Pub Date : 2021-09-01DOI: 10.53106/160792642021092205005
Fangzhe Chen, Xuwei Fan, Jianpeng Li, Min Zou, Lianfen Huang
Parkinson’s disease (PD) is a neurodegenerative disease that often occurs in elderly people. Its symptoms are static tremor and slow movement, which affect the life of the patient seriously. With the development of medical technology, the early diagnosis of PD has attracted widespread attention. Many studies have shown that abnormal gait characteristics are potential bases for judging whether suffering from Parkinson’s disease. If PD can be diagnosed in the early stage, it will benefit the control of the disease and subsequent treatment. However, the diagnosis of PD is a complex task which often relies on the doctor’s experience and subjective evaluation. In this stage, because of the lack of professional knowledge of doctors or errors in subjective judgment, it is easy to misdiagnose and miss the best treatment time. In response to this problem, this paper designs an auxiliary diagnosis system for PD based on abnormal gait, composed of embedded devices, mobile terminals and servers. The embedded device uses the accelerometer to collect the patient’s six-dimensional gait data, then the data are transmitted to the mobile phone via Bluetooth and sent to the server. The server analyzes the data by 1D convolutional neural network model and monitors the abnormality of the patient’s gait. Herein, we proved that the use of 1D convolutional neural network for analysis has better performance with five-fold cross-validation, and its recognition accuracy rate reaches 91.4%.
{"title":"Gait Analysis Based Parkinson’s Disease Auxiliary Diagnosis System","authors":"Fangzhe Chen, Xuwei Fan, Jianpeng Li, Min Zou, Lianfen Huang","doi":"10.53106/160792642021092205005","DOIUrl":"https://doi.org/10.53106/160792642021092205005","url":null,"abstract":"Parkinson’s disease (PD) is a neurodegenerative disease that often occurs in elderly people. Its symptoms are static tremor and slow movement, which affect the life of the patient seriously. With the development of medical technology, the early diagnosis of PD has attracted widespread attention. Many studies have shown that abnormal gait characteristics are potential bases for judging whether suffering from Parkinson’s disease. If PD can be diagnosed in the early stage, it will benefit the control of the disease and subsequent treatment. However, the diagnosis of PD is a complex task which often relies on the doctor’s experience and subjective evaluation. In this stage, because of the lack of professional knowledge of doctors or errors in subjective judgment, it is easy to misdiagnose and miss the best treatment time. In response to this problem, this paper designs an auxiliary diagnosis system for PD based on abnormal gait, composed of embedded devices, mobile terminals and servers. The embedded device uses the accelerometer to collect the patient’s six-dimensional gait data, then the data are transmitted to the mobile phone via Bluetooth and sent to the server. The server analyzes the data by 1D convolutional neural network model and monitors the abnormality of the patient’s gait. Herein, we proved that the use of 1D convolutional neural network for analysis has better performance with five-fold cross-validation, and its recognition accuracy rate reaches 91.4%.","PeriodicalId":50172,"journal":{"name":"Journal of Internet Technology","volume":"22 1","pages":"989-997"},"PeriodicalIF":1.6,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47291682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-01DOI: 10.53106/160792642021092205001
Jeng-Shyang Pan, Jiao Wang, Jinfeng Lai, Hao Luo, S. Chu
Two factors, accuracy and cost, have always plagued the node positing in wireless sensor networks (WSN). If positioning is required to be accurate enough, the cost of equipment required for the location must increase significantly. Conversely, the lower cost will bring some problems like the big bias of positioning. DV-hop is a widely used positioning algorithm due to its low dependence on the device and the low operating cost. Many modified DV-hop algorithms improve the estimation accuracy of the average jump distance and the distance between the unknown and known nodes by adding weights, applying least squares, and using heuristic algorithms. In this paper, a novel algorithm based on the modes communication for the parallel cat swarm optimization is proposed so as to improve the location accuracy of DV-hop.
{"title":"A Modes Communication of Cat Swarm Optimization Based WSN Node Location Algorithm","authors":"Jeng-Shyang Pan, Jiao Wang, Jinfeng Lai, Hao Luo, S. Chu","doi":"10.53106/160792642021092205001","DOIUrl":"https://doi.org/10.53106/160792642021092205001","url":null,"abstract":"Two factors, accuracy and cost, have always plagued the node positing in wireless sensor networks (WSN). If positioning is required to be accurate enough, the cost of equipment required for the location must increase significantly. Conversely, the lower cost will bring some problems like the big bias of positioning. DV-hop is a widely used positioning algorithm due to its low dependence on the device and the low operating cost. Many modified DV-hop algorithms improve the estimation accuracy of the average jump distance and the distance between the unknown and known nodes by adding weights, applying least squares, and using heuristic algorithms. In this paper, a novel algorithm based on the modes communication for the parallel cat swarm optimization is proposed so as to improve the location accuracy of DV-hop.","PeriodicalId":50172,"journal":{"name":"Journal of Internet Technology","volume":"22 1","pages":"949-956"},"PeriodicalIF":1.6,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48546075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-01DOI: 10.53106/160792642021092205013
Feifei Xu, Wenkai Zhang, Haizhou Du, Shanlin Zhou
Machine Reading Comprehension (MRC) is a challenging but meaningful task in natural language processing (NLP) that requires us to teach a machine to read and understand a given passage and answer questions related to that passage. In this paper, we present a rich knowledge-enhanced reader (RKE-Reader), a hierarchical MRC model that employs double knowledge bases with an NER system as its knowledge enhancement unit. Besides, we are the first to propose an offline answer-imporving method to help model to determine the uncertain answer without extra online training process. Our experimental results indicate that on most datasets, the RKE-Reader significantly outperforms most of the published models that do not have knowledge base, especially on datasets that need commonsense reasoning. And the ablation study also reflects that external knowledge bases and answer-selecting unit do make a positive contribution in the entire model.
{"title":"Enhancing Machine Comprehension Using Multi-Knowledge Bases and Offline Answer Span Improving System","authors":"Feifei Xu, Wenkai Zhang, Haizhou Du, Shanlin Zhou","doi":"10.53106/160792642021092205013","DOIUrl":"https://doi.org/10.53106/160792642021092205013","url":null,"abstract":"Machine Reading Comprehension (MRC) is a challenging but meaningful task in natural language processing (NLP) that requires us to teach a machine to read and understand a given passage and answer questions related to that passage. In this paper, we present a rich knowledge-enhanced reader (RKE-Reader), a hierarchical MRC model that employs double knowledge bases with an NER system as its knowledge enhancement unit. Besides, we are the first to propose an offline answer-imporving method to help model to determine the uncertain answer without extra online training process. Our experimental results indicate that on most datasets, the RKE-Reader significantly outperforms most of the published models that do not have knowledge base, especially on datasets that need commonsense reasoning. And the ablation study also reflects that external knowledge bases and answer-selecting unit do make a positive contribution in the entire model.","PeriodicalId":50172,"journal":{"name":"Journal of Internet Technology","volume":"22 1","pages":"1093-1105"},"PeriodicalIF":1.6,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44726287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-01DOI: 10.53106/160792642021092205017
Jingwei Zhang, Chen Jing, Ya Zhou, Qing Yang
Group recommendation derives from a phenomenon that a group with similar interests have formed various communities, which creates the requirements that a group of users in one community want to share personalized services. Different from traditional recommendations that focus on individuals, group recommendation needs to consider the differences in preference of group members. How to build a proper model for group members to aggregate different preferences is still a challenging problem: (1) the influence of group members is quite different; (2) a user decision is directly or indirectly influenced by other members in the same group. This paper proposed a Group Recommendation model covering User Importance and automatic Feature Interaction (GRUIFI), which can model interaction data of group member and learn group potential preference representation. Our model exploits an attention mechanism to obtain the weights of group members that represent user importance, and those dynamic user weights are integrated to learn a group representation. Then we design a neural network that combines the multi-head attention to automatically learn fine-grained interactions between groups and items, and further capture the interdependency between group members. Finally, the experiments on the two real-world datasets show that GRUIFI performs significantly better than baseline methods.
{"title":"GRUIFI: A Group Recommendation Model Covering User Importance and Feature Interaction","authors":"Jingwei Zhang, Chen Jing, Ya Zhou, Qing Yang","doi":"10.53106/160792642021092205017","DOIUrl":"https://doi.org/10.53106/160792642021092205017","url":null,"abstract":"Group recommendation derives from a phenomenon that a group with similar interests have formed various communities, which creates the requirements that a group of users in one community want to share personalized services. Different from traditional recommendations that focus on individuals, group recommendation needs to consider the differences in preference of group members. How to build a proper model for group members to aggregate different preferences is still a challenging problem: (1) the influence of group members is quite different; (2) a user decision is directly or indirectly influenced by other members in the same group. This paper proposed a Group Recommendation model covering User Importance and automatic Feature Interaction (GRUIFI), which can model interaction data of group member and learn group potential preference representation. Our model exploits an attention mechanism to obtain the weights of group members that represent user importance, and those dynamic user weights are integrated to learn a group representation. Then we design a neural network that combines the multi-head attention to automatically learn fine-grained interactions between groups and items, and further capture the interdependency between group members. Finally, the experiments on the two real-world datasets show that GRUIFI performs significantly better than baseline methods.","PeriodicalId":50172,"journal":{"name":"Journal of Internet Technology","volume":"22 1","pages":"1141-1153"},"PeriodicalIF":1.6,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42506455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-01DOI: 10.53106/160792642021092205011
Hoonyong Park, Jiyoon Kim, Sangmin Lee, Daniel Gerbi Duguma, I. You
The Internet of Things (IoT) is vulnerable to a wide range of security risks, which can be effectively mitigated by applying Cyber Threat Intelligence (CTI) sharing as a proactive mitigation approach. In realizing CTI sharing, it is of paramount importance to guarantee end-to-end protection of the shared information as unauthorized disclosure of CTI is disastrous for organizations using IoT. Furthermore, resource-constrained devices should be supported through lightweight operations. Unfortunately, the aforementioned are not satisfied by the Hypertext Transfer Protocol Secure (HTTPS), which state-of-the-art CTI sharing systems mainly depends on. As a promising alternative to HTTPS, Ephemeral Diffie-Hellman over COSE (EDHOC) can be considered because it meets the above requirements. However, EDHOC in its current version contains several security flaws, most notably due to the unprotected initial message. Consequently, we propose a lightweight end-to-end privacy-preserving security protocol that improves the existing draft EDHOC protocol by utilizing previously shared keys and keying materials while providing ticket-based optimized re-authentication. The proposed protocol is not only formally validated through BAN-logic and AVISPA, but also proved to fulfill essential security properties such as mutual authentication, secure key exchange, perfect forward secrecy, anonymity, confidentiality, and integrity. Also, comparing the protocol’s performance to that of the EDHOC protocol reveals a substantial improvement with a single roundtrip to allow frequent CTI sharing.
物联网(IoT)容易受到各种安全风险的影响，通过将网络威胁情报(CTI)共享作为一种主动缓解方法，可以有效缓解这些安全风险。在实现CTI共享时，确保共享信息的端到端保护至关重要，因为未经授权的CTI泄露对使用物联网的组织来说是灾难性的。此外，应该通过轻量级操作支持资源受限的设备。不幸的是，上述内容不能满足于最先进的CTI共享系统主要依赖的超文本传输协议安全(HTTPS)。EDHOC (Ephemeral Diffie-Hellman over COSE)是一种很有前途的HTTPS替代方案，因为它符合上述要求。然而，EDHOC在其当前版本中包含几个安全漏洞，最明显的是由于未受保护的初始消息。因此，我们提出了一种轻量级的端到端隐私保护安全协议，该协议通过利用先前共享的密钥和密钥材料来改进现有的EDHOC协议草案，同时提供基于票证的优化重新认证。该协议不仅通过ban -逻辑和AVISPA进行了正式验证，而且还证明了该协议具有相互认证、安全密钥交换、完全前向保密、匿名性、机密性和完整性等基本安全特性。此外，将该协议的性能与EDHOC协议的性能进行比较，可以发现单次往返允许频繁的CTI共享有了实质性的改进。
{"title":"lwEPSep: A Lightweight End-to-end Privacy-preserving Security Protocol for CTI Sharing in IoT Environments","authors":"Hoonyong Park, Jiyoon Kim, Sangmin Lee, Daniel Gerbi Duguma, I. You","doi":"10.53106/160792642021092205011","DOIUrl":"https://doi.org/10.53106/160792642021092205011","url":null,"abstract":"The Internet of Things (IoT) is vulnerable to a wide range of security risks, which can be effectively mitigated by applying Cyber Threat Intelligence (CTI) sharing as a proactive mitigation approach. In realizing CTI sharing, it is of paramount importance to guarantee end-to-end protection of the shared information as unauthorized disclosure of CTI is disastrous for organizations using IoT. Furthermore, resource-constrained devices should be supported through lightweight operations. Unfortunately, the aforementioned are not satisfied by the Hypertext Transfer Protocol Secure (HTTPS), which state-of-the-art CTI sharing systems mainly depends on. As a promising alternative to HTTPS, Ephemeral Diffie-Hellman over COSE (EDHOC) can be considered because it meets the above requirements. However, EDHOC in its current version contains several security flaws, most notably due to the unprotected initial message. Consequently, we propose a lightweight end-to-end privacy-preserving security protocol that improves the existing draft EDHOC protocol by utilizing previously shared keys and keying materials while providing ticket-based optimized re-authentication. The proposed protocol is not only formally validated through BAN-logic and AVISPA, but also proved to fulfill essential security properties such as mutual authentication, secure key exchange, perfect forward secrecy, anonymity, confidentiality, and integrity. Also, comparing the protocol’s performance to that of the EDHOC protocol reveals a substantial improvement with a single roundtrip to allow frequent CTI sharing.","PeriodicalId":50172,"journal":{"name":"Journal of Internet Technology","volume":"22 1","pages":"1067-1079"},"PeriodicalIF":1.6,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44109261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-01DOI: 10.53106/160792642021092205007
C. Ezeh, Tao Ren, Yan-Jie Xu, Shixuan Sun, Zhe Li
Several research works had been carried out to discover suitable algorithms to quantify node centralities. Among the many existing centrality metrics, only few consider centrality at the sub-graph level or deal with structural hole capabilities of pivot nodes. Research has proven the importance of sub-graph information in distinguishing influential nodes. In this work, two centrality metrics are proposed to distinguish and rank nodes in complex networks. The first metric called Sub-graph Degree Information centrality is based on entropy quantification of a node’s sub-graph degree distribution to determine its influence. The second metric called Sub-graph Degree and Structural Hole centrality considers a node’s sub-graph degree distribution and its structural hole property. The two metrics are designed to efficiently support weighted and unweighted networks. Performance evaluations were done on five real world datasets and one artificial network. The proposed metrics were equally compared against some classic centrality metrics. The results show that the proposed metrics can accurately distinguish and rank nodes distinctly on complex networks. They can equally discover highly influential and spreader nodes capable of causing epidemic spread and maximum network damage.
{"title":"Entropy and Structural-Hole Based Node Ranking Methods","authors":"C. Ezeh, Tao Ren, Yan-Jie Xu, Shixuan Sun, Zhe Li","doi":"10.53106/160792642021092205007","DOIUrl":"https://doi.org/10.53106/160792642021092205007","url":null,"abstract":"Several research works had been carried out to discover suitable algorithms to quantify node centralities. Among the many existing centrality metrics, only few consider centrality at the sub-graph level or deal with structural hole capabilities of pivot nodes. Research has proven the importance of sub-graph information in distinguishing influential nodes. In this work, two centrality metrics are proposed to distinguish and rank nodes in complex networks. The first metric called Sub-graph Degree Information centrality is based on entropy quantification of a node’s sub-graph degree distribution to determine its influence. The second metric called Sub-graph Degree and Structural Hole centrality considers a node’s sub-graph degree distribution and its structural hole property. The two metrics are designed to efficiently support weighted and unweighted networks. Performance evaluations were done on five real world datasets and one artificial network. The proposed metrics were equally compared against some classic centrality metrics. The results show that the proposed metrics can accurately distinguish and rank nodes distinctly on complex networks. They can equally discover highly influential and spreader nodes capable of causing epidemic spread and maximum network damage.","PeriodicalId":50172,"journal":{"name":"Journal of Internet Technology","volume":"22 1","pages":"1009-1017"},"PeriodicalIF":1.6,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46330114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An ultra-wideband (UWB) positioning system consists of at least three anchors and a tag. Via the UWB transceiver mounted on each device in the system, we can use some techniques to obtain the distance between each anchor and the tag. Then we can further realize the tag localization by some classic algorithms. However, in the real environment, the uncertain measurement may bring incorrect distance as well as positioning information. Therefore, in this research, we intend to reconsider the positioning issue by incorporating some machine learning approaches with uncertain measurement in the real environment. Particularly, we utilize the concept of machine learning for overall consideration instead of using a model to evaluate the uncertainty. The experimental results show that our method can be applied to different cases, and some interesting properties in the practical experiments are presented.
{"title":"Using Machine Learning Approaches to Improve Ultra-Wideband Positioning","authors":"Che-Cheng Chang, Hong-Wen Wang, Yu-Xiang Zeng, Jin-Da Huang","doi":"10.53106/160792642021092205008","DOIUrl":"https://doi.org/10.53106/160792642021092205008","url":null,"abstract":"An ultra-wideband (UWB) positioning system consists of at least three anchors and a tag. Via the UWB transceiver mounted on each device in the system, we can use some techniques to obtain the distance between each anchor and the tag. Then we can further realize the tag localization by some classic algorithms. However, in the real environment, the uncertain measurement may bring incorrect distance as well as positioning information. Therefore, in this research, we intend to reconsider the positioning issue by incorporating some machine learning approaches with uncertain measurement in the real environment. Particularly, we utilize the concept of machine learning for overall consideration instead of using a model to evaluate the uncertainty. The experimental results show that our method can be applied to different cases, and some interesting properties in the practical experiments are presented.","PeriodicalId":50172,"journal":{"name":"Journal of Internet Technology","volume":"22 1","pages":"1019-1029"},"PeriodicalIF":1.6,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46221281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-01DOI: 10.53106/160792642021092205006
Qiang Chen, Linzhou Li
Spectrally efficient frequency division multiplexing (SEFDM) is a bandwidth-compressed non-orthogonal multicarrier communication scheme, which provides improved spectral efficiency compared to orthogonal frequency division multiplexing (OFDM) system. The loss of orthogonality yields the self-introduced inter-carrier interference (ICI) complicating the equalizer design. In this work, a deep learning (DL) -based SEFDM equalization scheme is proposed to characterize the ICI and to detect the transmitted information bits. The DL-based equalization scheme is trained offline using randomly-generated data and then deployed online. The performance of the equalization scheme is tested by extensive numerical simulations. The results show that the proposed equalization scheme outperforms the linear equalization based equalization scheme, such as zero forcing (ZF), minimum mean squared error (MMSE) and truncated singular value decomposition (TSVD), under additive white Gaussian noise (AWGN) channel in terms of the bit-error rate (BER). Especially for BPSK, the uncoded BER performance approaches the traditional OFDM even for the compression ratio of 0.7, which saves the bandwidth by 30%.
{"title":"A Deep Learning Based Equalization Scheme for Bandwidthcompressed Non-orthogonal Multicarrier Communication","authors":"Qiang Chen, Linzhou Li","doi":"10.53106/160792642021092205006","DOIUrl":"https://doi.org/10.53106/160792642021092205006","url":null,"abstract":"Spectrally efficient frequency division multiplexing (SEFDM) is a bandwidth-compressed non-orthogonal multicarrier communication scheme, which provides improved spectral efficiency compared to orthogonal frequency division multiplexing (OFDM) system. The loss of orthogonality yields the self-introduced inter-carrier interference (ICI) complicating the equalizer design. In this work, a deep learning (DL) -based SEFDM equalization scheme is proposed to characterize the ICI and to detect the transmitted information bits. The DL-based equalization scheme is trained offline using randomly-generated data and then deployed online. The performance of the equalization scheme is tested by extensive numerical simulations. The results show that the proposed equalization scheme outperforms the linear equalization based equalization scheme, such as zero forcing (ZF), minimum mean squared error (MMSE) and truncated singular value decomposition (TSVD), under additive white Gaussian noise (AWGN) channel in terms of the bit-error rate (BER). Especially for BPSK, the uncoded BER performance approaches the traditional OFDM even for the compression ratio of 0.7, which saves the bandwidth by 30%.","PeriodicalId":50172,"journal":{"name":"Journal of Internet Technology","volume":"22 1","pages":"1001-1009"},"PeriodicalIF":1.6,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42780889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-01DOI: 10.53106/160792642021092205003
Xin You, Daxin Tian, Chen Liu, Xiao-Xuan Yu, Liangliang Song
Due to the dim light in the tunnel and the characteristics of natural electromagnetic shielding, drivers are prone to accidents in the tunnel and are unable to inform the navigation system in time. Therefore, it is still a bottleneck in the field of intelligent transportation how to obtain real-time vehicle position information and vehicle state information when the vehicle is running at high speeds in the tunnel. In this paper, a new technology is proposed to achieve accurate real-time positioning in the tunnel scene by combining the downlink time difference of arrival (DL-TDOA) with UWB technology. The DL-TDOA technology is based on Ultra Wide Band (UWB) data transmission technology, which can effectively reduce the interference of other electromagnetic waves and reduce the data transmission time. By calculating the transmission time of the wireless electromagnetic wave between the vehicle and the fixed base station, the technology can determine the real-time position of the vehicle and greatly reduce the time loss of data in transmission. DL-TDOA based on UWB technology has high precision, while DL-TDOA based on UWB technology has many advantages, such as high precision, strong anti-jamming ability, low power consumption and a high transmission rate which are suitable for accurate positioning and navigation in tunnel scenarios. In the final tunnel experiment, several tests were carried out at speeds of 30km/h, 60km/h and 80km/h respectively. By comparing the coordinate position after the conversion with the satellite coordinate, the real-time kinematics (RTK), it was concluded that the position error of vehicles in the tunnel is less than 1m, and the real-time positioning of vehicles in the tunnel is realized.
{"title":"Vehicles Positioning in Tunnel: A Real-Time Localization System Using DL-TDOA Technology","authors":"Xin You, Daxin Tian, Chen Liu, Xiao-Xuan Yu, Liangliang Song","doi":"10.53106/160792642021092205003","DOIUrl":"https://doi.org/10.53106/160792642021092205003","url":null,"abstract":"Due to the dim light in the tunnel and the characteristics of natural electromagnetic shielding, drivers are prone to accidents in the tunnel and are unable to inform the navigation system in time. Therefore, it is still a bottleneck in the field of intelligent transportation how to obtain real-time vehicle position information and vehicle state information when the vehicle is running at high speeds in the tunnel. In this paper, a new technology is proposed to achieve accurate real-time positioning in the tunnel scene by combining the downlink time difference of arrival (DL-TDOA) with UWB technology. The DL-TDOA technology is based on Ultra Wide Band (UWB) data transmission technology, which can effectively reduce the interference of other electromagnetic waves and reduce the data transmission time. By calculating the transmission time of the wireless electromagnetic wave between the vehicle and the fixed base station, the technology can determine the real-time position of the vehicle and greatly reduce the time loss of data in transmission. DL-TDOA based on UWB technology has high precision, while DL-TDOA based on UWB technology has many advantages, such as high precision, strong anti-jamming ability, low power consumption and a high transmission rate which are suitable for accurate positioning and navigation in tunnel scenarios. In the final tunnel experiment, several tests were carried out at speeds of 30km/h, 60km/h and 80km/h respectively. By comparing the coordinate position after the conversion with the satellite coordinate, the real-time kinematics (RTK), it was concluded that the position error of vehicles in the tunnel is less than 1m, and the real-time positioning of vehicles in the tunnel is realized.","PeriodicalId":50172,"journal":{"name":"Journal of Internet Technology","volume":"22 1","pages":"965-976"},"PeriodicalIF":1.6,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46466583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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