This paper presents the design and implementation of an Internet of Things (IoT)-enabled wheelchair system using the ESP32 microcontroller platform. The project aims to enhance the mobility and quality of life for individuals with limited mobility by integrating various sensors, connectivity options like Bluetooth And wifi, and a user-friendly mobile application. The ESP32 microcontroller is employed as the central processing unit to gather data from sensors, communicate wirelessly with a mobile app, and control the wheelchair's movement. The wheelchair is equipped with ultrasonic sensors to detect obstacles and ensure safe navigation.
This paper explores how blockchain technology can enhance collaborative Distributed Denial of Service (DDoS) mitigation strategies. As DDoS attacks become more complex and widespread, traditional mitigation approaches often require revision, underscoring the necessity for innovative solutions. The study explores how blockchain’s inherent characteristics—decentralization, transparency, and immutability—can be leveraged to create a robust, trust-based environment for sharing threat intelligence and coordinating defense mechanisms across multiple organizations. We will examine blockchain’s potential to facilitate real-time information exchange, improve attack detection, and enable coordinated responses among participating entities of collaborative ddos mitigation. The research will also address the security analysis of collaborative DDoS mitigation framework such as Decentralization, Hash based Threat Intelligence, SDN controller for load balancing, automated mitigation using smart contract. We will explore blockchain-based approaches’ potential benefits, limitations, and feasibility in DDoS mitigation through a literature review, theoretical analysis, and conceptual modeling. The findings of this study are expected to contribute significantly to the field of cybersecurity.
Now a days, cloud computing services are being used exponentially which in turn increases in multi-tenant distributed service(MTDS). Targeted attacks in MTDS propagated through side channel, shared memories, network access, and configuration. These attacks could be discovered after analyzing side channel data. This paper proposes a novel anomaly approach for virtual machine placement (VM) considering anomaly scores of VM and the CPU utilization of the servers. The result reveals that the proposed approach ensures secure VM migrations among the servers during VM placement, and also ensures that the MTDS environment remains secure.
Countless individuals around the world are impacted by breast cancer, making it a substantial global health challenge. Detecting this disease at an early stage is essential for effective treatment, and recent technological progress has provided fresh opportunities to enhance the precision of breast cancer diagnosis. Decentralized machine learning commonly known as federated learning is one of the privacy preservation technique, where machine learning models are trained locally and tested globally [1]. In our research we employed it for breast cancer detection while taking into account the privacy considerations of healthcare institutions. Decentralized machine learning (DML) commonly known as Federated Learning (FL) surpasses centralized machine learning (ML) in preserving privacy. In centralized ML, raw patient data is gathered and stored in a central location, posing privacy risks. Decentralised Machine Learning (DML), however, ensures privacy by sharing model insights and updates derived from local datasets, avoiding the transfer of identifiable patient information. Aggregation of the parameters collected by individual nodes plays crucial role in the performance of the global machine learning model. In this research we analysed the performance of commonly adopted aggregation algorithms FedAvg and FedProx under different parametric conditions. Comprehensive analysis of the performance metrics, includes accuracy and sensitivity. This comparison helps determine which approach offers superior diagnostic capabilities.
Cloud computing is the most emerging technology. It has the capability to provide various services like infrastructure, security, data management, databases, and network over the internet. Several cloud service providers in the market provide computing resources. Cloud brokering in cloud computing is a crucial part as it acts as a middleware between cloud users and cloud services providers. In this paper we aim to provide an overview on adaptive data management middleware for cloud brokers. To manage the heterogeneous data and other factors, policy-based task scheduling can be integrated with the cloud broker, which supports dynamic data placement and takes decisions and actions at the runtime considering the SLA (service level agreements) and QoS (quality of service) requirements without any manual involvement. By aggregation of data, the next task of scheduling and assigning priority will be taken carefully. At the end service requests should be fulfilled by utilizing minimum resources.
The advent of online shopping has revolutionized the way people procure goods, favoring the convenience and efficiency it offers over traditional stores. However, this transition to e-commerce comes with its own set of challenges. Instances of security breaches, where hackers exploit vulnerabilities in e-commerce platforms to steal sensitive user data, have become increasingly common. Additionally, concerns over privacy arise as some companies monetize user data, leaving individuals susceptible to various online threats such as phishing scams and spam communications.Another key disadvantage of current e-commerce systems is a lack of transparency in the supply chain, which frequently leaves consumers unsure about the legitimacy and condition of the products they buy. To address these issues, we propose the development of a decentralized marketplace. By leveraging blockchain technology, specifically Ethereum, and implementing smart contracts to govern transactions, we aim to create a platform where the supply chain is transparent to all involved parties. Instead of relying on traditional email addresses and passwords, users will utilize Decentralized Identifiers (DIDs) to authenticate themselves within the system. This innovative approach seeks to enhance security, protect user privacy, and instill trust in online transactions.
The integrity and efficiency of drug supply chain management represents pivotal concerns in contemporary pharmaceutical discourse. It is imperative to design innovative solutions that can overcome the existing challenges of drug supply chain such as, counterfeit drugs, supply chain vulnerabilities, and regulatory complexities. In response, this research explores the transformative potential of blockchain technology in fortifying and optimizing drug supply chain operations. The proposed Ethereum blockchain-based solution has procedure to avoid counterfeit drugs, and to trace the journey of drug. Our framework promises to engender trust, enhance regulatory oversight, and safeguard public health interests. We have conducted the security and cost analysis of executed smart contract to evaluate the effectiveness of contract.
Artificial Intelligence revolutionizes digital applications through simulation of human intelligence in machines, driving unprecedented progress in technology and decision-making. By adeptly processing large volumes of data, AI systems excel in pattern recognition, learning from experience, and autonomously making decisions. However, the present AI ecosystem suffers from data inequity, with major corporations dominating the market while smaller businesses still struggling. Access to AI data is the privilege of tech giants. In this paper, we propose DataHarbour, a blockchain-based marketplace that addresses issues with AI data access. DataHarbour's decentralized platform seeks to democratize data access and promote cooperation. Built on blockchain– a disruptive emerging technology that buttresses decentralized systems, it leverages smart contracts, incorporates data verification, and a reputation system. This paper also delineates the design and operation of AI data market place and portrays the system’s ability to foster a more inclusive AI environment. DataHarbour aims to accelerate AI innovation and close the data divide by facilitating safe data interchange and fostering integrity.
In the era of digital technology, the widespread availability of multimedia content has presented major challenges in confirming the genuineness and reliability of digital media. This paper introduces a novel technology that utilizes the InterPlanetary File System (IPFS) and Ethereum to authenticate multimedia content, ensuring its provenance and prohibiting tampering. The suggested approach brings together a decentralized ledger system with cryptographic hashing and smart contracts to create an immutable record of content ownership and modification history. By integrating cryptographic hashes of multimedia files into blockchain transactions, we establish a secure, transparent, and tamper-proof mechanism for tracking content authenticity. Our methodology improves confidence and dependability in digital media by establishing a strong structure for creators, distributors, and consumers to authenticate the authenticity of multimedia materials. Experimental results confirm the effectiveness and efficiency of our system in preserving the integrity of various types of multimedia content, laying the path for a wider utilization areas such as digital rights management, content distribution, and forensic analysis, etc.
E-ticket generation for tourist services aspires to offer a comprehensive solution that encompasses all facets of a tourist's expenditure when embarking on their desired journeys. The project underscores the importance of E-ticket generation and the development of a website that fulfils the critical need for secure and efficient E-ticket services at tourist destinations. E-ticket validation through QR codes emphasizes both time efficiency and security, providing a secure means to access various facilities at tourist destinations using E-tickets.The project consists of a user interface (UI) meticulously crafted using JavaScript, ensuring a seamless and captivating user experience. To optimize response times, we have incorporated APIs. We have leveraged the powerful MERN stack, highlighting the integration of MongoDB, Express.js, React.js, and Node.js, ensuring the project's robust functionality in both frontend and backend development.The project effectively addresses common challenges and serves as a dependable resource for planning and savouring memorable journeys to diverse destinations.
Serverless architectures are becoming super popular with demanding growth and better functionality. This architecture is a fantastic option for building and launching applications in the cloud. Why Serverless? Well, they can scale up easily, save some costs, and help reduce the time and amount of work required to perform a particular operation. But as more folks dive into serverless computing, some big security problems have come to light. One of the big worries that is not easily identified is the Denial of Wallet (DoW) attacks. This type of attack aims to drain resources and hit cloud service providers hard in their pockets. To counter DoW attacks, we need to grasp their characteristics and how they behave in serverless environments. This paper takes a deep dive into our way of preventing this attack by creating a dataset designed for detecting the DoW attack in the serverless architecture. The dataset which will be given by us will play a crucial role in helping researchers and experts. It helps build and improve strong techniques for spotting DoW attacks. Our work aims to secure containerized applications against ever-changing cyber threats by understanding these threats and enabling smarter reactions and approaches.
This paper brings about the integration of biometric authentication with QR code technology as a new approach to ensuring security and providing convenience for users. We will investigate the design and implementation of an adaptive biometric authentication system using QR codes, which will come after recent improvements in the two domains. The literature review considered herein highlights the importance of biometric features and QR code security measures. This will ensure that fingerprint biometrics and QR codes are used together in ensuring user authentication to prevent unauthorized access. The practical implementation of the system shall be demonstrated through a detailed methodology on data conversion, user interface development, and encryption algorithms. The hardware architecture used includes Arduino UNO and Fingerprint module R307, which facilitate the collection of fingerprint data, while the Fernet Algorithm is used in ensuring secure data transmission. The system is applied in access control, financial services, health, and government operations, among others. The results obtained are promising, referring to both security improvement and user acceptance. Future research directions are envisioned for further refinement and extension of the system's capacity.
Intellihealth is an electronic health record system that operates online with a decentralized structure, setting it apart from conventional systems that store data locally. This decentralized approach relies on blockchain technology to ensure the security of patient data. Within this system, patients can easily access their medical information, including their history, prescriptions, and recommendations, but they are not granted editing privileges. Doctors, on the other hand, can add reports, access patient medical histories, and offer medical advice. Every transaction requires the consent of both parties involved. The system's owner, typically a hospital representative, has the authority to add patients and doctors to the database. Upon logging in, patients are assisted by a chatbot to navigate the system effectively. Additionally, a predictive analysis system featuring various machine learning models has been integrated. This allows patients to upload medical reports, such as X-rays and MRI scans, for the detection of diseases like pneumonia or brain tumors, providing them with a preliminary diagnosis. Intellihealth is a web application compatible with all major web browsers. It utilizes Angular with TypeScript for the front-end, IPFS for the database, Django with python for Back-end and blockchain software like Truffle and Ganache. Account sign-in on the decentralized platform is facilitated through a Metamask wallet and is linked to the user's Aadhar Number as the primary identifier.
This paper proposes the integration of blockchain technology, specifically Hyperledger Fabric, to address challenges in organ donation management and electronic health records (EHRs) sharing. Blockchain's inherent features such as transparency, immutability, and decentralization are leveraged to streamline organ procurement processes, mitigate fraud, and build trust among stakeholders. Our framework includes organ tracking, consent verification, and secure data sharing among medical institutions through smart contracts, ensuring automation while maintaining data privacy and patient confidentiality. This solution is designed to accommodate the modern trend of seeking healthcare services from multiple providers, providing patients ownership of their EHRs and enabling seamless transfer across hospitals. By empowering healthcare providers with a unified system for organ donation integrity and EHR management, our approach aims to enhance collaboration, reduce administrative burdens, and improve the efficiency of organ transplantation systems, all while ensuring data security and promoting continuity of care.
These days cyberattacks are growing rapidly and becoming increasingly complex. Along with that data privacy is a very big concern. So, building systems powered by technologies like Machine Learning and Blockchain is crucial. There is a need to develop networks that can defend themselves, and keep the data confidential. To address this challenge, Federated Learning can be a powerful solution. This paper proposes a groundbreaking approach for setting up a intrusion detection system combining Federated Learning and Blockchain technology. Traditional intrusion detection systems have their limitations. They rely on pre-defined signatures to identify malicious activity. But these are vulnerable to attack. Federated Learning leverages large amounts of data distributed in the client devices and allows multiple clients to work hand-in-hand in order to train a Machine Learning model without the need to share sensitive data. Firstly, data collection occurs at various clients after which each client processes the data locally. Then various pre-processing and data standardization tasks are performed like feature extraction and normalization. Finally the updates are sent to a central system for aggregation. Now, a new version of global model is distributed back to all the client devices. In this project, we have also used blockchain technology which helps in enhancing security, trust, and reliability in the system. Continuous model updates are made possible without compromising system integrity. Finally, the decision is based on the network data, analyzed by the trained model, to determine if the intrusion is normal or an anomaly. We aim to build a system that is self-reliant in overcoming security threats while keeping data privacy in mind.
The agriculture and food supply chain faces significant hurdles, including vulnerabilities in centralized systems, insider manipulation, single-point failures, and high transaction costs. To tackle these challenges, blockchain technology has emerged as a transformative solution.In leveraging blockchain for agriculture, the focus is on enhancing transparency, traceability, and accountability. This involves establishing secure and tamper-proof records that are accessible to farmers, suppliers, and consumers. These records offer valuable insights into the origin, quality, and safety of agricultural products, effectively countering food fraud and fostering consumer trust. Additionally, blockchain streamlines administrative processes, lowers transaction costs, and widens financial access for small-scale farmers.This paper delves into the practical implementation of blockchain alongside smart contracts in the agriculture and food supply chain. It investigates how smart contracts can bolster the effectiveness of blockchain in overcoming the sector’s challenges. Furthermore, the paper provides insights into potential enhancements and optimizations, aiming to refine the integration of blockchain and smart contracts for a more resilient and efficient agricultural supply chain system.
In today’s digital age, the verification of documents is a critical aspect of numerous transactions and processes across various industries. However, traditional methods of document verification are often susceptible to fraud, tampering, and inefficiencies. In response to these challenges, this project proposes a novel solution leveraging blockchain technology to enhance the security and integrity of document verification processes. The objective of this project is to develop a decentralized system for document verification using blockchain, aiming to provide a secure, transparent, and tamper-proof platform for verifying the authenticity of digital documents. By harnessing the immutable and transparent nature of blockchain, the proposed system seeks to address the shortcomings of traditional verification methods and establish a trusted environment for document verification. Key features of the proposed system include the use of cryptographic hashing techniques to create unique digital fingerprints for each document, which are then stored on a blockchain network. Smart contracts are utilized to automate the verification process, enabling seamless and transparent verification without the need for intermediaries.The methodology involves the design and implementation of the blockchain-based document verification platform, incorporating essential components such as user authentication, document uploading, verification, and retrieval functionalities. The system is developed using Ethereum, a popular blockchain platform, and is deployed on a test network for evaluation and testing. Results from experimental testing demonstrate the effectiveness and reliability of the proposed system in providing tamper-proof document verification services. Furthermore, comparative analysis with existing centralized solutions highlights the advantages of blockchain-based verification in terms of security, transparency, and efficiency.
In this modern age where everything is developing and moving forward to achieve new heights, Fraud and scam-related cases are also increasing. That’s why we need to be more careful than ever before. nowadays whether we want to open a bank account or use a financial app we first need to verify ourselves by doing e-KYC. But the problem is this e-KYC verification is needed in so many sectors and we have to do it multiple times. This paper shows how this situation can be solved by using a blockchain-based e-KYC (Electronic Know Your Customer) system. This will be a one-stop solution for e-KYC, where people need to do KYC only one time and then they can use it in multiple organizations. Because of blockchain technology, our system will be transparent, decentralized, efficient, and trustworthy. Where users can choose whom to give the e-KYC information and can transfer all the information or the state of the information within a second. The proposed e-KYC systems’ motive is to create a more secure and widespread digitalized ecosystem that will bring benefits to any country.
The persistent issue of food grain wastage due to inefficient storage, transportation, and management at Fair Price Shops within the Public Distribution System (PDS) demands innovative solutions. A decentralized PDS model utilizing blockchain technology is proposed to enhance transparency and accountability in the supply chain. Blockchain provides precise monitoring across food grain procurement, transportation, and storage processes, yielding valuable insights into potential areas of wastage and facilitating efficient auditing. Enhanced traceability within this system helps minimize wastage, detect suspicious activities, and streamline auditing and record-keeping in the food supply chain. The adoption of blockchain fosters a culture of accountability and responsible practices while introducing automation to the distribution process. Additionally, blockchain's proven capabilities in improving food traceability address existing challenges related to system reliability, scalability, and accuracy. Integrating blockchain with IoT further strengthens data integrity and boosts efficiency. Examples such as blockchain-enabled crop insurance and platforms like AgroChain demonstrate its potential to transform agriculture through increased transparency, efficiency, and trust. However, challenges like scalability, energy consumption, and infrastructural constraints need to be addressed for widespread implementation. The evolution of the PDS, particularly under the Targeted Public Distribution System (TPDS) and the National Food Security Act (NFSA), underscores the need for enhanced supply chain management to ensure food security. Blockchain technology offers a promising approach to improving the reliability, accountability, and efficiency of the PDS, promoting the equitable distribution of essential goods.
Northern Bangladesh is home to the most lychee growing, which has a major economic impact. Lychee output and quality are reduced by various leaf and fruit diseases. Deep learning is used to construct a disease detection system for lychee diseases to detect them early and accurately. We correctly identified 6,000 photos of healthy and unhealthy lychee foliage and fruits. Six deep learning algorithms—VGG16, CustomCNN, MobileNet, InceptionV3, ResNet50, and Vision Transformer— classified the photos. Normalization and augmentation improved model resilience during data preparation. F1-score, recall, accuracy, and precision were utilized to train and evaluate the models. Vision Transformer (ViT) scores 99.91% in accuracy, recall, and F1-score, outperforming the other models. This achievement shows that ViT can better recognize complex lychee disease traits. The ViT algorithm for disease detection can help Bangladeshi farmers identify illnesses quickly and accurately. Reduced lychee loss and improved fruit quality and attractiveness should boost lychee sales in domestic and international markets.
