Fog-Computing-Based Cyber–Physical System for Secure Food Traceability through the Twofish Algorithm
Abstract
:1. Introduction
- The integration of novel fog modules with the CPS-based food traceability system for the identification of poor-quality products from the supply chain by maintaining reliable product information to increase the trust level;
- The utilization of novel sensing features along with monitoring and interface layer to gather product information from different layers to maintain the product’s actual information;
- To maintain the integrity, the details captured by the sensors along with the distribution of the information collected from the monitoring layers and stored in the fog;
- The utilization of a hybrid authentication mechanism with the integrated modification ability wherein the system can select users’ authentication criteria.
2. Related Work
3. Proposed Traceability System
3.1. Requirements, Identification, and Tracking
- Requirements: The food product traceability solution is based on three basic requirements, i.e., place identification, food product identification, and tracking movement;
- Food place identification: During the production process in the food supply chain, it is important to trace every involved phase uniquely. As we know that food products start from raw materials and then move step-by-step to completion, this should be traceable by anyone. In the supply chain, a food product changes its location time-to-time till completion; therefore, the location needs to be identified;
- Food product identification: It is also important to track and trace a food product throughout the supply chain. From the raw materials, i.e., from the initial phase till finishing, it must be distinctively identified;
- Movement tacking: The movement of food is very important to track and trace. It is the actual key element in the supply chain to be monitored during its production [40].
3.2. CPS-Based Secure Food Traceability Architecture
- Real-world layer: This is the ground or physical-world layer where the actual processes are performed physically. These processes consist of farming production where farmers produce the products, followed by the food processing layer, food packaging, and food storage. After this, the food is distributed to the retailer where customers can buy and are also able to obtain the product information from the intermediate layer;
- Sensing layer: The sensing layer is the primary layer equipped with the CPS sensors placed at different geographical locations of the supply chain. The CPS sensors consist of three main components, i.e., communication, computation, and control. The major functions performed at this layer are data processing, data conversion into operations, and encryption. The encryption and decryption are performed through the Twofish algorithm using a 256 bit key. In every phase of the supply chain, sensors are placed at different locations, which can sense the actual product information in an explicit form. This layer is further connected to the virtual storage layer;
- Virtual storage layer: The encrypted data transmitted by the sensing layer are sent to the virtual storage devices, and a copy of the information is forwarded to the parallel layer for monitoring purposes. Furthermore, the sensing layer is connected to virtual storage and communication devices to exchange content between the below and above layers. A virtual storage layer can also be utilized for collecting food product data. The key responsibility of the virtual layer provides services to the top layer for decision-making in addition to providing the facility to monitor the supply chain during the production process;
- Monitoring layer: The monitoring layer is the most significant one for food traceability because it is connected to the sensing layer and virtual storage layer and also gathers the information directly from food distribution vehicles. The monitoring layer has two significant components, i.e., decision-making and supply chain information. The incoming information from the sensing, virtual storage, and food distribution layers is integrated using supply chain components and forwarded to the decision-making components to evaluate the accuracy of the product information. The collection of information from different layers for evaluation purposes makes the proposed mechanism robust against modification attacks;
- Interface layer: This layer provides an interface to consumers, which contains food supply chain information regarding the product quality, name, manufacturing date, expiry, and weight. If customers find all information on the Internet and the same product is delivered to them, then it increases the trust and reliability among them. If there is any ambiguity in the object information, then it will cause a negative impression for the consumers and markets, which may ultimately reduce the trust.
3.3. Twofish-Algorithm-Based Data Encryption
Algorithm 1 Hybrid user authentication process. |
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3.4. Utilization of Fog Computing in the Proposed CPS
4. Simulation Environment
4.1. Encryption Time
4.2. Trusted Data Identification and Error Estimation
4.3. Computational Cost
4.4. Modification Attack
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Approach | Contribution | Limitation |
---|---|---|
[29] | Use of video surveillance cameras along with a novel dynamic background model for object monitoring. | Compression and decompression of stream data may take excessive time. |
[30] | Creation of a 3D city model and utilization of deep learning for object identification. | Requires abundant resources. |
[26] | Stream monitoring with fuzzy rules and stream mapping. | Integrity challenges due to the traceability database. |
[31] | Utilization of fog computing to reduce latency and communication cost. | T-S fuzzy and ANN-based fog increases the computational complexity. |
[32] | Division of layers into monitored, control, and cloud servers. | Depending on the cellular network and GPRS, may cause transmission delay. |
[33] | IoT later to capture information with the fog and cloud. | Increased latency and transmission cost. |
[34] | Utilization of real-time processing for prompt decision-making. | The A* algorithm is a blind search algorithm that may increase time and waste resources. |
Block Size | 192 bit | Average Percentage | ||
Key Size | 128 bit | 192 bit | 256 bit | |
FogAuth-FT | 60 | 90 | 110 | 86.67 |
Stream-FT | 90 | 120 | 150 | 120 |
FogCloud-FT | 110 | 145 | 120 | 125 |
Block Size | 256 bit | Average Percentage | ||
Key Size | 128 bit | 192 bit | 256 bit | |
FogAuth-FT | 70 | 105 | 140 | 105 |
Stream-FT | 100 | 130 | 180 | 136.67 |
FogCloud-FT | 110 | 165 | 220 | 165 |
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Awan, K.A.; Din, I.U.; Almogren, A.; Kim, B.-S. Fog-Computing-Based Cyber–Physical System for Secure Food Traceability through the Twofish Algorithm. Electronics 2022, 11, 283. https://doi.org/10.3390/electronics11020283
Awan KA, Din IU, Almogren A, Kim B-S. Fog-Computing-Based Cyber–Physical System for Secure Food Traceability through the Twofish Algorithm. Electronics. 2022; 11(2):283. https://doi.org/10.3390/electronics11020283
Chicago/Turabian StyleAwan, Kamran Ahmad, Ikram Ud Din, Ahmad Almogren, and Byung-Seo Kim. 2022. "Fog-Computing-Based Cyber–Physical System for Secure Food Traceability through the Twofish Algorithm" Electronics 11, no. 2: 283. https://doi.org/10.3390/electronics11020283