Detecting Smart Home Automation Application Interferences with Domain Knowledge
Trigger-action programming (TAP) is a widely used development paradigm that simplifies the Internet of Things (loT) automation. However, the exceptional interactions between automation applications may result in interferences, such as conflicts and infinite loops, which cause undesirable consequence...
Gespeichert in:
| Veröffentlicht in: | IEEE/ACM International Conference on Automated Software Engineering : [proceedings] S. 1086 - 1097 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
IEEE
11.09.2023
|
| Schlagworte: | |
| ISSN: | 2643-1572 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Trigger-action programming (TAP) is a widely used development paradigm that simplifies the Internet of Things (loT) automation. However, the exceptional interactions between automation applications may result in interferences, such as conflicts and infinite loops, which cause undesirable consequences and even security and safety risks. While several techniques have been proposed to address this problem, they are often restricted in handling explicit and simple conflicts without considering contextual influences. In addition, they suffer from performance issues when applying to large-scale applications. To address these challenges, we design an effective and practical tool KnowDetector with comprehensive domain knowledge to detect application interferences. To detect application interferences, KnowDetector constructs an automation graph with 1) events, conditions, and actions from automation applications, 2) vertices representing physical environment channels, and 3) edges derived from potential semantic relations between the vertices. In order to make the graph extensively capture the interactions between automation applications, we propose a knowledge model named KnowloT that accurately characterizes loT devices with command-level loT services and the intricate relations between these services and the contextual environment. We abstract the interference detection into a graph pattern-matching problem and summarize ten application interference patterns of four types. Finally, KnowDetector can efficiently detect application interferences by searching for sub-graphs matching the patterns within the automation graph. We evaluated KnowDetector on three real-world datasets. The results demonstrated that it outperformed the other state-of-the-art tools with the highest precision, recall, and F-measure. In addition, KnowDetector is scalable to detect application interferences within a large number of applications with a minimal time overhead. |
|---|---|
| AbstractList | Trigger-action programming (TAP) is a widely used development paradigm that simplifies the Internet of Things (loT) automation. However, the exceptional interactions between automation applications may result in interferences, such as conflicts and infinite loops, which cause undesirable consequences and even security and safety risks. While several techniques have been proposed to address this problem, they are often restricted in handling explicit and simple conflicts without considering contextual influences. In addition, they suffer from performance issues when applying to large-scale applications. To address these challenges, we design an effective and practical tool KnowDetector with comprehensive domain knowledge to detect application interferences. To detect application interferences, KnowDetector constructs an automation graph with 1) events, conditions, and actions from automation applications, 2) vertices representing physical environment channels, and 3) edges derived from potential semantic relations between the vertices. In order to make the graph extensively capture the interactions between automation applications, we propose a knowledge model named KnowloT that accurately characterizes loT devices with command-level loT services and the intricate relations between these services and the contextual environment. We abstract the interference detection into a graph pattern-matching problem and summarize ten application interference patterns of four types. Finally, KnowDetector can efficiently detect application interferences by searching for sub-graphs matching the patterns within the automation graph. We evaluated KnowDetector on three real-world datasets. The results demonstrated that it outperformed the other state-of-the-art tools with the highest precision, recall, and F-measure. In addition, KnowDetector is scalable to detect application interferences within a large number of applications with a minimal time overhead. |
| Author | Chen, Wei Wei, Jun Wang, Tao Liu, Liwei Wu, Guoquan Huang, Tao |
| Author_xml | – sequence: 1 givenname: Tao surname: Wang fullname: Wang, Tao email: wangtao19@otcaix.iscas.ac.cn organization: Institute of Software, Chinese Academy of Sciences,State Key Lab of Computer Sciences,Beijing,China – sequence: 2 givenname: Wei surname: Chen fullname: Chen, Wei email: wchen@otcaix.iscas.ac.cn organization: Institute of Software, Chinese Academy of Sciences,State Key Lab of Computer Sciences,Beijing,China – sequence: 3 givenname: Liwei surname: Liu fullname: Liu, Liwei email: liuliwei19@otcaix.iscas.ac.cn organization: Institute of Software, Chinese Academy of Sciences,State Key Lab of Computer Sciences,Beijing,China – sequence: 4 givenname: Guoquan surname: Wu fullname: Wu, Guoquan email: gqwu@otcaix.iscas.ac.cn organization: Institute of Software, Chinese Academy of Sciences,State Key Lab of Computer Sciences,Beijing,China – sequence: 5 givenname: Jun surname: Wei fullname: Wei, Jun email: wj@otcaix.iscas.ac.cn organization: Institute of Software, Chinese Academy of Sciences,State Key Lab of Computer Sciences,Beijing,China – sequence: 6 givenname: Tao surname: Huang fullname: Huang, Tao email: tao@otcaix.iscas.ac.cn organization: Institute of Software, Chinese Academy of Sciences,State Key Lab of Computer Sciences,Beijing,China |
| BookMark | eNotjk1OwzAUhA0Cibb0BLDwBRLs5yS2l1F_aEWlLgrrynGei1HiVIlRxe0JKpuZ-aTRaKbkLnQBCXniLOWc6ZfysMoLAJ0CA5EyxiS7IXMttRI5E6B1kd2SCRSZSHgu4YFMh-GLsXwEOSH7JUa00YcTPbSmj3TTtUjL79i1Jvou0PJ8bry95m2I2DvsMVgc6MXHT7ocez7Qt9BdGqxP-EjunWkGnP_7jHysV--LTbLbv24X5S4xoLKY1EIURoGUCnXlwOmscK6yyqI1I0OuuRB_4molubOIzFRQaWmNqjNbiRl5vu56RDyeez-e_zlyBlrlXIpfHktSqg |
| CODEN | IEEPAD |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/ASE56229.2023.00070 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISBN | 9798350329964 |
| EISSN | 2643-1572 |
| EndPage | 1097 |
| ExternalDocumentID | 10298517 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China grantid: No.037800KK58190001 funderid: 10.13039/501100001809 |
| GroupedDBID | 6IE 6IF 6IH 6IK 6IL 6IM 6IN 6J9 AAJGR AAWTH ABLEC ACREN ADYOE ADZIZ AFYQB ALMA_UNASSIGNED_HOLDINGS AMTXH BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO IEGSK IPLJI M43 OCL RIE RIL |
| ID | FETCH-LOGICAL-a284t-d336a82778e9bf2f946ffbc8cecabf22591335913fd871fcee0ab2b97ca8d4cb3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 3 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001103357200087&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| IngestDate | Wed Aug 27 02:32:41 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-a284t-d336a82778e9bf2f946ffbc8cecabf22591335913fd871fcee0ab2b97ca8d4cb3 |
| PageCount | 12 |
| ParticipantIDs | ieee_primary_10298517 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-Sept.-11 |
| PublicationDateYYYYMMDD | 2023-09-11 |
| PublicationDate_xml | – month: 09 year: 2023 text: 2023-Sept.-11 day: 11 |
| PublicationDecade | 2020 |
| PublicationTitle | IEEE/ACM International Conference on Automated Software Engineering : [proceedings] |
| PublicationTitleAbbrev | ASE |
| PublicationYear | 2023 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0051577 ssib057256115 |
| Score | 2.2585495 |
| Snippet | Trigger-action programming (TAP) is a widely used development paradigm that simplifies the Internet of Things (loT) automation. However, the exceptional... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 1086 |
| SubjectTerms | Automation automation application interference Interference Internet of Things Production Programming Redundancy Semantics smart home platform Smart homes TAP |
| Title | Detecting Smart Home Automation Application Interferences with Domain Knowledge |
| URI | https://ieeexplore.ieee.org/document/10298517 |
| WOSCitedRecordID | wos001103357200087&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV27asMwFBVN6NApfaT0jYauai1ZtqQxNAmFQhpIC9mCnpChTkmcfn-vFLvp0qGLsY0wQg-Oz7069yB0H0kF0wUlgmpPeO4Y0ZQqUhRCiYLJwEVIZhNiMpHzuZo2YvWkhfHep8Nn_iHeply-W9ltDJXBDmcqesl3UEeIcifWahdPIQC8Kf359wWcFqIpM0Qz9TiYjQDqWdSmsFjUNIv2xL8MVRKejHv_7Mkx6u-VeXj6gzkn6MBXp6jXWjPgZqeeodehj9kBaINnH7A6cLRDx4NtvdpJFfFgn7jGKSrYfH2DY2QWD6HdssIvbcStj97Ho7enZ9J4JxANgFMTl-ellkwI6ZUJLChehmCstN5qeAbSA-Q0XoIDyhSg25k2zChhtXTcmvwcdatV5S8QDo4bzwstc-q4ppm2JaCeAapjgC5Rc4n6cYAWn7vyGIt2bK7-eH-NjuIcxEMXlN6gbr3e-lt0aL_q5WZ9lyb1G-nEokQ |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PT8IwFH5RNNET_sD42x68Tteuo-uRCAQDIgmYcCNt1yYcHAaGf7-vYwMvHrws29IsTX_k2_dev_cBPHpSwVRMA0GVDXiUskBRKoM4FlLELHFcuMJsQgyHyXQqR6VYvdDCWGuLw2f2yd8Wufx0YdY-VIY7nEnvJb8PBzHnLNzItarlEwuEb0q3f7-I1EKUhYZoKJ9b4w6CPfPqFObLmobeoPiXpUqBKN36P_tyAo2dNo-MtqhzCns2O4N6Zc5Ayr16Du9t6_MD2IaMP3F9EG-ITlrrfLERK5LWLnVNirhg-fUV8bFZ0sZ284z0q5hbAz66nclLLyjdEwKFkJMHaRQ1VcKESKzUjjnJm85pkxhrFD4j7UF66i8uRdLksNuh0kxLYVSScqOjC6hli8xeAnEp15bHKoloyhUNlWki7mkkOxoJE9VX0PADNPvaFMiYVWNz_cf7BzjqTd4Gs8HrsH8Dx34-_BEMSm-hli_X9g4OzXc-Xy3viwn-AYhmpYs |
| 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%3Abook&rft.genre=proceeding&rft.title=IEEE%2FACM+International+Conference+on+Automated+Software+Engineering+%3A+%5Bproceedings%5D&rft.atitle=Detecting+Smart+Home+Automation+Application+Interferences+with+Domain+Knowledge&rft.au=Wang%2C+Tao&rft.au=Chen%2C+Wei&rft.au=Liu%2C+Liwei&rft.au=Wu%2C+Guoquan&rft.date=2023-09-11&rft.pub=IEEE&rft.eissn=2643-1572&rft.spage=1086&rft.epage=1097&rft_id=info:doi/10.1109%2FASE56229.2023.00070&rft.externalDocID=10298517 |