Pendekatan Self-Sovereign Identity dan Blockchain pada Smart Egg Incubator: Systematic Literature Review

Denilson Aji Santoso; Imam Riadi; Herman Herman

doi:10.33506/insect.v12i2.5882

Authors

Denilson Aji Santoso Program Studi Magister Informatika, Universitas Ahmad Dahlan, Yogyakarta
Imam Riadi Program Studi Sistem Informasi, Universitas Ahmad Dahlan, Yogyakarta https://orcid.org/0000-0003-4927-6385
Herman Herman Program Studi Magister Informatika, Universitas Ahmad Dahlan, Yogyakarta

DOI:

https://doi.org/10.33506/insect.v12i2.5882

Keywords:

Review, Blockchain, Internet of Things, Self-Sovereign Identity, Smart Incubator

Abstract

Implementasi Internet of Things (IoT) pada Smart Egg Incubator sangat krusial untuk memastikan parameter penting dalam penetasan telur terkontrol dengan baik. Namun, arsitektur cloud terpusat rentan terhadap Single Point of Failure (SPoF) serta serangan unauthorized access dan privilege escalation yang dapat memicu kematian embrio massal. Konvergensi Self-Sovereign Identity (SSI) melalui Decentralized Identifiers (DID) dan Verifiable Credentials (VC) dengan smart contract blockchain hadir sebagai solusi otorisasi mesin-ke-mesin (machine-to-machine) dan penyedia audit trail yang tamper-proof. Systematic Literature Review (SLR) hibrida ini bertujuan memetakan lanskap ancaman, mengevaluasi mekanisme mitigasi desentralisasi, serta menyintesis metrik kinerja operasional perangkat dari integrasi teknologi SSI dan Smart Contract Blockchain pada perangkat IoT bersumber daya terbatas (resource-constrained). Berdasarkan protokol PRISMA, dilakukan analisis kritis terhadap 25 artikel primer terkurasi (2022–2026). Hasil sintesis menunjukkan arsitektur hibrida SSI-Blockchain unggul dalam mengeliminasi SPoF dan meningkatkan mitigasi kontrol akses dibandingkan sistem tradisional. Kendati demikian, terdapat trade-off operasional berupa computational overhead, peningkatan latensi, dan limitasi memori mikrokontroler. Sebagai solusinya, penelitian ini merumuskan cetak biru arsitektur kolaboratif berbantuan Edge Gateway lokal dan penyimpanan off-chain berbasis IPFS untuk menjaga efisiensi daya perangkat sekaligus melindungi keselamatan fisik aset biologis secara real-time.

References

[1] A. U. Bhuiyan et al., IoT Enabled Smart Egg-incubator with Remote Monitoring and Control System, vol. 1, no. 1. Association for Computing Machinery, 2024. doi: 10.1145/3723178.3723193.

[2] K. S. Masia, P. A. Idowu, K. A. Nephawe, J. N. Ngcobo, M. C. Modiba, and T. J. Mpofu, “Effect of incubation temperature on hatchability , chick quality and post-hatch performance – Review,” Anim. Sci. Genet., vol. 21, no. 4, pp. 59–87, 2025, doi: 10.5604/01.3001.0055.2705.

[3] A. O. Adewusi, N. R. Chiekezie, and N. L. Eyo-udo, “Securing smart agriculture : Cybersecurity challenges and solutions in IoT-driven farms,” World J. Adv. Res. Rev., 2022, doi: https://doi.org/10.30574/wjarr.2022.15.3.0887.

[4] R. K. Maddumala, “Global Journal of Engineering Innovations & Interdisciplinary Research Comparison of Edge vs . Cloud Computing Architectures in IoT-Based Smart Agriculture Systems,” Glob. J. Eng. Innov. Interdiscip. Res., vol. 5, no. 6, pp. 1–6, 2025, doi: https://doi.org/10.33425/3066-1226.1180.

[5] I. Riadi and P. A. Raharja, “Vulnerability Analysis of E-voting Application using Open Web Application Security Project ( OWASP ) Framework,” Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 11, pp. 135–143, 2019, doi: https://dx.doi.org/10.14569/IJACSA.2019.0101118.

[6] T. Irlon, Muryanto and A. Alvionita, “IoT Security Model with Machine Learning and Edge Computing for Smart Poultry Farm,” Interdiscip. Soc. Stud., 2024, doi: https://doi.org/10.55324/iss.v4i3.876.

[7] I. Riadi and W. P. Wicaksono, “Implementasi Quality of Service M enggunakan Metode Hie rarchical Token Bucket Landasan Teori,” JUSI, vol. 1, no. 2, pp. 93–104, 2011.

[8] F. Albanna and I. Riadi, “Forensic Analysis of Frozen Hard Drive Using Static Forensics Method,” Int. J. Comput. Sci. Inf. Secur., vol. 15, no. 1, pp. 173–178, 2017.

[9] H. Wintolo, I. Riadi, and A. Yudhana, “Enhancing Private Cloud Security Using Knowledge Understanding Assessment Defense Method for Distributed Denial of Service Attack Mitigation,” Int. J. Saf. Secur. Eng., vol. 15, no. 11, pp. 2323–2331, 2025, doi: https://doi.org/10.18280/ijsse.151112.

[10] A. Kurniawan, I. Riadi, and A. Luthfi, “Forensic Analysis and Prevent of Cross Site Scripting in Single Victim Attack using Open Web Application Security Project ( OWASP ) Framework,” J. Theor. Appl. Inf. Technol., vol. 95, no. 6, pp. 1363–1371, 2017.

[11] T. Rochmadi, I. Riadi, and P. Yudi, “Live Forensics for Anti-Forensics Analysis on Private Portable Web Browser,” Int. J. Comput. Appl., vol. 164, no. 8, pp. 31–37, 2017.

[12] S. Rudrakar, P. Rughani, and L. Sadineni, “Digital forensics and incident response management model for IoT based agriculture,” Sci. Rep., pp. 1–23, 2025, doi: https://doi.org/10.1038/s41598-025-02635-2.

[13] P. Widiandana and I. Riadi, “Forensik Digital Cyberbullying pada Grup WhatsApp Menggunakan National Institute of Standards and Technology,” Insect (Informatics Secur. J. Tek. Inform., vol. 12, no. 01, 2026, doi: https://doi.org/10.33506/insect.v12i01.5275.

[14] R. Umar, I. Riadi, and R. S. Kusuma, “Analysis of Conti Ransomware Attack on Computer Network with Live Forensic Method,” Int. J. Informatics Dev., vol. 10, no. 1, pp. 53–61, 2021, doi: https://doi.org/10.14421/ijid.2021.2423.

[15] I. Riadi, Sunardi, and Sahiruddin, “Analisis Forensik Recovery pada Smartphone Android Menggunakan Metode National Institute Of Justice ( NIJ ),” JURTI, vol. 3, no. 1, pp. 87–95, 2019.

[16] M. Campoverde-molina, “Computers & Security Cybersecurity in smart agriculture : A systematic literature review,” Comput. Secur., vol. 150, no. December 2024, 2025, doi: https://doi.org/10.17632/5ddfvwfj9p.1.

[17] I. Riadi and R. Umar, “Experimental Study of Honeypot-Based Cyber Attack and Data Collection in Campus Network Environment,” Int. J. Informatics Comput., vol. 8, no. 1, 2026, doi: 10.35842/ijicom.

[18] I. Riadi, S. Winiarti, H. Yuliansyah, and M. ‘Arif Bin Mohamad, “Machine Learning Model for Classifying the Severity Level of Cyber Security Attacks,” Int. J. Adv. Intell. Informatics, 2026, doi: https://ijain.org/index.php/IJAIN/article/view/2245%7Cto_array%3A0.

[19] R. Singh, D. Kukreja, and D. K. Sharma, “Blockchain-enabled access control to prevent cyber attacks in IoT : Systematic literature review,” Front. Big Data, 2023, doi: https://doi.org/10.3389/fdata.2022.1081770.

[20] A. Kurniawan and I. Riadi, “Detection and Analysis Cerber Ransomware Based on Network Forensics Behavior,” Int. J. Netw. Secur., vol. 20, no. 5, pp. 836–843, 2018, doi: DOI: 10.6633/IJNS.201809 20(5).04.

[21] M. K. Umair and M. S. Khan, “Systematic Literature Review on Blockchain Based IoT Solutions,” ICCK Trans. Adv. Comput. Syst., 2026, doi: http://dx.doi.org/10.62762/TACS.2025.327681.

[22] A. Naseer, M. Shmoon, T. Shakeel, S. U. Rehman, A. Ahmad, and V. Gruhn, “A Systematic Literature Review of the IoT in Agriculture—Global Adoption, Innovations, Security, and Privacy Challenges,” IEEE Access, vol. 12, pp. 60986–61021, 2024, doi: 10.1109/ACCESS.2024.3394617.

[23] A. Akinbi and A. M. Ismael, “Forensic Science International : Digital Investigation A systematic literature review of blockchain-based Internet of Things ( IoT ) forensic investigation process models,” vol. 43, 2022, doi: 10.1016/j.fsidi.2022.301470.

[24] I. Riadi, A. Yudhana, M. Caesar, and F. Putra, “Akuisisi Bukti Digital Pada Instagram Messenger Berbasis Android Menggunakan Metode National Institute Of Justice ( NIJ ),” J. Tek. Inform. dan Sist. Inf., vol. 4, pp. 219–227, 2018, doi: http://dx.doi.org/10.28932/jutisi.v4i2.769.

[25] A. Fauzan, I. Riadi, and A. Fadlil, “Analisis Forensik Digital Pada Line Messenger Untuk Penanganan Cybercrime,” Prosiding, vol. 2, no. 1, pp. 159–163, 2016.

[26] F. Schardong and R. Custódio, “Self-Sovereign Identity: A Systematic Review, Mapping and Taxonomy,” Sensors, pp. 1–43, 2022, doi: https://doi.org/10.3390/s22155641.

[27] N. M. Banua and Q. W. Boon, “Blockchain for Security and Privacy: A Systematic Literature Review,” Appl. Inf. Technol. Comput. Sci., vol. 5, no. 1, pp. 1360–1382, 2024, doi: https://doi.org/10.30880/aitcs.2024.05.01.078.

[28] S. Algarni, F. Eassa, K. Almarhabi, A. Almalaise, and E. Albassam, “Blockchain-Based Secured Access Control in an IoT System,” Appl. Sci., pp. 1–16, 2021, doi: https://doi.org/10.3390/app11041772.

[29] R. Mubarak, I. Riadi, and T. Sutikno, “Integration of blockchain and cryptographic algorithms for education certification and verification : a systematic literature,” J. Soft Comput. Explor., 2026, doi: https://doi.org/10.00000/joscex.0000.00.00.000.

[30] A. Odeh and A. A. Taleb, “Federated Learning and Blockchain Framework for Scalable and Secure IoT Access Control,” Comput. Mater. Contin., vol. 84, no. 1, pp. 447–461, 2025, doi: https://doi.org/10.32604/cmc.2025.065426.

[31] S. A. Fadhel and A. S. Nori, “Lam-Iot : Lightweight Authentication Model for Monitoring Systems in Iot-Enabled Smart Homes Based on Did Using Iota,” Int. J. Comput. Digit. Syst., vol. 18, no. 1, pp. 1–13, 2025, doi: http://dx.doi.org/10.12785/ijcds/1571111345.

[32] M. M. Merlec and H. P. In, “SC-CAAC: A Smart-Contract-Based Context-Aware Access Control Scheme for Blockchain-Enabled IoT Systems,” IEEE Internet Things J., vol. 11, no. 11, pp. 19866–19881, 2024, doi: 10.1109/JIOT.2024.3371504.

[33] M. Usman, M. S. Sarfraz, M. U. Aftab, U. Habib, and S. Javed, “A Blockchain Based Scalable Domain Access Control Framework for Industrial Internet of Things,” IEEE Access, vol. 12, pp. 56554–56570, 2024, doi: 10.1109/ACCESS.2024.3390842.

[34] A. Ferrer-Rojas, B. T. Maharaj, and M. C. Hlophe, “Blockchain-Enhanced Attribute-Based Encryption Architecture With Feasibility Analysis,” IEEE Access, vol. 13, pp. 57629–57638, 2025, doi: 10.1109/ACCESS.2025.3554643.

[35] H. Cheng and J. Lu, “Blockchain-assisted attribute-based multi-keyword search for dynamic encrypted data in cloud-edge-IoT,” Internet of Things, vol. 36, no. December 2025, p. 101838, 2026, doi: 10.1016/j.iot.2025.101838.

[36] P. Liu, Q. He, B. Zhao, B. Guo, and Z. Zhai, “Efficient Multi-Authority Attribute-Based Searchable Encryption Scheme with Blockchain Assistance for Cloud-Edge Coordination,” Comput. Mater. Contin., 2023, doi: 10.32604/cmc.2023.041167.

[37] D.-M. Ngo, D. Lightbody, A. Temko, C. C. Murphy, and E. Popovici, “A Scalable Security Approach in IoT Networks: Smart Contracts and Anomaly-Based IDS for Gateways Using Hardware Accelerators,” IEEE Access, vol. 12, pp. 159519–159533, 2024, doi: 10.1109/ACCESS.2024.3486605.

[38] Y. Ding, J. Yu, S. Li, H. Sato, and M. G. Machizawa, “Data Aggregation Management With Self-Sovereign Identity in Decentralized Networks,” IEEE Trans. Netw. Serv. Manag., vol. 21, no. 6, pp. 6174–6189, 2024, doi: 10.1109/TNSM.2024.3451995.

[39] A. A. M. Sharadqh et al., “Hybrid Chain : Blockchain Enabled Framework for Bi-Level Intrusion Detection and Graph-Based Mitigation for Security Provisioning in Edge Assisted IoT Environment,” IEEE Access, vol. 11, no. March 2023, pp. 27433–27449, 2025, doi: 10.1109/ACCESS.2023.3256277.

[40] Y. Kunwei, Y. Bo, W. Tao, and Z. Yanwei, “Zero-Cerd : A Self-Blindable Anonymous Authentication System Based on Blockchain,” vol. 32, no. 3, 2023, doi: 10.23919/cje.2022.00.047.

[41] U. Khalil et al., “Extending ERC721 : Design and implementation of a novel secure NFT framework for IoT asset authentication in cyber-physical systems,” Blockchain Res. Appl., vol. 7, no. 2, p. 100456, 2026, doi: 10.1016/j.bcra.2026.100456.

[42] S. Suhail, M. Iqbal, R. Hussain, S. Ur, R. Malik, and R. Jurdak, “TRIPLE: A blockchain-based digital twin framework for cyber – physical systems security,” J. Ind. Inf. Integr., vol. 42, no. July 2023, p. 100706, 2024, doi: 10.1016/j.jii.2024.100706.

[43] P. S. Akshatha and S. M. D. Kumar, “MQTT and blockchain sharding : An approach to user-controlled data access with improved security and efficiency,” Blockchain Res. Appl., vol. 4, no. 4, p. 100158, 2023, doi: 10.1016/j.bcra.2023.100158.

[44] A. Pathak, I. Al-Anbagi, and H. J. Hamilton, “TABI: Trust-Based ABAC Mechanism for Edge-IoT Using Blockchain Technology,” IEEE Access, vol. 11, pp. 36379–36398, 2023, doi: 10.1109/ACCESS.2023.3265349.

[45] I. Mustafa, A. Salimi, and M. Scriney, “Secure IoT Communication and Policy Enforcement Framework ( SCOPE ),” IEEE Internet Things J., vol. 13, no. 3, pp. 4546–4563, 2026, doi: 10.1109/JIOT.2025.3633459.

[46] I. Kulkarni, G. Rathee, C. Abdelaziz, and N. Jaglan, “Adaptive ML-blockchain framework for intrusion detection and trust management in IoT networks,” Sustain. Cities Soc. Adv., vol. 2, no. 2, p. 100055, 2026, doi: 10.1016/j.scsadv.2026.100055.

[47] Z. Gao and W. Yan, “The real-time data processing framework for blockchain and edge computing,” Alexandria Eng. J., vol. 120, pp. 50–61, 2025, doi: https://doi.org/10.1016/j.aej.2025.01.092.

[48] H. Huang, P. Ye, M. Hu, and J. Wu, “A multi-point collaborative DDoS defense mechanism for IIoT environment,” Digit. Commun. Networks, vol. 9, no. 2, pp. 590–601, 2023, doi: 10.1016/j.dcan.2022.04.008.

[49] G. Baralla, L. Cocco, M. Di Francesco, and R. Tonelli, “Integrating Blockchain, SSI, and RBAC for the Secure Management of Defense Heritage Buildings,” IEEE Access, vol. 13, pp. 86290–86307, 2025, doi: 10.1109/ACCESS.2025.3570809.

[50] M. M. Asiri, H. Alfraihi, and Y. Said, “Securing Consumer Electronics Devices : A Blockchain-Based Access Management Approach Enhanced by Deep Learning Threat Modeling for IoT Ecosystems,” IEEE Access, vol. 12, no. July, pp. 110671–110680, 2024, doi: 10.1109/ACCESS.2024.3441094.

[51] S. Siddiqui, S. Hameed, S. Attique, J. Arshad, and Y. Ahmed, “A smart-contract-based adaptive security governance architecture for smart city service interoperations,” Sustain. Cities Soc., vol. 113, no. December 2023, p. 105717, 2024, doi: 10.1016/j.scs.2024.105717.

[52] W. Jiang, Z. Lin, and J. Tao, “An access control scheme for distributed Internet of Things based on adaptive trust evaluation and blockchain,” High-Confidence Comput., vol. 3, no. 1, p. 100104, 2023, doi: 10.1016/j.hcc.2023.100104.

[53] V. Dineshbabu and M. Vigenesh, “SecureFLACF : Secure Federated Learning Access Control Framework with Blockchain-Infused Intrusion Detection System for IIoT,” vol. 21, pp. 939–966, 2025, doi: 10.13052/jmm1550-4646.2155.

[54] C. Beis-penedo, R. P. Díaz-redondo, A. Fernández-vilas, M. Fernández-veiga, and F. Troncoso-pastoriza, “HLF-FSL : A decentralized federated split learning solution for IoT on hyperledger fabric,” Array, vol. 29, no. January, p. 100685, 2026, doi: 10.1016/j.array.2026.100685.

[55] W. Issa, N. Moustafa, B. Turnbull, and K.-K. R. Choo, “DT-BFL: Digital Twins for Blockchain-enabled Federated Learning in Internet of Things networks,” Ad Hoc Networks, vol. 178, p. 103934, 2025, doi: https://doi.org/10.1016/j.adhoc.2025.103934.

[56] D. Bernal, O. Ledesma, P. Lamo, and J. Bermejo, “IOTA-Based Authentication for IoT Devices in Satellite Networks,” Comput. Mater. Contin., 2025, doi: 10.32604/cmc.2025.069746.

[57] V. Dineshbabu and M. Vigenesh, “SecureFLACF: Secure Federated Learning Access Control Framework with Blockchain-Infused Intrusion Detection System for IIoT,” J. Mob. Multimed., vol. 21, no. 5, pp. 939–966, 2025, doi: 10.13052/jmm1550-4646.2155.

[58] S. K. Lee, M. M. Merlec, N. K. Sinai, S. Hong, and H. P. In, “ZK-Sandbox : Zero-Knowledge Data Sandbox System for Enhancing End-to-End Security and Privacy of Verifiable Credentials,” IEEE Internet Things J., vol. PP, no. X, p. 1, 2026, doi: 10.1109/JIOT.2026.3662381.