SURVEY ARTICLE
Towards Secure and Efficient Data Aggregation in Blockchain-Driven IoT Environments: A Comprehensive and Systematic Study
Xujun Tong,
Marzieh Hamzei,
Nima Jafari,
Corresponding Author
Xujun Tong
Public Basic College, Anhui Medical College, HeFei, China
Correspondence:
Xujun Tong ([email protected])
Nima Jafari ([email protected])
Search for more papers by this authorMarzieh Hamzei
Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
Search for more papers by this authorCorresponding Author
Nima Jafari
Future Technology Research Center, National Yunlin University of Science and Technology, Douliou, Yunlin, Taiwan
Correspondence:
Xujun Tong ([email protected])
Nima Jafari ([email protected])
Search for more papers by this authorXujun Tong,
Marzieh Hamzei,
Nima Jafari,
Corresponding Author
Xujun Tong
Public Basic College, Anhui Medical College, HeFei, China
Correspondence:
Xujun Tong ([email protected])
Nima Jafari ([email protected])
Search for more papers by this authorMarzieh Hamzei
Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
Search for more papers by this authorCorresponding Author
Nima Jafari
Future Technology Research Center, National Yunlin University of Science and Technology, Douliou, Yunlin, Taiwan
Correspondence:
Xujun Tong ([email protected])
Nima Jafari ([email protected])
Search for more papers by this authorFirst published: 14 February 2025
Funding: This study was supported by Anhui Province Key Teaching and Research Project: Computer Application Basic Course Ideological and Political Demonstration Course, No. 2020szsfkc0506; Anhui Province Natural Research Project: “Design and Practical Application Research of Artificial Intelligence Based Post Internship Management System” (Project No. ZR2021B002); Anhui Province Quality Engineering Project: “Integrated Practice Research of Major + Course in Nursing (Xinjiang) Medical Computer Culture Basic Course from the Perspective of Curriculum Ideology and Politics” (Project No. 2022jyxm790), Information Technology Provincial Course Ideological and Political Demonstration Course (Project No. 2022kcsz150), Provincial Public Virtual Simulation Training Base (Project No. 2022xnfzjd012).
ABSTRACT
The rapid evolution of the Internet of Things (IoT) has revolutionized various sectors, fostering seamless intercommunication and real-time monitoring. Central to this transformation is integrating blockchain technology, which ensures data integrity and security in IoT networks. This paper provides a meticulous exploration of data aggregation techniques within the context of blockchain-based IoT systems. The study categorizes data aggregation algorithms into Privacy-Preserving, Machine Learning-Based, Hierarchical, Real-Time, and Custom Aggregation Algorithms, each tailored to specific IoT requirements. Privacy-Preserving Aggregation Algorithms focus on safeguarding sensitive data through encryption and secure protocols. Machine Learning-Based Aggregation adapts dynamically to data patterns, offering predictive insights and real-time adaptability. Hierarchical Aggregation organizes devices into a structured hierarchy, optimizing data processing. Real-Time Aggregation processes data instantly, ensuring low latency for time-sensitive applications. Custom Aggregation Algorithms are bespoke solutions tailored to unique application demands, emphasizing efficiency and security. Through a comparative analysis of these techniques, this paper explores their advantages, disadvantages, and applicability, addressing the challenges and suggesting future research directions. The integration of blockchain-based data aggregation techniques not only enhances IoT network efficiency but also ensures the longevity and security of modern technological infrastructures. This study builds upon prior research in the field of IoT and blockchain technology by extending the exploration of data aggregation techniques and their implications for network efficiency and security. SLR method has been used to investigate each one in terms of influential properties such as the main idea, advantages, disadvantages, and strategies. The results indicate most of the articles were published in 2021 and 2022. Moreover, some important parameters such as privacy and security, latency, data processing, energy consumption, complexity, and reliability were involved in these investigations.
Open Research
Data Availability Statement
Data sharing is not applicable to this article as no new data were created or analyzed in this study.
References
- 1M. Krichen, “A Survey on Formal Verification and Validation Techniques for Internet of Things,” Applied Sciences 13, no. 14 (2023): 8122.
- 2A. Balasundaram, S. Routray, A. Prabu, P. Krishnan, P. P. Malla, and M. Maiti, “Internet of Things (IoT) Based Smart Healthcare System for Efficient Diagnostics of Health Parameters of Patients in Emergency Care,” IEEE Internet of Things Journal 10 (2023): 18563–18570.
- 3M. S. Abdalzaher, M. M. Fouda, H. A. Elsayed, and M. M. Salim, “Toward Secured IoT-Based Smart Systems Using Machine Learning,” IEEE Access 11 (2023): 20827–20841.
- 4A. Islam and S. Y. Shin, “A Digital Twin-Based Drone-Assisted Secure Data Aggregation Scheme With Federated Learning in Artificial Intelligence of Things,” IEEE Network 37, no. 2 (2023): 278–285.
- 5A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, “Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications,” IEEE Communications Surveys & Tutorials 17, no. 4 (2015): 2347–2376.
- 6M. Hamzei and N. J. Navimipour, “Toward Efficient Service Composition Techniques in the Internet of Things,” IEEE Internet of Things Journal 5, no. 5 (2018): 3774–3787.
- 7M. Hamzei, S. Khandagh, and N. Jafari Navimipour, “A Quality-of-Service-Aware Service Composition Method in the Internet of Things Using a Multi-Objective Fuzzy-Based Hybrid Algorithm,” Sensors 23, no. 16 (2023): 7233.
- 8A. Rejeb, K. Rejeb, H. Treiblmaier, et al., “The Internet of Things (IoT) in Healthcare: Taking Stock and Moving Forward,” Internet of Things 22 (2023): 100721.
- 9E. Ghaferi, R. Malekhosseini, F. Rad, and K. Bagherifard, “A Clustering Method for Locating Services Based on Fog Computing for the Internet of Things,” Journal of Supercomputing 78, no. 11 (2022): 13756–13779.
- 10M. Darbandi, “Proposing New Intelligent System for Suggesting Better Service Providers in Cloud Computing Based on Kalman Filtering,” International Journal of Technology Innovations and Research 24, no. 1 (2017): 1–9, https://doi.org/10.5281/Zenodo.1034475.
10.5281/Zenodo.1034475 Google Scholar
- 11A. Yousefpour, G. Ishigaki, R. Gour, and J. P. Jue, “On Reducing IoT Service Delay via Fog Offloading,” IEEE Internet of Things Journal 5, no. 2 (2018): 998–1010.
- 12M. Darbandi, “Proposing New Intelligence Algorithm for Suggesting Better Services to Cloud Users Based on Kalman Filtering,” Journal of Computer Sciences and Applications 5, no. 1 (2017): 11–16, https://doi.org/10.12691/JCSA-5-1-2.
10.12691/JCSA?5?1?2 Google Scholar
- 13A. Heidari, N. J. Navimipour, M. A. J. Jamali, and S. Akbarpour, “A Hybrid Approach for Latency and Battery Lifetime Optimization in IoT Devices Through Offloading and CNN Learning,” Sustainable Computing Informatics & Systems 39 (2023): 100899.
- 14A. Razaque, Y. Jararweh, B. Alotaibi, M. Alotaibi, and M. Almiani, “Hybrid Energy-Efficient Algorithm for Efficient Internet of Things Deployment,” Sustainable Computing Informatics & Systems 35 (2022): 100715.
- 15M. Tiwari, I. Maity, and S. Misra, “FedServ: Federated Task Service in Fog-Enabled Internet of Vehicles,” IEEE Transactions on Intelligent Transportation Systems 23, no. 11 (2022): 20943–20952.
- 16M. Darbandi, M. Abedi, S. Fard, and S. Nakhodchi, “ Involving Kalman Filter Technique for Increasing the Reliability and Efficiency of Cloud Computing,” in Proceedings of the International Conference on Scientific Computing (CSC) (Steering Committee of The World Congress in Computer Science, Computer, 2012), 1.
- 17M. S. Ali, M. Vecchio, M. Pincheira, K. Dolui, F. Antonelli, and M. H. Rehmani, “Applications of Blockchains in the Internet of Things: A Comprehensive Survey,” IEEE Communications Surveys & Tutorials 21, no. 2 (2018): 1676–1717.
- 18H. F. Atlam, A. Alenezi, M. O. Alassafi, and G. Wills, “Blockchain With Internet of Things: Benefits, Challenges, and Future Directions,” International Journal of Intelligent Systems and Applications 10, no. 6 (2018): 40–48.
10.5815/ijisa.2018.06.05 Google Scholar
- 19K. Wüst and A. Gervais, “ Do You Need a Blockchain?,” in 2018 Crypto Valley Conference on Blockchain Technology (CVCBT) (IEEE, 2018), 45–54.
10.1109/CVCBT.2018.00011 Google Scholar
- 20N. Adhikari and M. Ramkumar, “IoT and Blockchain Integration: Applications, Opportunities, and Challenges,” Network 3, no. 1 (2023): 115–141.
10.3390/network3010006 Google Scholar
- 21H. Xue, D. Chen, N. Zhang, H.-N. Dai, and K. Yu, “Integration of Blockchain and Edge Computing in Internet of Things: A Survey,” Future Generation Computer Systems 144 (2023): 307–326.
- 22Y. Zhang, C. Xu, H. Li, H. Yang, and X. Shen, “ Chronos: Secure and Accurate Time-Stamping Scheme for Digital Files via Blockchain,” in ICC 2019–2019 IEEE International Conference on Communications (ICC) (IEEE, 2019), 1–6.
10.1109/ICC.2019.8762071 Google Scholar
- 23Y. Dai, D. Xu, S. Maharjan, Z. Chen, Q. He, and Y. Zhang, “Blockchain and Deep Reinforcement Learning Empowered Intelligent 5G Beyond,” IEEE Network 33, no. 3 (2019): 10–17.
- 24R. Yang, F. R. Yu, P. Si, Z. Yang, and Y. Zhang, “Integrated Blockchain and Edge Computing Systems: A Survey, Some Research Issues and Challenges,” IEEE Communications Surveys & Tutorials 21, no. 2 (2019): 1508–1532.
- 25S. Namasudra, G. C. Deka, P. Johri, M. Hosseinpour, and A. H. Gandomi, “The Revolution of Blockchain: State-of-the-Art and Research Challenges,” Archives of Computational Methods in Engineering 28 (2021): 1497–1515.
- 26D. Liu, A. Alahmadi, J. Ni, X. Lin, and X. Shen, “Anonymous Reputation System for IIoT-Enabled Retail Marketing Atop PoS Blockchain,” IEEE Transactions on Industrial Informatics 15, no. 6 (2019): 3527–3537.
- 27M. He, J. Ni, D. Liu, H. Yang, and X. Shen, “ Private, Fair, and Verifiable Aggregate Statistics for Mobile Crowdsensing in Blockchain Era,” in 2020 IEEE/CIC International Conference on Communications in China (ICCC) (IEEE, 2020), 160–165.
10.1109/ICCC49849.2020.9238921 Google Scholar
- 28Y. Zhang, C. Xu, J. Ni, H. Li, and X. S. Shen, “Blockchain-Assisted Public-Key Encryption With Keyword Search Against Keyword Guessing Attacks for Cloud Storage,” IEEE Transactions on Cloud Computing 9, no. 4 (2019): 1335–1348.
- 29D. Liu, J. Ni, X. Lin, and X. Shen, “Transparent and Accountable Vehicular Local Advertising With Practical Blockchain Designs,” IEEE Transactions on Vehicular Technology 69, no. 12 (2020): 15694–15705.
- 30D. Liu, C. Huang, J. Ni, X. Lin, and X. Shen, “Blockchain-Based Smart Advertising Network With Privacy-Preserving Accountability,” IEEE Transactions on Network Science and Engineering 8, no. 3 (2020): 2118–2130.
10.1109/TNSE.2020.3027796 Google Scholar
- 31Z. Guan, X. Lu, N. Wang, J. Wu, X. Du, and M. Guizani, “Towards Secure and Efficient Energy Trading in IIoT-Enabled Energy Internet: A Blockchain Approach,” Future Generation Computer Systems 110 (2020): 686–695.
- 32Y. Lu, “The Blockchain: State-of-the-Art and Research Challenges,” Journal of Industrial Information Integration 15 (2019): 80–90.
- 33A. Miglani, N. Kumar, V. Chamola, and S. Zeadally, “Blockchain for Internet of Energy Management: Review, Solutions, and Challenges,” Computer Communications 151 (2020): 395–418.
- 34Z. Zheng, S. Xie, H. Dai, X. Chen, and H. Wang, “ An Overview of Blockchain Technology: Architecture, Consensus, and Future Trends,” in 2017 IEEE International Congress on Big Data (BigData Congress) (IEEE, 2017), 557–564.
10.1109/BigDataCongress.2017.85 Google Scholar
- 35A. Banafa, “IoT and Blockchain Convergence: Benefits and Challenges,” IEEE Internet of Things Journal 9 (2017): 2017.
- 36R. Gasmi, S. Hammoudi, M. Lamri, and S. Harous, “Recent Reinforcement Learning and Blockchain Based Security Solutions for Internet of Things: Survey,” Wireless Personal Communications 132 (2023): 1–39.
- 37F. Al-Doghman, Z. Chaczko, and J. Jiang, “ A Review of Aggregation Algorithms for the Internet of Things,” in 2017 25th International Conference on Systems Engineering (ICSEng) (IEEE, 2017), 480–487.
10.1109/ICSEng.2017.43 Google Scholar
- 38N. Chandnani and C. N. Khairnar, “ A Comprehensive Review and Performance Evaluation of Recent Trends for Data Aggregation and Routing Techniques in IoT Networks,” in Social Networking and Computational Intelligence: Proceedings of SCI-2018 (Springer Singapore, 2020), 467–484.
10.1007/978-981-15-2071-6_37 Google Scholar
- 39S. Norozpour and M. Darbandi, “Proposing New Method for Clustering and Optimizing Energy Consumption in WSN,” Talent Development and Excellence 12 (2020): 36.
- 40Y. Shen, T. Zhang, Y. Wang, H. Wang, and X. Jiang, “Microthings: A Generic Iot Architecture for Flexible Data Aggregation and Scalable Service Cooperation,” IEEE Communications Magazine 55, no. 9 (2017): 86–93.
- 41M. H. Homaei, E. Salwana, and S. Shamshirband, “An Enhanced Distributed Data Aggregation Method in the Internet of Things,” Sensors 19, no. 14 (2019): 3173.
- 42L. Krishnamachari, D. Estrin, and S. Wicker, “ The Impact of Data Aggregation in Wireless Sensor Networks,” in Proceedings 22nd International Conference on Distributed Computing Systems Workshops (IEEE, 2002), 575–578.
10.1109/ICDCSW.2002.1030829 Google Scholar
- 43B. A. Begum and S. V. Nandury, “Data Aggregation Protocols for WSN and IoT Applications—A Comprehensive Survey,” Journal of King Saud University, Computer and Information Sciences 35, no. 2 (2023): 651–681, https://doi.org/10.1016/j.jksuci.2023.01.008.
- 44M. Darbandi, “Kalman Filtering for Estimation and Prediction Servers With Lower Traffic Loads for Transferring High-Level Processes in Cloud Computing,” International Journal of Technology Innovations and Research 23, no. 1 (2017): 10–20, https://doi.org/10.5281/Zenodo.345288.
10.5281/Zenodo.345288 Google Scholar
- 45C. Garg, D. K. Mishra, D. Raj, and P. S. Mehra, “ A Survey on Integration of Blockchain and IoT (BIoT): Open Issues, Challenges & Solution,” in 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) (IEEE, 2022), 880–886.
10.1109/ICICICT54557.2022.9917632 Google Scholar
- 46M. Sandeep and B. Chandavarkar, “ Data Processing in IoT, Sensor to Cloud: Survey,” in 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT) (IEEE, 2021), 1–7.
- 47S. Yousefi, H. Karimipour, and F. Derakhshan, “Data Aggregation Mechanisms on the Internet of Things: A Systematic Literature Review,” Internet of Things 15 (2021): 100427.
- 48A. Ullah, M. Azeem, H. Ashraf, A. A. Alaboudi, M. Humayun, and N. Z. Jhanjhi, “Secure Healthcare Data Aggregation and Transmission in IoT—A Survey,” IEEE Access 9 (2021): 16849–16865.
- 49B. Pourghebleh and N. J. Navimipour, “Data Aggregation Mechanisms in the Internet of Things: A Systematic Review of the Literature and Recommendations for Future Research,” Journal of Network and Computer Applications 97 (2017): 23–34.
- 50S. Kumar and A. K. Pundir, “ Integration of IoT and Blockchain Technology for Enhancing Supply Chain Performance: A Review,” in 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) (IEEE, 2020), 396–401.
10.1109/IEMCON51383.2020.9284890 Google Scholar
- 51A. M. Sirajudheen and D. J. David, “ A Review: Secure and Efficient Data Aggregation in Wireless Sensor Networks Using the Internet of Things,” in 2023 International Conference on Computer Communication and Informatics (ICCCI) (IEEE, 2023), 1–7.
10.1109/ICCCI56745.2023.10128207 Google Scholar
- 52Z. Wei, M. Zhu, N. Zhang, et al., “UAV-Assisted Data Collection for Internet of Things: A Survey,” IEEE Internet of Things Journal 9, no. 17 (2022): 15460–15483.
- 53Z. Liu, H.-Y. Lin, and Y. Liu, “Long-Term Privacy-Preserving Aggregation With User-Dynamics for Federated Learning,” IEEE Transactions on Information Forensics and Security 18 (2023): 2398–2412.
- 54M. Liu, N. Lu, J. Yin, Q. Cheng, and W. Shi, “ BA-Audit: Blockchain-Based Public Auditing for Aggregated Data Sharing in Edge-Assisted IoT,” in International Conference on Security and Privacy in New Computing Environments (Springer, 2021), 204–218.
- 55F. Loukil, C. Ghedira-Guegan, K. Boukadi, and A.-N. Benharkat, “Privacy-Preserving IoT Data Aggregation Based on Blockchain and Homomorphic Encryption,” Sensors 21, no. 7 (2021): 2452.
- 56R. Mishra, D. Ramesh, and N. Mohammad, “ RBDA: Redactable-Blockchain Based Secure Data Aggregation Scheme for IoT Enabled Cloud Paradigm,” in 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Workshops) (IEEE, 2022), 409–414.
10.1109/PerComWorkshops53856.2022.9767416 Google Scholar
- 57X. Tang, X. Lan, L. Li, Y. Zhang, and Z. Han, “Incentivizing Proof-of-Stake Blockchain for Secured Data Collection in UAV-Assisted IoT: A Multi-Agent Reinforcement Learning Approach,” IEEE Journal on Selected Areas in Communications 40, no. 12 (2022): 3470–3484.
- 58V. Boiarkin and M. Rajarajan, “ A Novel Blockchain-Based Data-Aggregation Scheme for Edge-Enabled Microgrid of Prosumers,” in 2022 Fourth International Conference on Blockchain Computing and Applications (BCCA) (IEEE, 2022), 63–68.
10.1109/BCCA55292.2022.9922099 Google Scholar
- 59S.-H. Sim and Y.-S. Jeong, “Multi-Blockchain-Based IoT Data Processing Techniques to Ensure the Integrity of IoT Data in AIoT Edge Computing Environments,” Sensors 21, no. 10 (2021): 3515.
- 60K. Rantos, G. Drosatos, A. Kritsas, C. Ilioudis, A. Papanikolaou, and A. P. Filippidis, “A Blockchain-Based Platform for Consent Management of Personal Data Processing in the IoT Ecosystem,” Security and Communication Networks 2019 (2019): 1–15.
- 61R. Fan, L. Yin, S. Gao, W. Zhang, Q. Li, and F. Zhao, “ Blockchain Based Energy IoT Data Trusted Collection and Transmission,” in 2022 IEEE 5th International Conference on Electronic Information and Communication Technology (ICEICT) (IEEE, 2022), 96–99.
10.1109/ICEICT55736.2022.9909359 Google Scholar
- 62M. Merah, Z. Aliouat, Y. Harbi, and M. S. Batta, “Machine Learning-Based Clustering Protocols for Internet of Things Networks: An Overview,” International Journal of Communication Systems 36 (2023): e5487.
- 63S. Mehta, B. Bhushan, and R. Kumar, “ Machine Learning Approaches for Smart City Applications: Emergence, Challenges and Opportunities,” in Recent Advances in Internet of Things and Machine Learning: Real-World Applications (Springer International Publishing, 2022), 147–163.
10.1007/978-3-030-90119-6_12 Google Scholar
- 64A. Ahmed, S. Abdullah, M. Bukhsh, I. Ahmad, and Z. Mushtaq, “An Energy-Efficient Data Aggregation Mechanism for IoT Secured by Blockchain,” IEEE Access 10 (2022): 11404–11419.
- 65A. Islam, A. Al Amin, and S. Y. Shin, “FBI: A Federated Learning-Based Blockchain-Embedded Data Accumulation Scheme Using Drones for Internet of Things,” IEEE Wireless Communications Letters 11, no. 5 (2022): 972–976.
- 66X. Xu, H. Zhao, H. Yao, and S. Wang, “A Blockchain-Enabled Energy-Efficient Data Collection System for UAV-Assisted IoT,” IEEE Internet of Things Journal 8, no. 4 (2020): 2431–2443.
- 67X. Lan, X. Tang, D. Zhai, D. Wang, and Z. Han, “ Blockchain-Secured Data Collection for UAV-Assisted Iot: A Ddpg Approach,” in 2021 IEEE Global Communications Conference (GLOBECOM) (IEEE, 2021), 1–6.
10.1109/GLOBECOM46510.2021.9685008 Google Scholar
- 68C. H. Liu, Q. Lin, and S. Wen, “Blockchain-Enabled Data Collection and Sharing for Industrial IoT With Deep Reinforcement Learning,” IEEE Transactions on Industrial Informatics 15, no. 6 (2018): 3516–3526.
- 69X. Wang, S. Garg, H. Lin, G. Kaddoum, J. Hu, and M. S. Hossain, “A Secure Data Aggregation Strategy in Edge Computing and Blockchain-Empowered Internet of Things,” IEEE Internet of Things Journal 9, no. 16 (2020): 14237–14246.
10.1109/JIOT.2020.3023588 Google Scholar
- 70X. Huang, Y. Wu, C. Liang, Q. Chen, and J. Zhang, “Distance-Aware Hierarchical Federated Learning in Blockchain-Enabled Edge Computing Network,” IEEE Internet of Things Journal 10 (2023): 19163–19176.
- 71L. Zhang, F. Li, P. Wang, R. Su, and Z. Chi, “A Blockchain-Assisted Massive IoT Data Collection Intelligent Framework,” IEEE Internet of Things Journal 9, no. 16 (2021): 14708–14722.
10.1109/JIOT.2021.3049674 Google Scholar
- 72W. Lu, Z. Ren, J. Xu, and S. Chen, “Edge Blockchain Assisted Lightweight Privacy-Preserving Data Aggregation for Smart Grid,” IEEE Transactions on Network and Service Management 18, no. 2 (2021): 1246–1259.
- 73Y.-S. Jeong, “Blockchain Processing Technique Based on Multiple Hash Chains for Minimizing Integrity Errors of IoT Data in Cloud Environments,” Sensors 21, no. 14 (2021): 4679.
- 74M. S. Mahdavinejad, M. Rezvan, M. Barekatain, P. Adibi, P. Barnaghi, and A. P. Sheth, “Machine Learning for Internet of Things Data Analysis: A Survey,” Digital Communications and Networks 4, no. 3 (2018): 161–175.
- 75H. Mahmoud, M. A. Azad, J. Arshad, and A. Aneiba, “A Framework for Decentralized, Real-Time Reputation Aggregation in IoV,” IEEE Internet of Things Magazine 6, no. 2 (2023): 44–48.
10.1109/IOTM.001.2300033 Google Scholar
- 76L. Zhang, Y. Gu, R. Wang, et al., “Enabling Real-Time Quality-of-Service and Fine-Grained Aggregation for Wireless TSN,” Sensors 22, no. 10 (2022): 3901.
- 77A. I. Taloba, A. Elhadad, A. Rayan, et al., “A Blockchain-Based Hybrid Platform for Multimedia Data Processing in IoT-Healthcare,” Alexandria Engineering Journal 65 (2023): 263–274.
- 78B. Y. Kim, S. S. Choi, and J. W. Jang, “ Data Managing and Service Exchanging on IoT Service Platform Based on Blockchain With Smart Contract and Spatial Data Processing,” in Proceedings of the 1st International Conference on Information Science and Systems (IEEE, 2018), 59–63.
10.1145/3209914.3209916 Google Scholar
- 79S. M. Karim, A. Habbal, S. A. Chaudhry, and A. Irshad, “BSDCE-IoV: Blockchain-Based Secure Data Collection and Exchange Scheme for IoV in 5G Environment,” IEEE Access 11 (2023): 36158–36175.
- 80G. Rathee, A. Sharma, H. Saini, R. Kumar, and R. Iqbal, “A Hybrid Framework for Multimedia Data Processing in IoT-Healthcare Using Blockchain Technology,” Multimedia Tools and Applications 79, no. 15–16 (2020): 9711–9733.
