), the limited coverage of Base Stations (BS), and the constrained computation capability of Unmanned Aerial Vehicle (UAV), we propose a hybrid communication framework that utilizes UAVs as relays to optimize the collection of sensing data. We aim to minimize the average AoI of the data among all sensor nodes while considering the energy consumption constraints of sensor nodes, which is formulated as a Mixed Integer NonLinear Programming (MINLP). To address this problem, we decompose it into communication resource allocation and computation resource allocation. Finally, the average AoI of the whole system is minimized and the average energy consumption constraint of sensor nodes is satisfied. The simulation results show that our proposed method can achieve significant performance improvement. In specific, our proposed method can reduce the average AoI by 20%, 11%, and 43% compared to the three counterparts, Data Transmission Directly Algorithm (DTDA), Max Weight Algorithm (MWA), and matching algorithm, respectively.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 3","pages":"1060-1078"},"PeriodicalIF":6.6,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817717","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-30DOI: 10.26599/TST.2023.9010104
Yihao Guo;Longye Qiao;Zhixiong Yang;Jianping Xiang;Xinlong Feng;Hongbing Ma
Distinguishing genuine news from false information is crucial in today's digital era. Most of the existing methods are based on either the traditional neural network sequence model or graph neural network model that has become more popularity in recent years. Among these two types of models, the latter solve the former's problem of neglecting the correlation among news sentences. However, one layer of the graph neural network only considers the information of nodes directly connected to the current nodes and omits the important information carried by distant nodes. As such, this study proposes the Extendable-to-Global Heterogeneous Graph Attention network (namely EGHGAT) to manage heterogeneous graphs by cleverly extending local attention to global attention and addressing the drawback of local attention that can only collect information from directly connected nodes. The shortest distance matrix is computed among all nodes on the graph. Specifically, the shortest distance information is used to enable the current nodes to aggregate information from more distant nodes by considering the influence of different node types on the current nodes in the current network layer. This mechanism highlights the importance of directly or indirectly connected nodes and the effect of different node types on the current nodes, which can substantially enhance the performance of the model. Information from an external knowledge base is used to compare the contextual entity representation with the entity representation of the corresponding knowledge base to capture its consistency with news content. Experimental results from the benchmark dataset reveal that the proposed model significantly outperforms the state-of-the-art approach. Our code is publicly available at https://github.com/gyhhk/EGHGAT_FakeNewsDetection.
{"title":"Fake News Detection: Extendable to Global Heterogeneous Graph Attention Network with External Knowledge","authors":"Yihao Guo;Longye Qiao;Zhixiong Yang;Jianping Xiang;Xinlong Feng;Hongbing Ma","doi":"10.26599/TST.2023.9010104","DOIUrl":"https://doi.org/10.26599/TST.2023.9010104","url":null,"abstract":"Distinguishing genuine news from false information is crucial in today's digital era. Most of the existing methods are based on either the traditional neural network sequence model or graph neural network model that has become more popularity in recent years. Among these two types of models, the latter solve the former's problem of neglecting the correlation among news sentences. However, one layer of the graph neural network only considers the information of nodes directly connected to the current nodes and omits the important information carried by distant nodes. As such, this study proposes the Extendable-to-Global Heterogeneous Graph Attention network (namely EGHGAT) to manage heterogeneous graphs by cleverly extending local attention to global attention and addressing the drawback of local attention that can only collect information from directly connected nodes. The shortest distance matrix is computed among all nodes on the graph. Specifically, the shortest distance information is used to enable the current nodes to aggregate information from more distant nodes by considering the influence of different node types on the current nodes in the current network layer. This mechanism highlights the importance of directly or indirectly connected nodes and the effect of different node types on the current nodes, which can substantially enhance the performance of the model. Information from an external knowledge base is used to compare the contextual entity representation with the entity representation of the corresponding knowledge base to capture its consistency with news content. Experimental results from the benchmark dataset reveal that the proposed model significantly outperforms the state-of-the-art approach. Our code is publicly available at https://github.com/gyhhk/EGHGAT_FakeNewsDetection.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 3","pages":"1125-1138"},"PeriodicalIF":6.6,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817715","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-30DOI: 10.26599/TST.2024.9010086
Zhengyu Zhu;Mengfei Gong;Gangcan Sun;Peijia Liu;De Mi
A simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided integrated sensing and communication (ISAC) dual-secure communication system is studied in this paper. The sensed target and legitimate users (LUs) are situated on the opposite sides of the STAR-RIS, and the energy splitting and time switching protocols are applied in the STAR-RIS, respectively. The long-term average security rate for LUs is maximized by the joint design of the base station (BS) transmit beamforming and receive filter, along with the STAR-RIS transmitting and reflecting coefficients, under guarantying the echo signal-to-noise ratio thresholds and rate constraints for the LUs. Since the channel information changes over time, conventional convex optimization techniques cannot provide the optimal performance for the system, and result in excessively high computational complexity in the exploration of the long-term gains for the system. Taking continuity control decisions into account, the deep deterministic policy gradient and soft actor-critic algorithms based on off-policy are applied to address the complex non-convex problem. Simulation results comprehensively evaluate the performance of the proposed two reinforcement learning algorithms and demonstrate that STAR-RIS is remarkably better than the two benchmarks in the ISAC system.
{"title":"AI-Enabled STAR-RIS Aided MISO ISAC Secure Communications","authors":"Zhengyu Zhu;Mengfei Gong;Gangcan Sun;Peijia Liu;De Mi","doi":"10.26599/TST.2024.9010086","DOIUrl":"https://doi.org/10.26599/TST.2024.9010086","url":null,"abstract":"A simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided integrated sensing and communication (ISAC) dual-secure communication system is studied in this paper. The sensed target and legitimate users (LUs) are situated on the opposite sides of the STAR-RIS, and the energy splitting and time switching protocols are applied in the STAR-RIS, respectively. The long-term average security rate for LUs is maximized by the joint design of the base station (BS) transmit beamforming and receive filter, along with the STAR-RIS transmitting and reflecting coefficients, under guarantying the echo signal-to-noise ratio thresholds and rate constraints for the LUs. Since the channel information changes over time, conventional convex optimization techniques cannot provide the optimal performance for the system, and result in excessively high computational complexity in the exploration of the long-term gains for the system. Taking continuity control decisions into account, the deep deterministic policy gradient and soft actor-critic algorithms based on off-policy are applied to address the complex non-convex problem. Simulation results comprehensively evaluate the performance of the proposed two reinforcement learning algorithms and demonstrate that STAR-RIS is remarkably better than the two benchmarks in the ISAC system.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 3","pages":"998-1011"},"PeriodicalIF":6.6,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817769","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-30DOI: 10.26599/TST.2024.9010028
Chao Wang;Jingjing Yu;Zhi Pei;Qidi Wang;Chunlei Hong
Integer factorization, the core of the Rivest-Shamir-Adleman (RSA) attack, is an exciting but formidable challenge. As of this year, a group of researchers' latest quantum supremacy chip remains unavailable for cryptanalysis. Quantum annealing (QA) has a unique quantum tunneling advantage, which can escape local extremum in the exponential solution space, finding the global optimal solution with a higher probability. Consequently, we consider it an effective method for attacking cryptography. According to Origin Quantum Computing, QA computers are able to factor numbers several orders of magnitude larger than universal quantum computers. We try to transform the integer factorization problem in RSA attacks into a combinatorial optimization problem by using the QA algorithm of D-Wave quantum computer, and attack RSA-2048 which is composed of a class of special integers. The experiment factored this class of integers of size 2 2048 , $N=ptimes q$ As an example, the article gives the results of 10 RSA-2048 attacks in the appendix. This marks the first successful factorization of RSA-2048 by D-Wave quantum computer, regardless of employing mathematical or quantum techniques, despite dealing with special integers, exceeding 2 1061 −1 of California State University. This experiment verifies that the QA algorithm based on D-Wave is an effective method to attack RSA.
{"title":"A First Successful Factorization of RSA-2048 Integer by D-Wave Quantum Computer","authors":"Chao Wang;Jingjing Yu;Zhi Pei;Qidi Wang;Chunlei Hong","doi":"10.26599/TST.2024.9010028","DOIUrl":"https://doi.org/10.26599/TST.2024.9010028","url":null,"abstract":"Integer factorization, the core of the Rivest-Shamir-Adleman (RSA) attack, is an exciting but formidable challenge. As of this year, a group of researchers' latest quantum supremacy chip remains unavailable for cryptanalysis. Quantum annealing (QA) has a unique quantum tunneling advantage, which can escape local extremum in the exponential solution space, finding the global optimal solution with a higher probability. Consequently, we consider it an effective method for attacking cryptography. According to Origin Quantum Computing, QA computers are able to factor numbers several orders of magnitude larger than universal quantum computers. We try to transform the integer factorization problem in RSA attacks into a combinatorial optimization problem by using the QA algorithm of D-Wave quantum computer, and attack RSA-2048 which is composed of a class of special integers. The experiment factored this class of integers of size 2\u0000<sup>2048</sup>\u0000, \u0000<tex>$N=ptimes q$</tex>\u0000 As an example, the article gives the results of 10 RSA-2048 attacks in the appendix. This marks the first successful factorization of RSA-2048 by D-Wave quantum computer, regardless of employing mathematical or quantum techniques, despite dealing with special integers, exceeding 2\u0000<sup>1061</sup>\u0000−1 of California State University. This experiment verifies that the QA algorithm based on D-Wave is an effective method to attack RSA.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 3","pages":"1270-1282"},"PeriodicalIF":6.6,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817698","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-30DOI: 10.26599/TST.2023.9010116
Kaili Qi;Minqing Zhang;Fuqiang Di;Chao Jiang
Until now, some reversible data hiding in encrypted images (RDH-EI) schemes based on secret sharing (SIS-RDHEI) still have the problems of not realizing diffusivity and high embedding capacity. Therefore, this paper innovatively proposes a high capacity RDH-EI scheme that combines adaptive most significant bit (MSB) prediction with secret sharing technology. Firstly, adaptive MSB prediction is performed on the original image and cryptographic feedback secret sharing strategy encrypts the spliced pixels to spare embedding space. In the data hiding phase, each encrypted image is sent to a data hider to embed the secret information independently. When $r$ copies of the image carrying the secret text are collected, the original image can be recovered lossless and the secret information can be extracted. Performance evaluation shows that the proposed method in this paper has the diffusivity, reversibility, and separability. The last but the most important, it has higher embedding capacity. For $512 times 515$ grayscale images, the average embedding rate reaches 4.7358 bits per pixel (bpp). Compared to the average embedding rate that can be achieved by the Wang et al.'s SIS-RDHEI scheme, the proposed scheme with (2, 2), (2, 3), (2, 4), (3, 4), and (3, 5)-threshold can increase by 0.7358 bpp, 2.0658 bpp, 2.7358 bpp, 0.7358 bpp, and 1.5358 bpp, respectively.
{"title":"High Capacity Reversible Data Hiding Algorithm in Encrypted Images Based on Image Adaptive MSB Prediction and Secret Sharing","authors":"Kaili Qi;Minqing Zhang;Fuqiang Di;Chao Jiang","doi":"10.26599/TST.2023.9010116","DOIUrl":"https://doi.org/10.26599/TST.2023.9010116","url":null,"abstract":"Until now, some reversible data hiding in encrypted images (RDH-EI) schemes based on secret sharing (SIS-RDHEI) still have the problems of not realizing diffusivity and high embedding capacity. Therefore, this paper innovatively proposes a high capacity RDH-EI scheme that combines adaptive most significant bit (MSB) prediction with secret sharing technology. Firstly, adaptive MSB prediction is performed on the original image and cryptographic feedback secret sharing strategy encrypts the spliced pixels to spare embedding space. In the data hiding phase, each encrypted image is sent to a data hider to embed the secret information independently. When \u0000<tex>$r$</tex>\u0000 copies of the image carrying the secret text are collected, the original image can be recovered lossless and the secret information can be extracted. Performance evaluation shows that the proposed method in this paper has the diffusivity, reversibility, and separability. The last but the most important, it has higher embedding capacity. For \u0000<tex>$512 times 515$</tex>\u0000 grayscale images, the average embedding rate reaches 4.7358 bits per pixel (bpp). Compared to the average embedding rate that can be achieved by the Wang et al.'s SIS-RDHEI scheme, the proposed scheme with (2, 2), (2, 3), (2, 4), (3, 4), and (3, 5)-threshold can increase by 0.7358 bpp, 2.0658 bpp, 2.7358 bpp, 0.7358 bpp, and 1.5358 bpp, respectively.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 3","pages":"1139-1156"},"PeriodicalIF":6.6,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817719","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-30DOI: 10.26599/TST.2023.9010115
Hongtao Zhang;Qingguo Zhou
Geological hazard risk assessment and severity prediction are of great significance for disaster prevention and mitigation. Traditional methods require a long time to evaluate and rely heavily on human experience. Therefore, based on the key factors affecting landslides, this paper designs a geological disaster prediction model based on Monte Carlo neural network (MCNN). Firstly, based on the weights of evidence method, a correlation analysis was conducted on common factors affecting landslides, and several key factors that have the greatest impact on landslide disasters, including geological lithology, slope gradient, slope type, and rainfall, were identified. Then, based on the monitoring data of Lanzhou City, 18 367 data records were collected and collated to form a dataset. Subsequently, these multiple key influencing factors were used as inputs to train and test the landslide disaster prediction model based on MCNN. After determining the hyperparameters of the model, the training and prediction capabilities of the model were evaluated. Through comparison with several other artificial intelligence models, it was found that the prediction accuracy of the model studied in this paper reached 89%, and the Macro-Precision, Macro-Recall, and Macro-F1 indicators were also higher than other models. The area under curve (AUC) index reached 0.8755, higher than the AUC value based on a single influencing factor in traditional methods. Overall, the method studied in this paper has strong predictive ability and can provide certain decision support for relevant departments.
{"title":"Multi-Influencing Factors Landslide Susceptibility Prediction Model Based on Monte Carlo Neural Network","authors":"Hongtao Zhang;Qingguo Zhou","doi":"10.26599/TST.2023.9010115","DOIUrl":"https://doi.org/10.26599/TST.2023.9010115","url":null,"abstract":"Geological hazard risk assessment and severity prediction are of great significance for disaster prevention and mitigation. Traditional methods require a long time to evaluate and rely heavily on human experience. Therefore, based on the key factors affecting landslides, this paper designs a geological disaster prediction model based on Monte Carlo neural network (MCNN). Firstly, based on the weights of evidence method, a correlation analysis was conducted on common factors affecting landslides, and several key factors that have the greatest impact on landslide disasters, including geological lithology, slope gradient, slope type, and rainfall, were identified. Then, based on the monitoring data of Lanzhou City, 18 367 data records were collected and collated to form a dataset. Subsequently, these multiple key influencing factors were used as inputs to train and test the landslide disaster prediction model based on MCNN. After determining the hyperparameters of the model, the training and prediction capabilities of the model were evaluated. Through comparison with several other artificial intelligence models, it was found that the prediction accuracy of the model studied in this paper reached 89%, and the Macro-Precision, Macro-Recall, and Macro-F1 indicators were also higher than other models. The area under curve (AUC) index reached 0.8755, higher than the AUC value based on a single influencing factor in traditional methods. Overall, the method studied in this paper has strong predictive ability and can provide certain decision support for relevant departments.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 3","pages":"1215-1228"},"PeriodicalIF":6.6,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817721","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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