Skip to main content
SpringerLink
Log in

Solving the IoT Cascading Failure Dilemma Using a Semantic Multi-agent System

  • Conference paper
  • First Online:
The Semantic Web – ISWC 2023 (ISWC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14266))

Included in the following conference series:

  • 883 Accesses

Abstract

Managing interdependent Internet of Things (IoT) devices can be challenging because different actors, e.g., operators and service providers, propose siloed Device Management (DM) solutions that are unable to handle cascading failures across multiple devices. To address this issue, we propose a novel approach based on a cooperative Multi-agent System (MAS) allowing siloed DM solutions to manage IoT cascading failures automatically and coordinately. The proposed MAS leverages Semantic Web standards to establish a common understanding of device dependencies and failures. It relies on the Digital Twin technology to represent dynamic device dependencies accurately for failure root cause identification. Our approach has been effective in handling cascading failures in Smart Home scenarios, reducing time to repair failure, saving Customer Care costs, and minimizing resource consumption in IoT infrastructure such as energy consumption.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://aws.amazon.com/fr/iot-device-management/.

  2. 2.

    https://liveobjects.orange-business.com/.

  3. 3.

    MAS refers to a network of software agents that operate independently while being loosely connected to address complex problems that are beyond the individual capacities or knowledge of each agent.

  4. 4.

    A Semantic Digital Twin is a virtual and synchronized representation of real-world entities and processes built using a semantic description.

  5. 5.

    This study is limited by the information available online.

  6. 6.

    https://www.avsystem.com.

  7. 7.

    https://aws.amazon.com/fr/iot-device-management/.

  8. 8.

    Automation rules allow the automated composition of IoT services in a connected environment.

  9. 9.

    SmartThings is Samsung’s IoT platform that enables automation rules across IoT devices in Smart Homes.

  10. 10.

    https://iotfontology.github.io/.

  11. 11.

    https://saref.etsi.org/core/v3.1.1/.

  12. 12.

    https://webstore.iec.ch/publication/26359.

  13. 13.

    https://www.iso.org/standard/52256.html.

  14. 14.

    https://csa-iot.org/all-solutions/matter/.

  15. 15.

    https://usp-data-models.broadband-forum.org/.

  16. 16.

    https://iotdontology.github.io/.

  17. 17.

    https://www.w3.org/TR/shacl-af/.

  18. 18.

    https://tech2.thinginthefuture.com/.

  19. 19.

    https://github.com/jacamo-lang/jacamo.

  20. 20.

    https://jason.sourceforge.net/wp/.

  21. 21.

    https://cartago.sourceforge.net/.

  22. 22.

    https://github.com/apache/jena.

References

  1. Aïssaoui, F., Berlemont, S., Douet, M., Mezghani, E.: A semantic model toward smart IoT device management. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds.) Web, Artificial Intelligence and Network Applications, pp. 640–650. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-44038-1_59

    Chapter  Google Scholar 

  2. Ali, N., Hong, J.E.: Failure detection and prevention for cyber-physical systems using ontology-based knowledge base. Computers 7(4) (2018). https://doi.org/10.3390/computers7040068. https://www.mdpi.com/2073-431X/7/4/68

  3. Alsabilah, N., Rawat, D.B.: Anomaly detection in smart home networks using Kalman filter. In: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 1–6 (2021). https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484507

  4. Benazzouz, Y., Keir Aktouf, O.E., Parissis, I.: A fault fuzzy-ontology for large scale fault-tolerant wireless sensor networks. Procedia Comput. Sci. 35, 203–212 (2014). https://doi.org/10.1016/j.procs.2014.08.100. Knowledge-Based and Intelligent Information & Engineering Systems 18th Annual Conference, KES-2014 Gdynia, Poland, September 2014 Proceedings

  5. Boissier, O., Bordini, R., Hubner, J., Ricci, A.: Multi-Agent Oriented Programming: Programming Multi-Agent Systems Using JaCaMo. Intelligent Robotics and Autonomous Agents Series. MIT Press (2020)

    Google Scholar 

  6. Borhani, A., Zarandi, H.R.: Thingsdnd: IoT device failure detection and diagnosis for multi-user smart homes. In: 2022 18th European Dependable Computing Conference (EDCC), pp. 113–116 (2022). https://doi.org/10.1109/EDCC57035.2022.00028. Fault detection on sensors in smart home settings

  7. Bratman, M.: Intention, Plans, and Practical Reason. Harvard University Press, Cambridge (1987)

    Google Scholar 

  8. Celik, Z.B., Tan, G., Mcdaniel, P.: Iotguard: dynamic enforcement of security and safety policy in commodity IoT. In: Proceedings 2019 Network and Distributed System Security Symposium (2019)

    Google Scholar 

  9. Chakraborty, T., et al.: Fall-curve: a novel primitive for IoT fault detection and isolation. In: Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems, SenSys 2018, pp. 95–107. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3274783.3274853

  10. Chen, Y., Zhen, Z., Yu, H., Xu, J.: Application of fault tree analysis and fuzzy neural networks to fault diagnosis in the internet of things (IoT) for aquaculture. Sensors 17(1) (2017). https://doi.org/10.3390/s17010153

  11. Chi, Y., Dong, Y., Wang, Z.J., Yu, F.R., Leung, V.C.M.: Knowledge-based fault diagnosis in industrial internet of things: a survey. IEEE Internet Things J. 9(15), 12886–12900 (2022). https://doi.org/10.1109/JIOT.2022.3163606

    Article  Google Scholar 

  12. Chi, Y., Wang, Z.J., Leung, V.C.M.: Distributed knowledge inference framework for intelligent fault diagnosis in IIoT systems. IEEE Trans. Netw. Sci. Eng. 9(5), 3152–3165 (2022)

    Article  Google Scholar 

  13. Emmanouilidis, C., Gregori, M., Al-Shdifat, A.: Context ontology development for connected maintenance services. IFAC-PapersOnLine 53(2), 10923–10928 (2020). https://doi.org/10.1016/j.ifacol.2020.12.2833. 21st IFAC World Congress

  14. Guittoum, A.: IoT-F ontology documentation. https://github.com/Orange-OpenSource/collaborativeDM-IoTF-ontology-documentation

  15. Guittoum, A.: OSAMA agents in the Smart home use case. https://github.com/Orange-OpenSource/collaborativeDM-OSAMA-agent

  16. Guittoum, A.: The FMSim simulator. https://github.com/Orange-OpenSource/collaborativeDM-FM-Simulator

  17. Guittoum, A., et al.: Inferring threatening IoT dependencies using semantic digital twins toward collaborative IoT device management. In: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing. SAC 2023. Association for Computing Machinery, New York (2023). https://doi.org/10.1145/3555776.3578573

  18. Gupta, H., Dastjerdi, A.V., Ghosh, S.K., Buyya, R.: iFogSim: a toolkit for modeling and simulation of resource management techniques in internet of things, edge and fog computing environments (2016)

    Google Scholar 

  19. Jia, Y., et al.: Who’s in control? On security risks of disjointed IoT device management channels. In: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, CCS 2021, pp. 1289–1305. Association for Computing Machinery, New York (2021)

    Google Scholar 

  20. Kapitanova, K., Hoque, E., Stankovic, J.A., Whitehouse, K., Son, S.H.: Being smart about failures: assessing repairs in smart homes. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, UbiComp 2012, pp. 51–60. Association for Computing Machinery, New York (2012). https://doi.org/10.1145/2370216.2370225

  21. Kodeswaran, P., Kokku, R., Sen, S., Srivatsa, M.: Idea: a system for efficient failure management in smart IoT environments. In: MobiSys 2016 - Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services pp. 43–56 (2016). https://doi.org/10.1145/2906388.2906406

  22. Lazarova-Molnar, S., Shaker, H.R., Mohamed, N., Jorgensen, B.N.: Fault detection and diagnosis for smart buildings: state of the art, trends and challenges. In: 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC), pp. 1–7 (2016). https://doi.org/10.1109/ICBDSC.2016.7460392

  23. Li, J., Guo, Y., Wall, J., West, S.: Support vector machine based fault detection and diagnosis for HVAC systems. Int. J. Intell. Syst. Technol. Appl. 18, 204 (2019)

    Google Scholar 

  24. Najari, N., Berlemont, S., Lefebvre, G., Duffner, S., Garcia, C.: Robust variational autoencoders and normalizing flows for unsupervised network anomaly detection. In: Barolli, L., Hussain, F., Enokido, T. (eds.) AINA 2022. LNNS, vol. 450, pp. 281–292. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-99587-4_24

    Chapter  Google Scholar 

  25. Najeh, H.: Diagnosis in building: new challenges. Theses, Université Grenoble Alpes; École nationale d’ingénieurs de Gabès (Tunisie) (2019)

    Google Scholar 

  26. Nishiguchi, Y., Yano, A., Ohtani, T., Matsukura, R., Kakuta, J.: IoT fault management platform with device virtualization. In: IEEE World Forum on Internet of Things, WF-IoT 2018 - Proceedings 2018-January, pp. 257–262 (2018)

    Google Scholar 

  27. Norris, M., et al.: Iotrepair: systematically addressing device faults in commodity IoT. In: 2020 IEEE/ACM Fifth International Conference on Internet-of-Things Design and Implementation (IoTDI), pp. 142–148 (2020)

    Google Scholar 

  28. Norris, M., et al.: Iotrepair: flexible fault handling in diverse IoT deployments. ACM Trans. Internet Things 3(3) (2022). https://doi.org/10.1145/3532194

  29. Ozeer, U.I.Z.: Autonomic resilience of distributed IoT applications in the Fog. Theses, Université Grenoble Alpes (2019)

    Google Scholar 

  30. Perez Abreu, D., Velasquez, K., Curado, M., Monteiro, E.: A comparative analysis of simulators for the cloud to fog continuum. Simul. Model. Pract. Theory 101, 102029 (2020)

    Google Scholar 

  31. Power, A.: A predictive fault-tolerance framework for IoT systems. Ph.D. thesis, Lancaster University (2020). https://doi.org/10.17635/lancaster/thesis/1063

  32. Rao, A.S.: AgentSpeak(L): BDI agents speak out in a logical computable language. In: Van de Velde, W., Perram, J.W. (eds.) MAAMAW 1996. LNCS, vol. 1038, pp. 42–55. Springer, Heidelberg (1996). https://doi.org/10.1007/BFb0031845

    Chapter  Google Scholar 

  33. Samsung: SmartThings rule. https://developer-preview.smartthings.com/docs/automations/rules/. Accessed 6 Avril 2022

  34. Sanislav, T., Mois, G.: A dependability analysis model in the context of cyber-physical systems. In: 2017 18th International Carpathian Control Conference (ICCC), pp. 146–150 (2017)

    Google Scholar 

  35. Sanislav, T., Zeadally, S., Mois, G.D., Fouchal, H.: Reliability, failure detection and prevention in cyber-physical systems (CPSS) with agents. Concurr. Comput. Pract. Exp. 31(24), e4481 (2019). https://doi.org/10.1002/cpe.4481

  36. Sharma, A.B., Golubchik, L., Govindan, R.: Sensor faults: detection methods and prevalence in real-world datasets. ACM Trans. Sen. Netw. 6(3) (2010)

    Google Scholar 

  37. Shibuya, M., Hasegawa, T., Yamaguchi, H.: A study on device management for IoT services with uncoordinated device operating history. In: ICN 2016, p. 84 (2016)

    Google Scholar 

  38. Silva, L.D., Meneguzzi, F., Logan, B.: BDI agent architectures: a survey. In: Bessiere, C. (ed.) Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, pp. 4914–4921. International Joint Conferences on Artificial Intelligence Organization (2020)

    Google Scholar 

  39. Sinche, S., et al.: A survey of IoT management protocols and frameworks. IEEE Commun. Surv. Tutor. 22(2), 1168–1190 (2020)

    Article  Google Scholar 

  40. Steenwinckel, B., et al.: Towards adaptive anomaly detection and root cause analysis by automated extraction of knowledge from risk analyses. In: SSN@ISWC (2018)

    Google Scholar 

  41. Suárez-Figueroa, M.C., Gómez-Pérez, A., Fernández-López, M.: The NeOn methodology for ontology engineering. In: Suárez-Figueroa, M.C., Gómez-Pérez, A., Motta, E., Gangemi, A. (eds.) Ontology Engineering in a Networked World, pp. 9–34. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-24794-1_2

    Chapter  Google Scholar 

  42. Carnegie Mellon University: Intelligent Software Agents. https://www.cs.cmu.edu/~softagents/multi.html

  43. Wilhelm, Y., Reimann, P., Gauchel, W., Mitschang, B.: Overview on hybrid approaches to fault detection and diagnosis: combining data-driven, physics-based and knowledge-based models. Procedia CIRP 99, 278–283 (2021). https://doi.org/10.1016/j.procir.2021.03.041. 14th CIRP Conference on Intelligent Computation in Manufacturing Engineering, 15–17 July 2020

  44. Xing, L.: Cascading failures in internet of things: review and perspectives on reliability and resilience. IEEE Internet Things J. 8(1), 44–64 (2021)

    Article  Google Scholar 

  45. Xu, F., Liu, X., Chen, W., Zhou, C., Cao, B.: Ontology-based method for fault diagnosis of loaders. Sensors 18(3) (2018). https://doi.org/10.3390/s18030729

  46. Zhang, H., Zhang, Q., Liu, J., Guo, H.: Fault detection and repairing for intelligent connected vehicles based on dynamic Bayesian network model. IEEE Internet Things J. 5(4), 2431–2440 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Orange Innovation, Meylan, France

    Amal Guittoum, François Aïssaoui & Sébastien Bolle

  2. University of Grenoble Alpes - LIG, Grenoble, France

    Amal Guittoum, Fabienne Boyer & Noel De Palma

Authors
  1. Amal Guittoum
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. François Aïssaoui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sébastien Bolle
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fabienne Boyer
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Noel De Palma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amal Guittoum .

Editor information

Editors and Affiliations

  1. University of Liverpool, Liverpool, UK

    Terry R. Payne

  2. University of Bologna, Bologna, Italy

    Valentina Presutti

  3. Southeast University, Nanjing, China

    Guilin Qi

  4. Universidad Politécnica de Madrid, Madrid, Spain

    María Poveda-Villalón

  5. Huawei Technologies R&D UK, Edinburgh, UK

    Giorgos Stoilos

  6. Centrum Wiskunde and Informatica, Amsterdam, The Netherlands

    Laura Hollink

  7. IT University of Copenhagen, Copenhagen, Denmark

    Zoi Kaoudi

  8. Nanjing University, Nanjing, China

    Gong Cheng

  9. Tsinghua University, Beijing, China

    Juanzi Li

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Guittoum, A., Aïssaoui, F., Bolle, S., Boyer, F., De Palma, N. (2023). Solving the IoT Cascading Failure Dilemma Using a Semantic Multi-agent System. In: Payne, T.R., et al. The Semantic Web – ISWC 2023. ISWC 2023. Lecture Notes in Computer Science, vol 14266. Springer, Cham. https://doi.org/10.1007/978-3-031-47243-5_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-47243-5_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47242-8

  • Online ISBN: 978-3-031-47243-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation