Ensuring the reliability and trustworthiness of massive IoT-generated data processed in cloud-based systems is paramount for data integrity in IoT-Cloud platforms. The integration of Blockchain (BC) technology, particularly through BC-assisted data Edge Verification combined with a consensus system, utilizes BC's decentralized and immutable nature to secure data at the IoT network's edge. BC has garnered attention across diverse domains like smart agriculture, intellectual property, and finance, where its security features complement technologies such as SDN, AI, and IoT. The choice of a consensus algorithm in BC plays a crucial role and significantly impacts the overall effectiveness of BC solutions, with considerations including PBFT, PoW, PoS, and Ripple in recent years. In this study, I developed a Football Game Algorithm with Deep learning-based Data Edge Verification with a Consensus Approach (FGADL-DEVCA) for BC assisted IoT-cloud platforms. The major drive of the FGADL-DEVCA algorithm was to incorporate BC technology to enable security in the IoT cloud environment, and the DL model could be applied for fault detection efficiently. In the FGADL-DEVCA technique, the IoT devices encompassed considerable decentralized decision-making abilities for reaching an agreement based on the performance of the intrablock transactions. Besides, the FGADL-DEVCA technique exploited deep autoencoder (DAE) for the recognition and classification of faults in the IoT-cloud platform. To boost the fault detection performance of the DAE approach, the FGADL-DEVCA technique applied FGA-based hyperparameter tuning. The experimental result analysis of the FGADL-DEVCA technique was performed concerning distinct metrics. The experimental values demonstrated the betterment of the FGADL-DEVCA approach with other existing methods concerning various aspects.
Citation: Fahad F. Alruwaili. Ensuring data integrity in deep learning-assisted IoT-Cloud environments: Blockchain-assisted data edge verification with consensus algorithms[J]. AIMS Mathematics, 2024, 9(4): 8868-8884. doi: 10.3934/math.2024432
Ensuring the reliability and trustworthiness of massive IoT-generated data processed in cloud-based systems is paramount for data integrity in IoT-Cloud platforms. The integration of Blockchain (BC) technology, particularly through BC-assisted data Edge Verification combined with a consensus system, utilizes BC's decentralized and immutable nature to secure data at the IoT network's edge. BC has garnered attention across diverse domains like smart agriculture, intellectual property, and finance, where its security features complement technologies such as SDN, AI, and IoT. The choice of a consensus algorithm in BC plays a crucial role and significantly impacts the overall effectiveness of BC solutions, with considerations including PBFT, PoW, PoS, and Ripple in recent years. In this study, I developed a Football Game Algorithm with Deep learning-based Data Edge Verification with a Consensus Approach (FGADL-DEVCA) for BC assisted IoT-cloud platforms. The major drive of the FGADL-DEVCA algorithm was to incorporate BC technology to enable security in the IoT cloud environment, and the DL model could be applied for fault detection efficiently. In the FGADL-DEVCA technique, the IoT devices encompassed considerable decentralized decision-making abilities for reaching an agreement based on the performance of the intrablock transactions. Besides, the FGADL-DEVCA technique exploited deep autoencoder (DAE) for the recognition and classification of faults in the IoT-cloud platform. To boost the fault detection performance of the DAE approach, the FGADL-DEVCA technique applied FGA-based hyperparameter tuning. The experimental result analysis of the FGADL-DEVCA technique was performed concerning distinct metrics. The experimental values demonstrated the betterment of the FGADL-DEVCA approach with other existing methods concerning various aspects.
| [1] | S. Ma, S. Wang, W. T. Tsai, Y. Zhang, Delay Optimization for Consensus Communication in Blockchain-Based End-Edge-Cloud Network, In International Symposium on Advanced Parallel Processing Technologies (pp 241–262), Singapore: Springer Nature Singapore, 2023. https://doi.org/10.1007/978-981-99-7872-4_14 |
| [2] |
S. Wadhwa, S. Rani, S. Verma, J. Shafi, M. Wozniak, Energy efficient consensus approach of blockchain for IoT networks with edge computing, Sensors, 22 (2022), 3733. https://doi.org/10.3390/s22103733 doi: 10.3390/s22103733
|
| [3] |
Y. Zhang, B. Li, B. Liu, Y. Hu, H. Zheng, A privacy-aware PUFs-based multiserver authentication protocol in cloud-edge IoT systems using blockchain, IEEE Internet Things, 8 (2021), 13958–13974. https://doi.org/10.1109/JIOT.2021.3068410 doi: 10.1109/JIOT.2021.3068410
|
| [4] |
Y. Tang, J. Yan, C. Chakraborty, Y. Sun, Hedera: A permissionless and scalable hybrid blockchain consensus algorithm in multi-access edge computing for IoT, IEEE Internet Things, 2023. https://doi.org/10.1109/JIOT.2023.3279108 doi: 10.1109/JIOT.2023.3279108
|
| [5] | K. Wang, S. P. Xu, C. M. Chen, S. H. Islam, M. M. Hassan, C. Savaglio, et al., A trusted consensus scheme for collaborative learning in the edge ai computing domain, IEEE Network, 35 (2021), 204–210. https://doi.org/10.1109/MNET.011.2000249 |
| [6] |
M. M. Alhejazi, R. M. A. Mohammad, Enhancing the blockchain voting process in IoT using a novel blockchain Weighted Majority Consensus Algorithm (WMCA), Inf. Secur. J., 31 (2022), 125–143. https://doi.org/10.1080/19393555.2020.1869356 doi: 10.1080/19393555.2020.1869356
|
| [7] |
Z. Liao, S. Cheng, RVC: A reputation and voting based blockchain consensus mechanism for edge computing-enabled IoT systems, J. Network Comput. Appl., 209 (2023), 103510. https://doi.org/10.1016/j.jnca.2022.103510 doi: 10.1016/j.jnca.2022.103510
|
| [8] |
W. Wang, H. Huang, L. Xue, Q. Li, R. Malekian, Y. Zhang, Blockchain-assisted handover authentication for intelligent telehealth in multi-server edge computing environment, J. Syst. Archit., 115 (2021), 102024. https://doi.org/10.1016/j.sysarc.2021.102024 doi: 10.1016/j.sysarc.2021.102024
|
| [9] |
X. Fu, H. Wang, P. Shi, X. Zhang, Teegraph: A Blockchain consensus algorithm based on TEE and DAG for data sharing in IoT, J. Syst. Archit., 122 (2022), 102344. https://doi.org/10.1016/j.sysarc.2021.102344 doi: 10.1016/j.sysarc.2021.102344
|
| [10] | W. Li, Q. Zhang, S. Deng, B. Zhou, B. Wang, J. Cao, Q-learning improved lightweight consensus algorithm for blockchain-structured internet of things, IEEE Internet Things, 2023. |
| [11] | T. Vaiyapuri, K. Shankar, S. Rajendran, S. Kumar, S. Acharya, H. Kim, Blockchain Assisted Data Edge Verification with Consensus Algorithm for Machine Learning Assisted IoT, IEEE Access, 2023. https://doi.org/10.1109/ACCESS.2023.3280798 |
| [12] | G. Xu, H. Bai, J. Xing, T. Luo, N. N. Xiong, X. Cheng, et al., SG-PBFT: A secure and highly efficient distributed blockchain PBFT consensus algorithm for intelligent Internet of vehicles, J. Paral. Distr. Comput., 164 (2022), 1–11. https://doi.org/10.1016/j.jpdc.2022.01.029 |
| [13] |
Y. Fan, H. Wu, H. Y. Paik, DR-BFT: A consensus algorithm for blockchain-based multi-layer data integrity framework in dynamic edge computing system, Future Gener. Comp. Syst., 124 (2021), 33–48. https://doi.org/10.1016/j.future.2021.04.020 doi: 10.1016/j.future.2021.04.020
|
| [14] | Y. Li, J. Shen, S. Ji, Y. H. Lai, Blockchain-Based Data Integrity Verification Scheme in AIoT Cloud-Edge Computing Environment, IEEE Transactions on Engineering Management, 2023. |
| [15] | Y. Du, Z. Wang, J. Li, L. Shi, D. N. K. Jayakody, Q. Chen, et al., Blockchain-aided edge computing market: Smart contract and consensus mechanisms, IEEE T. Mobile Comput., 2022. |
| [16] | Z. Li, G. Li, M. Bilal, D. Liu, T. Huang, X. Xu, Blockchain-assisted Server Placement with Elitist Preserved Genetic Algorithm in Edge Computing, IEEE Internet Things, 2023. |
| [17] | M. Poongodi, S. Bourouis, A. N. Ahmed, M. Vijayaragavan, K. G. S. Venkatesan, W. Alhakami, et al., A novel secured multi-access edge computing based vanet with neuro fuzzy systems based blockchain framework, Comput. Comm., 192 (2022), 48–56. https://doi.org/10.1016/j.comcom.2022.05.014 |
| [18] |
Y. Zhao, Y. Qu, Y. Xiang, Y. Zhang, L. Gao, A Lightweight Model-Based Evolutionary Consensus Protocol in Blockchain as a Service for IoT, IEEE T. Serv. Computi., 2023. https://doi.org/10.1016/j.comcom.2022.05.014 doi: 10.1016/j.comcom.2022.05.014
|
| [19] |
Z. Chen, J. Zhang, Z. Huang, P. Wang, Z. Yu, W. Miao, Computation offloading in blockchain-enabled MCS systems: A scalable deep reinforcement learning approach, Future Gener. Comp. Syst., 153 (2024), 301–311. https://doi.org/10.1016/j.future.2023.12.004 doi: 10.1016/j.future.2023.12.004
|
| [20] |
Z. Chen, Z. Yu, Intelligent offloading in blockchain-based mobile crowdsensing using deep reinforcement learning, IEEE Commun. Mag., 61 (2023), 118–123. https://doi.org/10.1016/j.future.2023.12.004 doi: 10.1016/j.future.2023.12.004
|
| [21] | M. Firdaus, H. T. Larasati, K. H. Rhee, A blockchain-assisted distributed edge intelligence for privacy-preserving vehicular networks, Comput. Mater. Con., 76 (2023). https://doi.org/10.32604/cmc.2023.039487 |
| [22] |
A. K. Mousa, M. N. Abdullah, An improved deep learning model for DDoS detection based on hybrid stacked autoencoder and checkpoint network, Future Internet, 15 (2023), 278. https://doi.org/10.32604/cmc.2023.039487 doi: 10.32604/cmc.2023.039487
|
| [23] |
Z. H. Ahmed, F. Maleki, M. Yousefikhoshbakht, H. Haron, Solving the vehicle routing problem with time windows using modified football game algorithm, Egypt. Inform. J., 24 (2023), 100403. https://doi.org/10.32604/cmc.2023.039487 doi: 10.32604/cmc.2023.039487
|
Figures(9) / Tables(5)
Fahad F. Alruwaili. Ensuring data integrity in deep learning-assisted IoT-Cloud environments: Blockchain-assisted data edge verification with consensus algorithms[J]. AIMS Mathematics, 2024, 9(4): 8868-8884. doi: 10.3934/math.2024432
DownLoad: