Land ownership management is a critical administrative process that requires secure record maintenance, transparent ownership verification, and efficient property transfer mechanisms. Conventional land registry systems primarily depend on centralized databases and paper-based documentation, making them susceptible to document forgery, unauthorized modifications, duplicate ownership claims, lengthy verification procedures, and administrative inefficiencies. These limitations often result in ownership disputes, reduced public trust, and delays in property transactions. This paper presents a Blockchain-Enabled Secure Land Registry Framework that leverages blockchain technology to establish a decentralized, transparent, and tamper-resistant platform for land registration and ownership management. The proposed system integrates a React.js-based user interface with a Node.js and Express.js backend, while Firebase Authentication and Firebase Firestore manage user authentication, supporting documents, and application data. Ethereum smart contracts developed using Solidity are employed to securely record land registration, government verification, and ownership transfer transactions on the blockchain, with Ganache serving as the blockchain testing environment. Every approved transaction generates a unique blockchain transaction hash, enabling secure verification, complete traceability, and immutable ownership history. The hybrid architecture combines the scalability of cloud-based data management with the integrity of blockchain technology to ensure efficient record retrieval while preventing unauthorized alterations. The implemented framework demonstrates secure land registration, transparent ownership transfer, simplified government verification, and reliable auditability with minimal operational complexity. The proposed solution provides a scalable and cost-effective approach for modern digital land administration and establishes a strong foundation for future integration with national land registries, electronic identity verification, GIS-based property mapping, and mobile-enabled citizen services.
Secure and transparent attendance management has become increasingly important in educational institutions as conventional attendance systems often face challenges such as proxy attendance, unauthorized record modification, and limited traceability. Most existing solutions rely on centralized databases, making them susceptible to data tampering, accidental loss, and single-point failures. This paper presents a Blockchain-Based Attendance Management System that leverages blockchain technology to provide a decentralized and immutable mechanism for recording and verifying attendance information. The proposed framework integrates a React.js-based user interface with a Node.js and Express.js backend, while Firebase Authentication and Firestore manage user authentication and application data. Attendance records are securely stored through Ethereum smart contracts executed on the Ganache blockchain network, with transaction hashes linked to Firebase for efficient retrieval and verification. This hybrid architecture combines the scalability of cloud-based data management with the integrity and transparency of blockchain technology. Once attendance is recorded, the information cannot be altered without detection, ensuring reliable auditability and improved trust among students, faculty members, and administrators. The implemented system demonstrates secure attendance recording, fast verification, and efficient transaction management while reducing the possibility of record manipulation. The proposed solution offers a practical, scalable, and cost-effective approach for modern attendance management and provides a strong foundation for future enhancements such as biometric authentication, QR code-based attendance, and cloud-enabled blockchain deployment.
The study investigated digital currency and blockchain technology in the 21st century financial ecosystem. The empirical study adopted a descriptive survey design. A questionnaire was used for data collection in a sample size of 121 selected randomly from the staff and students of Abia State Polytechnic, Aba. The data collected from the respondents were analyzed with the frequency distribution table and chi-square (x2 ) statistical technique. The findings revealed the imperativeness of digital currency and blockchain technology in the 21st century financial ecosystem. In other words, digital currency and blockchain technology has significant effect with financial ecosystem. The study, therefore, recommended among others that Central bank of Nigeria, legislators and financial stakeholders should collaborate to establish compliance standards and best practices for digital currency and blockchain integration in financial ecosystem. These standards should ensure that digital currency algorithms and blockchain technology conform with regulatory requirements and ethical principles, while promoting transparency and accountability.
Hadeer Khayoon Ashour, Noor Salah Alramadan, Hamid Mohsin Jadah
There is growing interest in using blockchain technology to overcome the flaws of legacy payment systems and banking operations, few empirical efforts have examined the possible use of blockchain by large institutions. The study examines how blockchain is changing the payment systems and banking services with a focus on Citigroup (Citi) and various Citi blockchain projects, specifically Citi Token Services. The study aims to assess the impact of blockchain’s adoption on efficiency, cost reduction, customer confidence and service accessibility. A quantitative research study was conducted in a longitudinal design, and data were analysed using multiple linear regression in the SPSS program from 2020–2024 to check the relationship between variables. The results indicate that blockchain implementation offers considerable transaction speed, operational and transactional cost reduction (up to 80 percent), increased customer trust and broader service access with 24/7 transactions. The regression model explains 51.9 percent of the variance in performance. Although promising, blockchain for banking is still in its infancy and facing a variety of challenges that need to be solved for wider application, such as scalability, regulatory compliance, and integration with existing systems.
The rapid growth of digital education and online recruitment has significantly increased the demand for reliable academic credential verification. Conventional certificate verification methods are often centralized, time-consuming, and susceptible to document forgery, unauthorized modification, and administrative delays. To address these challenges, this paper presents a Blockchain-Enabled Decentralized Framework for Secure Academic Certificate Issuance and Real-Time Verification. The proposed framework utilizes Ethereum blockchain technology through Solidity smart contracts to establish an immutable and transparent repository of certificate records, ensuring that issued credentials cannot be altered without detection. A SHA-256 cryptographic hashing mechanism is employed to generate unique digital fingerprints for each certificate, while Firebase Authentication and Cloud Firestore provide secure identity management and efficient off-chain metadata storage. The user interface is developed using React.js, enabling educational institutions to issue certificates and allowing employers, universities, and other stakeholders to verify credentials instantly through a simple web-based platform. During verification, the system recomputes the certificate hash and compares it with the blockchain record to detect tampering and validate authenticity in real time. Experimental evaluation on a local Ethereum network demonstrates reliable certificate issuance, rapid verification with sub-second response times, secure transaction handling, and effective resistance against certificate forgery. The proposed framework enhances transparency, trust, and operational efficiency while minimizing manual verification efforts. Furthermore, its modular architecture facilitates future migration to public blockchain networks and decentralized storage platforms, making it suitable for scalable deployment across educational institutions and digital credential ecosystems.
Dodi Setiawan, Sri Sutjiningtyas, A. Eka Hermia Fitrianingsy, Ronald Naibaho · 5 authors
Blockchain consensus mechanisms are critical for ensuring security, efficiency, and scalability in decentralized networks. This study qualitatively examines ten widely used consensus algorithms—Proof of Work (PoW), Proof of Stake (PoS), Delegated PoS (DPoS), PBFT, Raft, Proof of Authority (PoA), Hybrid PoW/PoS, DAG/IOTA, Hashgraph, and Tendermint—within the research context of Bintan, Riau Islands, Indonesia. Performance was evaluated through literature review and simulated network observations, focusing on transaction throughput (TPS), latency, energy consumption, and network stability. Results indicate that DAG/IOTA and Hashgraph achieve the highest throughput with minimal latency, making them suitable for IoT and enterprise-scale applications. PoS and PoA offer energy-efficient alternatives, while PoW provides high security at the cost of high energy usage. Hybrid PoW/PoS demonstrates balanced performance across multiple metrics. Qualitative analysis highlights trade-offs among energy efficiency, throughput, latency, and decentralization. These findings provide practical guidance for selecting consensus mechanisms according to network requirements, operational constraints, and sustainability considerations, contributing a consolidated perspective on blockchain efficiency and scalability.
Pipelines that pair a large language model with a static analyzer, feeding findings back as repair instructions, appear throughout recent smart contract repair research. They rest on a rarely examined assumption: that the analyzer output serving as the oracle faithfully records what the analyzer found. I report three ways that assumption fails, identified during a four-contract instrument-validation exercise preceding a planned repair study. First, Mythril v0.24.8 can exit without reaching the analysis phase while returning exit status zero, empty standard error, and a findings array byte-identical to that of a genuinely clean scan; the failure is reported in a sibling JSON field that finding-extraction code has no reason to read. Second, 12 of 23 Slither findings in my validation set fell outside the high, medium, and low impact bands, so an unfiltered count measures a composite whose components may not behave alike under repair. Third, keying finding identity on source location breaks across repair rounds. On the one contract carried through three rounds, location-based keying inflated resolved findings from 7 to 12 and introduced findings from 2 to 7. The underlying instability is established in the warning-tracking literature; my contribution is its consequence for repair metrics, where it biases both transition counts upward and can confound comparison between methods producing differently sized .patches. I separately report an executed exploit showing a specification-level authorization defect that produced no high or medium impact finding. I propose calibration procedures for each hazard and release the harness, contracts, and raw analyzer output at doi:10.5281/zenodo.21586404.
Crypto currency is one of most interesting financial innovation of 21st century. Crypto currency trading not only involve financial literacy while trading but also there are psychological factors affecting the decision of traders. Keeping in view the psychological factors and investors’ decision, this research study is designed to investigate the complex interplay between psychological triggers and market dynamics in the cryptocurrency sector in Pakistan, specifically examining how these elements coalesce to drive investor behavior and market volatility. While traditional financial models often attribute asset fluctuations to technological or fundamental shifts, this study posits that cryptocurrency markets are fundamentally driven by human perception and emotional reactivity. Utilizing a quantitative methodological approach, data was collected from a sample of 175 experienced traders to analyze the impact of emotional states, market sentiment, and behavioral discipline on trading outcomes. The empirical results, derived through multiple linear regression analysis, reveal that the model possesses a high level of explanatory power, accounting for 56% of the variance in emotional trading behavior (R2=0.56R2=0.56). Market sentiment emerged as the primary determinant of impulsive trading (β=0.48β=0.48), demonstrating that external social cues often exert a stronger influence on decision-making than internal emotional states. Among specific psychological variables, Fear, Uncertainty, and Doubt (FUD) were identified as the most significant predictors of rash choices (β=0.34β=0.34), while the Fear of Missing Out (FOMO) also demonstrated a substantial, though secondary, effect (β=0.21β=0.21). Conversely, the study found that trading experience and the application of systematic strategies serve as vital moderating factors that decrease emotional reactivity and enhance behavioral stability (β=−0.19β=−0.19). The findings contribute to the fields of behavioral finance and digital economics by illustrating that the volatility inherent in digital assets is a systemic byproduct of individual psychological biases aggregated through digital narratives. The research concludes that achieving a sustainable financial ecosystem requires moving beyond purely technical regulations. Instead, it advocates for the implementation of behaviorally-informed safeguards, such as algorithmic "cooling-off" periods and sentiment-aware trading tools, to mitigate the risks associated with reactive investing. Ultimately, this work provides a blueprint for a more resilient digital financial future by prioritizing human factors in market governance.
Initial Coin Offerings (ICOs) have emerged as an innovative mechanism for raising capital, particularly for blockchain-based projects. However, the lack of regulatory oversight and the prevalence of low-quality information raise important questions about what truly drives ICO success. While existing literature focuses predominantly on technical and signalling variables, the role of investor decision-making remains theoretically underdeveloped and empirically underexplored. This paper addresses this gap by pursuing two objectives. First, we identify the drivers of ICO success using a probit model applied to an original sample of 535 ICOs conducted between January 2016 and May 2021. Second, we investigate investor decision-making patterns using a novel dataset of 200 active crypto-forum participants over the same period. Our results have three main findings, though with modest statistical strength than initially estimated. (I) Marketing channels are the most consistent predictor of ICO success across the sample period, clearing conventional significance thresholds only in the pooled sample (z = 1.90, p<0.10), with each additional channel raising the probability of soft-cap achievement by approximately 1.0 percentage point. (II) Team presentation and video presentation show no meaningful influence on success in any period. (III) Whitepaper availability is not statistically significant even in pooled sample, reinforcing rather than qualifying its irrelevance as a predictor; the number of accepted cryptocurrency price speculation rather than project fundamentals, consistent with mood and sentiment dominating information-based decision making in ICO markets, though this finding should be read alongside the data limitations discussed in 3.B. These findings contribute to the behavioural finance literature by providing an operational definition of ‘investor mood’ and demonstrating its empirical relevance in crypto markets. We conclude that understanding investor mood is not a secondary question but a necessary complement to technical analysis of ICO success.
Tokenized representations of cash-like instruments, comprising stablecoins, tokenized money market funds, and tokenized real-world assets, are increasingly positioned as core on-chain financial infrastructure, yet empirical evidence on how these instruments behave in practice remains limited. This paper reports a comparative empirical examination of public transaction-level blockchain data, covering adoption patterns, usage dynamics, and operational characteristics across three parallel case studies: USDC (stablecoin, Circle), BENJI (tokenized money market fund, Franklin Templeton), and BUIDL (tokenized U.S. Treasury, BlackRock via Securitize). On-chain metrics covering issuance and redemption activity, transfer behavior, wallet concentration, velocity proxies, and cross-chain deployment are interpreted against a four-layer reference architecture (asset representation, control-plane governance, settlement and finality, and composability). Results reveal systematic behavioral differences aligned with product intent and governance design: stablecoins function as high-velocity settlement instruments with broad address distribution, while tokenized investment products exhibit batch-oriented issuance, low circulation intensity, and concentrated holdings consistent with institutional custody and regulatory constraints. A live-pipeline extraction for BUIDL on Ethereum over the 90-day window ending 31 January 2026 yields a holder-level Gini coefficient of 0.8706 with a bootstrap 95% confidence interval of [0.7672, 0.9208] and a top-ten concentration share of 98.96%. Cross-chain deployment expands access but preserves reliance on dominant settlement layers. These patterns constitute an evidence-based framework for evaluating tokenized finance as production-grade financial market infrastructure.
Internet of Things (IoT) technologies in the healthcare industry, also known as the Internet of Medical Things (IoMT), have proven to greatly improve patient monitoring, diagnostics, and clinical decision-making. The increasing prevalence of resource-challenged medical devices, wireless connectivity, and cloud services, however, has brought new risks around security and privacy concerns that can now directly impact patient safety and data integrity. In this paper, a thorough study of 41 peer-reviewed research papers from January 2018 through May 2025 revealed the current state of security vulnerabilities and resilience strategies in healthcare IoT systems. It provides a comprehensive analysis of security threats at the device, network, and application levels such as unauthorized access, malware and ransomware, data breaches, and denial-of-service attacks delivered in a systematic manner. This contrasts with existing surveys, which consider single security mechanisms and improve upon various multi-layered security means such as AI-enabled anomaly detection, blockchain-based authentication and auditability, low-compute cryptographic techniques, and privacy-preserving methods such as federated learning. The outcomes also show that although emerging technologies add a great deal of security and trust capabilities, issues on scalability, interoperability, deployment, and regulations are not yet fully addressed. This review highlights important knowledge gaps and offers structured knowledge and future directions for research to address the design of secure, resilient, and practically deployable IoMT architectures for real-world healthcare environments.
Decentralised finance (DeFi) is a relatively new trend in finance that uses blockchain, smart contracts, and distributed ledger technology to offer financial services in a decentralised manner. Although scholars have made many theoretical advances in decentralised finance in recent years, knowledge of its theoretical structure and future research areas remains limited. This is why this study provides a bibliometric analysis of 1002 articles on DeFi published in Scopus between 2012 and 2026. The analysis uses performance analysis and a science mapping approach based on citation analysis, co-authorship, bibliographic coupling and keyword co-occurrence analysis. The results reveal a remarkably high annual growth rate of 39.34% and DeFi’s dynamism and interdisciplinary nature. The three main countries involved in DeFi research are the USA, China, and the UK. Management Science, Energy Economics and Technological Forecasting and Social Change became the main scientific journals for disseminating knowledge about DeFi. Analysis of thematic changes showed a transition of scientific interests from blockchain and cryptocurrencies to new topics, like artificial intelligence, sustainability, governance, and financial inclusion. Overall, the current study provides a better understanding of the intellectual, conceptual, and social basis of DeFi and highlights possible research areas in the use of artificial intelligence in DeFi, decentralised governance, and sustainable digital financial system development.
The article discusses a decentralized electronic voting system based on blockchain technology. This study aims to improve the performance and fault tolerance of blockchain-based electronic voting systems by introducing the Automated Leaderless Byzantine Fault Tolerance (AL-BFT) consensus protocol. This study aims to develop and evaluate an electronic voting system model that applies the proposed AL-BFT consensus mechanism in a permissioned peer-to-peer network. The methods used include computer modeling of a peer-to-peer (P2P) network, implementation of a decentralized ledger, and experimental load testing of the consensus protocol. System performance is evaluated using key metrics, such as transaction latency, throughput (requests per second), fault tolerance threshold, and scalability. The study results include the development of a conceptual architecture for the electronic voting system, the identification of its core components, and the analysis of their interactions to ensure data integrity and the reliability of voting results. At each stage of the electoral process, data security is considered, and additional protection mechanisms are analyzed to enhance system robustness. Eliminating the leader election phase from the consensus process is a key feature of the proposed approach, thereby reducing coordination overhead and enabling more efficient agreement among nodes. The proposed AL-BFT protocol reduces transaction latency and improves throughput while maintaining the fault tolerance level of traditional Byzantine Fault Tolerance-based approaches. The results confirm improved efficiency compared to classical leader-based consensus mechanisms, particularly in small permissioned blockchain networks. Conclusions. A practical implementation of the system has been developed and tested under real simulated load conditions. The proposed solution ensures stable system operation and reliable consensus formation. The system can be effectively applied to university elections, organizational voting, and other scenarios that require transparency, security, and manipulation resistance
Secure distributed systems offer reliability and privacy guarantees that are crucial across applications ranging from blockchains and cloud computing to fault-tolerant distributed Cyber-Physical Systems (CPS). These protocols enable groups of mutually distrusting parties to collaborate and execute tasks at scale while maintaining robust security guarantees against faulty and adversarial behavior. Blockchains demonstrate that the reliability half of this promise is achievable in practice, with deployments spanning hundreds of parties over geo-distributed testbeds. The privacy half has {\it not} kept pace: despite rapidly growing demand from applications such as anonymous networks and privacy-preserving AI, systems at blockchain scale have been unable to offer privacy guarantees. At the other end of the spectrum, the reliability techniques that succeeded in the blockchain setting are far too expensive for emerging distributed CPS applications, where hardware and network conditions are substantially weaker. In both settings, existing solutions are too slow and resource-intensive to be deployed in practice. This thesis asks whether both guarantees can be delivered at the scale their applications demand, on the hardware those applications actually run on.The first half of this thesis builds Multi-Party Computation (MPC) protocols for systems with a hundred or more parties over real-world geo-distributed networks, motivated by modern blockchains. MPC enables $n$ mutually distrusting parties to jointly compute any function over their private inputs. We identify computationally expensive heavyweight cryptography based on number-theoretic hardness assumptions as the central scalability bottleneck and address it by designing protocols entirely using \emph{lightweight} cryptography such as symmetric-key encryption and cryptographic Hash functions. These tools are two orders of magnitude cheaper than heavyweight operations and additionally offer post-quantum security. We present three works in this line: HashRand, a random beacon protocol, Velox, an MPC protocol achieving fairness, and Aeternum, a framework for guaranteed output delivery in asynchronous MPC and dynamic proactive secret sharing. We implement and evaluate all three, showing that they outperform prior work by two orders of magnitude and scale to $100$ or more parties on geo-distributed testbeds with practical latency and communication costs.The second half turns to Asynchronous Approximate Agreement (AAA) for distributed CPS with a hundred or more parties, motivated by robot and drone swarms. Unlike randomized Byzantine Agreement (BA) protocols, which depend on expensive heavyweight cryptography to produce common coins, AAA protocols are deterministic and avoid these tools. These protocols still have a high cubic communication cost, which is unaffordable in the low-bandwidth CPS setting. We introduce \emph{Relaxed Validity}, an approximate validity property that allows nodes to trade the accuracy of the protocol's output for sub-cubic communication.Leveraging this property, we design SensorBFT and Delphi, both AAA protocols with sub-cubic communication overhead. We apply both to agreement problems in the CPS domain and experimentally demonstrate their scalability relative to prior works. Both consume an order of magnitude less energy than prior protocols based on randomized BA, a decisive metric on resource- and power-constrained sensor devices.
This informative document explores the evolving digital asset landscape, covering cryptocurrency, NFTs, blockchain technology, Web3, and emerging market trends. It provides readers with practical insights into digital ownership, market developments, and the importance of research when evaluating opportunities in the growing blockchain economy. Collective Shift
Metaverse interoperability has become necessary for uniform virtual experiences between different metaverse ecosystems. Blockchain technology along with decentralized oracle network have played a key role in representing and transferring metaverse assets across different metaverse environments. This paper discusses the concept of interoperability with respect to metaverse, representation of metaverse assets on blockchains, and shows the application of decentralized oracles in achieving metaverse interoperability. Various standards for representing assets in blockchain and their comparison is performed. In the paper, the ERC-721 standard was used for tokenizing assets and transferring them from the Avalanche Fuji testnet to the Ethereum Sepolia testnet and Arbitron Sepolia to Ethereum Sepolia. Chainlink CCIP was used to transfer NFTs between blockchains. A comparison in term of the total gas consumed in both the transactions was performed. The paper has shown that Distributed Oracle Networks (DON) have become a useful solution in providing accurate and secure data feeds as well as transferring assets between different blockchain networks.
Dileep Kumar Murala, G. Siva Krishna, P. N. V. M. Syamala Rao, Madhusudana Rao Nalluri · 5 authors
Integrating blockchain technology with cloud computing has enabled Blockchain as a Service (BaaS), a cloud-based paradigm that allows users to design, deploy, and manage customized blockchain applications, including smart contracts and domain-specific business functionalities. BaaS providers manage infrastructure provisioning, maintenance, and scalability while ensuring quality of service (QoS) compliance through service level agreements (SLAs). However, existing resource management approaches often struggle to satisfy dynamic performance requirements in a cost-efficient manner without increasing operational overhead or reducing provider efficiency. This paper presents a structured Adaptive SLA-based assistance framework for deploying Hyperledger Fabric on cloud platforms. The framework integrates automated performance monitoring using Hyperledger Caliper, SLA violation detection through programmed SLA chaincode, and automated VM scaling via the OpenStack4J API within a 3E (effective–efficient–economical) verification methodology. A greedy multi-objective scheduling mechanism guides parameter optimization by selecting scaling actions according to marginal performance gain per unit cost. Experiments were conducted on the Nectar Research Cloud using Hyperledger Fabric 2.5 to evaluate the impact of VM size, block size, peer count, and storage configuration on throughput (TPS) and average latency. Results demonstrate that the framework consistently achieves performance targets of 50%, 100%, and 200% above the baseline configuration through adaptive resource reconfiguration. Among evaluated strategies, Comb2, which combines dual block size adjustment with VM scaling, emerged as the optimal balanced configuration in terms of performance and cost efficiency. While automated monitoring, SLA enforcement, and VM scaling are fully implemented, continuous online scheduling under dynamically changing workloads remains future work. The proposed framework establishes a practical foundation for SLA-driven blockchain optimization in cloud environments and supports future extensibility to additional BaaS platforms.
The rapid growth of AI-driven workloads, IoT ecosystems, and distributed digital services has exposed fundamental limitations in existing cloud and edge infrastructures, particularly in guaranteeing robust data privacy under emerging quantum threats. Current blockchain-based systems provide integrity and decentralization but rely predominantly on computational cryptography and access-control mechanisms, leaving them vulnerable to future quantum adversaries and large-scale inference attacks. In this paper, we introduce Data Communities as a novel paradigm for privacy-preserving, blockchain-enabled cooperative digital infrastructures, formalized within the Cooperative Digital Infrastructure (CDI) framework. Our approach integrates three complementary privacy protection layers: (i) MuReQua, a quantum-resilient blockchain consensus mechanism leveraging CQKD for cryptographic robustness against Shor-type attacks; (ii) DeSSE, an information-theoretically secure distributed storage model based on n × m fragmentation, ensuring zero information leakage below reconstruction thresholds; and (iii) a multi-tier data sovereignty model (C0–C3) enforcing policy-driven data locality and regulatory compliance across federated nodes. We formalize privacy guarantees through an adversarial model encompassing classical, quantum, insider, and governance-level threats, and demonstrate that the proposed architecture achieves information-theoretic confidentiality, forward secrecy, and attack-resilient distributed governance. A privacy leakage analysis shows that the probability of data reconstruction under sub-threshold compromise is identical to zero, outperforming conventional blockchain storage models based on encryption alone. Simulation and case study results indicate that Data Communities achieve up to 99.999% service availability, 55% reduction in external data exposure, and 22–35% carbon-aware optimization, while maintaining strict privacy guarantees across distributed environments. Compared with existing blockchain systems (e.g., Ethereum, Hyperledger Fabric), the proposed framework shifts privacy protection from access-control and pseudonymity to structural, information-theoretic privacy by design. Overall, the results establish Data Communities as a scalable and quantum-resilient foundation for next-generation privacy-preserving blockchain infrastructures, bridging distributed AI, secure storage, and cooperative governance under a unified formal model.
Kapil K. Jajulwar, Priya Dasarwar, Uma Yadav, Bhakti Prasad Thakre · 6 authors
Blockchain consensus mechanisms are important to ensure the safe validation of transactions. However, the limitations of high computational complexity, energy consumption, and mining latency restrict the scalability of blockchain in large-scale IP-based and wireless network environments. Current methods mainly rely on single optimization methods without jointly optimizing miner selection and hash computation, resulting in inferior performance under dynamic network conditions. To fill this gap, this study presents a new hybrid bioinspired optimization framework for efficient blockchain mining, integrating Genetic Algorithm (GA), Firefly optimization, and Particle Swarm Optimization (PSO) into a unified architecture to take advantage of their complementary strengths. The proposed method uses both historical and real-time performance metrics to determine the best mining nodes. The Firefly algorithm is used to optimize the selection of hash ranges to reduce CPU workload. PSO is used to select high-performance neighboring nodes for collaborative mining. The model is implemented using the NS-2 simulator and tested over a network of 1000 wireless nodes under different consensus protocols. The experimental results illustrate 4.3% decrease in computational complexity, 4% decrease in energy consumption, and 5% decrease in mining delay. The model further reduces mining complexity by up to 34.2% and reduces the energy utilization from 24.5% to 16.6%, demonstrating its effectiveness for scalable and energy-efficient blockchain deployment.