The rapid convergence of social networks, cloud computing, and the Internet of Things (IoT) domains is redefining the scope and functionality of the digital environment. While these technologies have transformed various industries individually, their convergence presents new opportunities and challenges. However, the interdisciplinary integration of these domains remains underexplored, with existing surveys often focusing on isolated technologies rather than their synergistic potential. This gap limits our understanding of how combined architectures can address complex real-world problems such as scalability, interoperability, and security in integrated systems. This survey aims to bridge the knowledge gap by systematically examining the interactions of Social-IoT-Cloud, highlighting their synergies, and identifying key areas for further research. The study delves into the transformative convergence potential of these domains. We have extensively reviewed existing literature, convergence architectures, case studies, and practical applications. This study explains the foundational components and methodologies that drive the convergence of these domains. The presented work examines how this convergence reshapes various aspects of our interconnected world, including communication paradigms, data management strategies, and user experiences. A novel architecture for intelligent health monitoring systems is also presented, highlighting the potential of cross-domain analysis. This integrated ecosystem enables innovation across diverse areas, including healthcare, environmental monitoring, transportation, and smart cities. The manuscript also addresses privacy concerns, challenges, and future directions associated with cross-domain integration. Thus, the work offers valuable insights for researchers and policymakers to understand the complexities and implications of cross-domain integration. It provides a road map for leveraging the transformation potential of these interconnected domains.

Inadequate resource utilization of physical machines is a primary cause of high energy consumption in cloud environments. Virtual machine allocation and consolidation play a vital role in efficiently utilizing data center resources. Existing research typically focuses either on optimizing the allocation of virtual machines (VMs) to physical machines (PMs) or on consolidating VMs based solely on current workload information. This paper proposes a three-tier architecture that integrates VM allocation and consolidation by leveraging both current and predicted workloads. By incorporating accurate workload prediction instead of relying only on real-time usage, the proposed method proactively improves placement decisions, thereby minimizing frequent VM migrations caused by dynamic workload changes and reducing the risk of SLA violations.Tier 1 maps VMs to PMs optimally using a Non-dominated Sorting Genetic Algorithm II-based virtual machine allocation and consolidation (NSGAII-VMAC) algorithm. Tier 2 employs a Long Short-Term Memory (LSTM) model to predict VM workloads and compute the predicted workloads of PMs. Finally, Tier 3 performs consolidation in three steps: first, it identifies overloaded and underloaded PMs using current and predicted workloads; next, it selects VMs from overloaded PMs for migration using a VM selection method inspired by the Pareto front strategy; and lastly, it determines the destination PM for placement using NSGAII-VMAC. Extensive experiments demonstrated that the proposed approach reduced energy consumption by up to 50.93%, lowered the number of VM migrations by 30.57%, and reduced the number of active servers by 60.39% compared to baseline methods. For workload prediction, the LSTM model achieved an RMSE as low as 0.028 and an MAE as low as 0.021, outperforming state-of-the-art models. These results validate the effectiveness of a prediction-enhanced framework for energy-efficient cloud data center management.

Accurately identifying brain tumours in preoperative and intraoperative medical images poses significant challenges for neurosurgeons, especially when relying solely on visual examination. Image registration aids in effectively addressing this challenge by improving the alignment and accuracy of medical images. However, several conventional image registration methods often struggle to determine optimum rigid transformation parameters and fail to identify the global optima or approximate to global optima, which significantly increases computational time. To counter this challenge, a two-tier approach is presented for multimodality register image, integrating hybrid optimization approach with convolution neural network. During the initial stage, source and template image alignment is performed by determining optimum transformation using hybrid optimization, with similarity metric such as mutual information as fitness function. Next, VGG-19 is applied to enhance the quality and reliability of source and hybrid optimized image. Subsequently, dynamic inlier selection optimizes the feature matching process, thereby improving the robustness of registration. Finally, thin plate spline interpolation is used to calculate the transformation parameters for accurate image alignment. The proposed approach is evaluated on monomodal and multimodal medical images sourced from standard and real-world datasets. The results show significant improvements in RMSE decreased from 14.346 to 8.487, SSIM increased from 0.9332 to 0.9698, PSNR increased from 36.079 to 39.812, and CC increased from 0.9619 to 0.989, outperforming the performance of existing methods. The experimental results confirm that proposed framework offers a robust, accurate, and computationally efficient solution for multimodal medical image registration.

The main objective of the grain supply chain is to ensure that consumers will always have access to safe, high-quality grain around the world. The traditional grain supply chains usually have issues such as fraud, inefficiencies, logistics, and a lack of transparency that reduce the system's overall efficiency. Furthermore, grain traceability is a major issue since it becomes challenging to precisely determine the origin, quality, and transportation of grain products when many intermediaries are involved. Blockchain technology is developing as a viable solution to the present problems affecting the grain supply chain, that is undergoing a considerable transformation due to new technological breakthroughs. Blockchain provides smart contracts, decentralization, traceability, immutable recordkeeping, enhanced transparency, and higher sustainability. Blockchain Technology can improve the traditional grain supply chains by safeguarding the provenance and quality data of grain products that will increase operational efficiency and sustainability while fostering the trust among all stakeholders. This paper analyzes the use of blockchain in grain supply chain, highlighting its benefits for increased traceability, transparency and consumer confidence. Additionally, it represents the future research direction with a focus on scalability solutions and emerging technologies to maximize the efficiency and transparency of the grain supply chain using blockchain.

It is essential to allocate virtual machines efficiently in order to optimize the resource utilization and minimize the total energy consumption in data centers (DCs). Thus, reducing the number of operational physical machines will reduce the overall energy consumption of the cloud DC. In this paper, First Fit (FF), First Fit Decrease (FFD), Best Fit (BF), Best Fit Decrease (BFD) and Genetic Algorithm (GA) heuristics have been used to allocate virtual machines (VMs) to physical machines (PMs) to get the optimal mapping. Furthermore, we estimated the total energy consumption by the resource requirements of PMs that are required to process the assigned VMs. The experimental results indicate that optimizing the number of active PMs is not always enough to minimize the total energy consumption of the DC. GA is able to get the optimal number of active PMs, but it does not always reduce the total energy consumption of the DC compared to the other employed algorithms. This highlights the necessity of considering energy consumption as a separate objective during virtual machine allocation and consolidation planning as opposed to minimizing the number of active PMs to reduce the power consumption of the data center.

Traceability is a crucial component in the grain supply chain due to the food safety crises. The traditional supply chains rely on central platform that makes them prone to data tampering and fraud. This leads to poor traceability and lack of trust among stakeholders. This paper is aimed at solving these problems by incorporating blockchain technology in order to design a fully decentralized traceability system for grain supply chain. The blockchain ensures the system transparency by using the immutable records and secure transactions across the network. Additionally, AI is used to preserve the grain integrity. Smart contracts further streamline processes among stakeholders like payment, reduces delays, and reliance on third party. The performance of the proposed model is evaluated under the realistic conditions to determine the effectiveness of the system. The model is particularly designed to prioritize its efficiency and reliability. Experimental results shows that this model improves cost-effectiveness and transparency, while maintaining end-to-end traceability, thereby increasing consumer trust.

For a doctor to diagnose a patient and conduct quantitative analysis, medical images must be accurately registered. Deep learning-based image registration methods have been explored extensively, but accurate registration of medical images still remains a major concern. To address the issue of accurate alignment in medical images, this paper presents an approach employing unsupervised learning algorithm U-shaped convolution neural network (U-Net) model, followed by teaching learning-based optimization (TLBO) along with affine transformation for grayscale as well as colored medical image registration. The combined two-channel image generated using moving and fixed image is given as an input to encoder and decoder phase of U-Net model to learn image features and generate displacement field. To predict the spatial transformation parameters, a set of control points are generated to define the deformation field and moving image feature to produce the warped image. To improve the quality of warped image, TLBO with rigid transformation parameter (RTP) on fixed and U-Net warped image is applied by detecting the optimum value of transformation parameters. The proposed approach is implemented and evaluated using five different datasets for 2D and 3D monomodal medical MR and CT image modalities as well as multimodal clinical datasets. For 3D representation, transfer learning is used to obtain the warped images using the 3D pre-trained weights of VoxelMorph U-Net model. In comparison to state-of-the-art approaches like symmetric image normalization (SyN) and VoxelMorph, the Dice score value increases from 0.742 as reported by Balakrishnan (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2018) to 0.9542 and 0.710 as reported by Zhou (IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021) to 0.9024, while the Hausdorff distance decreases from 2.0521 Zhang (Appl Intell 12:1-18) to 1.5607 and 2.0446 Zhang (Appl Intell 12:1-18) to 1.0923, respectively, for deformable medical image registration under different image modalities. Higher value of Dice score and lower value of Hausdorff distance with our proposed approach in similarity metrics indicates better registration accuracy.

Given the rise in demand for cloud computing in the modern era, the effectiveness of resource utilization is eminent to decrease energy footprint and achieve economic services. With the emerging machine learning and artificial intelligence techniques to model and predict, it is essential to explore a principal method that provides the best solution for the accurate provisioning of forthcoming requests in a cloud data center. Recent studies used machine learning and other advanced analytics to predict resource usage; however, these do not consider long-range dependencies in the time series, which is essential to capture for better prediction. Further, they show limitations in handling noise, missing values, and outliers in datasets. In this paper, we explored the problem by studying three techniques that enabled us to answer improvements in short-term forecasting of physical machines’ resource usage if the above factors are considered. We evaluated the predictions using Transformer and Informer deep learning models that cover the above aspects and compared them with the Long short-term memory (LSTM) model. We used a real-world Google cluster trace usage dataset and employed Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm to select heterogeneous machines. The evaluation of the three models depicts that the Transformer architecture that considers long-range dependencies in time series and shortcomings with datasets shows improvement in forecasting with 14.2% reduction in RMSE than LSTM. However, LSTM shows better results for some machines than the Transformer, which depicts the importance of input sequence order. The Informer model, which considers both dependencies and is a hybrid of LSTM and Transformer, outperformed both models with 21.7% from LSTM and 20.8% from Transformer reduction in RMSE. The results also depict Informer model consistently performs better than the other models across all subsets of the dataset. Our study proves that considering long-range dependencies and sequence ordering for resource usage time series improves the prediction.

A subfield of forensic science called “digit al forensics” focuses on the examination and recovery of digital evidence from digital devices discovered at crime scenes. The gathered evidence must be securely stored to avoid manipulation or tampering. To ensure justice and make wise decisions, the integrity of the evidence is essential. Blockchain, a new technology, is used to preserve papers in a decentralized setting to address this problem. Blockchain creates a tamper-resistant ledger system by offering a safe and unchangeable chain of data, where each record is cryptographically connected to the one before it. In order to keep evidence in a dependable storage medium, this study suggests a safe chain of custody framework that makes use of blockchain. Only authorized persons can access or possess the evidence since every transmission of it is recorded on a private Ethereum blockchain from the moment it is seized. A digital evidence system that uses smart locks to physically store and secure the evidence is smoothly linked with the framework. The key to unlock the evidence is only in the possession of the Admin, an authorized entity, in order to maintain the integrity of the evidence submission and retrieval process. Multiple parties, including law enforcement organizations, attorneys, and forensic specialists, will benefit from our framework’s secure approach for maintaining the admissibility and integrity of the evidence.

This blockchain project aims to develop a decentralized supply chain management system leveraging blockchain technology. The primary objectives are to improve transparency, traceability, and security within the supply chain process. To achieve this, a permissioned blockchain will be utilized, granting access exclusively to authorized participants. By implementing this solution, all stakeholders involved in the supply chain will have access to an immutable and comprehensive record of all transactions and activities. Consequently, the risk of fraudulent activities will be minimized, efficiency will be increased, and costs associated with conventional supply chain management systems will be reduced. Smart contracts will be employed to automate various aspects of the supply chain process, including order processing, inventory management, and payment processing. This automation will streamline the overall process, resulting in reduced time and effort required for supply chain management. Moreover, the system will feature a user-friendly interface enabling participants to conveniently access and monitor their transactions and activities. Advanced analytics capabilities will also be integrated into the system, empowering participants to analyze their data and make well-informed decisions. In summary, the proposed blockchain-based supply chain management system holds immense potential to transform conventional supply chain practices. It will enhance transparency, traceability, and security while simultaneously driving down costs and improving overall efficiency.

In medical image registration, traditional approaches such as Harris corner detector, Oriented FAST and Rotated Brief (ORB), and scale invariant feature transform (SIFT) encounters several challenges for achieving precise alignment. These challenges arise from their limited ability to adapt scale, rotation, and illumination variations. To confront these challenges, the proposed methodology leverages the speeded-up robust features (SURF) algorithm for feature point extraction. Additionally, it employs the alpha trimmed spatial relation correspondence (ATSRC) algorithm as an efficient alternative to the conventional random sample consensus (RANSAC) method to effectively eliminate mismatches. In the pre-processing phase, the reference image undergoes Gaussian filtering to remove noise, and subsequent normalization is applied. Furthermore, feature detection is performed on both the reference and target images using SURF. A brute force (BF) matcher is applied, followed by alpha trimmed spatial relation correspondence to remove outliers. The process continues with computing the homography matrix and generating the registered image. Numerous experiments are conducted on monomodal and multimodal medical images acquired from the Kaggle dataset. The proposed methodology outperforms existing technique by significantly improving mutual information (MI) from 2.53 to 3.654, 2.257 to 3.17389 and cross correlation (CC) from 0.9219 to 0.95475.

In image registration, traditional optimization techniques are incapable of detecting the optimum value of geometric transformation parameters. To resolve this issue, a novel scheme of monomodal (isomodal) biomedical image registration employing teaching learning-based optimization (TLBO) is proposed. In pre-processing, reference image undergoes gaussian filtering to eliminate noise followed through normalization. During de-noising, contrast between anatomical features of an image is degraded. In order to create the floating image, rigid transformation is employed. These images are aligned by detecting optimum value of rigid transformation parameters (RTP) using TLBO with mutual information (MI) maximization as an objective function. MI and structural similarity index measure (SSIM) are used to evaluate visual quality of registered image. The proposed scheme is tested on several isomodal medical images such as magnetic resonance imaging (MRI) and computed tomography (CT). The value of MI, SSIM increases by 8

Traditional optimization methods encounter challenges in determining the optimal geometric transformation parameters and often lacking to achieve global optimum which leads to extended computational time required for convergence. To address this challenge, a dual-phase strategy is proposed for multimodal biomedical image registration by amalgamating the united equilibrium optimizer (UEO) with Visual Geometry Group (VGG-19). In first phase, the input images are aligned by identifying the optimal values of rigid transformation parameters through UEO, with mutual information (MI) maximization serving as an objective function. In second phase, VGG19 is employed to enhance the reliability of resulting registered image obtained through UEO optimization for extracting both the low- and high-level features of reference and target images. Following this, dynamic inlier selection is employed to refine the matching process, integrating expectation maximization for iterative updating of inlier selection, thus improving registration accuracy. Finally, thin plate spline interpolation is utilized to calculate the transformation matrix, ensuring precise alignment between reference and UEO registered image. Extensive experiments are conducted on multimodal medical images sourced from publicly available repositories and clinical(in-house) datasets to validate the reliability of the proposed scheme. These experiments showcase substantial improvements in RMSE from 22.21094 to 10.983654, SSIM from 0.7190832 to 0.8409321, PSNR from 30.176044 to 34.095471, and CC from 0.9304633 to 0.9809432, outperforming state-of-the-art methods.

Parkinson's disease (PD) is a prevalent neurodegenerative disease that hurts millions globally. For prompt intervention and successful therapy, Parkinson's disease (PD) must be identified early. However, a significant obstacle to creating progression-predicting models for early detection is the absence of extensive and readily available data. This research aims to present a robust method to forecast Parkinson's disease progression using minimal information. The Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is a commonly used metric for evaluating both motor and non-motor symptoms to determine the severity and course of Parkinson's disease. The ML models in this research were evaluated based on their ability to predict the MDS-UPDRS values with minimum error with respect to the actual measurements. The preprocessing of this data includes clustering based on patient ID and linear interpolation to fill values between contiguous readings. To create a baseline, MDS-UPDRS [1-4] are treated as unsupervised time series data by setting the time stamp as the input variable and the corresponding MDS-UPDRS value as the output variable. Then, the data is re-framed into a supervised format and tested on the same set of models to compare performance. The data for this research is sourced from the Accelerating Medicines Partnership-Parkinson's Disease (AMP-PD) Kaggle dataset. Among the machine learning algorithms assessed, the Multi Layer Perceptron (MLP) exhibited the most favorable predictive performance with an MAE of 0.52 and an MSE of 0.57 on average across the four clusters (U1, U2, U3, U4) for supervised format. This performance surpasses the second-best-performing algorithm, Random Forest, substantially. The MLP outperformed Random Forest by 25.46% in terms of MAE and 28.22% in MSE. Additionally, using a supervised format reduced MAE by 17.08% on average for all models and 41.73% for MLP compared to an unsupervised format. This considerable improvement suggests the robustness and efficacy of the MLP model and supervised re-framing in capturing the intricate progression trends of Parkinson's disease.

Medical image registration is vital for precise healthcare diagnosis, treatment planning, and disease progression tracking, but traditional methods fail to capture complex spatial transformations and anatomical variations. A Kernel Principal Component Analysis (KPCA) driven Teaching Learning based optimization (TLBO) approach is proposed to overcome these limitations. The proposed approach is categorized into three phases, i.e., pre-processed phase, contour extraction phase with feature extraction using KPCA, and evaluating robust affine transformation parameters leveraging TLBO for accurate alignment. In the pre-processing phase, gaussian filter is applied to remove noise from source and target images, followed by normalization process. Afterwards, contour extraction is carried out to create a feature image that represents the boundaries of an image. Centroid localization is then utilized to compute translation parameters, which determine the spatial alignment between the images. By utilizing KPCA, this method captures non-linear relationships in the data to enhance the representation of image features. TLBO is employed to optimize the rigid transformation parameters to improve the accurate alignment of source and target images. Extensive experiments are carried out on monomodal and multimodal medical images such as CT and MRI taken from the Harvard Brain ATLAS, Kaggle as well as in-house clinical dataset to demonstrate the effectiveness of the proposed approach. The proposed approach significantly outperforms state-of-the-art methods, with improvements of 44.37% in Root Mean Square Error (RMSE), 19.93% in Structural Similarity Index Measure (SSIM), 20.01% in Peak Signal-to-Noise Ratio (PSNR), and 16.21% in Cross Correlation (CC) quality assessment metrics.

The grain supply chain is crucial for any nation’s self-sustainability due to its huge impact on food security, economic stability, and the livelihoods of several people. The path grain takes from farmers to consumers is opaque and complicated, due to which consumers cannot trust grain quality and its origin. Although blockchain is widely used for fair and secure transactions between farmers and buyers, issues related to transparency and traceability in the grain supply chain, such as counterfeiting and middlemen involvement, have not been adequately addressed. To tackle these issues, a blockchain-based solution is proposed that unites farmers, warehouses, government central and state agencies, transporters, and food corporations on a single platform to enhance transparency, traceability, and trust among all parties. This system involves minting a non-fungible token (NFT) corresponding to each lot of grain approved by government officials. The NFT comprises grain quality, type, temperature data from sensors, weight, and ownership information, which updates as the grain lot moves across the supply chain from central agencies to state agencies and so on. NFTs enable stakeholders to track the grain lot from cultivation to end-users, providing insights into grain conditions and quality. An Internet of Things-based circuit is designed using a Digital-output relative humidity & temperature (DHT22) sensor, which offers real-time temperature and humidity readings, and geolocation coordinates are gathered from the GPS module across the supply chain. Farmers can directly interact with warehouses to sell grains, eliminating the need for middlemen and fostering trust among all parties. The proposed four-tier framework is implemented and deployed on the Ethereum network, with smart contracts interacting with React-based web pages. Analysis and results of the proposed model illustrate that it is viable, secure, and superior to the existing grain supply chain system.

Farming is a major sector required for any nation to become self-sustainable. Quality seeds heavily influence the effectiveness of farming. Seeds cultivated by breeders pass through several entities in order to reach farmers. The existing seed supply chain is opaque and intractable, which not only hinders the growth of crops but also makes the life of a farmer miserable. Blockchain has been widely employed to enable fair and secure transactions between farmers and buyers, but concerns related to transparency and traceability in the seed supply chain, counterfeit seeds, middlemen involvement, and inefficient processes in the agricultural ecosystem have not received enough attention. To address these concerns, a blockchain-based solution is proposed that brings breeders, farmers, warehouse owners, transporters, and food corporations to a single platform to enhance transparency, traceability, and trust among trust-less parties. A smart contract updates the status of seeds from a breeder from submitted to approved. Then, a non-fungible token (NFT) corresponding to approved seeds is minted for the breeder, which records the date of cultivation and its owner (breeder). The NFT enables farmers to keep track of seeds right from the date of their cultivation and their owner, which helps them to make better decisions about picking seeds from the correct owner. Farmers directly interact with warehouses to purchase seeds, which removes the need for middlemen and improves the trust among trust-less entities. Furthermore, a tender for the transportation of seeds is auctioned on the basis of the priority location locp, Score, and bid_amount of every transporter, which provides a fair chance to every transporter to restrict the monopoly of a single transporter. The proposed system achieves immutability, decentralization, and efficiency inherently from the blockchain. We implemented the proposed scheme and deployed it on the Ethereum network. Smart contracts deployed over the Ethereum network interact with React-based web pages. The analysis and results of the proposed model indicate that it is viable and secure, as well as superior to the current seed supply chain system.

In the last several decades, application of affine transformation and optimization approaches have received enough attention in the domain of monomodal/multimodal image registration. A novel and robust image registration method is proposed which leverages teaching learning-based optimization (TLBO) to obtain the optimal value of rigid transformation parameters. Further, speeded up robust features (SURF) framework is employed to extract features along with Random sample consensus (RANSAC) algorithm. Afterwards, projective transformation is used to obtain more accurate registered image. To remove noise, gaussian filter is used during pre-processing and then normalization is carried out. TLBO is used to identify the optimal geometric transformation parameters by considering the mutual information (MI) as an objective function. This method detects keypoints (features) using SURF then K-nearest neighbour (KNN) is employed to match the detected features. Furthermore, RANSAC eliminates false matches. The registered image with optimal value of rigid transformation parameters is obtained and then enhanced by using SURF-RANSAC followed by projective transformation. Experiments are conducted on Whole Brain ATLAS and KAGGLE datasets. Robustness of proposed approach is verified by the improvement in evaluation metric, such as structure similarity index measure (SSIM) by 8%, MI by 12.71% on monomodal and root mean square error (RMSE) by 41.44% on multimodal images as compared to recent state-of-the-art methods.

Mental health has become a major concern due to changing lifestyles and ever-increasing pressure at the workplace. Deadlines and goals are the prime reason for stress, which in turn leads to depression, anxiety as well as other mental illnesses. Hence, in the lure to improve the current situation, this paper proposed an ensemble stress detection mechanism that conveniently and accurately detects stress, depression as well anxiety. Few steps are conducted to detect the mental issue of any individual undertaking the test. The proposed ensemble mechanism comprises four basic detection modes: face detection, voice detection, Depression Anxiety Stress Scale (DASS), and a 22-parameter test. Face detection is a reliable source for detecting mental issues, whereas voice recognition confirms and aids the result provided by face detection. In addition, DASS test is a simple questionnaire conducted with a scaled answering system ranging from high to low, and finally, the 22-parameter test consists of 22 important physiological features of the patient. Experimental findings on different machine-learning datasets show that the proposed ensemble approach for stress detection is promising.

Imports and exports of a nation heavily impact its GDP, interest rates, exchange rate, and inflation rate. With the ongoing transition from traditional to digital methods, the procedure of importing-exporting goods also witnessed a trend in the paper-based traditional approach where the custom department's officers verify all of the paperwork in accordance with national customs law before approving imports and exports. However, this approach is extremely time-consuming and has a higher risk of manipulation/tampering with the documents to make illegal imports and exports happen. To digitalize the previous approach, the development of systems with centralized servers came into existence which could connect the exports and imports and thus, reduce the time factor in communication but increase the risk of data(documents) getting mutated or lost (in case of server crash). With the evolution of blockchain technology, an efficient alternative to providing solutions for the above-mentioned drawbacks in import–export exists. Security, encryption, and transactions amongst individuals (such as exporters, importers, banks, customs offices) that cannot be changed are all provided by blockchain technology. This paper presents a blockchain-based solution for automating Customs Clearance between trading countries by employing non-fungible tokens (NFTs) for Certificate of Origin (COO) and Letter of Credit (LCs) and communicating agreement files on the blockchain network using Inter Planetary File System (IPFS).