物联网异常节点定位方法的改进
传统的物联网异常节点定位方法采用模糊边缘覆盖方法，以节点之间的互信息量作为信息索引导，当节点规模较大和干扰较强时，定位准确度不高，为此，提出一种基于自适应信息融合跟踪检测的物联网异常节点定位算法．首先构建物联网节点之间通信传输信道模型，采用载波调制方法进行信道特征参量估计．然后利用自适应信息融合跟踪检测算法进行节点异常特征提取和检测，实现异常节点信息融合和滤波，通过接收端进行连续检测，实现异常节点的自适应分辨和定位．仿真结果表明，该方法在对物联网中异常节点定位时，误差能快速收敛到零，具有较好的准确性和实时性．...
Saved in:
| Published in | 西安工程大学学报 Vol. 31; no. 2; pp. 225 - 231 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
广州铁路职业技术学院信息工程系,广东广州,510000
2017
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 1674-649X |
| DOI | 10.13338/j.issn.1674-649x.2017.02.014 |
Cover
| Abstract | 传统的物联网异常节点定位方法采用模糊边缘覆盖方法，以节点之间的互信息量作为信息索引导，当节点规模较大和干扰较强时，定位准确度不高，为此，提出一种基于自适应信息融合跟踪检测的物联网异常节点定位算法．首先构建物联网节点之间通信传输信道模型，采用载波调制方法进行信道特征参量估计．然后利用自适应信息融合跟踪检测算法进行节点异常特征提取和检测，实现异常节点信息融合和滤波，通过接收端进行连续检测，实现异常节点的自适应分辨和定位．仿真结果表明，该方法在对物联网中异常节点定位时，误差能快速收敛到零，具有较好的准确性和实时性． |
|---|---|
| AbstractList | TN911; 传统的物联网异常节点定位方法采用模糊边缘覆盖方法,以节点之间的互信息量作为信息素引导,当节点规模较大和干扰较强时,定位准确度不高,为此,提出一种基于自适应信息融合跟踪检测的物联网异常节点定位算法.首先构建物联网节点之间通信传输信道模型,采用载波调制方法进行信道特征参量估计.然后利用自适应信息融合跟踪检测算法进行节点异常特征提取和检测,实现异常节点信息融合和滤波,通过接收端进行连续检测,实现异常节点的自适应分辨和定位.仿真结果表明,该方法在对物联网中异常节点定位时,误差能快速收敛到零,具有较好的准确性和实时性. 传统的物联网异常节点定位方法采用模糊边缘覆盖方法，以节点之间的互信息量作为信息索引导，当节点规模较大和干扰较强时，定位准确度不高，为此，提出一种基于自适应信息融合跟踪检测的物联网异常节点定位算法．首先构建物联网节点之间通信传输信道模型，采用载波调制方法进行信道特征参量估计．然后利用自适应信息融合跟踪检测算法进行节点异常特征提取和检测，实现异常节点信息融合和滤波，通过接收端进行连续检测，实现异常节点的自适应分辨和定位．仿真结果表明，该方法在对物联网中异常节点定位时，误差能快速收敛到零，具有较好的准确性和实时性． |
| Abstract_FL | Traditional networking abnormal node locatingus uses fuzzy edge covering method,with the mutual information between nodes as information element guide.When a node with a large scale and strong interference,positioning accuracy is not high.Thus,an abnormal node localization algorithm based on adaptive information fusion tracking algorithm is proposed.Firstly,the communication transmission channel model between physical network node is constructed,and characteristic parameters of channel estimation is performed using carrier modulation method.Then,an adaptive information fusion tracking algorithm is used to extract and detect abnormal nodes feature,and the abnormal node information fusion and filtering are realized.By continuous detection at the receiving end,the abnormal node adaptive identification and localization are achieved.The simulation results show that when the method is used to locate the abnormal nodes in the Internet of things,the error can converge to zero quickly,which has good accuracy and real-time performance. |
| Author | 张华 |
| AuthorAffiliation | 广州铁路职业技术学院信息工程系,广东广州510000 |
| AuthorAffiliation_xml | – name: 广州铁路职业技术学院信息工程系,广东广州,510000 |
| Author_FL | ZHANG Hua |
| Author_FL_xml | – sequence: 1 fullname: ZHANG Hua |
| Author_xml | – sequence: 1 fullname: 张华 |
| BookMark | eNo1j7tKA0EYhaeIYIx5CcEmsOv8c9mdKSV4g4BNCrswM9mNG3SiWcSNncFGwUKIFvEJbGxssmDwZdzLY7gSbc6Bw8f5_7OBanZkA4S2AbtAKRU7QzeKY-uC5zPHYzJxCQbfxcTFwGqo_p-frKNmHEcaE0JBMo_XUau4fytvZ8XyKfucZotF-TAtpmn2Pv9ePuYvaf7xXMzv8llafr1uorVQncVB888bqLu_120fOp3jg6P2bscxHmEO93xifMP6lEgQQnHJJNMKG6l8GXgCaB-EEYYB72PNSGhAaB5yoiUHIJo2UGtVe61sqOygNxxdjW11sJfo8GaQTBL9uw5Xwip4awWb05EdXEYVfjGOztV40qv-wIwIIugPczFi9w |
| ClassificationCodes | TN911 |
| ContentType | Journal Article |
| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| Copyright_xml | – notice: Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| DBID | 2RA 92L CQIGP W92 ~WA 2B. 4A8 92I 93N PSX TCJ |
| DOI | 10.13338/j.issn.1674-649x.2017.02.014 |
| DatabaseName | 维普_期刊 中文科技期刊数据库-CALIS站点 维普中文期刊数据库 中文科技期刊数据库-工程技术 中文科技期刊数据库- 镜像站点 Wanfang Data Journals - Hong Kong WANFANG Data Centre Wanfang Data Journals 万方数据期刊 - 香港版 China Online Journals (COJ) China Online Journals (COJ) |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| DocumentTitleAlternate | The improvement of abnormal node localization method for Internet of things |
| DocumentTitle_FL | The improvement of abnormal node localization method for Internet of things |
| EndPage | 231 |
| ExternalDocumentID | xbfzgxyxb201702014 672042828 |
| GrantInformation_xml | – fundername: 广东省公益研究与能力建设专项基金 funderid: (2015A010103001) |
| GroupedDBID | 2RA 92L ALMA_UNASSIGNED_HOLDINGS CDYEO CQIGP W92 ~WA -02 2B. 4A8 92I 93N CCEZO CDRFL GROUPED_DOAJ PSX TCJ |
| ID | FETCH-LOGICAL-c624-5672c7c4d329188a59494ba0c9a79e6813d18c8c415d0b42fc18b5f52b95112b3 |
| ISSN | 1674-649X |
| IngestDate | Thu May 29 04:07:46 EDT 2025 Wed Feb 14 10:02:33 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Keywords | Internet of things abnormal nodes 滤波 信息融合 information fusion 定位 location 物联网 filtering 异常节点 |
| Language | Chinese |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c624-5672c7c4d329188a59494ba0c9a79e6813d18c8c415d0b42fc18b5f52b95112b3 |
| Notes | Traditional networking abnormal node locatingus uses fuzzy edge covering method, with the mutual information between nodes as information element guide. When a node with a large scale and strong interference, positioning accuracy is not high. Thus, an abnormal node localization algorithm based on adaptive information fusion tracking algorithm is proposed. Firstly, the communication transmission channel model between physical network node is constructed, and characteristic parameters of channel estimation is performed using carrier modulation method.Then, an adaptive information fusion tracking algorithm is used to extract and detect abnormal nodes feature, and the abnormal node information fusion and filtering are realized. By continuous detection at the receiving end, the abnormal node adaptive identification and localization are achieved. The simulation results show that when the method is used to locate the abnormal nodes in the Internet of things, the error can converge to zero quickly, which has good acc |
| PageCount | 7 |
| ParticipantIDs | wanfang_journals_xbfzgxyxb201702014 chongqing_primary_672042828 |
| PublicationCentury | 2000 |
| PublicationDate | 2017 |
| PublicationDateYYYYMMDD | 2017-01-01 |
| PublicationDate_xml | – year: 2017 text: 2017 |
| PublicationDecade | 2010 |
| PublicationTitle | 西安工程大学学报 |
| PublicationTitleAlternate | Journal of Xi an University of Engineering Science and Technology |
| PublicationTitle_FL | Journal of Xi'an Polytechnic University |
| PublicationYear | 2017 |
| Publisher | 广州铁路职业技术学院信息工程系,广东广州,510000 |
| Publisher_xml | – name: 广州铁路职业技术学院信息工程系,广东广州,510000 |
| SSID | ssib022319465 ssib006595880 ssib008679780 ssib036435108 ssib051375645 ssib009686238 ssib005319384 ssj0003313605 |
| Score | 2.086034 |
| Snippet | 传统的物联网异常节点定位方法采用模糊边缘覆盖方法，以节点之间的互信息量作为信息索引导，当节点规模较大和干扰较强时，定位准确度不高，为此，提出一种基于自适应信息... TN911; 传统的物联网异常节点定位方法采用模糊边缘覆盖方法,以节点之间的互信息量作为信息素引导,当节点规模较大和干扰较强时,定位准确度不高,为此,提出一种基于自适应信... |
| SourceID | wanfang chongqing |
| SourceType | Aggregation Database Publisher |
| StartPage | 225 |
| SubjectTerms | 信息融合 定位 异常节点 滤波 物联网 |
| Title | 物联网异常节点定位方法的改进 |
| URI | http://lib.cqvip.com/qk/98149B/201702/672042828.html https://d.wanfangdata.com.cn/periodical/xbfzgxyxb201702014 |
| Volume | 31 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVEBS databaseName: EBSCOhost Academic Search Ultimate issn: 1674-649X databaseCode: ABDBF dateStart: 20150201 customDbUrl: https://search.ebscohost.com/login.aspx?authtype=ip,shib&custid=s3936755&profile=ehost&defaultdb=asn isFulltext: true dateEnd: 99991231 titleUrlDefault: https://search.ebscohost.com/direct.asp?db=asn omitProxy: true ssIdentifier: ssib009686238 providerName: EBSCOhost |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnR1NaxQxNNQtiBdRVKxVKWguwrQ7-c4xsztLEfRUobdlZnam1cO26hbW3ly8KHgQqof6C7x48dKCxT9ju_0ZvpeZXce2SBWWkHl5yXsvL5u8ZOa9EHIfdv6wKIswKArbC4TIeZDYJAyyNLcsl5lIBTo4P3qslp-Ih6tydebCUu2rpa1Buphtn-lX8j9aBRjoFb1k_0Gz00YBAHnQL6SgYUjPpWMaa2osdZbGBkxCagVCoja1IY0ljVrUMJ8x-EMc5yEa08hikYupdTQWWMu0aayoVb4IUk6tRGRAMMIXCV8ErXWojep2LQKdRHjZpvGNRxqB0IID0pEvEtRpn2lTp2oZhbw5OdF_xb5rYgb4MnH9eKL0w_RDySPaijDQszGNLbUcuwMZBdqdqndMy8tpvMCenml6qVoeZ8oKVIc-ZR65Q13okcMK56RIGrspimCk1jgpqbTO4I21pH_VUVsLlBaBEv6m3-liUS1ZT2t79mrmL_23KyOClXin1ifOfTj6Z57C4oTCED8x1D52bOlReyIE-DAttteGr4YpooF9jxe3zzI8gGqQWRe1o84fUyqvuR5juMj6FI3xFXXt2aKH0O8pmIOBCv0wfZYh13LyohqNG85DXnobT7vnIqET0Zb-JhhGL1nf6K89B5vMu8j1i6S_VrPmVq6Qy9U2bMGV_6mrZGZ7_Rp5MH775fj1zvjgw-H30eHe3vG70Xi0f_h19-fB-6NP-0ffPo533xzt7B__-HydrHTildZyUN0lEmSKiUAqzTKdiR5nNjQmkVZYkSbNzCba5sqEvBeazGRgzvaaqWBFFppUFpKlFnckKb9BGv2Nfn6TLCRaZjkTCdRSopfD_spkSdPqoqdZWoRqjsxPxexuliFjugrvgsLTjTlyrxK8W00kL7un1XvrXFjz5BLmywPB26QxeLGV3wETeZDerYbFLwjriFg |
| linkProvider | EBSCOhost |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=%E7%89%A9%E8%81%94%E7%BD%91%E5%BC%82%E5%B8%B8%E8%8A%82%E7%82%B9%E5%AE%9A%E4%BD%8D%E6%96%B9%E6%B3%95%E7%9A%84%E6%94%B9%E8%BF%9B&rft.jtitle=%E8%A5%BF%E5%AE%89%E5%B7%A5%E7%A8%8B%E5%A4%A7%E5%AD%A6%E5%AD%A6%E6%8A%A5&rft.au=%E5%BC%A0%E5%8D%8E&rft.date=2017&rft.pub=%E5%B9%BF%E5%B7%9E%E9%93%81%E8%B7%AF%E8%81%8C%E4%B8%9A%E6%8A%80%E6%9C%AF%E5%AD%A6%E9%99%A2%E4%BF%A1%E6%81%AF%E5%B7%A5%E7%A8%8B%E7%B3%BB%2C%E5%B9%BF%E4%B8%9C%E5%B9%BF%E5%B7%9E%2C510000&rft.issn=1674-649X&rft.volume=31&rft.issue=2&rft.spage=225&rft.epage=231&rft_id=info:doi/10.13338%2Fj.issn.1674-649x.2017.02.014&rft.externalDocID=xbfzgxyxb201702014 |
| thumbnail_s | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fimage.cqvip.com%2Fvip1000%2Fqk%2F98149B%2F98149B.jpg http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.wanfangdata.com.cn%2Fimages%2FPeriodicalImages%2Fxbfzgxyxb%2Fxbfzgxyxb.jpg |