This paper presents a privacy-preserving intrusion detection architecture tailored for smart home environments, addressing the dual challenge of maintaining data confidentiality while enabling accurate anomaly detection. The proposed system replaces conventional raw data analysis with a proof-driven mechanism leveraging Zero-Knowledge Proofs (ZKPs). Behavioral patterns from smart devices such as motion sensors, door contacts, and environmental monitors are abstracted into cryptographic representations, which are then processed by a zk-SNARK-compatible machine learning model. Inference results are accompanied by cryptographic proofs verifying the correctness of each decision without disclosing the input data. A private blockchain layer, implemented using Ethereum smart contracts, records event hashes, proof metadata, and decision outcomes to ensure tamper-evident logging and automated response handling. Experimental simulations on synthetic home automation datasets demonstrate that the architecture achieves over 92% anomaly detection accuracy while ensuring zero exposure of raw sensor streams. The system also exhibits low-latency proof generation (~400 ms) and end-to-end response time under 1.2 seconds, confirming its suitability for real-time smart home applications.
This research focuses on the design and development of a blockchain-based plastic waste tracking system aimed at enhancing transparency, efficiency, and accountability in plastic waste management. The system utilizes Hyperledger Fabric as a permissioned blockchain platform and integrates smart contracts to manage transactions between organizations, including waste generators, collectors, sorting warehouses, and final processing warehouses. This system records each stage of the plastic waste journey, from creation to final processing, in a permanent, transparent, and immutable manner. The testing results demonstrate that the system can accurately record the status and history of waste, manage transfers between organizations, and process plastic waste into recycled products. Moreover, the system shows a significant potential for carbon emission reduction, with an estimated reduction of up to 50% compared to traditional plastic waste management methods, such as incineration or landfilling. The study also explores how the implementation of blockchain can support global efforts in mitigating the environmental impacts of plastic waste. The blockchain-based system also provides real-time monitoring, ensuring that each transaction is verified and recorded immediately, contributing to more effective management. The implementation of smart contracts further guarantees that waste-related activities are executed automatically when predefined conditions are met, reducing administrative overhead. The study also explores how the implementation of blockchain can support global efforts in mitigating the environmental impacts of plastic waste. Ultimately, this system presents a scalable solution that could be adopted in various regions to improve global waste management strategies.
Rajasekaran P, M. Duraipandian, Johny Renoald Albert
The increasing rate of growth of the Internet of Things (IoT) in cloud-hospitality health has brought in data storage, transmission, and security challenges with the advent of quantum-enabled threats. Traditional compression methods struggle with computational inefficiency and the threat of invasion of privacy. This paper proposes a Quantum-Enhanced Zero-Knowledge Healthcare Compression Network for solving these challenges by combining Zero-Knowledge Proofs and Quantum-Inspired Deep Learning. The main goal is to provide privacy-preserving, efficient data compression along with optimizing computation costs and safeguarding sensitive healthcare records. Drawbacks in present cryptographic techniques, e.g., high computational costs in homomorphic encryption and scalability limitations in blockchain, require a novelty Adaptive Quantum-Assisted Zero-Knowledge Verification and Quantum Fusion-AutoCNN Encoder (QF-AutoCNN) to overcome this research. This work’s originality lies in combining Quantum zk-SNARKs, Hybrid Quantum Feature Encoding, and Reinforcement Learning-Based Challenge Optimization to provide better security, compression ratio, and verification efficiency. Experimental results show better accuracy (0.9816), improved F-measure (0.9709), and less computational overhead, better than other current methods such as convolutional neural networks-encryption and proxy re-encryption. This research greatly adds to safe cloud healthcare IoT by lessening privacy threats, maximizing storage space, and minimizing processing time, guaranteeing real-time handling of medical information.
The COVID-19 pandemic has accelerated the adoption of digital health solutions such as telemedicine, Internet of Medical Things (IoMT), and AI-based diagnostics, enabling remote monitoring and contactless consultations. While IoMT devices—including wearable sensors and implantables—have enhanced continuous healthcare delivery, they have also introduced challenges related to security, privacy, interoperability, and latency. Traditional blockchain frameworks, though effective in ensuring decentralized trust and immutability, are resource-intensive and unsuitable for constrained IoMT environments. To address these limitations, this study proposes a Lightweight Blockchain–IoMT framework tailored for secure remote healthcare in the post-pandemic era. The proposed architecture follows a three-tier design: (i) the IoMT Device Layer for real-time physiological data collection, (ii) the Fog/Edge Layer functioning as blockchain gateways for authentication and pre-processing, and (iii) the Cloud Layer for storage, analytics, and decision support. By incorporating lightweight consensus mechanisms such as Proof-of-Authentication (PoAh) or Delegated Proof-of-Stake (DPoS), the system minimizes latency and energy consumption compared to Proof-of-Work. Security is reinforced through elliptic curve cryptography (ECC) and smart contracts, ensuring data confidentiality, integrity, and controlled access, while complying with global standards such as HIPAA and GDPR. Experimental analysis demonstrates that the lightweight blockchain–IoMT framework outperforms conventional blockchain models in transaction throughput, scalability, and energy efficiency. Moreover, the integration of machine learning within the cloud layer supports predictive analytics and personalized care.
This paper presents the design and implementation of a blockchain-secured system for monitoring driver sobriety and real-time geolocation. The proposed platform integrates a Modular Sensor Battery (MSB) for detecting alcohol concentration in exhaled air, a centralized Data Collection Platform (DC Platform) for real-time data visualization and storage, and a complementary physiological monitoring device—the IoT Fit-Bit Smart Band (IFSB)—which captures heart rate and blood oxygen saturation as alternative indicators when breath-based sensing may be compromised. The MSB, the DC Platform, integration with the IoT FitBit Smart Band, and the blockchain-based data management architecture represent the authors’ direct contribution to both the conceptual design and technical implementation. These elements are introduced as part of a unified, fully integrated system designed to enable non-invasive sobriety monitoring and secure data integrity in vehicular contexts. To ensure data authenticity, a custom Ethereum smart contract stores cryptographic hashes of sensor readings, enabling decentralized, tamper-evident verification without exposing sensitive medical information. The system was validated in a controlled experimental environment, confirming its operational robustness and demonstrating its potential to improve road safety through secure, real-time sobriety detection and geolocation tracking.
The smart grid is the next evolution of electrical power systems, a continuation of the old grids that involves a mix of digital and traditional power grid technologies to allow the potential to communicate in both directions, decentralized energy production and real-time monitoring. However, such a connection exposes it to cyber attacks, data fraud, and unauthorized access as well. Blockchain technology is one of these technologies because it is transparent, immutable, and decentralized to overcome these security obstacles. In this paper, an overview of blockchain technology smart grid security, architecture, consensus algorithm, and application are presented. Some of the most notable blockchain works in the smart grid include secure energy trading, decentralised identity management, detecting attacks and preserving privacy. When applied in smart grids, reviewed blockchain protocols also comprise Proof of Work (PoW) and Proof of Stake (PoS) along with Practical Byzantine Fault Tolerance (PBFT). Top of that, there are hybrid types of blockchain such as artificial intelligence (AI) and the Internet of things (IoT) that are also covered as the next picture to enable the system to become more scalable and interoperable. Power consumption, time wastage, and regulation hurdle is greatly considered. This paper has concluded that blockchain is a bottom-up technology, which can cause smart grid infrastructures to be much more resilient, transparent, and efficient.
Bitcoin's limited scripting capabilities and lack of native interoperability mechanisms have constrained its integration into the broader blockchain ecosystem, especially decentralized finance (DeFi) and multi-chain applications. This paper presents a comprehensive taxonomy of Bitcoin cross-chain bridge protocols, systematically analyzing their trust assumptions, performance characteristics, and applicability to the Artificial Intelligence of Things (AIoT) scenarios. We categorize bridge designs into three main types: naive token swapping, pegged-asset bridges, and arbitrary-message bridges. Each category is evaluated across key metrics such as trust model, latency, capital efficiency, and DeFi composability. Emerging innovations like BitVM and recursive sidechains are highlighted for their potential to enable secure, scalable, and programmable Bitcoin interoperability. Furthermore, we explore practical use cases of cross-chain bridges in AIoT applications, including decentralized energy trading, healthcare data integration, and supply chain automation. This taxonomy provides a foundational framework for researchers and practitioners seeking to design secure and efficient cross-chain infrastructures in AIoT systems.
The rapid expansion of the Internet of Things (IoT) is driving the integration of billions of connected devices across various domains, including healthcare, transportation, and smart urban systems. Although this proliferation offers considerable advantages in terms of functionality and operational efficiency, it also brings to the forefront a range of pressing concerns, particularly in relation to security, reliability, and privacy. These challenges are largely rooted in the decentralized and dynamic architecture of IoT ecosystems. In this context, trust and reputation mechanisms have become increasingly vital for enabling secure and reliable interactions between devices and users. This paper examines recent advances in trust management models tailored to IoT environments, with a focus on approaches leveraging blockchain technologies, machine learning techniques, and edge or fog computing paradigms. We assess the practical implications of these solutions, discussing both their strengths and inherent limitations. Furthermore, we identify key open issues such as scalability, data protection, and interoperability across platforms, and we outline potential research directions to support the development of more robust and adaptable trust frameworks for the evolving IoT landscape.
Jonas Lopes de Vilas Boas, Ygor S. Costa, Rodrigo da Rosa Righi, Antônio Marcos Alberti · 5 authors
Reliable vaccine tracking and monitoring during transport and storage are essential to ensure dose effectiveness while minimizing waste. However, current solutions face challenges related to reliability, immutability, security, transparency, flexibility, extensibility, patient support, trust, and cost. Centralized systems are vulnerable to fraud, tampering, and manipulation, often relying on manual service contracts and lack of attested IoT devices to ensure data authenticity. Moreover, most existing platforms do not provide tamper-proof, near real-time monitoring, resulting in operational vulnerabilities and increased costs. This article presents Coldnet, a novel architecture for vaccine tracking and tracing that addresses these issues by: (i) integrating IoT device attestation with the registration of immutable data and flexible monitoring attributes; (ii) using Blockchain-based smart contracts to automatically manage tracking and monitoring clauses, improving security and enabling dynamic rule management; (iii) offering intuitive interfaces to support patient access to delivery information; and (iv) deploying an affordable, user-friendly IoT prototype to monitor and report vaccine status. A case study demonstrates Coldnet's feasibility, with an average delay of 20 seconds for recording and checking conditions — suitable for real operations. A simulation evaluating scalability and the impact of IoT attestation shows transaction costs of US$1.51 for ten vaccine batches with five monitored properties each, a cost deemed acceptable for the added features. Execution delays remained stable (0.85–0.92 seconds), with negligible impact from attestation. Coldnet contributes to reliable vaccine logistics, improving public health efforts by strengthening trust, transparency, and data integrity in vaccination campaigns.
Thi Thanh Thuy, Minh‐Ky Nguyen, Thuyet D. Bui, Hoang Phan Hải Yen · 10 authors
This paper explores how blockchain technology, widely known as the backbone of cryptocurrencies, can be harnessed to address limitations of traditional water quality monitoring (WQM) systems. Blockchain offers a decentralized, tamper-proof ledger that enables secure, transparent, and traceable data management across distributed networks. When applied to water quality monitoring, blockchain facilitates real-time data acquisition, enhances data integrity, and enables smart contracts for automated regulatory compliance and alerts. These features not only improve the accuracy and efficiency of WQM systems but also build public trust in the reported data. Key insights from current research and pilot applications highlight blockchain’s capacity to integrate with IoT devices for real-time sensing, support adaptive water governance, and empower local stakeholders through decentralized control and transparent access to information. The implications for policy and practice are significant: blockchain-based WQM can support stronger regulatory enforcement, encourage cross-sector collaboration, and provide a robust digital foundation for sustainable water management in smart cities and rural areas alike. As such, this review paper positions blockchain as a transformative tool in the digital transition toward more resilient and equitable water management systems.
Remote service delivery and automation using Blockchain and Internet of Things (IoT) are revolutionising healthcare operations. Enhancing healthcare data interchange and providing real-time treatment is becoming more difficult due to the exponential growth of patient populations around the globe. It is still very difficult to develop a digital healthcare platform that is entirely decentralised, secure, trustworthy, interoperable, and scalable, even if existing studies have improved these platforms to improve patient outcomes and reduce hospital visits. This paper proposes a robust and scalable healthcare architecture using Blockchain smart contracts and IoT, with the integration of the InterPlanetary File System (IPFS). By securely storing sensitive medical records, the framework enhances data privacy and interoperability for patients as well as healthcare professionals. A device proxy monitors potentially vulnerable IoT devices and uses cryptography to ensure that data remains private. Experimental evaluations of the system’s performance have focused on key factors, including healthcare record upload, download, access, and mining times. The results show that public healthcare systems based on the Blockchain considerably boost efficiency and performance by integrating IPFS.
This article explores how the integration of Artificial Intelligence (AI), Machine Learning (ML), Web3.0, Blockchain, Metaverse, and Non-Fungible Tokens (NFTs) will revolutionize various aspects of public life globally over the next decade. We introduce novel perspectives such as AI-driven decentralized governance, blockchain-based universal basic income, and metaverse-enabled global education platforms. These technologies will transform global supply chains through AI-driven forecasting and blockchain-verified logistics, ensuring transparency and efficiency. Healthcare will advance with AI-powered telemedicine and personalized treatments, reducing disparities in underserved regions. Autonomous systems will enhance urban mobility and disaster response, fostering sustainable smart cities. Web3.0 will empower users with decentralized digital identities and data sovereignty, redefining advertising and social media through token-based models. Blockchain will secure academic credentials, streamline insurance, and enable transparent philanthropy, while carbon credit markets promote sustainability. The metaverse will revolutionize remote work, healthcare consultations, and cultural preservation through immersive virtual environments. NFTs will democratize real estate and creative economies, enabling tokenized ownership and secure voting systems. Synergistically, these technologies will create decentralized e-commerce, disaster response systems, and virtual innovation hubs, fostering equitable digital ecosystems. However, challenges like digital divides, AI biases, and blockchain scalability must be addressed to ensure inclusive adoption. This article envisions a future where these advancements redefine governance, economies, and social interactions, paving the way for an innovative, equitable global society. AI-driven avatars and decentralized AI training platforms will further enhance virtual collaboration, while tokenized cultural assets empower communities, ensuring a resilient, inclusive digital future.
Journal of Theoretical and Applied Information Technology
Digital identity management that incorporates blockchain technology provides a safe and effective way to enhance safety systems. Typical issues with older methods of managing identities include data breaches, identity theft, and unauthorized access. This encrypted, decentralized, and irreversible blockchain method ensures 100% accurate identification verification by eliminating any weak points and consolidating power. By further automating the authentication process, smart contracts aim to boost transparency while lowering fraud risks. This paper focuses on how safety-critical settings may leverage blockchain-based digital identity management systems to provide data accessibility, integrity, and privacy. In addition to assisting corporations in meeting and exceeding security laws, blockchain, a distributed ledger technology, gives individuals more control over their data. The suggested method discourses problems like interoperability, scalability and regulatory constraints, paving the way for a more secure and reliable structure. The primary emphasis of this research is on the revolutionary potential of blockchain technology as it pertains to identity management. Therefore, current safety systems will have enhanced dependability, security and efficiency.
Rozina Chohan, Gulshan Naheed, Dr Khakoo Mal, Muhammad Jalil Afridi · 6 authors
The growing popularity of the Internet of Things (IoT) networks has called out a growing need in sound and scalable security. The blockchain technology and its features of decentralization, immutability, and transparency can offer an effective solution to manage key security vulnerabilities, namely, authentication, data integrity, and privacy. This paper discusses the use of blockchain to IoT security, namely: decentralized authentication systems, data integrity management, and the challenge of scalability of large IoT networks. The findings indicate that despite the fact that blockchain can greatly improve security by guaranteeing data integrity and scheme-level device authentication, the performance of blockchain faced with output times of heightened issuance, slow transaction processing, and network constraints only emerge as network sizes rise. It has been proven that Proof of Stake (PoS) performs better than Proof of Work (PoW) regarding energy consumption, transaction latency, and time of recovery after failures due to attacks. Nonetheless, the paper underlines that streamlining blockchain protocols can help to mitigate performance stalls during massive IoT usage. The paper shows that, despite blockchain offering a secure basis to IoT, refinements in autonomous agreements, privacy safety nets, and scalability methods are required to make it an opportunity with broad IoT implementations.
Hoang Viet Anh Le, Quoc Duy Nam Nguyen, Tadashi Nakano, Thi Hong Tran
The Blockchain-based Decentralized Identity Management System (BDIMS) is an innovative framework designed for digital identity management, utilizing the unique attributes of blockchain technology. The BDIMS categorizes entities into three distinct groups: identity providers, service providers, and end-users. The system’s efficiency in identifying and extracting information from identification cards is enhanced by the integration of artificial intelligence (AI) algorithms. These algorithms decompose the extracted fields into smaller units, facilitating optical character recognition (OCR) and user authentication processes. By employing Merkle Trees, the BDIMS ensures secure authentication with service providers without the need to disclose any personal information. This advanced system empowers users to maintain control over their private information, ensuring its protection with maximum effectiveness and security. Experimental results confirm that the BDIMS effectively mitigates identity fraud while maintaining the confidentiality and integrity of sensitive data.
This paper proposes the design of electronic medical record system supported by multimedia communication based on blockchain technology. Blockchain technology ensures the secure storage and sharing of patient information through distributed ledger and smart contract algorithm. In this system, smart contracts are used to automatically execute cross-institutional data access control and audit functions to ensure the transparency and compliance of data access. At the same time, this paper introduces multimedia communication technology to support the efficient transmission and sharing of medical data, especially in diagnostic images, videos and voice. Simulation results show that the electronic medical record system based on blockchain has significantly improved data processing efficiency, security and reliability compared with traditional systems.
Shilpa Kottapally, Sr. Software Development Engineer, Adjudication - Rxclaim developement Application, CVS Health, 2100 E lake cook road , Buffalo grove Illinois 60047, USA
International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
In recent years, several research and development initiatives have focused on developing secure and trustworthy systems for the healthcare industry via pervasive and mobile healthcare (mHealth) solutions. State-of-the-art mHealth solutions primarily rely on centralized storage, such as cloud computing servers, which may escalate the maintenance costs, require ever-increasing storage infrastructure, and pose privacy and security risks to the health-critical data produced, consumed, and transmitted over ad hoc networks. To overcome these limitations, we conducted this study intending to synergize mobile computing (devices to process health-critical data) and blockchain technology (infrastructure to secure storage and retrieval of health-critical data), specifically addressing data security and privacy using a blockchain mHealth system. The research employs an incremental method by (i) developing a framework that acts as a blueprint to architect blockchain-enabled mHealth systems, (ii) implementing a suite of algorithms as a proof-of-concept to automate the framework, and (iii) experimental evaluations to validate the scalability, computation, and energy efficiency of the proposed solution. The proposed framework has been implemented as a frontend using a mobile application interface that exploits the backend via the InterPlanetary File System (IPFS) system and Ethereum blockchain for secure management of mHealth data. We use a case-study-based approach demonstrating how health units, medics, and patients can securely access and distribute health-critical data. For evaluation, we deployed a smart contract prototype on the Ethereum TESTNET network in a Windows environment to test the proposed framework. Results of the evaluation indicate (a) scalability with query response time (range: 10–41 ms), (b) computational performance (CPU utilization: 1.5% – 2.5%), and (c) energy efficiency (gas consumption: 40000 units for 1000 bytes). The proposed solution – framework, algorithms, and experimental evaluation – aims to advance state-of-the-art architecting and implementing cybersecurity mHealth solutions using blockchain technology.
Herman Zahid, Adil Zulfiqar, Muhammad Adnan, Muhammad Sajid Iqbal · 7 authors
This review explores the transformative architecture of Smart Grid 3.0 by integrating cutting-edge technologies. It presents novel architectural frameworks to transform nanogrid, microgrid, and VPP topologies to their Grid 3.0 counterparts. This study systematically analyzes the application of advanced algorithms and technologies across all hierarchical subsystems—nanogrid 3.0, microgrid 3.0, VPP 3.0, and Smart Grid 3.0. These digital technologies have transformative capabilities. The digital twins can perform real-time monitoring, simulation, and predictive analysis; blockchain ensures secure, decentralized energy transactions; and the metaverse creates immersive, interactive environments for system management. This review also explores the role of AI in power grid which is to optimize energy scheduling, fault detection, and energy management. This paper adds to the literature by systematically addressing subsystems of Smart Grid 3.0, including energy generation, transmission, distribution, communication, and storage. Challenges such as interoperability, scalability, data integrity, and cybersecurity are discussed, and solutions are proposed which highlights the need of interdisciplinary approach. These include cyber-attack detection and mitigation mechanisms, advanced simulation tools, and robust policy frameworks. A thorough review of literature enabled this paper to present practical implementation strategies and real-world examples of digital technologies integrated smart grids. By integrating these technologies across hierarchical energy systems, this study establishes a foundation for future research in transforming conventional smart grid infrastructure into a resilient, efficient, and interconnected cyber-physical energy network called Smart Grid 3.0 as the peak of this evolution so far.
Background: With the enhanced data amount being created, it is significant to various organizations and their processing, and managing big data becomes a significant challenge for the managers of the data. The development of inexpensive and new computing systems and cloud computing sectors gave qualified industries to gather and retrieve the data very precisely however securely delivering data across the network with fewer overheads is a demanding work. In the decentralized framework, the big data sharing puts a burden on the internal nodes among the receiver and sender and also creates the congestion in network. The internal nodes that exist to redirect information may have inadequate buffer ability to momentarily take the information and again deliver it to the upcoming nodes that may create the occasional fault in the transmission of data and defeat frequently. Hence, the next node selection to deliver the data is tiresome work, thereby resulting in an enhancement in the total receiving period to allocate the information. Methods: multi-node data repetition. Blockchain is involved in offering a transparency to the application of transmission. A simultaneous multi-threading framework confirms quick data channeling to various network receivers in a very short time. Therefore, an advanced method to securely store and transfer the big data in a timely manner is developed in this work. A deep learning-based smart contract is initially designed. The dilated weighted recurrent neural network (DW-RNN) is used to design the smart contract for the Ethereum blockchain. With the aid of the DW-RNN model, the authentication of the user is verified before accessing the data in the Ethereum blockchain. If the authentication of the user is verified, then the smart contracts are assigned to the authorized user. The model uses elliptic Curve ElGamal cryptography (EC-EC), which is a combination of elliptic curve cryptography (ECC) and ElGamal encryption for better security, to make sure that big data transfers on the Ethereum blockchain are safe. The modified Al-Biruni earth radius search optimization (MBERSO) algorithm is used to make the best keys for this EC-EC encryption scheme. This algorithm manages keys efficiently and securely, which improves data security during blockchain operations. Results: smart contracts.
Manuel Jaramillo, Diego Carrión, Jorge Muñoz, Luis Tipán
This study presents a systematic bibliometric review of digital innovations in renewable energy-oriented power systems, with a focus on Blockchain, Artificial Intelligence (AI), the Internet of Things (IoT), and Data Analytics. The objective is to evaluate the research landscape, trends, and integration potential of these technologies within sustainable energy infrastructures. Peer-reviewed journal articles published between 2020 and 2025 were retrieved from Scopus using a structured search strategy. A total of 23,074 records were initially identified and filtered according to inclusion criteria based on relevance, peer-review status, and citation impact. No risk of bias assessment was applicable due to the nature of the study. The analysis employed bibliometric and keyword clustering techniques using VOSviewer and MATLAB to identify publication trends, citation patterns, and technology-specific application areas. AI emerged as the most studied domain, peaking with 1209 papers and 15,667 citations in 2024. IoT and Data Analytics followed in relevance, contributing to real-time system optimization and monitoring. Blockchain, while less frequent, is gaining traction in secure decentralized energy markets. Limitations include possible indexing delays affecting 2025 trends and the exclusion of gray literature. This study offers actionable insights for researchers and policymakers by identifying converging research fronts and recommending areas for regulatory, infrastructural, and collaborative focus. This review was not pre-registered. Funding was provided by the Universidad Politécnica Salesiana under project code 005-01-2025-02-07.
In the contemporary digital age, education is no longer limited to traditional educational environments. Many educational institutions shifted to depend on the smart learning process but expressed concern about this solution due to its various challenges in securing the learning process and learners' data. By virtue of the most recent technologies like blockchain and artificial intelligence, which played a significant role in solving many challenges that faced the educational sector and overcoming issues like fake certificates, manipulation, tracking learners' activities, and predicting learners' academic performance. The study proposed a smart framework based on blockchain and deep learning to enhance smart learning processes and provide solutions for challenges in the field. The framework is intended to store the learner's data on the blockchain through the interplanetary file system and reap the benefits of securing the learner's data and ensuring its integrity, as well as ensuring the confidentiality and authentication of the users through the wallets that are created on the Ethereum private blockchain platform. Then apply the deep learning model to this secured data to predict the learner's performance. The smart contract functions also play a role in enabling the university to issue learners' certificates that are stored on the blockchain to be available and verifiable by all the nodes in the network. Based on the experimental results, deep neural networks were used to model the encrypted data that was stored on the blockchain and predict the learner's performance and achieved a high degree of accuracy (91.29%) and low loss (about 0.18) in comparison to other studies that depended on the centralized nature of the data. As well, the university blockchain's functionality was tested, and it successfully returned all the functional requirements and showed its legitimacy.
This work focuses on multi-dimensional approach to incorporate the Support Vector Machine (SVM) models with Blockchain to secure distributed ledger against APTs.The classifying and high pattern recognition ability of SVM makes the proposed framework easily capture and neutralize malicious activities in the blockchain networks in realtime.The distribution of the blockchain technology and use of machine learning for predictive modeling guarantees a hard-coded countermeasure against new forms of cyber threats.As such, this work is centered on how these technologies can be integrated in harmony: attempting to enhance the accuracy of threat identification without compromising the functionality of the blockchain.This implementation shows the possibility of achieving strong, secure and scalable applications in different applications domains, and so make a way forward for upcoming decentralized cybersecurity solutions.
Smart waste management is vital for reducing environmental impact and improving quality of life in smart cities. This study presents an AI-driven waste classification model that integrates IoT and Blockchain technologies. IoT-connected bins transmit data to a central server, which uses blockchain to ensure secure, transparent data storage. AI algorithms, including machine learning (ML) and deep learning (DL), classify waste in real-time, optimizing waste collection and recycling. Blockchain ensures data integrity, while ML and DL models enhance sorting efficiency. The system aims to improve waste management and sustainability through intelligent decision-making and secure data handling. Performance will be assessed using retrieval metrics and visualization tools to evaluate the impact of hybrid ML and DL models on waste detection and classification.