journal article·2022-09-05Open access

A Fuzzy-Based Method for Objects Selection in Blockchain-Enabled Edge-IoT Platforms Using a Hybrid Multi-Criteria Decision-Making Model

Bhaskar B. Gardas, Arash Heidari, Nima Jafari Navimipour, Mehmet Unal

Applied Sciences· Volume 12· Issue 17

Abstract

The broad availability of connected and intelligent devices has increased the demand for Internet of Things (IoT) applications that require more intense data storage and processing. However, cloud-based IoT systems are typically located far from end-users and face several issues, including high cloud server load, slow response times, and a lack of global mobility. Some of these flaws can be addressed with edge computing. In addition, node selection helps avoid common difficulties related to IoT, including network lifespan, allocation of resources, and trust in the acquired data by selecting the correct nodes at a suitable period. On the other hand, the IoT’s interconnection of edge and blockchain technologies gives a fresh perspective on access control framework design. This article provides a novel node selection approach for blockchain-enabled edge IoT that provides a quick and dependable node selection. Moreover, fuzzy logic to approximation logic was used to manage numerical and linguistic data simultaneously. In addition, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a powerful tool for examining Multi-Criteria Decision-Making (MCDM) problems, is used. The suggested fuzzy-based technique employs three input criteria to select the correct IoT node for a given mission in IoT-edge situations. The outcomes of the experiments indicate that the proposed framework enhances the parameters under consideration.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: IoT and Edge/Fog Computing · Blockchain Technology Applications and Security · Caching and Content Delivery

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Mechanical Engineering, M.H. Saboo Siddik College of Engineering, University of Mumbai, 8, SabooSiddik Polytechnic Road, Mumbai 400 008, Maharashtra, India

Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz 1477893855, Iran

Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Kadir Has University, Istanbul 34083, Turkey

Department of Computer Engineering, Nisantasi University, Istanbul 34467, Turkey

References (51)

Show source-provided references
1. DeepBAN: A temporal convolution-based communication framework for dynamic WBANs10.1109/TCOMM.2021.3094581 ↗
2. Machine learning applications for COVID-19 outbreak management10.1007/s00521-022-07424-w ↗
3. Luo, G., Zhang, H., Yuan, Q., Li, J., and Wang, F.Y. (2022). ESTNet: Embedded Spatial-Temporal Network for Modeling Traffic Flow Dynamics. IEEE Trans. Intell. Transp. Syst., 1–12.10.1109/TITS.2022.3167019 ↗
4. Continuous authentication through finger gesture interaction for smart homes using WiFi10.1109/TMC.2020.2994955 ↗
5. Heidari, A., Jamali, M.A.J., Navimipour, N.J., and Akbarpour, S. (2022). Deep Q-Learning Technique for Offloading Offline/Online Computation in Blockchain-Enabled Green IoT-Edge Scenarios. Appl. Sci., 12.10.3390/app12168232 ↗
6. Recommendation Based on Large-Scale Many-Objective Optimization for the Intelligent Internet of Things System10.1109/JIOT.2021.3104661 ↗
7. Resource allocation in 5G IoV architecture based on SDN and fog-cloud computing10.1109/TITS.2020.3048844 ↗
8. RFID Reader Anticollision Based on Distributed Parallel Particle Swarm Optimization10.1109/JIOT.2020.3033473 ↗
9. Cao, K., Ding, H., Wang, B., Lv, L., Tian, J., Wei, Q., and Gong, F. (2022). Enhancing Physical Layer Security for IoT with Non-Orthogonal Multiple Access Assisted Semi-Grant-Free Transmission. IEEE Internet Things J., 1–13.10.1109/JIOT.2022.3193189 ↗
10. 6G-enabled network in box for internet of connected vehicles10.1109/TITS.2020.3034817 ↗
11. Large-scale many-objective deployment optimization of edge servers10.1109/TITS.2021.3059455 ↗
12. Heidari, A., Navimipour, N.J., and Unal, M. (2022). The History of Computing in Iran (Persia)—Since the Achaemenid Empire. Technologies, 10.10.3390/technologies10040094 ↗
13. Lv, Z., Li, Y., Feng, H., and Lv, H. (2021). Deep learning for security in digital twins of cooperative intelligent transportation systems. IEEE Trans. Intell. Transp. Syst., 1–10.10.1109/TITS.2021.3122566 ↗
14. A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain10.1016/j.compbiomed.2022.105461 ↗
15. A Structural Evolution-Based Anomaly Detection Method for Generalized Evolving Social Networks10.1093/comjnl/bxaa168 ↗
16. A privacy-preserving aggregation scheme based on negative survey for vehicle fuel consumption data10.1016/j.ins.2021.05.009 ↗
17. Chen, H., Xiong, Y., Li, S., Song, Z., Hu, Z., and Liu, F. (2022). Multi-Sensor Data Driven with PARAFAC-IPSO-PNN for Identification of Mechanical Nonstationary Multi-Fault Mode. Machines, 10.10.3390/machines10020155 ↗
18. Applications of ML/DL in the management of smart cities and societies based on new trends in information technologies: A systematic literature review10.1016/j.scs.2022.104089 ↗
19. Research on electric vehicle charging safety warning model based on back propagation neural network optimized by improved gray wolf algorithm10.1016/j.est.2022.104092 ↗
20. Blockchain-assisted secure fine-grained searchable encryption for a cloud-based healthcare cyber-physical system10.1109/JAS.2021.1004003 ↗
21. DBTP2SF: A deep blockchain-based trustworthy privacy-preserving secured framework in industrial internet of things systems10.1002/ett.4222 ↗
22. Method of reaching consensus on probability of food safety based on the integration of finite credible data on block chain10.1109/ACCESS.2021.3108178 ↗
23. Security and blockchain convergence with Internet of Multimedia Things: Current trends, research challenges and future directions10.1016/j.jnca.2020.102918 ↗
24. Data-driven dynamic active node selection for event localization in IoT applications-a case study of radiation localization10.1109/ACCESS.2019.2894956 ↗
25. The continuous pollution routing problem
26. Representative task self-selection for flexible clustered lifelong learning10.1109/TNNLS.2020.3042500 ↗
27. An effective relay node selection technique for energy efficient WSN-assisted IoT10.1007/s11277-020-07167-8 ↗
28. Optimal energy strategy for node selection and data relay in WSN-based IoT10.1007/s11036-015-0592-5 ↗
29. Many-Objective Deployment Optimization for a Drone-Assisted Camera Network10.1109/TNSE.2021.3057915 ↗
30. A hybrid IoT services recommender system using Social IoT
31. Power-frequency oscillation suppression algorithm for AC microgrid with multiple virtual synchronous generators based on fuzzy inference system10.1049/rpg2.12461 ↗
32. Chen, H., and Li, S. (2022). Multi-Sensor Fusion by CWT-PARAFAC-IPSO-SVM for Intelligent Mechanical Fault Diagnosis. Sensors, 22.10.3390/s22103647 ↗
33. Multisource heterogeneous unsupervised domain adaptation via fuzzy relation neural networks10.1109/TFUZZ.2020.3018191 ↗
34. Hybrid MCDM model for location of logistics hub: A case in China under the belt and road initiative10.1109/ACCESS.2021.3065100 ↗
35. A novel hybrid multi-criteria group decision-making approach with intuitionistic fuzzy sets to design reverse supply chains for COVID-19 medical waste recycling channels10.1016/j.cie.2022.108228 ↗
36. Ban, Y., Liu, M., Wu, P., Yang, B., Liu, S., Yin, L., and Zheng, W. (2022). Depth Estimation Method for Monocular Camera Defocus Images in Microscopic Scenes. Electronics, 11.10.3390/electronics11132012 ↗
37. Improving high-impact bug report prediction with combination of interactive machine learning and active learning10.1016/j.infsof.2021.106530 ↗
38. Data quality matters: A case study on data label correctness for security bug report prediction10.1109/TSE.2021.3063727 ↗
39. Diversified personalized recommendation optimization based on mobile data10.1109/TITS.2020.3040909 ↗
40. An indirect eavesdropping attack of keystrokes on touch screen through acoustic sensing10.1109/TMC.2019.2947468 ↗
41. Framework for benchmarking logistics performance using fuzzy AHP10.1504/IJBPSCM.2009.026267 ↗
42. Wudhikarn, R., Chakpitak, N., and Neubert, G. (2020). Improving the strategic benchmarking of intellectual capital management in logistics service providers. Sustainability, 12.10.3390/su122310174 ↗
43. Node selection and utility maximization for mobile edge computing–driven IoT10.1002/ett.3704 ↗
44. Optimal relay node selection for robust data forwarding over time-varying IoT networks10.1109/TVT.2019.2929856 ↗
45. Application of fuzzy logic for IoT node elimination and selection in opportunistic networks: Performance evaluation of two fuzzy-based systems10.1007/s11280-020-00835-6 ↗
46. Lu, M., and Wudhikarn, R. (2022, January 26–28). Using the best-worst method to develop intellectual capital indicators in financial service company. Proceedings of the 2020 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON), Chiang Rai, Thailand.10.1109/ECTIDAMTNCON53731.2022.9720385 ↗
47. Optimal relay node selection in time-varying IoT networks using apriori contact pattern information10.1016/j.adhoc.2019.102065 ↗
49. Sustainable partner selection: An integrated AHP-TOPSIS approach10.1504/IJOR.2020.109755 ↗
50. Fuzzy TOPSIS and fuzzy VIKOR in selecting green suppliers for sponge iron and steel manufacturing10.1007/s00500-021-05644-1 ↗
51. Interpretability application of the Just-in-Time software defect prediction model10.1016/j.jss.2022.111245 ↗