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A Survey on Conflict Detection in IoT-based Smart Homes (2310.04447v1)

Published 3 Oct 2023 in cs.HC

Abstract: As the adoption of IoT-based smart homes continues to grow, the importance of addressing potential conflicts becomes increasingly vital for ensuring seamless functionality and user satisfaction. In this survey, we introduce a novel conflict taxonomy, complete with formal definitions of each conflict type that may arise within the smart home environment. We design an advanced conflict model to effectively categorize these conflicts, setting the stage for our in-depth review of recent research in the field. By employing our proposed model, we systematically classify conflicts and present a comprehensive overview of cutting-edge conflict detection approaches. This extensive analysis allows us to highlight similarities, clarify significant differences, and uncover prevailing trends in conflict detection techniques. In conclusion, we shed light on open issues and suggest promising avenues for future research to foster accelerated development and deployment of IoT-based smart homes, ultimately enhancing their overall performance and user experience.

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References (122)
  1. 2017. Copyright. In Managing the Web of Things, Quan Z. Sheng, Yongrui Qin, Lina Yao, and Boualem Benatallah (Eds.). Morgan Kaufmann, Boston, iv. https://doi.org/10.1016/B978-0-12-809764-9.00022-6
  2. 2018. openHAB: empowering the smart home. https://www.openhab.org/. Accessed: 2022-04-05.
  3. 2018. SmartThings: One simple home system. http://www.smartthings.com. Accessed: 2022-04-05.
  4. 2019. Android Things. https://developer.android.com/things. Accessed: 2022-04-05.
  5. 2019. IFTTT: What is IFTTT? https://ifttt.com/explore. Accessed: 2022-04-05.
  6. 2019. Tasker: Total Automation for Android. https://tasker.joaoapps.com. Accessed: 2022-04-05.
  7. Testing autonomous cars for feature interaction failures using many-objective search. In 2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE). IEEE, 143–154. https://doi.org/10.1145/3238147.3238192
  8. Motaz Osman Ahmed et al. 2021. Adaptation Conflicts of Heterogeneous Devices in IOT Smart-Home. American Academic Scientific Research Journal for Engineering, Technology, and Sciences 81, 1 (2021), 64–78.
  9. Internet of things: A survey on enabling technologies, protocols, and applications. IEEE communications surveys & tutorials 17, 4 (2015), 2347–2376. https://doi.org/10.1109/COMST.2015.2444095
  10. Fadi Al-Turjman and Mohammad Abujubbeh. 2019. IoT-enabled smart grid via SM: An overview. Future generation computer systems 96 (2019), 579–590.
  11. A Alfakeeh and A Al-Bayatti. 2016. Feature interactions detection and resolution in smart homes systems. Int J Electron Electr Eng 4, 1 (2016), 66–73. https://doi.org/10.18178/ijeee.4.1.66-73
  12. Agent-based negotiation approach for feature interactions in smart home systems using calculus of the context-aware ambient. Transactions on Emerging Telecommunications Technologies 33, 2 (2022), e3808. https://doi.org/10.1002/ett.3808
  13. Ahmed Saeed Alghamdi. 2015. Features Interaction Detection and Resolution in Smart home systems Using Agent-Based Negotiation Approach. (2015).
  14. Scalable analysis of interaction threats in IoT systems. In Proceedings of the 29th ACM SIGSOFT international symposium on software testing and analysis. 272–285. https://doi.org/10.1145/3395363.3397347
  15. Fahd Alharithi. 2019. Detecting conflicts among autonomous devices in smart homes. Ph.D. Dissertation. Florida Institute of Technology.
  16. Internet of Things applications: A systematic review. Computer Networks 148 (2019), 241–261. https://doi.org/10.1016/j.comnet.2018.12.008
  17. The Internet of Things: A survey. Computer Networks 54, 15 (2010), 2787–2805. https://doi.org/10.1016/j.comnet.2010.05.010
  18. Juan Carlos Augusto and Andrés Muñoz. 2019. User preferences in intelligent environments. Applied Artificial Intelligence 33, 12 (2019), 1069–1091.
  19. Real-time analysis of privacy-(un) aware IoT applications. arXiv preprint arXiv:1911.10461 (2019).
  20. Debasis Bandyopadhyay and Jaydip Sen. 2011. Internet of things: Applications and challenges in technology and standardization. Wireless personal communications 58, 1 (2011), 49–69. https://doi.org/10.1007/s11277-011-0288-5
  21. Designing energy-efficient IoT-based intelligent transport system: need, architecture, characteristics, challenges, and applications. Energy Conservation for IoT Devices: Concepts, Paradigms and Solutions (2019), 209–233.
  22. Exploring end user programming needs in home automation. ACM Transactions on Computer-Human Interaction (TOCHI) 24, 2 (2017), 1–35.
  23. An ontology-based approach to conflict resolution in Home and Building Automation Systems. Expert systems with applications 41, 14 (2014), 6161–6173. https://doi.org/10.1016/j.eswa.2014.04.017
  24. Towards automatic conflict detection in home and building automation systems. Pervasive and Mobile Computing 12 (2014), 37–57. https://doi.org/10.1016/j.pmcj.2013.06.001
  25. IoTGuard: Dynamic Enforcement of Security and Safety Policy in Commodity IoT.. In NDSS.
  26. Dipankar Chaki and Athman Bouguettaya. 2020. Fine-grained conflict detection of iot services. In 2020 IEEE International Conference on Services Computing (SCC). IEEE, 321–328. https://doi.org/10.1109/SCC49832.2020.00049
  27. Dipankar Chaki and Athman Bouguettaya. 2021a. Adaptive priority-based conflict resolution of IoT services. In 2021 IEEE International Conference on Web Services (ICWS). IEEE, 663–668. https://doi.org/10.1109/ICWS53863.2021.00091
  28. Dipankar Chaki and Athman Bouguettaya. 2021b. Dynamic Conflict Resolution of IoT Services in Smart Homes. In International Conference on Service-Oriented Computing. Springer, 368–384. https://doi.org/10.1007/978-3-030-91431-8_23
  29. A Conflict Detection Framework for IoT Services in Multi-resident Smart Homes. In 2020 IEEE International Conference on Web Services (ICWS). IEEE Computer Society, Los Alamitos, CA, USA, 224–231. https://doi.org/10.1109/ICWS49710.2020.00036
  30. Multi-platform application interaction extraction for iot devices. In 2019 IEEE 25th international conference on parallel and distributed systems (ICPADS). IEEE, 990–995. https://doi.org/10.1109/ICPADS47876.2019.00151
  31. Cross-app interference threats in smart homes: Categorization, detection and handling. In 2020 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). IEEE, 411–423. https://doi.org/10.1109/DSN48063.2020.00056
  32. You Are What You Use: Usage-based Profiling in IoT Environments. arXiv preprint arXiv:2209.02298 (2022).
  33. Smart city IoT services creation through large-scale collaboration. IEEE Internet of Things Journal 7, 6 (2020), 5267–5275.
  34. From users’ intentions to IF-THEN rules in the Internet of Things. ACM Transactions on Information Systems (TOIS) 39, 4 (2021), 1–33. https://doi.org/10.1145/3447264
  35. Smart secure homes: a survey of smart home technologies that sense, assess, and respond to security threats. Journal of reliable intelligent environments 3, 2 (2017), 83–98. https://doi.org/10.1007/s40860-017-0035-0
  36. Development and optimization of artificial neural network algorithms for the prediction of building specific local temperature for HVAC control. International Journal of Energy Research 44, 11 (2020), 8513–8531. https://doi.org/10.1002/er.5537
  37. Empowering End Users to Customize Their Smart Environments: Model, Composition Paradigms, and Domain-Specific Tools. ACM Trans. Comput.-Hum. Interact. 24, 2, Article 12 (apr 2017), 52 pages. https://doi.org/10.1145/3057859
  38. A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Human–Computer Interaction 16, 2-4 (2001), 97–166.
  39. Wenbo Ding and Hongxin Hu. 2018. On the safety of iot device physical interaction control. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. 832–846. https://doi.org/10.1145/3243734.3243865
  40. IOTSAFE: Enforcing Safety and Security Policy with Real IoT Physical Interaction Discovery. In the 28th Network and Distributed System Security Symposium (NDSS 2021).
  41. ImAtHome: Making trigger-action programming easy and fun. Journal of Visual Languages & Computing 42 (2017), 60–75. https://doi.org/10.1016/j.jvlc.2017.08.003
  42. Radhika Garg and Hua Cui. 2022. Social contexts, agency, and conflicts: Exploring critical aspects of design for future smart home technologies. ACM Transactions on Computer-Human Interaction 29, 2 (2022), 1–30.
  43. Personalization of Context-Dependent Applications Through Trigger-Action Rules. ACM Trans. Comput.-Hum. Interact. 24, 2, Article 14 (apr 2017), 33 pages. https://doi.org/10.1145/3057861
  44. Policy conflict resolution in iot via planning. In Advances in Artificial Intelligence: 30th Canadian Conference on Artificial Intelligence, Canadian AI 2017, Edmonton, AB, Canada, May 16-19, 2017, Proceedings 30. Springer, 169–175.
  45. Tractable policy management framework for IoT. In Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VIII, Vol. 10190. SPIE, 48–54. https://doi.org/10.1117/12.2266597
  46. Context-based conflict management in pervasive platforms. In 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE, 250–255. https://doi.org/10.1109/PERCOMW.2017.7917567
  47. Jared Hall. [n.d.]. On Operational Policy Conflict Detection and Resolution in CPS-IoT systems. ([n. d.]).
  48. HSAS-MD Analyzer: A Hybrid Security Analysis System Using Model-Checking Technique and Deep Learning for Malware Detection in IoT Apps. Sensors 22, 3 (2022), 1079. https://doi.org/10.3390/s22031079
  49. Patterns of conflict among software components. Journal of Systems and Software 79, 4 (2006), 537–551.
  50. Chien-Chang Hsu and Liang-Zong Wang. 2008. A smart home resource management system for multiple inhabitants by agent conceding negotiation. In 2008 IEEE International Conference on Systems, Man and Cybernetics. IEEE, 607–612. https://doi.org/10.1109/ICSMC.2008.4811344
  51. Safechain: Securing trigger-action programming from attack chains. IEEE Transactions on Information Forensics and Security 14, 10 (2019), 2607–2622. https://doi.org/10.1109/TIFS.2019.2899758
  52. CoPI: Enabling Probabilistic Conflict Prediction in Smart Space Through Context-awareness. In 2022 IEEE/ACM Seventh International Conference on Internet-of-Things Design and Implementation (IoTDI). IEEE, 30–42.
  53. Conflict Detection in IoT-based Smart Homes. In 2021 IEEE International Conference on Web Services (ICWS). IEEE Computer Society, Los Alamitos, CA, USA, 303–313. https://doi.org/10.1109/ICWS53863.2021.00048
  54. A Multi-task Learning Approach for Predicting Intentions Using Smart Home IoT Services. In Service-Oriented Computing: 20th International Conference, ICSOC 2022, Seville, Spain, November 29–December 2, 2022, Proceedings. Springer, 413–421.
  55. A conflicts’ classification for IoT-based services: a comparative survey. PeerJ Computer Science 7 (2021), e480. https://doi.org/10.7717/peerj-cs.480
  56. A formal methods-based Rule Verification Framework for end-user programming in campus Building Automation Systems. Building and Environment 181 (2020), 106983. https://doi.org/10.1016/j.buildenv.2020.106983
  57. Hiroshi Igaki and Masahide Nakamura. 2010. Modeling and detecting feature interactions among integrated services of home network systems. IEICE transactions on information and systems 93, 4 (2010), 822–833. https://doi.org/10.1587/transinf.E93.D.822
  58. Security and privacy considerations for wireless sensor networks in smart home environments. In Proceedings of the 2012 IEEE 16th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 626–633.
  59. ContexloT: Towards providing contextual integrity to appified IoT platforms.. In ndss, Vol. 2. San Diego, 2–2.
  60. Multiple model method for aircraft conflict detection and resolution in intent and weather uncertainty. IEEE Trans. Aerospace Electron. Systems 55, 2 (2018), 1004–1020.
  61. A Review and State of Art of Internet of Things (IoT). Archives of Computational Methods in Engineering 29, 3 (2022), 1395–1413. https://doi.org/10.1007/s11831-021-09622-6
  62. Li Na Lee and Mi Jeong Kim. 2020. A critical review of smart residential environments for older adults with a focus on pleasurable experience. Frontiers in psychology 10 (2020), 3080. https://doi.org/10.3389/fpsyg.2019.03080
  63. Ya-Hua Lee and Fuchun Joseph Lin. 2019. Situation awareness and conflict resolution in smart home with multiple users. In 2019 IEEE 5th World Forum on Internet of Things (WF-IoT). IEEE, 852–857.
  64. Detecting feature interactions in home appliance networks. In 2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing. IEEE, 895–903. https://doi.org/10.1109/SNPD.2008.158
  65. DIAC: An Inter-app Conflicts Detector for Open IoT Systems. ACM Transactions on Embedded Computing Systems (TECS) 19, 6 (2020), 1–25. https://doi.org/10.1145/3391895
  66. Jian Liang and Ruxu Du. 2005. Thermal comfort control based on neural network for HVAC application. In Proceedings of 2005 IEEE Conference on Control Applications, 2005. CCA 2005. IEEE, 819–824. https://doi.org/10.1109/CCA.2005.1507230
  67. RemedioT: Remedial actions for internet-of-things conflicts. In Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. 101–110. https://doi.org/10.1145/3360322.3360837
  68. CityGuard: A Watchdog for Safety-Aware Conflict Detection in Smart Cities. In 2017 IEEE/ACM Second International Conference on Internet-of-Things Design and Implementation (IoTDI). 259–270.
  69. Detection of Runtime Conflicts among Services in Smart Cities. In 2016 IEEE International Conference on Smart Computing (SMARTCOMP). 1–10. https://doi.org/10.1109/SMARTCOMP.2016.7501688
  70. Evan Magill and Jesse Blum. 2016. Exploring conflicts in rule-based sensor networks. Pervasive and Mobile Computing 27 (2016), 133–154. https://doi.org/10.1016/j.pmcj.2015.08.005
  71. Smart Office Area Monitoring & Control Based on IOT. In 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN). IEEE, 450–453.
  72. Peter Marwedel. 2021. Specifications and Modeling. Springer International Publishing, Cham, 29–126. https://doi.org/10.1007/978-3-030-60910-8_2
  73. Sven Meyer and Andry Rakotonirainy. 2003. A Survey of Research on Context-Aware Homes. In Proceedings of the Australasian Information Security Workshop Conference on ACSW Frontiers 2003 - Volume 21 (Adelaide, Australia) (ACSW Frontiers ’03). Australian Computer Society, Inc., AUS, 159–168. https://doi.org/10.5555/827987.828005
  74. An empirical approach to modeling user-system interaction conflicts in smart homes. IEEE Transactions on Human-Machine Systems 50, 6 (2020), 573–583.
  75. IoT-based smart homes: A review of system architecture, software, communications, privacy and security. Internet of Things 1 (2018), 81–98.
  76. A Survey on Trend and Classification of Internet of Things Reviews. IEEE Access 8 (2020), 111763–111782. https://doi.org/10.1109/ACCESS.2020.3002932
  77. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Annals of internal medicine 151, 4 (2009), 264–269.
  78. Alberto Monge Roffarello. 2018. End User Development in the IoT: A Semantic Approach. In 2018 14th International Conference on Intelligent Environments (IE). 107–110. https://doi.org/10.1109/IE.2018.00026
  79. VISCR: intuitive and conflict-free automation for securing the dynamic consumer iot infrastructures. arXiv preprint arXiv:1907.13288 (2019).
  80. Feature interactions in integrated services of networked home appliances. In Proc. of Int’l. Conf. on Feature Interactions in Telecommunication Networks and Distributed Systems (ICFI’05). 236–251.
  81. Considering Impacts and Requirements for Better Understanding of Environment Interactions in Home Network Services. Comput. Netw. 57, 12 (aug 2013), 2442–2453. https://doi.org/10.1016/j.comnet.2013.02.024
  82. Constructing home network systems and integrated services using legacy home appliances and web services. International Journal of Web Services Research (IJWSR) 5, 1 (2008), 82–98. https://doi.org/10.4018/jwsr.2008010105
  83. Securing Fog Computing for Internet of Things Applications: Challenges and Solutions. IEEE Communications Surveys & Tutorials 20, 1 (2018), 601–628. https://doi.org/10.1109/COMST.2017.2762345
  84. Using argumentation to solve conflicting situations in users’ preferences in ambient assisted living. Applied Artificial Intelligence 35, 15 (2021), 2327–2369.
  85. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. International journal of surgery 88 (2021), 105906.
  86. Natural notation for the domestic internet of things. In International Symposium on End User Development. Springer, 25–41. https://doi.org/10.1007/978-3-319-18425-8_3
  87. Context aware computing for the internet of things: A survey. IEEE communications surveys & tutorials 16, 1 (2013), 414–454.
  88. Rule-based conflict resolution framework for Internet of Things device management in smart home environment. In 2016 IEEE 5th Global Conference on Consumer Electronics. IEEE, 1–2. https://doi.org/10.1109/GCCE.2016.7800444
  89. Pavana Pradeep and Krishna Kant. 2022. Conflict Detection and Resolution in IoT Systems: A Survey. IoT 3, 1 (2022), 191–218. https://doi.org/10.3390/iot3010012
  90. Automating Conflict Detection and Mitigation in Large-Scale IoT Systems. In 2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid). IEEE, 535–544. https://doi.org/10.1109/CCGrid51090.2021.00063
  91. Preclude: Conflict detection in textual health advice. In 2017 IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE, 286–296.
  92. User satisfaction in long term group recommendations. In International Conference on Case-Based Reasoning. Springer, 211–225. https://doi.org/10.1007/978-3-642-23291-6_17
  93. Parisa Rashidi and Diane J. Cook. 2013. COM: A Method for Mining and Monitoring Human Activity Patterns in Home-Based Health Monitoring Systems. ACM Trans. Intell. Syst. Technol. 4, 4, Article 64 (oct 2013), 20 pages. https://doi.org/10.1145/2508037.2508045
  94. Discovering Activities to Recognize and Track in a Smart Environment. IEEE Transactions on Knowledge and Data Engineering 23, 4 (2011), 527–539. https://doi.org/10.1109/TKDE.2010.148
  95. Conflict detection and resolution in home and building automation systems: a literature review. Journal of Ambient Intelligence and Humanized Computing 5, 5 (2014), 699–715. https://doi.org/10.1007/s12652-013-0184-9
  96. Smart HVAC control in IoT: Energy consumption minimization with user comfort constraints. The Scientific World Journal 2014 (2014). https://doi.org/10.1155/2014/161874
  97. Using semi-formal methods for detecting interactions among smart homes policies. Science of Computer Programming 67, 2-3 (2007), 125–161. https://doi.org/10.1016/j.scico.2006.11.002
  98. A systematic examination of inter-app conflicts detections in open IoT systems. Technical Report. North Carolina State University. Dept. of Computer Science.
  99. Kratos: Multi-user multi-device-aware access control system for the smart home. In Proceedings of the 13th ACM Conference on Security and Privacy in Wireless and Mobile Networks. 1–12.
  100. Energy conservation in IoT-based smart home and its automation. Energy conservation for IoT devices (2019), 155–177. https://doi.org/10.1007/978-981-13-7399-2_7
  101. Comparing formal models of IoT app coordination analysis. In Proceedings of the 3rd ACM SIGSOFT International Workshop on Software Security from Design to Deployment. 3–10. https://doi.org/10.1145/3416507.3423188
  102. Pursuing pleasance: Interrogating energy-intensive visions for the smart home. International Journal of Human-Computer Studies 136 (2020), 102379.
  103. Conflict detection scheme based on formal rule model for smart building systems. IEEE Transactions on Human-Machine Systems 45, 2 (2014), 215–227. https://doi.org/10.1109/THMS.2014.2364613
  104. Experiences with IoT and AI in a smart campus for optimizing classroom usage. IEEE Internet of Things Journal 6, 5 (2019), 7595–7607.
  105. Jun Tang. 2019. Conflict detection and resolution for civil aviation: A literature survey. IEEE Aerospace and Electronic Systems Magazine 34, 10 (2019), 20–35.
  106. Understanding and automatically detecting conflicting interactions between smart home iot applications. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 1215–1227. https://doi.org/10.1145/3368089.3409682
  107. Model-Driven Engineering for End-Users in the Loop in Smart Ambient Systems. Journal of Universal Computer Science 27, 7 (2021), 755–773. https://doi.org/10.3897/jucs.70515
  108. Comity-conflict avoidance in pervasive computing environments. In OTM Confederated International Conferences ”On the Move to Meaningful Internet Systems”. Springer, 763–772. https://doi.org/10.1007/978-3-540-76890-6_2
  109. Practical Trigger-Action Programming in the Smart Home. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Toronto, Ontario, Canada) (CHI ’14). Association for Computing Machinery, New York, NY, USA, 803–812. https://doi.org/10.1145/2556288.2557420
  110. Charting the attack surface of trigger-action IoT platforms. In Proceedings of the 2019 ACM SIGSAC conference on computer and communications security. 1439–1453. https://doi.org/10.1145/3319535.3345662
  111. Fear and logging in the internet of things. In Network and Distributed Systems Symposium.
  112. Attention-based dynamic user preference modeling and nonlinear feature interaction learning for collaborative filtering recommendation. Applied Soft Computing 110 (2021), 107652. https://doi.org/10.1016/j.asoc.2021.107652
  113. WM Wang and SL Ting. 2011. Development of a computational simulation model for conflict management in team building. IJEBM 3 (2011), 14. https://doi.org/10.5772/50932
  114. The Internet of Things—A survey of topics and trends. Information systems frontiers 17, 2 (2015), 261–274. https://doi.org/10.1007/s10796-014-9489-2
  115. Cognitive Internet of Things: A New Paradigm Beyond Connection. IEEE Internet of Things Journal 1, 2 (2014), 129–143. https://doi.org/10.1109/JIOT.2014.2311513
  116. A3ID: An Automatic and Interpretable Implicit Interference Detection Method for Smart Home via Knowledge Graph. IEEE Internet of Things Journal 7, 3 (2020), 2197–2211. https://doi.org/10.1109/JIOT.2019.2959063
  117. An application conflict detection and resolution system for smart homes. In 2015 IEEE/ACM 1st international workshop on software engineering for smart cyber-physical systems. IEEE, 33–39.
  118. Context as a service: realizing internet of things-aware processes for the independent living of the elderly. In International Conference on Service-Oriented Computing. Springer, 763–779. https://doi.org/10.1007/978-3-319-46295-0_54
  119. Yinbo Yu and Jiajia Liu. 2021. TAPInspector: Safety and Liveness Verification of Concurrent Trigger-Action IoT Systems. arXiv preprint arXiv:2102.01468 (2021).
  120. Victor Zamudio and Victor Callaghan. 2009. Understanding and avoiding interaction-based instability in pervasive computing environments. International Journal of Pervasive Computing and Communications 5, 2 (2009), 163–186.
  121. Kamal Aldein Mohammed Zeinab and Sayed Ali Ahmed Elmustafa. 2017. Internet of things applications, challenges and related future technologies. World Scientific News 67, 2 (2017), 126–148.
  122. Visualizing Differences to Improve End-User Understanding of Trigger-Action Programs (CHI EA ’20). Association for Computing Machinery, New York, NY, USA, 1–10. https://doi.org/10.1145/3334480.3382940
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