One major issue associated to microservices architectures is aspects related to data consistency and management of data within such a context. To measure the effect on system integrity, this research paper compares different data consistency models which are, eventual consistency, distributed transactions through Two-Phase Commit (2PC), the Saga pattern. Employing historical data analysis, case studies, and examining the recent technological advancements, the study gives a comparative assessment of various approaches in terms of their capabilities to achieve transaction latencies and throughput and possibility to guarantee consistent results. Based on the results, it is evident that although eventual consistency models provide high scale-out and availability characteristics, the models experience transitory data inconsistency. Consistency is high since data is accessed from, and committed to, multiple databases in a distributed transaction but they have performance penalties. The Saga patterning gives a balanced view but at the same time brings together additional design difficulty. New trends like Distributed Ledger Technology (DLT) as well as new consensus algorithms that can be used to solve problematic approaches like the Raft algorithm give new possibilities for improving consistency of the data but bring their own concerns. The study implies that one must choose the right consistency model depending on the need and performance Degree and the future research can consider the use of the combination of several models along with the improvement of technological solutions to have a better deal with data consistency in microservices.
Purpose: Enterprise Resource Planning (ERP) systems, such as SAP (Systems, Applications, and Products in Data Processing), are critical to modern enterprises, enabling the integration of core business functions and the management of essential data. Ensuring their availability, reliability, and adaptability is paramount, as disruptions can result in significant operational and financial consequences. Traditional Knowledge Management (KM) approaches emphasize the preservation of ERP-related knowledge but often lack responsiveness to emergent risks. This study introduces a novel framework grounded in the concept of antifragility—where systems grow stronger under stress—by simulating disruptions to enable continuous knowledge evolution and system adaptation. Methodology: A mixed-methods research design combines simulation-based inquiry with Design Science Research (DSR) to investigate antifragile KM within ERP environments. Artificial Intelligence (AI) tools are integrated into the KM system to analyse ERP failures, generate runbooks, and proactively manage recovery knowledge. Controlled simulations of kernel upgrades and failure scenarios—modelled on ITIL 4 incident typologies—serve as structured stressors to expose vulnerabilities. Lightweight LLMs, Retrieval-Augmented Generation (RAG) pipelines, and semantic search tools are employed to codify procedural knowledge and enhance the responsiveness of ERP operations. Findings: The results demonstrate that embedding antifragile principles into ERP KM improves organizational learning, responsiveness, and recovery capabilities. Transitioning from static knowledge repositories to dynamic, AI-enabled systems allows for autonomous decision-making, decentralized knowledge flow, and adaptive documentation. Each disruption becomes a learning event, reinforcing the resilience and self-improvement of the ERP knowledge ecosystem. Implications: Empirical insights suggest that AI-driven antifragile KM transforms ERP disruptions into opportunities for growth, rather than threats to stability. The proposed framework supports the development of systems that not only recover from failure but also become progressively more robust and adaptive through structured experimentation and continuous learning.
The rapid development of the Internet of Things (IoT) has intensified security and privacy challenges across data generation, transmission, and storage. This study introduces a blockchain-based secure edge storage model tailored for IoT environments and presents a lightweight consensus algorithm, Deep Q-Network (DQN)-Raft+, which incorporates deep reinforcement learning. By combining the decentralized features of edge computing and blockchain, the model enables automated data access control through smart contracts. Furthermore, it optimizes leader node selection in the Raft consensus process using a DQN, formulating the consensus as a Markov Decision Process to enhance responsiveness and privacy protection in dynamic network conditions. Experiments were performed in a simulated environment using TensorFlow 2.6 and a MySQL database. The performance of DQN-Raft+ was compared against traditional consensus algorithms, including Proof of Work, Proof of Stake, Practical Byzantine Fault Tolerance, and Delegated Byzantine Fault Tolerance. Results indicate that DQN-Raft+ significantly reduces block generation delay (175.77 ms) and achieves a high privacy protection score (0.95). It also maintains a low data loss rate of 0.01%, demonstrating enhanced robustness and real-time capability. These findings indicate that DQN-Raft+ effectively strengthens data security and privacy in IoT systems, offering a technically sound and efficient mechanism for secure data exchange. The study provides both a theoretical framework and practical direction for future research in secure IoT deployment.
This paper presents a comprehensive comparative analysis of three prominent blockchain networks: Solana (SOL), Ethereum (ETH), and SUI. The study examines transaction speeds, costs, transaction volumes, and evaluates the benefits and disadvantages of each cryptocurrency in real-world applications. Through detailed analysis of technical specifications, market performance, and ecosystem development, this research provides insights into the relative strengths and weaknesses of these blockchain platforms as they compete for market dominance in 2025.
With the growing demand for blockchain technology, the deployment of various applications has highlighted the critical chal- lenge of balancing scalability, security, and decentralization—termed the blockchain trilemma. To ensure blockchain’s effectiveness in real-world scenarios, resolving this trilemma with minimal trade-offs is crucial. Despite recent advancements, existing solutions, including Filecoin’s decentralized storage, have not fully addressed these challenges. While Filecoin reduces on-chain data re- dundancy through cryptographic proofs (PoRep/PoSt), its throughput remains insufficient (< 50 TPS) and vulnerable to adversarial attacks like selfish mining. In this study, we propose a novel Filecoin-based architecture that tackles the trilemma by reducing trans- action size to 50.6 bytes via Data Identification Numbers (DINs), achieving 145 TPS (a 20 × improvement over baseline Filecoin) with 1 MB blocks while maintaining 80% chain quality under 45% adversarial influence. Our approach enhances decentralization by minimizing storage requirements (292 bytes per reference block) and lowering hardware demands for mining nodes ($500/n- ode). Cost-efficiency analysis demonstrates a 99.9% reduction in energy consumption (0.001 kWh/Tx) compared to Bitcoin’s PoW (1,200 kWh/Tx) and a 3,448 × improvement in storage efficiency over baseline Filecoin. Security is preserved through PoRep/PoSt optimizations, resisting double-spending and Sybil attacks. Theoretical and empirical evaluations, including adversarial simulations and comparisons with Bitcoin, Ethereum, and Filecoin, validate unprecedented scalability-security-decentralization trade-offs. This work sets a new benchmark for blockchain systems, enabling decentralized applications to rival centralized systems in throughput, cost, and robustness.
Blockchain technology has rapidly evolved as a decentralized solution offering high security and transparency; however, several challenges still hinder the effective management of workflows within blockchain based environments. This study aims to develop an adaptive workflow management model that utilizes decentralized artificial intelligence (AI) and distributed ledger technology (DLT) to enhance the performance, security, and flexibility of processes in blockchainn networks. A mixed method approach combining simulation and experimentation on a dedicated blockchain platform was employed. The adaptive workflow model consists of a realtime process monitoring module, a decentralized AI module for adaptive decision making, and a DLT component that ensures data consistency and security. Statistical methods and system performance evaluations were used to analyze the experimental data. Results show that the proposed model can reduce workflow response times by up to 25% and increase the successful execution rate of smart contracts to 98%. Moreover, the integration of decentralized AI optimizes workload distribution across nodes, enabling network scalability improvements of up to 150% without significant performance degradation. The findings demonstrate that the adaptive workflow model combining AI and DLT enhances the flexibility and governance of blockchain networks through AI’s predictive capabilities and DLT’s security. Nevertheless, challenges such as high computational resource demands and technical complexities must be addressed. This research opens opportunities for further development to expand the scope of complex and dynamic blockchain applications and supports their integration with technologies like the Internet of Things (IoT).
Blockchain consensus algorithms face trade-offs between performance, security, decentralization, and energy efficiency. Proof-of-Work (PoW) ensures strong security but is energy-intensive. Proof-of-Stake (PoS) is efficient but may risk centralization. Byzantine Fault Tolerance (BFT) offers low latency but lacks scalability. This paper proposes an Adaptive Hybrid Consensus (AHC) algorithm that dynamically integrates PoW, PoS, and BFT elements. The AHC mechanism is designed for general-purpose blockchain environments and can adapt to real-time network conditions. AHC is a promising solution for next-generation blockchain systems as it has the potential to significantly improve latency, throughput, and energy consumption [13][14].
S M Mostaq Hossain, Amani Altarawneh, Maanak Gupta
As blockchain technologies are increasingly adopted in enterprise and research domains, the need for secure, scalable, and performance-transparent node infrastructure has become critical. While self-hosted Ethereum nodes offer operational control, they often lack elasticity and require complex maintenance. This paper presents a hybrid, service-oriented architecture for deploying and monitoring Ethereum full nodes using Amazon Managed Blockchain (AMB), integrated with EC2-based observability, IAM-enforced security policies, and reproducible automation via the AWS Cloud Development Kit. Our architecture supports end-to-end observability through custom EC2 scripts leveraging Web3.py and JSON-RPC, collecting over 1,000 real-time data points-including gas utilization, transaction inclusion latency, and mempool dynamics. These metrics are visualized and monitored through AWS CloudWatch, enabling service-level performance tracking and anomaly detection. This cloud-native framework restores low-level observability lost in managed environments while maintaining the operational simplicity of managed services. By bridging the simplicity of AMB with the transparency required for protocol research and enterprise monitoring, this work delivers one of the first reproducible, performance-instrumented Ethereum deployments on AMB. The proposed hybrid architecture enables secure, observable, and reproducible Ethereum node operations in cloud environments, suitable for both research and production use.
This study presents a comparative evaluation of five widely adopted blockchain consensus algorithms—Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS), Practical Byzantine Fault Tolerance (PBFT), and Proof of Authority (PoA)—based on energy efficiency and performance metrics. As blockchain systems scale into broader applications, key indicators such as transaction throughput (TPS), latency, security, centralization risk, and scalability become increasingly critical. Each algorithm is assessed through theoretical analysis, empirical data, and simulation-based studies found in the literature. The findings indicate that Proof of Work is the most secure, but it does not environmentally sustainable due to its energy needs. Proof of Stake and Delegated Proof of Stake are more energy-efficient and have a high throughput, but it can lead to centralization. A Practical Byzantine Fault Tolerant consensus algorithm is great in permissioned networks, but it doesn't scale well. Furthermore, those networks aren't really decentralized. Proof of Authority is the most environmentally sustainable option, but that energy efficiency comes at the cost of decentralization. This research highlights the lack of sustainable consensus mechanisms, that is, consensus protocols that are both secure and scalable and environmentally sustainable—as a gap that future research can address.
The emergence of blockchain technology has revolutionized decentralized data management by offering robust alternatives to traditional centralized database systems. This paper provides a systematic and comprehensive review of blockchain-based distributed databases, highlighting key architectural transformations, core enabling technologies such as Merkle Trees, PBFT, and Zero-Knowledge Proofs, and comparing them with conventional distributed databases. Real-world implementations including Hyperledger Fabric, BigchainDB, and OrbitDB are analyzed to assess their scalability, interoperability, and security capabilities. The paper also explores intrinsic security mechanisms, performance bottlenecks, and regulatory challenges that affect adoption. Finally, it identifies open research questions and future directions necessary for building scalable, privacy-aware, and interoperable decentralized database ecosystems suitable for enterprise and multi-stakeholder environments. Keywords— Blockchain databases, consensus mechanisms, data integrity, decentralized systems, distributed ledger, Merkle trees, Zero-Knowledge Proofs
Sunil P. Chinte, Prof. S. A. Thakare, Aarti R. Jaiswal, Nikunj Hasmukhrai Raja · 5 authors
The rapid expansion of Internet of Things (IoT) ecosystems has resulted in an unprecedented surge in data generation, necessitating reliable, scalable, and secure storage mechanisms. Traditional centralized storage systems suffer from inherent limitations such as single points of failure, limited scalability, and vulnerability to cyberattacks, which compromise the confidentiality and availability of critical IoT data. This study introduces a blockchain- based decentralized storage framework aimed at addressing these critical issues. By leveraging the distributed and immutable characteristics of blockchain technology, the proposed system enhances data integrity, ensures transparency, and facilitates trustless data exchange among heterogeneous IoT devices. The methodology includes mathematical modeling of key performance parameters such as latency, throughput, storage efficiency, and consensus delay. Smart contracts are integrated to automate validation and enforce rules among interconnected devices, while redundancy mechanisms like replication and erasure coding improve storage reliability and efficiency. The framework’s effectiveness is evaluated using simulation tools including Hyperledger Caliper and Ethereum Testnets for blockchain behavior, and NS-3 and OMNeT++ for modeling dynamic IoT network environments. Experimental results reveal a 30% improvement in data retrieval time, 25% gain in storage efficiency, 40% enhancement in system resilience, and a 50% increase in transaction throughput over conventional approaches. These metrics highlight the suitability of the proposed model for real-world applications requiring scalable and secure IoT data management, such as healthcare monitoring, smart cities, and industrial automation. The model’s reproducibility and modularity make it a robust solution for future research and deployment. Overall, this work demonstrates that blockchain-integrated decentralized storage frameworks present a transformative step toward resilient and scalable IoT infrastructures.
Abstract: The rapid proliferation of blockchain technology has intensified concerns over the energy inefficiency of traditional consensus mechanisms, particularly Proof of Work (PoW). This study investigates alternative, scalable consensus mechanisms with a focus on enhancing energy efficiency while maintaining performance and decentralization. The evaluation centers on Proof of Stake (PoS), Delegated Proof of Stake (DPoS), and Practical Byzantine Fault Tolerance (PBFT), using empirical simulations and statistical modeling to assess key performance indicators—energy consumption per transaction, transaction latency, and throughput. The results indicate that PoS is the most energy-efficient, consuming only 0.04 kWh per transaction, whereas DPoS offers the highest throughput at 1400 transactions per second with moderate energy requirements. PBFT demonstrates the lowest latency but at the cost of increased energy usage. Predictive regression analysis further reinforces the trade-offs between energy use and scalability across consensus mechanisms. These findings highlight the importance of selecting consensus algorithms based on specific application requirements and environmental considerations. The study provides a data-driven framework for guiding the design and adoption of energy-conscious blockchain infrastructures suitable for sustainable and large-scale deployment. Keywords: Blockchain, Consensus Mechanism, Energy Efficiency, Scalability, Proof of Stake, Delegated Proof of Stake, PBFT, Distributed Systems, Transaction Throughput, Green Blockchain Technologies
Blockchain has been widely recognized as a trusted computing paradigm underpinning Decentralized Applications (DApps). However, low performance and poor scalability of the existing mainstream blockchain designs and implementations render their prospects unattainable, so-called trilemma . Directed Acyclic Graph (DAG) emerged as an alternative blockchain architecture to address the performance and scalability issue. However, the capacity and feasibility of DAG-based blockchain systems still remain of concern, due to a lack of open source implementations and convincing published experimental results. In this article, we propose a DAG-based blockchain, named 3D-DAG , to greatly improve the scalability and performance without compromising security. The system addresses the trilemma of decentralization, scalability, and security. Technically, our 3D-DAG consists of two layers of chains: DAGchain and mainchain . The DAGchain organizes the transactions in DAG topology, without having to solve the forks. They select a small group of validators from a large number of miners to make up the committee for consensus procedures. The design of DAGchain improves scalability by enabling parallel transaction processing. Based on that, the mainchain plays the role of checkpoint, periodically packaging and recording received transactions from DAGchain for finality. We also provide a prototype implementation with detailed evaluations. Experimental results show our 3D-DAG can meet the performance (a peak value of \(9.82\times 10^{4}\) TPS) that is expected by DApps in the context of Web3.
As financial institutions increasingly migrate their core platforms to microservices-based architectures, the challenge of managing distributed transactions has gained critical importance. Banking APIs typically require atomicity and consistency across multiple services—such as account management, fraud detection, notifications, and audit trails all of which operate independently with isolated data stores. In such an ecosystem, ensuring consistency, performance, and fault tolerance becomes a balancing act that traditional and modern transaction patterns attempt to resolve differently. This paper explores and contrasts two dominant approaches to distributed transaction management: the Two-Phase Commit (2PC) protocol and the Saga Pattern, particularly in the context of mission-critical banking applications. 2PC has long been considered the gold standard for ensuring atomicity and strong consistency in distributed systems. However, its blocking nature, reliance on a centralized coordinator, and vulnerability to network partitions make it less suitable for high-throughput, globally distributed systems common in modern fintech platforms. On the other hand, the Saga Pattern, an eventual consistency model that orchestrates a sequence of local transactions with compensating rollback operations—offers better fault tolerance and non-blocking behavior. Yet, its trade-offs include the complexity of compensating logic, lack of strict ACID guarantees, and potential for data anomalies if not carefully implemented. To ground the discussion in real-world reliability needs, I introduce a chaos engineering-based simulation that demonstrates the behavior of both 2PC and Saga under controlled failure scenarios, such as inter-service latency spikes and partial service outages. We benchmark recovery times, resource locking, system availability, and data reconciliation behavior using a representative banking microservice architecture deployed in a containerized environment. My findings reveal that Saga outperforms 2PC in terms of availability and fault recovery, making it suitable for user-facing, latency-sensitive operations. However, 2PC remains superior for operations demanding immediate consistency and compliance with strict audit requirements, such as core ledger updates. Based on this analysis, we propose a hybrid transaction strategy that applies 2PC to core financial operations and Saga to surrounding auxiliary services, striking a balance between performance and correctness. This study offers practical design insights for architects building resilient, scalable, and regulation-compliant financial systems. It also highlights the need for adaptive orchestration platforms capable of dynamically selecting transaction models based on context and SLA requirements.
Web3 technologies have experienced unprecedented growth in the last decade, achieving widespread adoption. As various blockchain networks continue to evolve, we are on the cusp of a paradigm shift in which they could provide services traditionally offered by the Internet, but in a decentralized manner, marking the emergence of the Internet of Blockchains. While significant progress has been achieved in enabling interoperability between blockchain networks, existing solutions often assume that networks are already mutually aware. This reveals a critical gap: the initial discovery of blockchain networks remains largely unaddressed. This paper proposes a decentralized architecture for blockchain network discovery that operates independently of any centralized authority. We also introduce a mechanism for discovering assets and services within a blockchain from external networks. Given the decentralized nature of the proposed discovery architecture, we design an incentive mechanism to encourage nodes to actively participate in maintaining the discovery network. The proposed architecture implemented and evaluated, using the Substrate framework, demonstrates its resilience and scalability, effectively handling up to 130,000 concurrent requests under the tested network configurations, with a median response time of 5.5 milliseconds, demonstrating the ability to scale its processing capacity further by increasing its network size.
Bitcoin mining is highly energy-intensive, and improving its efficiency is critical for both economic and environmental sustainability. This project presents a web-based simulation tool that models key aspects of Bitcoin mining, including the SHA-256 hashing algorithm, nonce iteration, and target difficulty checks. The computational backend is integrated with real-time power and thermal models, enabling the simulator to reflect how hash rate influences energy consumption and temperature. Interactive controls for frequency, voltage, and resistance, along with graphical visualizations of power usage over time, allow users to explore trade-offs between energy efficiency and mining performance. The simulation also includes a financial trade-off analysis feature and supports extended runtime testing to evaluate long-term behavior under varying operational conditions.
Dumitrel Loghin, Shuang Liang, S. Liu, Xiong Liu · 6 authors
Zero-knowledge proofs (ZKP) are becoming a gold standard in scaling blockchains and bringing Web3 to life. At the same time, ZKP for transactions running on the Ethereum Virtual Machine require powerful servers with hundreds of CPU cores. The current zkProver implementation from Polygon is optimized for x86-64 CPUs by vectorizing key operations, such as Merkle tree building with Poseidon hashes over the Goldilocks field, with Advanced Vector Extensions (AVX and AVX512). With these optimizations, a ZKP for a batch of transactions is generated in less than two minutes. With the advent of cloud servers with ARM which are at least 10% cheaper than x86-64 servers and the implementation of ARM Scalable Vector Extension (SVE), we wonder if ARM servers can take over their x86-64 counterparts. Unfortunately, our analysis shows that current ARM CPUs are not a match for their x86-64 competitors. Graviton4 from Amazon Web Services (AWS) and Axion from Google Cloud Platform (GCP) are 1.6X and 1.4X slower compared to the latest AMD EPYC and Intel Xeon servers from AWS with AVX and AVX512, respectively, when building a Merkle tree with over four million leaves. This low performance is due to (1) smaller vector size in these ARM CPUs (128 bits versus 512 bits in AVX512) and (2) lower clock frequency. On the other hand, ARM SVE/SVE2 Instruction Set Architecture (ISA) is at least as powerful as AVX/AVX512 but more flexible. Moreover, we estimate that increasing the vector size to 512 bits will enable higher performance in ARM CPUs compared to their x86-64 counterparts while maintaining their price advantage.
With the increasing number of graduates each year, there is a growing need for verified and easily accessible academic certificates. This paper proposes a solution that addresses both verification and accessibility using blockchain technology, with validation as the top priority. Blockchain is a distributed ledger technology that ensures transparency and immutability across a decentralized network. It stores data in blocks, which are cryptographically linked to one another, forming a secure and tamper-proof chain. Due to its decentralized nature, there is no need for a central authority to manage or store the data. Blockchain adheres to the CIA triad—Confidentiality, Integrity, and Availability—while also supporting Authorization, making it a suitable candidate for secure certificate storage and verification. One major advantage of this system is the reduced burden on students to keep physical copies of their certificates, which can be easily lost or damaged. Moreover, in today’s world, creating counterfeit certificates is relatively easy. Blockchain offers a robust solution to this problem due to its immutable and verifiable structure. In this proposed system, when a certificate is issued, a unique hash is generated for it. Along with this hash, a QR (Quick Response) code is also created, linking directly to the certificate stored on the blockchain. By scanning the QR code, anyone can instantly verify the authenticity of the certificate via a secure website. This method is significantly faster and more reliable than traditional manual verification techniques. Blockchain technology presents an ideal solution for sectors that require low-cost, trustworthy, and efficient verification of important documents. Each block in the blockchain is validated through consensus by multiple participants in the network. Once validated, altering a block would require modifying all previous linked blocks and gaining control over more than 50% of the network's nodes—an extremely difficult and nearly impossible task. This ensures the integrity and security of the stored certificates.
Muhammad Iqbal, Kunal Raj, K.V. Narasimha Reddy, Mohd. Mudaseer Mazharuddin
In today's digital age, student academic data is still largely controlled by educational institutions, which creates major risks and limitations.Centralized systems are vulnerable to data loss due to natural disasters, political instability, or system failures.They also make it difficult for students to access or share their records when participating in exchange programs or pursuing lifelong learning across different platforms.To solve these issues, this paper introduces a decentralized approach where students have full control over their educational data.Using blockchain technology-specifically the Ethereum public network-and Web3 tools, we present DecentralEduChain, a framework that allows students to securely store and manage their academic records through smart contracts.Educational institutions can interact with these contracts via integrated Learning Management Systems (LMS), enabling both the reading and updating of student records without relying on centralized databases.This system not only enhances security and transparency but also empowers students with ownership of their data, making it easier to share academic credentials across institutions.The paper also outlines the practical steps for implementing the system, including smart contract creation and integration with LMS platforms, making it a promising solution for the future of educational data management.
Independent Researcher, USA, Damodar Bihani, Bright Chibunna Ubamadu, Signal Alliance Technology Holding, Nigeria · 6 authors
The integration of blockchain technology into the tokenization of real-world assets (RWAs) is revolutionizing how value is stored, transferred, and accessed globally. This paper proposes a scalable framework for cross-functional collaboration in Web3 product development focused on blockchain-based tokenized RWAs. Tokenization enables physical assets such as real estate, commodities, and intellectual property to be digitized into blockchain-based tokens, allowing for fractional ownership, increased liquidity, and enhanced accessibility. However, the successful development and deployment of such Web3 products require an interdisciplinary approach that combines technological innovation, legal compliance, financial modeling, and user experience design. Our framework addresses these needs by enabling seamless collaboration between developers, legal experts, financial analysts, and UX/UI designers throughout the product lifecycle. We present a modular architecture built on interoperable blockchain protocols such as Ethereum and Polkadot, integrating smart contracts, decentralized identifiers (DIDs), and oracles for real-time asset verification. The framework emphasizes agile product development practices and leverages decentralized autonomous organization (DAO) structures to facilitate decision-making and community governance. Furthermore, we explore how regulatory-compliant token standards, such as ERC-1400, can be incorporated to ensure adherence to jurisdiction-specific asset ownership and transfer laws. This study includes a case analysis of cross-functional product teams building tokenized real estate platforms and carbon credit marketplaces, demonstrating how scalable collaboration can accelerate time-to-market and improve transparency, trust, and user adoption. Our findings highlight that such a collaborative framework significantly reduces technical debt and improves legal and financial risk mitigation. The framework also enhances stakeholder alignment through integrated project management tools and on-chain documentation. By offering a structured, scalable, and adaptable approach, this framework positions Web3 product teams to unlock the full potential of tokenized RWAs in a decentralized economy. It serves as a critical guide for developers, entrepreneurs, regulators, and investors aiming to leverage blockchain technology in building trustworthy, scalable, and cross-functional Web3 applications.
Md. Shariful Islam, Mohammad Saifur Rahman, M. Sohel Rahman
Log management is crucial for ensuring the security, integrity, and compliance of modern information systems. Traditional log management solutions face challenges in achieving tamper-proofing, scalability, and real-time processing in distributed environments. This paper presents a blockchain-based log management framework that addresses these limitations by leveraging blockchain's decentralized, immutable, and transparent features. The framework integrates a hybrid on-chain and off-chain storage model, combining blockchain's integrity guarantees with the scalability of distributed storage solutions like IPFS. Smart contracts automate log validation and access control, while cryptographic techniques ensure privacy and confidentiality. With a focus on real-time log processing, the framework is designed to handle the high-volume log generation typical in large-scale systems, such as data centers and network infrastructure. Performance evaluations demonstrate the framework's scalability, low latency, and ability to manage millions of log entries while maintaining strong security guarantees. Additionally, the paper discusses challenges like blockchain storage overhead and energy consumption, offering insights for enhancing future systems.
The implementation of cloud-based distributed ledger technology in global supply chain environments offers transformative solutions for organizations struggling with coordination challenges, transparency deficits, and trust barriers. Despite technological advancements in supply chain management, persistent issues related to information asymmetry and verification difficulties continue to affect networks of manufacturers, suppliers, logistics providers, and retailers operating across geographical boundaries. The proposed architectural framework leverages blockchain's immutable and transparent properties to establish a shared digital infrastructure that enables secure transaction processing while eliminating traditional intermediaries. Through technical architecture development and case application insights, the distributed ledger model addresses fundamental operational inefficiencies in global supply chains. The framework provides implementation strategies for achieving real-time visibility across supply chain tiers, enhanced collaboration among stakeholders, and robust security protocols for fraud prevention. By contributing both theoretical foundations and practical implementation guidelines, this work advances supply chain digitalization with particular relevance for organizations seeking to build more resilient, efficient, and transparent global operations in complex business environments.
Flaviene Scheidt de Cristo, Jorge Augusto Meira, Jean-Philippe Eisenbarth, Radu State
Several distributed systems based on unstructured p2p networks, such as blockchains, rely on underlying protocols to disseminate messages in a fast and reliable way. As the state-of-the-art for message dissemination in blockchains, GossipSub guarantees delivery and resilience against attacks and byzantine faults by scaling pubsub dissemination without exceeding bandwidth or overloading peers. Although GossipSub relies heavily on the way its mesh is constructed, there is little insight into how different configuration parameters impact the overall performance of the system. This study analyzes the relationships between the configuration and the performance of GossipSub from a causal point of view using the concrete case of the XRPL. By employing graphical causal methods to investigate the strength of those connections, this study goes towards the direction of finding the best configuration for GossipSub for different domains, without the need for excessive empirical tests.