Self-Healing of Semantically Interoperable Smart and Prescriptive Edge Devices in IoT
Source abstract
Abstract
Smart homes enhance energy efficiency without compromising residents’ comfort. To support smart home deployment and services, an IoT network must be established, while energy-management techniques must be applied to ensure energy efficiency. IoT networks must perpetually operate to ensure constant energy and indoor environmental monitoring. In this paper, an advanced sensor-agnostic plug-n-play prescriptive edge-to-edge IoT network management with micro-services is proposed, supporting also the semantic interoperability of multiple smart edge devices operating in the smart home network. Furthermore, IoT health-monitoring algorithms are applied to inspect network anomalies taking proper healing actions/prescriptions without the need to visit the residency. An autoencoder long short-term memory (AE-LSTM) is selected for detecting problematic situations, improving error prediction to 99.4%. Finally, indicative evaluation results reveal the mitigation of the IoT system breakdowns.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Anomaly Detection Techniques and Applications · Network Security and Intrusion Detection · IoT and Edge/Fog Computing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Department of Cultural Technology and Communication, University of the Aegean, Intelligent Systems Lab, 81100 Mytilene, Greece
Management Science and Technology Department, International Hellenic University (IHU), 65404 Kavala, Greece
References (58)
Show source-provided references
1. (2022, August 26). REPowerEU: A Plan to Rapidly Reduce Dependence on Russian Fossil Fuels and Fast forward the Green Transition. Available online: https://ec.europa.eu/commission/presscorne.
2. (2022, August 26). European Commission, EU ‘Save Energy’. Available online: https://eur-lex.europa.eu/homepage.html.
3. NRG4-U: A novel home energy management system for a unique loadprofile
4. Garzia, F., Van Thillo, L., Verbeke, S., Pozza, C., and Audenaert, A. (2022, January 22–25). Co-benefits of building automation and control systems: An analysis of smart office buildings. Proceedings of the CLIMA 2022 Conference, Rotterdam, The Netherlands.
5. Smart home security: Challenges, issues and solutions at different IoT layers10.1007/s11227-021-03825-1 ↗
6. (2022, September 22). Carlo Gavazzi Automations. Available online: https://www.carlogavazzi.com/.
7. Wicaksono, R., Rif’an, M., and Anugerah, R. (2021, January 31). IoT Based Smart Energy Meter Using Modbus Protocol as Electricity Saving Effort. Proceedings of the Conference on Broad Exposure to Science and Technology 2021 (BEST 2021), Online.
8. Dadi, V., Pathakamuri, N., Ashik, M., Patnaik, M.R., Reddy, D.V.R.K., and Ravichandra, B. (2022). Manual (Wired) and Control (Wireless) Modes of Automation System with Multi-level Voice Strings. Sustainable Communication Networks and Application, Springer.10.1007/978-981-16-6605-6_28 ↗
9. Chinchero, H.F., Alonso, J.M., and Ortiz, T.H. (2020, January 16–19). LED Lighting System with Magnetic Control and IoT Sensor Integration for Smart Buildings. Proceedings of the 2020 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), Aipur, India.10.1109/PEDES49360.2020.9379580 ↗
10. A review paper on wireless sensor network techniques in Internet of Things (IoT)10.1016/j.matpr.2021.05.067 ↗
11. A review of IoT network management: Current status and perspectives
12. (2022, September 22). Smart Home—Europe. Available online: https://www.statista.com/outlook/dmo/smart-home/europe.
13. Survey on LoRa technology: Solution for internet of things
14. IoT network design using open-source LoRa coverage emulator10.1109/ACCESS.2021.3070976 ↗
15. Ahmad, Z., Shahid Khan, A., Nisar, K., Haider, I., Hassan, R., Haque, M.R., Tarmizi, S., and Rodrigues, J.J.P.C. (2021). Anomaly detection using deep neural network for IoT architecture. Appl. Sci., 11.10.3390/app11157050 ↗
16. Ullah, I., and Mahmoud, Q.H. (2020, January 11–14). A Technique for Generating a Botnet Dataset for Anomalous Activity Detection in IoT Networks. Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics SMC, Toronto, ON, Canada.10.1109/SMC42975.2020.9283220 ↗
17. Adi, P.D.P., Sihombing, V., Siregar, V.M.M., Yanris, G.J., Sianturi, F.A., Purba, W., Tamba, S.P., Simatupang, J., Arifuddin, R., and Subairi, S. (2021, January 9–11). A performance evaluation of ZigBee mesh communication on the Internet of Things (IoT). Proceedings of the 2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT), Surabaya, Indonesia.10.1109/EIConCIT50028.2021.9431875 ↗
18. Lightweight and secure authentication scheme for IoT network based on publish–subscribe fog computing model10.1016/j.comnet.2021.108465 ↗
19. A review on fog computing: Architecture, fog with IoT, algorithms and research challenges10.1016/j.icte.2021.05.004 ↗
20. Design of anomaly-based intrusion detection system using fog computing for IoT network10.3103/S0146411621020085 ↗
21. Network management system for IoT based on dynamic systems10.1155/2021/9102095 ↗
22. Improvement and implementation of wireless network topology system based on SNMP protocol for router equipment10.1016/j.comcom.2019.12.038 ↗
23. Toward an AI-enabled SDN-based 5G & IoT network10.5539/nct.v5n2p7 ↗
24. Abdulrazak, B., Codjo, J.A., and Paul, S. (2022). Self-Healing approach for IoT Architecture: AMI Platform. International Conference on Smart Homes and Health Telematics, Springer.10.1007/978-3-031-09593-1_1 ↗
25. Dias, J.P., Lima, B., Faria, J.P., Restivo, A., and Ferreira, H.S. (2020). Visual self-healing modelling for reliable internet-of-things systems. International Conference on Computational Science, Springer.10.1007/978-3-030-50426-7_27 ↗
26. (2022, November 08). Node-RED. Available online: https://nodered.org//.
27. Aktas, M.S., and Astekin, M. (2019). Provenance aware run-time verification of things for self-healing Internet of Things applications. Concurr. Comput., 31.10.1002/cpe.4263 ↗
28. Ferreira, H.S., Sousa, T.B., Restivo, A., and Dias, J.P. (2020, January 1–4). A pattern-language for self-healing Internet-of-Things systems. Proceedings of the European Conference on Pattern Languages of Programs 2020, Virtual Event.10.1145/3424771.3424804 ↗
29. Dias, J.P., Restivo, A., and Ferreira, H.S. (2021, January 3). Empowering visual Internet-of-Things mashups with self-healing capabilities. Proceedings of the 2021 IEEE/ACM 3rd International Workshop on Software Engineering Research and Practices for the IoT (SERP4IoT), Madrid, Spain.10.1109/SERP4IoT52556.2021.00014 ↗
30. Optimization scheduling of home appliances in smart home: A model based on a niche technology with sharing mechanism10.1016/j.ijepes.2022.108126 ↗
31. Internet of things-based intelligent smart home control system10.1155/2021/9928254 ↗
32. Design, implementation, and deployment of an IoT based smart energy management system10.1109/ACCESS.2021.3070960 ↗
33. Tiwari, P., Garg, V., and Agrawal, R. (2022). Changing world: Smart homes review and future. Smart IoT for Research and Industry, Springer.10.1007/978-3-030-71485-7_9 ↗
34. (2022, October 13). MQTT Specification. Available online: https://mqtt.org/mqtt-specification.
35. (2022, October 19). System and Service Manager. Available online: https://systemd.io/.
36. Hsu, T.H.-C. (2018). Hands-On Security in DevOps: Ensure Continuous Security, Deployment, and Delivery with DevSecOps, Packt Publishing Ltd.
37. Xu, L. (2021, January 28–30). Computer Network Security Problems and Solutions Based on Big Data. Proceedings of the 2nd International Conference on Computing and Data Science, Stanford, CA, USA.10.1145/3448734.3450838 ↗
38. Landscape of architecture and design patterns for iot systems10.1109/JIOT.2020.3003528 ↗
39. Long short-term memory10.1162/neco.1997.9.8.1735 ↗
40. A review of recurrent neural networks: LSTM cells and network architectures10.1162/neco_a_01199 ↗
41. A survey on LSTM memristive neural network architectures and applications10.1140/epjst/e2019-900046-x ↗
42. Gensler, A., Henze, J., Sick, B., and Raabe, N. (2016, January 9–12). Deep Learning for solar power forecasting—An approach using AutoEncoder and LSTM Neural Networks. Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Budapest, Hungary.10.1109/SMC.2016.7844673 ↗
43. Staudemeyer, R.C., and Morris, E.R. (2019). Understanding LSTM–A tutorial into long short-term memory recurrent neural networks. arXiv.
44. Forecasting and Anomaly Detection approaches using LSTM and LSTM Autoencoder techniques with the applications in supply chain management10.1016/j.ijinfomgt.2020.102282 ↗
45. Homayouni, H., Ghosh, S., Ray, I., Gondalia, S., Duggan, J., and Kahn, M.G. (2020, January 10–13). An autocorrelation-based lstm-autoencoder for anomaly detection on time-series data. Proceedings of the 2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA.10.1109/BigData50022.2020.9378192 ↗
46. Unsupervised anomaly detection with LSTM autoencoders using statistical data-filtering10.1016/j.asoc.2021.107443 ↗
47. Agarap, A.F. (2018). Deep learning using rectified linear units (relu). arXiv.
48. The three sigma rule10.1080/00031305.1994.10476030 ↗
49. (2022, October 19). Web of Things (WoT) Architecture W3C Recommendation 9 April 2020. Available online: https://www.w3.org/TR/wot-architecture/.
50. Korkas, C., Dimara, A., Michailidis, I., Krinidis, S., Marin-Perez, R., Martínez García, A.I., and Tzovaras, D. (2022). Integration and Verification of PLUG-N-HARVEST ICT Platform for Intelligent Management of Buildings. Energies, 15.10.3390/en15072610 ↗
51. TensorFlow (2022, October 19). TensorFlow Lite|ML for Mobile and Edge Devices. Available online: https://www.tensorflow.org/lite.
52. TensorFlow (2022, October 07). Tensorflow Lite Converter. Available online: https://www.tensorflow.org/lite/convert.
53. Kogler, A., Weber, D., Haubenwallner, M., Lipp, M., Gruss, D., and Schwarz, M. (2022, January 22–26). Finding and Exploiting CPU Features using MSR Templating. Proceedings of the 2022 IEEE Symposium on Security and Privacy (SP), San Francisco, CA, USA.10.1109/SP46214.2022.9833599 ↗
54. Liu, F.T., Ting, K.M., and Zhou, Z.H. (2008, January 15–19). Isolation forest. Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, Pisa, Italy.10.1109/ICDM.2008.17 ↗
55. Ma, J., and Perkins, S. (2003, January 20–24). Time-series novelty detection using one-class support vector machines. Proceedings of the International Joint Conference on Neural Networks, Portland, OR, USA.
56. Alghushairy, O., Alsini, R., Soule, T., and Ma, X. (2020). A review of local outlier factor algorithms for outlier detection in big data streams. Big Data Cogn. Comput., 5.10.3390/bdcc5010001 ↗
57. Çelik, M., Dadaşer-Çelik, F., and Dokuz, A.Ş. (2011, January 15–18). Anomaly detection in temperature data using DBSCAN algorithm. Proceedings of the 2011 International Symposium on Innovations in Intelligent Systems and Applications, Istanbul, Turkey.10.1109/INISTA.2011.5946052 ↗
58. Le, X.H., Ho, H.V., Lee, G., and Jung, S. (2019). Application of long short-term memory (LSTM) neural network for flood forecasting. Water, 11.10.3390/w11071387 ↗