Autonomous Market Makers (AMMs) rely on arbitrage to facilitate passive price updates. Liquidity fragmentation poses a complex challenge across different blockchain networks. This paper proposes FluxLayer, a solution to mitigate fragmented liquidity and capture the maximum extractable value (MEV) in a cross-chain environment. FluxLayer is a three-layer framework that integrates a settlement layer, an intent layer, and an under-collateralised leverage lending vault mechanism. Our evaluation demonstrates that FluxLayer can effectively enhance cross-chain MEV by capturing more arbitrage opportunities, reducing costs, and improving overall liquidity.
Sybil attacks pose a significant security threat to blockchain ecosystems, particularly in token airdrop events. This paper proposes a novel sybil address identification method based on subgraph feature extraction lightGBM. The method first constructs a two-layer deep transaction subgraph for each address, then extracts key event operation features according to the lifecycle of sybil addresses, including the time of first transaction, first gas acquisition, participation in airdrop activities, and last transaction. These temporal features effectively capture the consistency of sybil address behavior operations. Additionally, the method extracts amount and network structure features, comprehensively describing address behavior patterns and network topology through feature propagation and fusion. Experiments conducted on a dataset containing 193,701 addresses (including 23,240 sybil addresses) show that this method outperforms existing approaches in terms of precision, recall, F1 score, and AUC, with all metrics exceeding 0.9. The methods and results of this study can be further applied to broader blockchain security areas such as transaction manipulation identification and token liquidity risk assessment, contributing to the construction of a more secure and fair blockchain ecosystem.
Hope Leticia Nakayiza, Love Allen Chijioke Ahakonye, Dong‐Seong Kim, Jae‐Min Lee
Increased automation, connectivity, and data sharing enabled by 6G technology have heightened the vulnerability of Internet of Vehicles (IoV) networks. Addressing this challenge requires an intrusion detection system (IDS) capable of accurately identifying data falsification within IoV while adhering to real-time constraints. This paper presents a Blockchain-enhanced feature-engineered IDS to ensure precise attack detection and classification with minimal computational overhead in in-vehicle networks (IVNs). The proposed lightweight IDS utilizes a hybrid Pearson’s Correlation Coefficient (PCC) feature selection technique designed for deployment on the Telematics Control Unit (TCU). Furthermore, we propose a custom private blockchain network utilizing the proof of authority and association (PoA) consensus mechanism, deployable on the Roadside Unit (RSU), for the secure logging of vehicle Electronic Control Unit (ECU) information, detection results, and the automatic isolation of malicious ECUs via smart contracts. Experimentation analysis demonstrates that the proposed approach achieves notable performance, with a 99.9% detection accuracy and minimal computation times of 0.24s and 1.32s on the CICIoV2024 and CAN-Intrusion datasets. Furthermore, the system achieves high blockchain scalability, maintaining stable throughput of 16 tx/s and low transaction latency of 0.062s under increasing ECU density and RSU coverage.
Ahmed M. Tawfik, Ayman Al-Ahwal, Adly S. Tag Eldien, Hala H. Zayed
Ensuring privacy and confidentiality in healthcare data management remains a critical challenge. Traditional centralized access control mechanisms are susceptible to security breaches, including unauthorized access, data leakage, and single points of failure, as well as privacy violations such as patient record exposure and improper data sharing. To address these issues, this paper proposes ACHealthChain, a blockchain-based framework leveraging Hyperledger Fabric for decentralized and transparent access control. The framework integrates the InterPlanetary File System (IPFS) for decentralized storage and ensures privacy through Hyperledger Fabric channels. ACHealthChain features PolicyChain for fine-grained access control and revocation, structuring patient health data into separate subchains for EHRs and diagnoses with permissioned access. Additionally, LogChain enhances auditing and accountability. A series of experiments evaluate ACHealthChain's performance and scalability, considering metrics such as throughput, latency, and resource utilization. Results demonstrate that ACHealthChain improves throughput by 19.7% and reduces latency by 87%, outperforming existing frameworks built on the same platform. The scalability analysis further confirms the framework's capability to handle increasing workloads within an expanding blockchain network. ACHealthChain presents a promising solution for secure and efficient healthcare data sharing with potential real-world applications.
Whether financial assets movements exhibit correlation and memory has been an intriguing question for physicists. This study aims to investigate whether financial shocks exhibit non-Markovian behavior. In particular, it explores the presence of long-term memory and non-local fluctuations during financial crises. The non-Markovian behavior of volatility and return during the cryptocurrency crashes of 2017–2021 and 2021–2024 cycles are examined. The analysis shows that a scaling relation, which is valid for a singular Markovian process, breaks down in data sets spanning approximately 1 year and 3 years after the onset of the 2017 crash. A similar pattern was observed in the 2021 crash, although the analysis does not work for some data sets. In these time intervals, the crash process shows non-Markovian behavior with financial shocks demonstrating non-local fluctuations and evidence of long-term memory.
This document provides a comprehensive analysis of sustainable data engineering practices, focusing on the ecological implications of contemporary methodologies. It examines power usage, carbon dioxide output, and electronic waste production in data centers, while exploring eco-friendly approaches such as energy-conserving hardware, streamlined data handling processes, and the adoption of sustainable power sources. The potential of AI enhanced optimization techniques, quantum computation, and distributed ledger systems to reduce environmental impact is also examined. The paper concludes with actionable strategies for corporations and regulators to enhance the sustainability of data engineering practices, ensuring that the expansion of our digital landscape does not occur at the cost of environmental health.
The cryptocurrency market is characterized by its high volatility and complex temporal dependencies, posing significant challenges for accurate price prediction. This study introduces advanced hybrid Recurrent Neural Network (RNN) architectures—LSTM-GRU, GRU-BiLSTM, and LSTM-BiLSTM—to enhance the predictive accuracy of cryptocurrency price forecasting. By leveraging the strengths of each RNN variant, the hybrid models effectively capture intricate time-series patterns and nonlinear dependencies inherent in cryptocurrency data. The research follows a comprehensive methodology, including the collection of historical price data for Bitcoin (BTC), Ethereum (ETH), and Litecoin (LTC), rigorous data preprocessing, and the integration of hybrid architectures. Extensive experiments are conducted, and the models are evaluated using key performance metrics, such as Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). Results highlight the superior performance of hybrid RNNs, with LSTM-BiLSTM excelling in BTC price prediction, GRU-BiLSTM and LSTM-GRU demonstrating robust performance for ETH and LTC. This study not only establishes the efficacy of hybrid RNN architectures for time-series forecasting but also underscores their potential for real-world applications in trading strategies. The findings set a new standard for leveraging deep learning in cryptocurrency markets, paving the way for more accurate, reliable, and adaptive forecasting systems. Future work will focus on extending this approach to a broader range of cryptocurrencies and incorporating external market factors to further enhance predictive capabilities.
This comparative policy analysis examines the education systems of South Korea, Taiwan, and Singapore—East Asia’s “Asian Tigers”—focusing on their historical development, philosophies, objectives, structures, financing, administration, and teacher policies. Utilizing Bereday’s (1964) comparative method, the study synthesizes secondary sources, including government reports and academic journals, to explore how these nations leverage education for economic and social progress within distinct political and cultural contexts. South Korea’s system emphasizes fierce competition and STEM excellence, driven by high-stakes exams like the CSAT, yet grapples with equity issues due to private tutoring prevalence. Taiwan prioritizes holistic development, bilingualism, and a 12-year compulsory framework, fostering inclusivity but facing rural-urban disparities. Singapore champions meritocracy, aligning its streamlined 6-4-2 structure with economic needs through early streaming and robust public funding, though it risks rigidity. Commonalities include centralized governance, rigorous academic standards, and public-private partnerships, while differences in financing and decentralization reflect contextual priorities. The findings highlight policy coherence as a driver of educational success, offering lessons for developing nations like Ethiopia, such as investing in teacher quality, early education, and equitable access. This study underscores the transformative potential of education when aligned with national goals, providing actionable insights for global education reform in an interconnected world.
Existing smart contract honeypot detection approaches exhibit high false negatives and positives due to (i) their inability to generate transaction sequences triggering order-dependent traps and (ii) their limited code coverage from traditional fuzzing’s random mutations. In this paper, we propose a hybrid fuzzing framework for smart contract honeypot detection based on taint analysis, SCH-Hunter. SCH-Hunter conducts source-code-level feature analysis of smart contracts and extracts data dependency relationships between variables from the generated Control Flow Graph to construct specific transaction sequences for fuzzing. A symbolic execution module is also introduced to resolve complex conditional branches that fuzzing alone fails to penetrate, enabling constraint solving. Furthermore, real-time dynamic taint propagation monitoring is implemented using taint analysis techniques, leveraging taint flow information to optimize seed mutation processes, thereby directing mutation resources toward high-value code regions. Finally, by integrating EVM (Ethereum Virtual Machine) code instrumentation with taint information flow analysis, the framework effectively identifies and detects security-sensitive operations, ultimately generating a comprehensive detection report. Empirical results are as follows. (i) For code coverage, SCH-Hunter performs better than the state-of-art tool, HoneyBadger, achieving higher average code coverage rates on both datasets, surpassing it by 4.79% and 17.41%, respectively. (ii) For detection capabilities, SCH-Hunter is not only roughly on par with HoneyBadger in terms of precision and recall rate but also capable of detecting a wider variety of smart contract honeypot techniques. (iii) For the evaluation of components, we conducted three ablation studies to demonstrate that the proposed modules in SCH-Hunter significantly improve the framework’s detection capability, code coverage, and detection efficiency, respectively.
The application of smart contracts in electric power systems is widespread. However, vulnerabilities in smart contracts can cause significant economic losses and require careful attention. Smart contracts in electric power systems have domain-specific characteristics that differ from traditional public blockchain applications. As a result, existing vulnerability detection tools cannot be directly applied to these systems. To address this challenge, we design a vulnerability detection tool called E-Guard specifically for smart contracts in electric power systems. E-Guard uses a tailored intermediate representation (IR) known as EIR, which provides control flow and data flow information more suited to the business logic of electric power systems than traditional static analysis tools. We identify and summarize three types of vulnerabilities unique to electric power systems based on expert knowledge. Experimental results show that E-Guard significantly outperforms traditional static analysis tools in detecting these three types of vulnerabilities. Additionally, the extra overhead generated by using EIR is minimal and negligible. This demonstrates that E-Guard is an effective and efficient tool for enhancing the security of smart contracts in electric power systems.
The Ethereum blockchain has transformed decentralized finance (DeFi) and is widely used to issue ERC20 tokens. However, many of these tokens rely on unverified smart contracts, which pose serious security risks. Hackers can take advantage of vulnerabilities in these unverified ERC20 tokens, leading to scams, financial losses, and a decline in user trust. Although several tools are available to audit smart contracts, their effectiveness in analyzing unverified ERC20 tokens remains uncertain. This study examines three auditing tools HoneyBadger, Maian, and Mythril by testing how well they detect security issues in unverified ERC20 tokens. The SmartBugs framework was used to support the auditing process, enabling parallel execution, standardized reports, and bulk auditing of contracts. For a thorough evaluation, two datasets were used: one from 50,581 Ethereum blockchain blocks and another from the DappRadar list of blacklisted ERC20 tokens. These datasets were chosen to provide a broad and realistic view of how the tools perform on both typical and high-risk contracts. The tools were compared based on their ability to detect issues, their execution speed, and their overall effectiveness. The results revealed clear differences in performance: some tools were better at finding vulnerabilities accurately, while others focused more on speed than depth. This study emphasizes the need to improve smart contract auditing methods and highlights the importance of developing more effective security tools to strengthen the Ethereum blockchain.
Marina Ricci, Alessandra Scarcelli, Annalisa Di Roma
This paper presents the outcomes of the Moda 4.0 research project—carried out by the Design_Kind Lab at Politecnico di Bari in collaboration with Emme Evolution S.r.l.—showing how digital transformation drives sustainability in the fashion retail sector. By developing and integrating digital systems and tools for multimedia content creation, distribution, and consumption, the study illustrates how emerging technologies inform new skill sets in product and service design while fostering novel cultural values. The research guides the partner fashion company's digital transition through a structured, multidisciplinary design approach, emphasizing sustainability across products and processes. The project delineates three digital strategies related to technologies: (I) metaverse and virtual worlds, (II) Virtual and Augmented Reality, and (III) Non-Fungible Tokens. Ultimately, the findings highlight that holistic, future-oriented digital strategies enhance creative expression and customer experiences and reinforce environmentally responsible and agile innovation in the fashion industry.
ABSTRACT The emergence of the Metaverse has introduced significant challenges in task offloading and data processing due to its virtual universe nature with immersive environments and a multitude of interconnected users and devices. The abundance of data in the Metaverse poses security challenges in local processing, necessitating traditional methods such as data transfer to Mobile Edge Computing (MEC) and subsequently to the cloud, thereby emphasizing security concerns. In this paper, a novel approach to address these challenges has been introduced: An Ethereum Blockchain‐based MEC framework uses smart contracts designed to ensure secure task offloading. It enables authentication in the Metaverse through smart contracts, followed by modeling the task offloading issue as a Markov Decision Process (MDP). To solve this MDP problem, a hybrid algorithm integrating Deep Q‐Networks (DQN) with Bidirectional Long Short‐Term Memory (Bi‐LSTM), known as BRL‐Net (Bi‐LSTM Reinforcement Learning Network), has been proposed. This framework enables secure and efficient task offloading in dynamic Metaverse environments. BRL‐Net outperforms Proximal Policy Optimization (PPO), achieving a 9.93% higher reward and greater stability. The BRL‐Net's performance across Blockchain consensus mechanisms shows Delegated Proof of Stake (DPoS) as the most efficient, reducing latency by 49.96%, increasing throughput by 10.48%, and lowering energy consumption by 50.24%, compared to Proof of Stake (PoS), thereby optimizing Metaverse performance.
Abstract: In modern democracies, secure and transparent voting mechanisms are critical for ensuring public trust and electoral integrity. Traditional voting systems often face challenges such as tampering, identity fraud, and lack of transparency. This paper proposes a Blockchain-Based Voting System designed to address these issues by integrating advanced technologies including Zero-Knowledge Proofs (ZKP), InterPlanetary File System (IPFS), and the Polygon Proof-of-Stake (PoS) blockchain. The system incorporates Aadhaar-based identity verification with OTP authentication to ensure that only eligible citizens can vote, while preserving voter anonymity through the implementation of ZKP. All sensitive data, including votes and candidate information, are recorded on the decentralized Polygon network, ensuring immutability and transparency. IPFS is employed for storing large files such as candidate profiles and voting records in a secure and distributed manner. Smart contracts automate the core election functions such as vote casting, validation, and result declaration, thereby minimizing the risk of human error and manipulation. A modular user interface is provided for both voters and election administrators, facilitating real-time monitoring, seamless authentication, and secure participation. By leveraging blockchain’s trustless architecture and privacypreserving cryptographic protocols, the proposed system aims to modernize the electoral process, enhance voter confidence, and strengthen democratic institutions in the digital age.The architecture ensures end-to-end verifiability, making each vote independently auditable without compromising confidentiality. This integration of privacy, security, and scalability offers a robust foundation for next-generation electoral systems.
Phumudzo Lloyd Seabe, Claude Rodrigue Bambe Moutsinga, Edson Pindza
Abstract Predicting cryptocurrency prices is challenging due to market volatility and external influences like social media sentiment. This study integrates Twitter sentiment analysis with deep learning models (LSTM, GRU, Bi-LSTM, and Temporal Attention Model) to enhance Bitcoin price forecasting. Sentiment features were extracted using VADER and RoBERTa, with findings showing that RoBERTa-based models significantly outperform VADER. Bi-LSTM (RoBERTa) achieved the lowest MAPE of 2.01%, demonstrating the effectiveness of deep contextual embeddings. SHAP analysis identified Sentiment Momentum, RoBERTa Compound Score, and VADER Negativity Score as key predictors of price movements. These results highlight the value of sentiment-driven forecasting and provide insights for traders, investors, and researchers.
Decentralized Finance (DeFi) has revolutionized financial transactions by enabling open, permissionless access to financial services. However, its lack of centralized oversight and pseudonymous architecture have also brought by fraudulent activities. This study presents a novel framework for fraud detection in DeFi that integrates graph neural networks (GNNs) with multi-agent reinforcement learning (MARL). Leveraging a directed transaction graph comprising 50,000 Ethereum addresses and over 120,000 token transfers, this paper evaluates four detection pipelines: extreme gradient-boosted decision trees (XGBoost), a GNN-only model (GCN), a standalone reinforcement learning agent (PPO), and a proposed GNN+RL hybrid model. The hybrid system combines graph-based embeddings with adversarial policy learning, where a fraudster and a detector co-evolve through a multi-agent PPO setup using PettingZoo’s ParallelEnv. Synthetic fraud strategies are generated using a GAN and projected into the GCN embedding space to simulate adaptive threats. Experimental results show that while GCNs outperform flat-feature models, the GNN+RL hybrid achieves superior balance across accuracy (84.58%), AUC (0.8176), and F1 score (0.7493), capturing both structural and behavioral fraud signals. Reward convergence curves further illustrate emergent adversarial dynamics. The proposed framework demonstrates the effectiveness of combining relational inductive biases, dynamic decision-making, and adversarial augmentation for resilient fraud detection. Future work includes extending to cross-chain analytics and enriching contextual understanding through integration with large language models.
Blockchain technology has emerged as one of the most transformative digital innovations of the 21st century. This paper presents a comprehensive review of blockchain's fundamental architecture, tracing its development from Bitcoin's initial implementation to current enterprise applications. We examine the core technical components including distributed consensus algorithms, cryptographic principles, and smart contract functionality that enable blockchain's unique properties. The historical progression from cryptocurrency-focused systems to robust platforms for decentralized applications is analyzed, highlighting pivotal developments in scalability, privacy, and interoperability. Additionally, we identify critical challenges facing widespread blockchain adoption, including technical limitations, regulatory hurdles, and integration complexities with existing systems. By providing this foundational understanding of blockchain technology, this paper contributes to ongoing research efforts addressing blockchain's potential to revolutionize data management across industries.
Blockchain is a type of decentralized distributed database. Unlike traditional relational database management systems, it does not require management or maintenance by a third party. All data management and update processes are open and transparent, solving the trust issues of centralized database management systems. Blockchain ensures network-wide consistency, consensus, traceability, and immutability. Under the premise of mutual distrust between nodes, blockchain technology integrates various technologies, such as P2P protocols, asymmetric encryption, consensus mechanisms, and chain structures. Data is distributed and stored across multiple nodes, maintained by all nodes, ensuring transaction data integrity, undeniability, and security. This facilitates trusted information sharing and supervision. The basic principles of blockchain form the foundation for all related research. Understanding the working principles is essential for further study of blockchain technology. There are many platforms based on blockchain technology, and they differ from one another. This paper will analyze the architecture of blockchain systems at each layer, focusing on the principles and technologies of blockchain platforms such as Bitcoin, Ethereum, and Hyperledger Fabric. The analysis will cover their scalability and security and highlight their similarities, differences, advantages, and disadvantages.
This study aims to design a halal fresh meat traceability system based on dual blockchain technology and the Internet of Things (IoT). This system will utilize the Point of Authority (PoA) method to validate information on the implementation of halal assurance in the halal fresh meat food chain based on beef, starting from the slaughterhouse, halal slaughterer, and slaughterhouse supervisor. This system will also provide information on temperature and humidity conditions in the delivery service of halal fresh meat products to consumers in traditional markets. In this study, we developed a software system with the waterfall method for a web-based halal traceability system using the Hypertext Preprocessor (PHP) programming language and the Laravel framework, blockchain using SQLite, and IoT technology using C programming. Consumers can obtain product delivery information, halal fresh product assurance information, and halal guarantor information transparently in the halal fresh meat supply chain by scanning the product's Quick Response Code (QR code). This study offers a basic framework for policymakers to improve the halal integrity of the halal fresh meat supply chain, and the concept of halal assurance in traditional markets. It also allows consumers to monitor the halal status of fresh beef products in conventional markets. This study contributes to the beef-based halal fresh beef supply chain research by developing a traceability system for halal fresh meat using blockchain and IoT.
Smart contract classification holds significant application value in the field of blockchain. However, existing methods suffer from inefficiencies and high computational complexity when dealing with smart contract data. To address these issues, this paper proposes a Cluster-BERT model based on neural clustering techniques. The model reduces the computational burden of self-attention mechanisms by clustering attention heads, thereby improving training efficiency. The Cluster-BERT model comprises multiple modules. Module 1 preprocesses smart contract data, converting abstract syntax trees and graph structure features into text representations suitable for BERT models. Module 2 serves as the core of the model, introducing neural clustering methods to reduce computational complexity. Module 3 further optimizes the model by finding the optimal number of centroids, achieving a balance between training efficiency and classification accuracy. Experimental results show that our proposed Cluster-BERT achieved an accuracy of 91.42%, a recall of 91.44%, and an F1 score of 91.43%, which indicates a noticeable improvement over the baseline model. Our model reduces computational complexity from quadratic to linear, resulting in an average reduction of 8.48% in training time and 7.88% in prediction time compared to the baseline model. On the smart contract dataset, the accuracy and precision of our model outperformed other models proposed in recent years by 1% to 2% points on average.
The technology through which records are kept will complicate hacking systems and even forging data stored in the blockchain, which is connectible to safety.It is known as distributed ledger technology or public ledger: distributed digital recording devices which record transactions and supplementary data appearing in various locations simultaneously.A digital currency transacts business in which a decentralised network does receipt and verification through a public ledger and cryptographic methods instead of a bank or other central authority.Decentralised cryptocurrencies such as Bitcoin now provide an outlet for personal wealth beyond restriction and confiscation."As Bitcoin gains ground, more companies have started accepting the cryptocurrency.
Imre Mátyás Kovács, Ágnes Fűrész, András Szeberényi
The convergence of artificial intelligence (AI) and blockchain is fostering the emergence of AI-powered cryptocurrencies, which offer sustainable alternatives to energy-intensive digital finance systems. This paper examines how AI supports the environmental, social, and governance (ESG) performance of blockchain ecosystems. AI integration enables the use of energy-efficient consensus mechanisms, improves decentralized finance operations, and facilitates ESG compliance. Projects like Render Network, Fetch.ai, and Ocean Protocol illustrate how AI can increase data processing efficiency, minimize redundant computation, and promote carbon-neutral tokenomics. These platforms support smarter asset distribution through decentralized data marketplaces and machine learning applications. The research methodology involves a review of academic literature, analysis of market statistics, and energy consumption data comparison. The results demonstrate that AI-powered cryptocurrencies can achieve significant energy savings—up to 35% compared to centralized systems—and exhibit strong market growth, with their total capitalization rising more than tenfold between 2021 and 2024. The findings suggest that AI-enhanced blockchain technologies play an essential role in advancing sustainability in digital finance. This study provides guidance for regulators, developers, and investors aiming to align blockchain innovation with green technology principles and responsible market practices.
A S M Touhidul Hasan, Rakib Ul Haque, Larry Wigger, Anthony Vatterott
Counterfeit products cause financial losses for both the manufacturer and the enduser; e.g., fake foods and medicines pose significant risks to the public’s health. Moreover, it is challenging to ensure trust in a product’s supply chain, preventing counterfeit goods from being distributed throughout the network. However, fake product detection methods are expensive and need to be more scalable, whereas a unified traceability system for packaged products is not available. Therefore, this research proposes a product traceability system, named Trusted Traceability Service (TTS), using Blockchain and Self-Sovereign Identity (SSI). The TTS can be incorporated across diverse industries because of its generic and manageable four-layer product packaging strategy. Blockchain-enabled SSI empowers distributed nodes, to verify them without a centralized client–server authorization architecture. Moreover, due to its distributed nature, the proposed TTS framework is scalable and robust, with the use of web3.0 distributed application development. The adoption of Fantom, a public blockchain infrastructure, allows the proposed system to handle thousands of successful transactions more cost-effectively than the Ethereum network. The deployment of the proposed framework in both public and private blockchain networks demonstrated its superiority in execution time and number of successful transactions.
Charles Nicholas, Charles Dwumfour Osei, David Kwao-Sarbah
The success of decentralization efforts in developing countries, such as Ghana, is closely tied to the capacity for robust infrastructure delivery at the local level, where local governments are mandated to drive development but often operate on shoestring budgets. This study critically examines the performance of Internally Generated Funds (IGF) collection in the Ahafo Ano-South West District in Ghana, with a specific focus on revenue trends from 2016 to 2022. Using time series data, the study applies the Corrected Revenue Collection Index (CRCI) to assess how well various revenue streams performed. The findings reveal a striking pattern where property rates emerged as the most consistent and high-performing source of IGF, while revenues from land royalties and administrative fees lagged significantly. Rental income from lands and buildings, and licenses, showed moderate but promising results. These disparities highlight the untapped potential within local revenue systems and point to key areas for reform and strategic investment. By offering new empirical insights, this study contributes meaningfully to the broader discourse on local government financing and sustainable development. It underscores the urgent need for improved revenue mobilization strategies and greater fiscal accountability to empower district assemblies in Ghana and similar contexts to deliver on their developmental mandates. Strengthening IGF collection is not just a financial necessity but a pathway to stronger and more self-reliant local governance. Keywords: Revenue mobilization, Internally generated fund, District Assembly, Local Government, Decentralization, Ghana.