Toward an Intelligent Blockchain IoT-Enabled Fish Supply Chain: A Review and Conceptual Framework
Abstract
:1. Introduction
2. Literature Review
2.1. Industry Perspective
2.2. Academic Perspective
3. Blockchain Architectural Design Considerations for Traceability in Supply Chain
3.1. Traceability in Traditional Supply Chain Systems
3.2. Traceability in Smart Supply Chain Systems
3.3. Blockchain-Based Supply Chain Design Considerations
- Blockchain Type
- Data Storage and Computation
- Consensus Protocols
4. Proposed Blockchain IoT-Enabled Fish Supply Chain
4.1. System Requirements
4.2. Architectural Framework
4.2.1. Supply Chain Layer
- 1.
- Harvester (raw fish supplier): fish catching, registering, and packaging.
- 2.
- Manufacturer (factories, fishing docks): grading, fish processing, manufacturing, registering products each with a unique number, and packaging. Fish products must be re-registered and identified with a unique batch number if they are repackaged into batches.
- 3.
- Distributor (delivery companies, warehouses, storage hubs): packaging, delivering, classifying, quality checking, standardizing, tracking, and storing.
- 4.
- Safety and quality regulators (government food safety inspectors, certifiers, auditors): inspecting, grading, penalizing, licensing, and standardizing. Inspectors, certifiers, and auditors can also be classified as system users responsible for inspection, auditing, test reporting, and issuing product certifications [57].
- 5.
- Retailers (markets, supermarkets, wholesale stores, retail shops): receiving, packaging, classifying, selling, storing, distributing, and marketing.
- 6.
- Customers: buying, returning, quality checking, reporting, and consuming. Customers or end-consumers usually interact with the system interface by querying fish product data, which are permanently and securely stored in the Blockchain. Blockchain-based SC systems do not usually classify the consumer as a stakeholder since they consume the product at the end of its lifecycle; therefore, they do not need an account on the Blockchain.
- 7.
- Other actors may involve software developers and project coordinators.
4.2.2. IoT LAYER
- 1.
- Wireless sensors are usually placed at strategic points on the chain and have identification and sensing capabilities. These sensors communicate precise measurements continuously, called polling, or upon request. For example, a truck’s temperature monitoring sensors continuously measure cargo hold temperature and report the readings to the system. Each reading, in the form of a data transaction, must contain a unique track-and-trace number for the current measurement associated with the sensor device’s physical address [63]. The sensor keeps the observations in the sensors’ memory to be communicated with the server or can be automated to be reported regularly. Smart contracts can be utilized to control and regulate the sensors and to trigger the system if the readings are out of the specified limits.
- 2.
- IoT-enabled optical scanning devices can read RFID tags or machine-readable optical labels such as QR codes, and barcodes report product information with the track-and-trace readings. For example, RFID tags are unique digital cryptographic identifiers that connect physical items to their virtual identities [64], typically attached to the fish containers or packing cases and programmed to log trace data. Barcodes are usually used to label individual products; however, RFID is more convenient than barcodes but has a higher cost.
- 3.
- Smart weighing devices are used to weigh the fish caught during fishing operations. Weight logging could be automated to forecast the time between the landing date and the selected destination.
- 4.
- On-board survey cameras and electronic monitoring systems can identify interactions with by-catches and protected fish species.
- 5.
- GPS trackers can be used for real-time location determination and detailed tracking information, including geo-location, speed, and time.
- 6.
- Automated handheld imaging inspection devices can check fish freshness and possible microbiological and chemical contamination in fish species or fish farms. For example, the Adulteration and Traceability (QAT) handheld device is a proprietary technology proposed and developed by SafetySpect [65,66] that can be used to measure fish freshness and fecal contamination. This device can also be used to inspect several other types of meat products. The QAT device is portable, easy to use, and efficiently detect possible contamination.
4.2.3. Knowledge Layer
4.2.4. Application Layer
5. Machine Learning Integration with Blockchain IoT-Enabled Supply Chain
5.1. Related Work
5.2. Commercial Use Case
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
- Cook, B.; Zealand, W. Blockchain: Transforming the Seafood Supply Chain; World Wide Fund for Nature: Gland, Switzerland, 2018. [Google Scholar]
- Xiong, X.; D’Amico, P.; Guardone, L.; Castigliego, L.; Guidi, A.; Gianfaldoni, D.; Armani, A. The uncertainty of seafood labeling in China: A case study on Cod, Salmon and Tuna. Mar. Policy 2016, 68, 123–135. [Google Scholar] [CrossRef]
- Lindley, J. Food regulation and policing: Innovative technology to close the regulatory gap in Australia. J. Consum. Prot. Food Saf. 2022, 17, 127–136. [Google Scholar] [CrossRef] [PubMed]
- Gopi, K.; Mazumder, D.; Sammut, J.; Saintilan, N. Determining the provenance and authenticity of seafood: A review of current methodologies. Trends Food Sci. Technol. 2019, 91, 294–304. [Google Scholar] [CrossRef]
- De Coning, E.; Witbooi, E. Towards a new’fisheries crime’ paradigm: South Africa as an illustrative example. Mar. Policy 2015, 60, 208–215. [Google Scholar] [CrossRef]
- Callinan, C.; Vega, A.; Clohessy, T.; Heaslip, G. Blockchain adoption factors, enablers, and barriers in fisheries supply chain: Preliminary findings from a systematic literature review. J. Br. Blockchain Assoc. 2022, 5, 32437. [Google Scholar] [CrossRef]
- Chandan, A.; John, M.; Potdar, V. Achieving UN SDGs in Food Supply Chain Using Blockchain Technology. Sustainability 2023, 15, 2109. [Google Scholar] [CrossRef]
- Reddy, K.R.K.; Gunasekaran, A.; Kalpana, P.; Sreedharan, V.R.; Kumar, S.A. Developing a Blockchain framework for the automotive supply chain: A systematic review. Comput. Ind. Eng. 2021, 157, 107334. [Google Scholar] [CrossRef]
- Brookbanks, M.; Parry, G. The impact of a Blockchain platform on trust in established relationships: A case study of wine supply chains. Supply Chain. Manag. Int. J. 2022, 27, 128–146. [Google Scholar] [CrossRef]
- Chiacchio, F.; D’Urso, D.; Oliveri, L.M.; Spitaleri, A.; Spampinato, C.; Giordano, D. A non-fungible token solution for the track and trace of pharmaceutical supply chain. Appl. Sci. 2022, 12, 4019. [Google Scholar] [CrossRef]
- Torky, M.; Hassanein, A.E. Integrating Blockchain and the internet of things in precision agriculture: Analysis, opportunities, and challenges. Comput. Electron. Agric. 2020, 178, 105476. [Google Scholar] [CrossRef]
- Rejeb, A.; Keogh, J.G.; Treiblmaier, H. Leveraging the internet of things and Blockchain technology in supply chain management. Future Internet 2019, 11, 161. [Google Scholar] [CrossRef]
- Oliveira, J.; Lima, J.E.; da Silva, D.; Kuprych, V.; Faria, P.M.; Teixeira, C.; Cruz, E.F.; da Cruz, A.M.R. Traceability system for quality monitoring in the fishery and aquaculture value chain. J. Agric. Food Res. 2021, 5, 100169. [Google Scholar] [CrossRef]
- Wu, M.; Wang, K.; Cai, X.; Guo, S.; Guo, M.; Rong, C. A comprehensive survey of Blockchain: From theory to IoT applications and beyond. IEEE Internet Things J. 2019, 6, 8114–8154. [Google Scholar] [CrossRef]
- Tokkozhina, U.; Martins, A.L.; Ferreira, J.C.; Casaca, A. Traceable Distribution of Fish Products: State of the Art of Blockchain Technology Applications to Fish Supply Chains. In Proceedings of the Intelligent Transport Systems: 6th EAI International Conference (INTSYS 2022), Lisbon, Portugal, 15–16 December 2022; Springer: Berlin/Heidelberg, Germany, 2023; pp. 89–100. [Google Scholar]
- Vanany, I.; Ali, M.H.; Tan, K.H.; Kumar, A.; Siswanto, N. A supply chain resilience capability framework and process for mitigating the COVID-19 pandemic disruption. IEEE Trans. Eng. Manag. 2021. [Google Scholar] [CrossRef]
- Tasnim, Z. Disruption in global Food Supply Chain (FSCs) due to Covid-19 pandemic and impact of digitalization through block chain technology in FSCs management. Eur. J. Bus. Manag. 2020, 12, 73–84. [Google Scholar]
- Marine Stewardship Council. Available online: https://www.msc.org/ (accessed on 28 January 2023).
- Wong, S.; Yeung, J.K.W.; Lau, Y.Y.; So, J. Technical sustainability of cloud-based Blockchain integrated with machine learning for supply chain management. Sustainability 2021, 13, 8270. [Google Scholar] [CrossRef]
- Da Xu, L.; Lu, Y.; Li, L. Embedding Blockchain technology into IoT for security: A survey. IEEE Internet Things J. 2021, 8, 10452–10473. [Google Scholar]
- Hang, L.; Ullah, I.; Kim, D.H. A secure fish farm platform based on Blockchain for agriculture data integrity. Comput. Electron. Agric. 2020, 170, 105251. [Google Scholar] [CrossRef]
- Howson, P. Building trust and equity in marine conservation and fisheries supply chain management with Blockchain. Mar. Policy 2020, 115, 103873. [Google Scholar] [CrossRef]
- Ferreira, J.C.; Martins, A.L.; Tokkozhina, U.; Helgheim, B.I. Fish Control Process and Traceability for Value Creation Using Blockchain Technology. In Proceedings of the International Conference on Innovations in Bio-Inspired Computing and Applications, Seattle, WA, USA, 16–18 December 2021; pp. 761–773. [Google Scholar]
- Xu, X.; Weber, I.; Staples, M.; Zhu, L.; Bosch, J.; Bass, L.; Pautasso, C.; Rimba, P. A taxonomy of Blockchain-based systems for architecture design. In Proceedings of the 2017 IEEE International Conference on Software Architecture (ICSA), Gothenburg, Sweden, 3–7 April 2017; pp. 243–252. [Google Scholar]
- Kshetri, N. 1 Blockchain’s roles in meeting key supply chain management objectives. Int. J. Inf. Manag. 2018, 39, 80–89. [Google Scholar] [CrossRef]
- Dorri, A.; Kanhere, S.S.; Jurdak, R.; Gauravaram, P. Blockchain for IoT security and privacy: The case study of a smart home. In Proceedings of the 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Kona, HI, USA, 13–17 March 2017; pp. 618–623. [Google Scholar]
- Mondragon, A.E.C.; Mondragon, C.E.C.; Coronado, E.S. Feasibility of Internet of Things and Agnostic Blockchain Technology Solutions: A Case in the Fisheries Supply Chain. In Proceedings of the 2020 IEEE 7th International Conference on Industrial Engineering and Applications (ICIEA), Bangkok, Thailand, 16–21 April 2020; pp. 504–508. [Google Scholar]
- Wang, X.; Yu, G.; Liu, R.P.; Zhang, J.; Wu, Q.; Su, S.W.; He, Y.; Zhang, Z.; Yu, L.; Liu, T.; et al. Blockchain-Enabled Fish Provenance and Quality Tracking System. IEEE Internet Things J. 2021, 9, 8130–8142. [Google Scholar] [CrossRef]
- Larissa, S.; Parung, J. Designing supply chain models with Blockchain technology in the fishing industry in Indonesia. IOP Conf. Ser. Mater. Sci. Eng. 2021, 1072, 012020. [Google Scholar] [CrossRef]
- Rejeb, A. Blockchain potential in Tilapia supply chain in Ghana. Acta Tech. Jaurinensis 2018, 11, 104–118. [Google Scholar] [CrossRef]
- Tokkozhina, U.; Martins, A.L.; Ferreira, J.C. Multi-tier supply chain behavior with Blockchain technology: Evidence from a frozen fish supply chain. Oper. Manag. Res. 2023, 1–15. [Google Scholar] [CrossRef]
- Maroufi, M.; Abdolee, R.; Tazekand, B.M. On the convergence of Blockchain and internet of things (iot) technologies. arXiv 2019, arXiv:1904.01936. [Google Scholar]
- Kawaguchi, N. Application of Blockchain to supply chain: Flexible Blockchain technology. Procedia Comput. Sci. 2019, 164, 143–148. [Google Scholar] [CrossRef]
- Kumar, P.; Kumar, R.; Srivastava, G.; Gupta, G.P.; Tripathi, R.; Gadekallu, T.R.; Xiong, N.N. PPSF: A privacy-preserving and secure framework using Blockchain-based machine-learning for IoT-driven smart cities. IEEE Trans. Netw. Sci. Eng. 2021, 8, 2326–2341. [Google Scholar] [CrossRef]
- Narayanaswami, C.; Nooyi, R.; Govindaswamy, S.R.; Viswanathan, R. Blockchain anchored supply chain automation. IBM J. Res. Dev. 2019, 63, 7:1–7:11. [Google Scholar] [CrossRef]
- Khalil, A.A.; Franco, J.; Parvez, I.; Uluagac, S.; Shahriar, H.; Rahman, M.A. A literature review on Blockchain-enabled security and operation of cyber-physical systems. In Proceedings of the 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), Los Alamitos, CA, USA, 27 June–1 July 2022; pp. 1774–1779. [Google Scholar]
- Attia, O.; Khoufi, I.; Laouiti, A.; Adjih, C. An IoT-Blockchain architecture based on hyperledger framework for health care monitoring application. In Proceedings of the NTMS 2019—10th IFIP International Conference on New Technologies, Mobility and Security, Canary Islands, Spain, 24–26 June 2019; IEEE Computer Society: Washington, DC, USA, 2019; pp. 1–5. [Google Scholar]
- Musleh, A.S.; Yao, G.; Muyeen, S.M. Blockchain Applications in Smart Grid–Review and Frameworks. IEEE Access 2019, 7, 86746–86757. [Google Scholar] [CrossRef]
- Fraga-Lamas, P.; Fernández-Caramés, T.M. A review on Blockchain technologies for an advanced and cyber-resilient automotive industry. IEEE Access 2019, 7, 17578–17598. [Google Scholar] [CrossRef]
- Wang, X.; Li, D. Value added on food traceability: A supply chain management approach. In Proceedings of the 2006 IEEE International Conference on Service Operations and Logistics, and Informatics, Shanghai, China, 21–23 June 2006; pp. 493–498. [Google Scholar]
- Hardt, M.J.; Flett, K.; Howell, C.J. Current barriers to large-scale interoperability of traceability technology in the seafood sector. J. Food Sci. 2017, 82, A3–A12. [Google Scholar] [CrossRef] [PubMed]
- Kumperščak, S.; Medved, M.; Terglav, M.; Wrzalik, A.; Obrecht, M. Traceability systems and technologies for better food supply chain management. Qual. Prod. Improv.-QPI 2019, 1, 567–574. [Google Scholar] [CrossRef]
- Kishore Kumar, A.; Aeri, M.; Grover, A.; Agarwal, J.; Kumar, P.; Raghu, T. Secured supply chain management system for fisheries through IoT. Meas. Sens. 2023, 25, 100632. [Google Scholar] [CrossRef]
- Qin, J.; Vasefi, F.; Hellberg, R.S.; Akhbardeh, A.; Isaacs, R.B.; Yilmaz, A.G.; Hwang, C.; Baek, I.; Schmidt, W.F.; Kim, M.S. Detection of fish fillet substitution and mislabeling using multimode hyperspectral imaging techniques. Food Control 2020, 114, 107234. [Google Scholar] [CrossRef]
- da Costa, N.L.; da Costa, M.S.; Barbosa, R. A review on the application of chemometrics and machine learning algorithms to evaluate beer authentication. Food Anal. Methods 2021, 14, 136–155. [Google Scholar] [CrossRef]
- Garriga, M.; Dalla Palma, S.; Arias, M.; De Renzis, A.; Pareschi, R.; Andrew Tamburri, D. Blockchain and cryptocurrencies: A classification and comparison of architecture drivers. Concurr. Comput. Pract. Exp. 2021, 33, e5992. [Google Scholar] [CrossRef]
- Monteiro, R.; Ribeiro, M.; Viana, C.; de Lima Moreira, M.W.; Araújo, G.; Rodrigues, J.J. Fish Recognition Model for Fraud Prevention using Convolutional Neural Networks. Adv. Comput. Intell. 2023, 3, 2. [Google Scholar] [CrossRef]
- Xu, X.; Pautasso, C.; Zhu, L.; Gramoli, V.; Ponomarev, A.; Tran, A.B.; Chen, S. The Blockchain as a software connector. In Proceedings of the 2016 13th Working IEEE/IFIP Conference on Software Architecture (WICSA), Venice, Italy, 5–8 April 2016; pp. 182–191. [Google Scholar]
- Hepp, T.; Sharinghousen, M.; Ehret, P.; Schoenhals, A.; Gipp, B. On-chain vs. off-chain storage for supply-and Blockchain integration. It-Inf. Technol. 2018, 60, 283–291. [Google Scholar] [CrossRef]
- Lin, Q.; Wang, H.; Pei, X.; Wang, J. Food safety traceability system based on Blockchain and EPCIS. IEEE Access 2019, 7, 20698–20707. [Google Scholar] [CrossRef]
- Yang, X.; Li, M.; Yu, H.; Wang, M.; Xu, D.; Sun, C. A trusted Blockchain-based traceability system for fruit and vegetable agricultural products. IEEE Access 2021, 9, 36282–36293. [Google Scholar] [CrossRef]
- Hasan, S.S.; Sultan, N.H.; Barbhuiya, F.A. Cloud data provenance using IPFS and Blockchain technology. In Proceedings of the Seventh International Workshop on Security in Cloud Computing, Auckland, New Zealand, 8 July 2019; pp. 5–12. [Google Scholar]
- An, A.C.; Diem, P.T.X.; Van Toi, T.; Binh, L.D.Q. Building a product origins tracking system based on Blockchain and PoA consensus protocol. In Proceedings of the 2019 International Conference on Advanced Computing and Applications (ACOMP), Nha Trang, Vietnam, 26–28 November 2019; pp. 27–33. [Google Scholar]
- Nurgazina, J.; Pakdeetrakulwong, U.; Moser, T.; Reiner, G. Distributed ledger technology applications in food supply chains: A review of challenges and future research directions. Sustainability 2021, 13, 4206. [Google Scholar] [CrossRef]
- Sangeetha, A.; Shunmugan, S.; Murugan, G. Blockchain for IoT enabled supply chain management-A systematic review. In Proceedings of the 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India, 7–9 October 2020; pp. 48–52. [Google Scholar]
- Yang, R.; Wakefield, R.; Lyu, S.; Jayasuriya, S.; Han, F.; Yi, X.; Yang, X.; Amarasinghe, G.; Chen, S. Public and private Blockchain in construction business process and information integration. Autom. Constr. 2020, 118, 103276. [Google Scholar] [CrossRef]
- Perboli, G.; Musso, S.; Rosano, M. Blockchain in logistics and supply chain: A lean approach for designing real-world use cases. IEEE Access 2018, 6, 62018–62028. [Google Scholar] [CrossRef]
- Al-Rakhami, M.S.; Gumaei, A.; Rahman, S.; Mizanur, M.; Al-Amri, A. Decentralized Blockchain-based model for Edge Computing. arXiv 2021, arXiv:2106.15050. [Google Scholar]
- Li, G.; Ren, X.; Wu, J.; Ji, W.; Yu, H.; Cao, J.; Wang, R. Blockchain-based mobile edge computing system. Inf. Sci. 2021, 561, 70–80. [Google Scholar] [CrossRef]
- Shi, W.; Dustdar, S. The Promise of Edge Computing. Computer 2016, 49, 78–81. [Google Scholar] [CrossRef]
- Menon, S.; Shah, S. An overview of digitalisation in conventional supply chain management. In MATEC Web of Conferences, Proceedings of the 23rd International Conference on Circuits, Systems, Communications and Computers (CSCC 2019), Athens, Greece, 14–17 July 2019; EDP Sciences: Les Ulis, France, 2019; Volume 292, p. 01013. [Google Scholar]
- Wu, Y.; Dai, H.N.; Wang, H. Convergence of Blockchain and edge computing for secure and scalable IIoT critical infrastructures in industry 4.0. IEEE Internet Things J. 2020, 8, 2300–2317. [Google Scholar] [CrossRef]
- Bocek, T.; Rodrigues, B.B.; Strasser, T.; Stiller, B. Blockchains everywhere—A use-case of Blockchains in the pharma supply-chain. In Proceedings of the 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), Lisbon, Portugal, 8–12 May 2017; pp. 772–777. [Google Scholar]
- Tian, F. An agri-food supply chain traceability system for China based on RFID & Blockchain technology. In Proceedings of the 2016 13th International Conference on Service Systems and Service Management(ICSSSM), Kunming, China, 24–26 June 2016; pp. 1–6. [Google Scholar]
- Gorji, H.T.; Shahabi, S.M.; Sharma, A.; Tande, L.Q.; Husarik, K.; Qin, J.; Chan, D.E.; Baek, I.; Kim, M.S.; MacKinnon, N.; et al. Combining deep learning and fluorescence imaging to automatically identify fecal contamination on meat carcasses. Sci. Rep. 2022, 12, 2392. [Google Scholar] [CrossRef]
- Sueker, M.; Stromsodt, K.; Gorji, H.T.; Vasefi, F.; Khan, N.; Schmit, T.; Varma, R.; Mackinnon, N.; Sokolov, S.; Akhbardeh, A.; et al. Handheld multispectral fluorescence imaging system to detect and disinfect surface contamination. Sensors 2021, 21, 7222. [Google Scholar] [CrossRef]
- Kim, H.M.; Laskowski, M. Toward an ontology-driven Blockchain design for supply-chain provenance. Intell. Syst. Account. Financ. Manag. 2018, 25, 18–27. [Google Scholar] [CrossRef]
- Glaser, F. Pervasive decentralisation of digital infrastructures: A framework for Blockchain enabled system and use case analysis. In Proceedings of the 50th Hawaii International Conference on System Sciences (HICSS-50), Waikoloa Village, HI, USA, 4–7 January 2017. [Google Scholar]
- Mohanta, B.K.; Panda, S.S.; Jena, D. An overview of smart contract and use cases in Blockchain technology. In Proceedings of the 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Bengaluru, India, 10–12 July 2018; pp. 1–4. [Google Scholar]
- Akhtar, M.M.; Rizvi, D.R. Traceability and detection of counterfeit medicines in pharmaceutical supply chain using Blockchain-based architectures. In Sustainable and Energy Efficient Computing Paradigms for Society; Springer: Berlin/Heidelberg, Germany, 2021; pp. 1–31. [Google Scholar]
- Chang, S.E.; Chen, Y. When Blockchain meets supply chain: A systematic literature review on current development and potential applications. IEEE Access 2020, 8, 62478–62494. [Google Scholar] [CrossRef]
- Vyas, N.; Beije, A.; Krishnamachari, B. Blockchain and the Supply Chain: Concepts, Strategies and Practical Applications; Kogan Page Publishers: London, UK, 2019. [Google Scholar]
- Zareen, H.; Awan, S.; Sajid, M.B.E.; Baig, S.M.; Faisal, M.; Javaid, N. Blockchain and IPFS based service model for the internet of things. In Proceedings of the Complex, Intelligent and Software Intensive Systems: Proceedings of the 15th International Conference on Complex, Intelligent and Software Intensive Systems (CISIS-2021), Asan, Republic of Korea, 1–3 July 2021; pp. 259–270. [Google Scholar]
- Athanere, S.; Thakur, R. Blockchain based hierarchical semi-decentralized approach using IPFS for secure and efficient data sharing. J. King Saud Univ.-Comput. Inf. Sci. 2022, 34, 1523–1534. [Google Scholar] [CrossRef]
- Dutta, P.; Choi, T.M.; Somani, S.; Butala, R. Blockchain technology in supply chain operations: Applications, challenges and research opportunities. Transp. Res. Part E Logist. Transp. Rev. 2020, 142, 102067. [Google Scholar] [CrossRef]
- Li, Z.; Guo, H.; Wang, W.M.; Guan, Y.; Barenji, A.V.; Huang, G.Q.; McFall, K.S.; Chen, X. A Blockchain and automl approach for open and automated customer service. IEEE Trans. Ind. Inform. 2019, 15, 3642–3651. [Google Scholar] [CrossRef]
- Tirkolaee, E.B.; Sadeghi, S.; Mooseloo, F.M.; Vandchali, H.R.; Aeini, S. Application of machine learning in supply chain management: A comprehensive overview of the main areas. Math. Probl. Eng. 2021, 2021, 1476043. [Google Scholar] [CrossRef]
- Köhler, S.; Pizzol, M. Technology assessment of Blockchain-based technologies in the food supply chain. J. Clean. Prod. 2020, 269, 122193. [Google Scholar] [CrossRef]
- Jensen, T.; Hedman, J.; Henningsson, S. How tradelens delivers business value with Blockchain technology. MIS Q. Exec. 2019, 18, 221–224. [Google Scholar] [CrossRef]
- Shahbazi, Z.; Byun, Y.C. A procedure for tracing supply chains for perishable food based on Blockchain, machine learning and fuzzy logic. Electronics 2020, 10, 41. [Google Scholar] [CrossRef]
- Abbas, K.; Afaq, M.; Ahmed Khan, T.; Song, W.C. A Blockchain and machine learning-based drug supply chain management and recommendation system for smart pharmaceutical industry. Electronics 2020, 9, 852. [Google Scholar] [CrossRef]
- Saurabh, S.; Dey, K. Blockchain technology adoption, architecture, and sustainable agri-food supply chains. J. Clean. Prod. 2021, 284, 124731. [Google Scholar] [CrossRef]
- Open Sc. Available online: https://opensc.org/ (accessed on 23 September 2022).
- TradeLens. Available online: https://www.tradelens.com (accessed on 23 September 2022).
- Saeed, R.; Feng, H.; Wang, X.; Xiaoshuan, Z.; Zetian, F. Fish quality evaluation by sensor and machine learning: A mechanistic review. Food Control 2022, 137, 108902. [Google Scholar] [CrossRef]
- Konovalov, D.A.; Saleh, A.; Efremova, D.B.; Domingos, J.A.; Jerry, D.R. Automatic weight estimation of harvested fish from images. In Proceedings of the 2019 Digital image computing: Techniques and applications (DICTA), Perth, WA, Australia, 2–4 December 2019; pp. 1–7. [Google Scholar]
- Iqbal, M.A.; Wang, Z.; Ali, Z.A.; Riaz, S. Automatic fish species classification using deep convolutional neural networks. Wirel. Pers. Commun. 2021, 116, 1043–1053. [Google Scholar] [CrossRef]
- Griesche, C.; Baeumner, A.J. Biosensors to support sustainable agriculture and food safety. TrAC Trends Anal. Chem. 2020, 128, 115906. [Google Scholar] [CrossRef]
- Neethirajan, S.; Ragavan, V.; Weng, X.; Chand, R. Biosensors for sustainable food engineering: Challenges and perspectives. Biosensors 2018, 8, 23. [Google Scholar] [CrossRef]
- Tsolakis, N.; Schumacher, R.; Dora, M.; Kumar, M. Artificial intelligence and Blockchain implementation in supply chains: A pathway to sustainability and data monetisation? Ann. Oper. Res. 2022, 1–54. [Google Scholar] [CrossRef]
Quality Attribute | Design Considerations |
---|---|
Traceability | Track and trace the moving product throughout the SC during its lifetime, influenced by a variety of factors such as: (1) smart contracts, (2) business logic design, (3) transactional rate and processing speed, and (4) data structure. |
Performance | Blockchain-based system performance is affected by resource availability in terms of storage and processing. Factors to consider: (1) Blockchain platform ( private or consortium is preferred), (2) data structure and block size, (3) transactional rate and processing speed, and (4) choice of consensus protocol. |
Security | Consider possible malicious activities and security risks, especially with the integration of IoT-enabled devices. Factors to consider: (1) choice of a consensus protocol, (2) access control lists (ACLs) and trust establishment. |
Privacy | Ensure the confidentiality of stakeholders’ sensitive information. Factors to consider: (1) user identity and product authentication and (2) Blockchain platform (private is preferred). |
Interoperability | Ability to communicate and access information across various Blockchain systems by standardizing Blockchain implementation and ensuring regulation compliance especially with IoT integration. Factors to consider: (1) Blockchain-based layered architecture, (2) standardized language, and (3) on-chain computation. |
Scalability | Adapt to a growing number of users and IoT-enabled devices, data ledger size, transactional rate and processing speed, and data transmission latency. Factors to consider: (1) Blockchain platform, such as private or consortium, (2) network size, (3) block size and transaction size, (4) off-chain and cloud storage, (5) choice of consensus protocol, and (6) sharing. |
Latency | Reduce the time taken to transfer the data. Latency is affected by transactional rate, processing speed, and security check time. Factors to consider: (1) off-chain and cloud storage, and (2) Blockchain platform (private is preferred to avoid long commitment times compared to public). |
Integrity | Manage an immutable and permanent data ledger that cannot be altered or deleted and maintain identical copies across the nodes. Factors to consider: (1) off-chain storage and (2) choice of consensus protocol. |
Usability | Ease of use and user ability to achieve the desired outcome and to obtain access to data. Factors to consider: (1) user-friendly application programming interface (API) and (2) multi-factor authentication. |
Use Case | |
---|---|
Use Case Name | Automatically reading observations from an IoT-enabled sensor. |
Primary Actor | IoT-enabled sensor. |
Description | Reading an observation from an IoT-enabled sensor. |
Input Data | Data readings from an IoT-enabled sensor such as temperature, humidity, geo-location. |
Pre-Condition | The IoT-enabled sensor is connected to the network, either wired or wireless. The IoT-enabled sensor is active and able to detect and measure physical surroundings automatically and promptly. |
Post-Condition | A new observation is sent over the network in the form of a data transaction. The Blockchain system validates and verifies the transaction to be combined with other transactions in a new block and added to the chain. |
Main Flow | The IoT-enabled sensor observes a new reading automatically and promptly. Data observation uses a procedure with input and output. The IoT-enabled sensor generates a write transaction for the observation. The transaction is sent to be verified and validated and then added to the database. |
Alternative Flow | None. |
Misuse Case | |
---|---|
Misuse Case Name | Fraud, product data tampering. |
Category | Security Attacks. |
Goal | Adulteration by tampering with the data stored on the product tag and code, such as RFID tag and QR code. |
Primary Actor | System, fraudulent stakeholder. |
Description | Fraud through false tag data. |
Input Data | Data stored on RFID tag, QR code, and the system. |
Pre-Condition | Each fish product is assigned a unique RFID identifier and QR code. The fish product data stored on the RFID tag or QR code consists of: (1) production origin, area, state, and country; (2) product weight at the time of packaging, which may have several packaging stages; (3) packaging time and date; and (4) additional product information such as product grade, product quality assessment features, and expiration date. The same data on the RFID tag and QR code are stored on the Blockchain. The RFID tag and QR code are attached to the fish product or container. |
Attack Flow | Fraudulent participant may tamper with the data from the product’s RFID tag or QR code, or false information is injected. |
Post-Condition | The system has false information about the product. |
Detection Flow | Trace-back is performed to detect any possible fraud. The likelihood of product fraud is inferred if the information on the Blockchain and RFID tag or QR code does not match. No product fraud is inferred if the information on the Blockchain and RFID tags matches. |
Ref/Type | Machine Learning Technique | Product Type | Study Objective | Dataset |
---|---|---|---|---|
[83]/Industry | N/A | Fish | Verifying the vessel’s GPS locations to determine if it is within legal fishing zones | Dataset of satellite imagery, live video monitoring |
[84]/Industry | N/A | Shipment and logistics | Enhancing supply chain services such as transport cost reduction and shipment schedule optimization | Geo-location data collected through GPS sensors |
[80]/Academic | Bayesian Regression and Random Forest | Food | Predicting the product’s estimated expiration date | Real-time dataset that was collected from temperature and humidity sensors |
[81]/Academic | LightGBM | Drug | Recommending drugs to system users | Drug reviews dataset provided by the UCI |
[82]/Academic | Dummy Variable Regression | Grape wine | Informed decisions for smart transportation and logistics and quality control | Survey questionnaire for collecting data from the actors |
Integration Roles | Description |
---|---|
Fishing | Ensuring safe and legal fishing zones. Reducing the time needed for video review and lowering the cost of electronic monitoring. Visual tracking for automatic fish counting. Decision making in the management of fishing activities such as season fishing dates and allowable catch tonnage based on demand prediction. |
Manufacturing | Developing ML models to assist manufacturers in factories to make informed decisions on the quality of the fish and to decide the best preliminary processing or refining steps. |
Shipping and Transportation | Ensuring optimized shipping routes and excluding shipment danger zones. Predicting shipping time and routes (dynamic routing). Identifying the direction of the tagged fish. Predicting transport time and routes to shorten the distribution time to guarantee fish freshness. |
Logistics | Dynamic inventory management to predict demand/sales of fresh fish in the near future, allowing stock to be purchased on time. |
Customer Service | Providing real-time recommendations to consumers about products using models developed with datasets collected from consumer reviews. |
Health | Sustainable food fishing for the health of ecosystems (marine). Predicting water freshness for the health of the fish product. Estimating product expiry dates for consumer health. |
Quality | Finding fish quality patterns for each supplier and automating track-and-trace reporting. Automating quality inspection throughout the supply chain phases, for example, determining damage in shipping containers, classifying it by damage type and time, and recommending the best corrective action to repair the assets. |
Assets Maintenance and Replacement | Finding patterns in hardware asset usage to establish the factors that most influence devices and machinery performance. |
Labor and stakeholders assessment | Performance assessment, labor status prediction. |
Pricing | Finding optimal pricing based on seasonal demand, market feedback, the grade/species, and prevailing wholesale market price. |
Fraud Detection | Predicting the occurrence of fish fraud and other malicious threats and identifying the factors leading to fraudulent activities. |
Contamination | Predicting the hazard type. Detecting fish contamination with the new specialized handheld contamination and inspection devices. |
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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Ismail, S.; Reza, H.; Salameh, K.; Kashani Zadeh, H.; Vasefi, F. Toward an Intelligent Blockchain IoT-Enabled Fish Supply Chain: A Review and Conceptual Framework. Sensors 2023, 23, 5136. https://doi.org/10.3390/s23115136
Ismail S, Reza H, Salameh K, Kashani Zadeh H, Vasefi F. Toward an Intelligent Blockchain IoT-Enabled Fish Supply Chain: A Review and Conceptual Framework. Sensors. 2023; 23(11):5136. https://doi.org/10.3390/s23115136
Chicago/Turabian StyleIsmail, Shereen, Hassan Reza, Khouloud Salameh, Hossein Kashani Zadeh, and Fartash Vasefi. 2023. "Toward an Intelligent Blockchain IoT-Enabled Fish Supply Chain: A Review and Conceptual Framework" Sensors 23, no. 11: 5136. https://doi.org/10.3390/s23115136
APA StyleIsmail, S., Reza, H., Salameh, K., Kashani Zadeh, H., & Vasefi, F. (2023). Toward an Intelligent Blockchain IoT-Enabled Fish Supply Chain: A Review and Conceptual Framework. Sensors, 23(11), 5136. https://doi.org/10.3390/s23115136