We introduce the DeTerministic Virtual Machine (DTVM) Stack, a next-generation smart contract execution framework designed to address critical performance, determinism, and ecosystem compatibility challenges in blockchain networks. Building upon WebAssembly (Wasm) while maintaining full Ethereum Virtual Machine (EVM) ABI compatibility, DTVM introduces a Deterministic Middle Intermediate Representation (dMIR) and a hybrid lazy-JIT compilation engine to balance compilation speed and execution efficiency. DTVM further accommodates diverse instruction set architectures (e.g., EVM, RISC-V) through modular adaptation layers. This enables seamless integration with DTVM's hybrid lazy-JIT compilation engine, which dynamically optimizes performance while preserving deterministic execution guarantees across heterogeneous environments. The key contributions including: 1). The framework achieves up to 2$\times$ acceleration over evmone in dominant Ethereum contract (e.g. ERC20/721/1155) execution and reduces fibonacci computation latency by 11.8$\sim$40.5% compared to Wasm based VMs. 2). A novel trampoline hot-switch mechanism enables sub-millisecond (0.95ms) post-deployment invocation times, outperforming up to about 23$\times$ in compilation and invocation efficiency. 3). It supports multi-language development (Solidity, C++, Rust, Java, Go, and AssemblyScript) through unified bytecode conversion while maintaining EVM ABI compatibility for seamless invocation. It reduces machine code object sizes by 30.0$\sim$72.6%, coupled with a minimized Trusted Computing Base. 4). It offers SmartCogent, an AI-driven full-stack development experience, leveraging fine-tuned LLMs and retrieval-augmented generation to automate tasks across the smart contract lifecycle: development, debugging, security auditing, and deployment. DTVM Stack has been open-sourced (https://github.com/DTVMStack).
Esam Mahdi, Carlos MartÃn-Barreiro, Xavier Cabezas
In this article, we introduce a novel deep learning hybrid model that integrates attention Transformer and Gated Recurrent Unit (GRU) architectures to improve the accuracy of cryptocurrency price predictions. By combining the Transformer's strength in capturing long-range patterns with the GRU's ability to model short-term and sequential trends, the hybrid model provides a well-rounded approach to time series forecasting. We apply the model to predict the daily closing prices of Bitcoin and Ethereum based on historical data that include past prices, trading volumes, and the Fear and Greed index. We evaluate the performance of our proposed model by comparing it with four other machine learning models: two are non-sequential feedforward models: Radial Basis Function Network (RBFN) and General Regression Neural Network (GRNN), and two are bidirectional sequential memory-based models: Bidirectional Long-Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU). The performance of the model is assessed using several metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), along with statistical validation through the nonparametric Friedman test followed by a post hoc Wilcoxon signed rank test. The results demonstrate that our hybrid model consistently achieves superior accuracy, highlighting its effectiveness for financial prediction tasks. These findings provide valuable insights for improving real-time decision making in cryptocurrency markets and support the growing use of hybrid deep learning models in financial analytics.
In recent years, Learning Management Systems (LMS) have acquired substantial appeal, notably because to the COVID-19 pandemic, delivering greater efficacy and efficiency. Within LMS, online tests have evolved as a crucial instrument for measuring students’ performance and knowledge of course content, playing a vital part in deciding their development. It is critical that online test results be both trustworthy and easily accessible. Students' grades might be negatively affected by any security flaw, such hacking. Conventional online test systems frequently store data centrally in databases like MySQL, leaving them subject to unwanted access and modification. Secure, peer-to-peer administration and assessment of academic tests is made possible in this article using a blockchain-based infrastructure. To guarantee data integrity, the framework utilises hashing algorithms. To strengthen security, it incorporates proof of stake processes. Blockchain effectively protects data integrity due to its decentralised data storage and the use of cryptographic hashing for each block. The study illustrates the usage of blockchain for designing online tests, recording each question and response directly on the blockchain. We were able to do this by developing a module that communicates with Moodle, an LMS. By comparing it to Moodle's default centralised storage, our addon alters the storage of exam results, making the data stored on the blockchain safe and impenetrable. Exam data is securely encrypted using the blockchain, which prevents tampering and ensures its integrity. Based on our findings, there are no inconsistencies when comparing data saved on the blockchain to Moodle's conventional method. To safeguard student information from tampering, the blockchain network offers a trustworthy and unchangeable platform. Finally, our blockchain-based paradigm provides a strong answer to the problem of how to make online test scores more secure and trustworthy. We guarantee data integrity and transparency by using blockchain's decentralised and tamper-proof nature. This allows for a more reliable evaluation of academic achievement.
Faris Kamil Hasan Mihna, Hazim Akram Sallal, Lobna Abdalhusen Easa Al-Seedi, Hasan Ali Al- Tameemi · 7 authors
The metaverse indicates an ever-evolving digital ecosystem where virtual real estate has now become an asset class. These properties, subject to smart contracts on the blockchain and represent as non-fungible tokens (NFTs), gives rise to new legal and cyber issues due to the decentralized and dematerialized nature of these digital assets .This paper proposes a machine learning approach to classify the digital real estate contracts into Ownership and Lease contracts. The study utilizes a dataset of one thousand digital real estate contracts collected from platforms such as Decentraland and The Sandbox. The dataset also included attributes such as plot size, plot location, transaction value, and contract duration. Preprocessing of data included encoding categorical data, standardization of numerical variables, and UTF-8 encoded text to preserve data quality. Two classification models were used: Logistic Regression and Random Forest. The model's evaluation used accuracy, precision, recall, and F1-score as evaluation criteria. The Random Forest outperformed with a perfect classification score showing that it may have been better suited to dealing with the complexity and dimensionality of the dataset. The outcomes of the study highlight the role AI could play in automating the analysis of contracts, at the same time highlighting that cybersecurity practices are important when working with data. The framework of this study seeks to support the development of a regulatory regime and add further transparency to real estate contracts in the metaverse - as a scalable tool for future digital real estate management.
Blockchain consensus protocols enable participants to agree on consistent views of the blockchain that may be ahead or behind relative to each other but do not fork into different chains. A number of recently popular Byzantine-fault-tolerant (BFT) protocols first construct a directed acyclic graph (DAG) that partially orders transactions, then linearize the DAG into a blockchain that totally orders transactions. The definitions and correctness proofs of these DAG-based protocols typically assume that the set of participants is fixed, which is impractical in long-lived blockchains. Additionally, only a few of those proofs have been machine-checked, uncovering errors in some published proofs. We developed a formal model of a DAG-based BFT protocol with dynamic stake, where participants can join and leave at every block, with stake used to weigh decisions in the protocol. We formally proved that blockchains never fork in the model, also clarifying how BFT bounds on faulty participants generalize to these highly dynamic sets of participants. Our model and proofs are formalized in the ACL2 theorem prover, apply to arbitrarily long executions and arbitrarily large system states, and are verified in 1 minute by ACL2.
Abstract The rise of Decentralized Finance (DeFi) is revolutionizing the financial sector by leveraging blockchain technology to offer alternatives to traditional banking, lending, and investment mechanisms. By eliminating intermediaries, DeFi facilitates peer-to-peer transactions through smart contracts and decentralized applications (dApps), enhancing efficiency, transparency, and financial inclusion. This paper explores the fundamental components of DeFi, including decentralized exchanges (DEXs), lending platforms, stablecoins, and yield farming, while assessing their implications for conventional financial institutions. Despite its rapid growth, DeFi presents several challenges, including regulatory uncertainty, security vulnerabilities, smart contract risks, and liquidity constraints. The absence of centralized oversight raises concerns regarding compliance, fraud, and investor protection. Moreover, the volatility of digital assets and reliance on algorithmic protocols introduce financial stability risks. Through case studies and market analysis, this study evaluates whether DeFi can complement or disrupt traditional financial systems. While DeFi offers significant innovation and democratization of financial services, its long-term sustainability depends on regulatory developments, technological advancements, and mainstream adoption. By addressing existing limitations, DeFi has the potential to reshape financial intermediation, bridging the gap between decentralized and traditional finance in the evolving digital economy. Keywords: Decentralized Finance, Blockchain, Smart Contracts, Financial Intermediation, Banking Disruption, FinTech, Regulatory Challenges .
Mohammad R. Shakournia, Pooya Jamshidi, Hamid Reza Faragardi, Nasser Yazdani
Blockchain technology offers a decentralized and secure method for storing and authenticating data, rendering it well-suited for various applications such as digital currencies, supply chain management, and voting systems. However, the decentralized nature of blockchain also exposes it to vulnerabilities, particularly Byzantine faults, which arise when nodes in the network behave maliciously or encounter unexpected failures. Such incidents can result in inconsistencies within the blockchain and, in extreme scenarios, lead to a breakdown in consensus. Byzantine fault-tolerant consensus algorithms are crafted to tackle this challenge by ensuring that network nodes can agree on the blockchain's state even in the presence of faulty or malicious nodes. To bolster the system's resilience against these faults, it is imperative to detect them within the system. However, our examination of existing literature reveals a prevalent assumption: solutions typically operate under constraints regarding the number of faulty nodes. Such constraints confine the proposed solutions to ideal environments, limiting their practical applicability. In response, we propose a novel approach inspired by social paradigms, employing a trusted and fully monitored communication sub-process to detect Byzantine nodes. Upon detection, these nodes can be either disregarded in the consensus-building process, subjected to penalties, or undergo modifications as per the system's policy. Finally, we statistically demonstrate that our approach achieves a detection probability that exceeds 95\% for Byzantine nodes. In essence, our methodology ensures that if Byzantine nodes exhibit malicious behavior, healthy nodes can identify them with a confidence level of 95\%.
Edge computing-based Next-Generation Wireless Networks (NGWN)-IoT offer enhanced bandwidth capacity for large-scale service provisioning but remain vulnerable to evolving cyber threats. Existing intrusion detection and prevention methods provide limited security as adversaries continually adapt their attack strategies. We propose a dynamic attack detection and prevention approach to address this challenge. First, blockchain-based authentication uses the Deoxys Authentication Algorithm (DAA) to verify IoT device legitimacy before data transmission. Next, a bi-stage intrusion detection system is introduced: the first stage uses signature-based detection via an Improved Random Forest (IRF) algorithm. In contrast, the second stage applies feature-based anomaly detection using a Diffusion Convolution Recurrent Neural Network (DCRNN). To ensure Quality of Service (QoS) and maintain Service Level Agreements (SLA), trust-aware service migration is performed using Heap-Based Optimization (HBO). Additionally, on-demand virtual High-Interaction honeypots deceive attackers and extract attack patterns, which are securely stored using the Bimodal Lattice Signature Scheme (BLISS) to enhance signature-based Intrusion Detection Systems (IDS). The proposed framework is implemented in the NS3 simulation environment and evaluated against existing methods across multiple performance metrics, including accuracy, attack detection rate, false negative rate, precision, recall, ROC curve, memory usage, CPU usage, and execution time. Experimental results demonstrate that the framework significantly outperforms existing approaches, reinforcing the security of NGWN-enabled IoT ecosystems
Yu Gao, Yu Zhang, Matija Piškorec, Claudio J. Tessone
Monero, a privacy-focused cryptocurrency, employs a decentralized peer-to-peer (P2P) network that plays a critical role in transaction propagation and consensus formation. While much research has explored Monero's privacy transaction mechanisms, its underlying P2P network architecture has remained relatively underexplored. In this study, building on our recent work on Monero network detection, we further investigate the network topology of Monero's P2P structure, which has evolved following recent protocol updates that enhanced security by obscuring peer information. Using k-core decomposition, we confirm that the Monero network exhibits a core-periphery structure, where a tightly interconnected core of supernodes is crucial for maintaining network cohesion, while peripheral nodes rely on these core nodes for connectivity. This structure explains why targeting central nodes does not easily lead to the rapid disintegration of the network's largest connected component while also providing a deeper understanding of the true architecture of Monero's peer protocol.
The Monero blockchain enables anonymous transactions through advanced cryptography in its peer-to-peer network, which underpins decentralization, security, and trustless interactions. However, privacy measures obscure peer connections, complicating network analysis. This study proposes a method to infer peer connections in Monero's latest protocol version, where timestamp data is unavailable. We collect peerlist data from TCP flows, validate our inference algorithm, and map the network structure. Our results show high accuracy, improving with longer observation periods. This work is the first to reveal connectivity patterns in Monero's updated protocol, providing visualizations and insights into its topology. Our findings enhance the understanding of Monero's P2P network, including the role of supernodes, and highlight potential protocol and security improvements.
Gerardo Iuliano, Luigi Allocca, Matteo Cicalese, Dario Di Nucci
The security of smart contracts is critical in blockchain systems, where even minor vulnerabilities can lead to substantial financial losses. Researchers proposed several vulnerability detection tools evaluated using existing benchmarks. However, most benchmarks are outdated and focus on a narrow set of vulnerabilities. This work evaluates whether mutation seeding can effectively inject vulnerabilities into Solidity-based smart contracts and whether state-of-the-art static analysis tools can detect the injected flaws. We aim to automatically inject vulnerabilities into smart contracts to generate large and wide benchmarks. We propose MuSe, a tool to generate vulnerable smart contracts by leveraging pattern-based mutation operators to inject six vulnerability types into real-world smart contracts. We analyzed these vulnerable smart contracts using Slither, a static analysis tool, to determine its capacity to identify them and assess their validity. The results show that each vulnerability has a different injection rate. Not all smart contracts can exhibit some vulnerabilities because they lack the prerequisites for injection. Furthermore, static analysis tools fail to detect all vulnerabilities injected using pattern-based mutations, underscoring the need for enhancements in static analyzers and demonstrating that benchmarks generated by mutation seeding tools can improve the evaluation of detection tools.
This paper investigates real-time detection of spoofing activity in limit order books, focusing on cryptocurrency centralized exchanges. We first introduce novel order flow variables based on multi-scale Hawkes processes that account both for the size and placement distance from current best prices of new limit orders. Using a Level-3 data set, we train a neural network model to predict the conditional probability distribution of mid price movements based on these features. Our empirical analysis highlights the critical role of the posting distance of limit orders in the price formation process, showing that spoofing detection models that do not take the posting distance into account are inadequate to describe the data. Next, we propose a spoofing detection framework based on the probabilistic market manipulation gain of a spoofing agent and use the previously trained neural network to compute the expected gain. Running this algorithm on all submitted limit orders in the period 2024-12-04 to 2024-12-07, we find that 31% of large orders could spoof the market. Because of its simple neuronal architecture, our model can be run in real time. This work contributes to enhancing market integrity by providing a robust tool for monitoring and mitigating spoofing in both cryptocurrency exchanges and traditional financial markets.
Decentralized exchanges, such as those employing constant product market makers (CPMMs) like Uniswap V2, play a crucial role in the blockchain ecosystem by enabling peer-to-peer token swaps without intermediaries. Despite the increasing volume of transactions, there remains limited research on identifying optimal trading paths across multiple DEXs. This paper presents a novel line-graph-based algorithm (LG) designed to efficiently discover profitable trading routes within DEX environments. We benchmark LG against the widely adopted Depth-First Search (DFS) algorithm under a linear routing scenario, encompassing platforms such as Uniswap, SushiSwap, and PancakeSwap. Experimental results demonstrate that LG consistently identifies trading paths that are as profitable as, or more profitable than, those found by DFS, while incurring comparable gas costs. Evaluations on Uniswap V2 token graphs across two temporal snapshots further validate LG's performance. Although LG exhibits exponential runtime growth with respect to graph size in empirical tests, it remains viable for practical, real-world use cases. Our findings underscore the potential of the LG algorithm for industrial adoption, offering tangible benefits to traders and market participants in the DeFi space.
Building on our prior threshold-based analysis of six months of Poloniex trading data, we have extended both the temporal span and granularity of our study by incorporating minute-level OHLCV records for 1021 tokens around each confirmed pump-and-dump event. First, we algorithmically identify the accumulation phase, marking the initial and final insider volume spikes, and observe that 70% of pre-event volume transacts within one hour of the pump announcement. Second, we compute conservative lower bounds on insider profits under both a single-point liquidation at 70% of peak and a tranche-based strategy (selling 20% at 50%, 30% at 60%, and 50% at 80% of peak), yielding median returns above 100% and upper-quartile returns exceeding 2000%. Third, by unfolding the full pump structure and integrating social-media verification (e.g., Telegram announcements), we confirm numerous additional events that eluded our initial model. We also categorize schemes into "pre-accumulation" versus "on-the-spot" archetypes-insights that sharpen detection algorithms, inform risk assessments, and underpin actionable strategies for real-time market-integrity enforcement.
Fernando Castillo, Jonathan Heiss, Sebastian Werner, Stefan Tai
Blockchain and distributed ledger technologies (DLTs) facilitate decentralized computations across trust boundaries. However, ensuring complex computations with low gas fees and confidentiality remains challenging. Recent advances in Confidential Computing -- leveraging hardware-based Trusted Execution Environments (TEEs) -- and Proof-carrying Data -- employing cryptographic Zero-Knowledge Virtual Machines (zkVMs) -- hold promise for secure, privacy-preserving off-chain and layer-2 computations. On the other side, a homogeneous reliance on a single technology, such as TEEs or zkVMs, is impractical for decentralized environments with heterogeneous computational requirements. This paper introduces the Trusted Compute Unit (TCU), a unifying framework that enables composable and interoperable verifiable computations across heterogeneous technologies. Our approach allows decentralized applications (dApps) to flexibly offload complex computations to TCUs, obtaining proof of correctness. These proofs can be anchored on-chain for automated dApp interactions, while ensuring confidentiality of input data, and integrity of output data. We demonstrate how TCUs can support a prominent blockchain use case, such as federated learning. By enabling secure off-chain interactions without incurring on-chain confirmation delays or gas fees, TCUs significantly improve system performance and scalability. Experimental insights and performance evaluations confirm the feasibility and practicality of this unified approach, advancing the state of the art in verifiable off-chain services for the blockchain ecosystem.
Fernando Castillo, Oscar Castillo, Eduardo Brito, Simon Espinola
Decentralized Autonomous Machines (DAMs) represent a transformative paradigm in automation economy, integrating artificial intelligence (AI), blockchain technology, and Internet of Things (IoT) devices to create self-governing economic agents participating in Decentralized Physical Infrastructure Networks (DePIN). Capable of managing both digital and physical assets and unlike traditional Decentralized Autonomous Organizations (DAOs), DAMs extend autonomy into the physical world, enabling trustless systems for Real and Digital World Assets (RDWAs). In this paper, we explore the technological foundations, and challenges of DAMs and argue that DAMs are pivotal in transitioning from trust-based to trustless economic models, offering scalable, transparent, and equitable solutions for asset management. The integration of AI-driven decision-making, IoT-enabled operational autonomy, and blockchain-based governance allows DAMs to decentralize ownership, optimize resource allocation, and democratize access to economic opportunities. Therefore, in this research, we highlight the potential of DAMs to address inefficiencies in centralized systems, reduce wealth disparities, and foster a post-labor economy.
This paper studies Mean Field Games (MFGs) in which agent dynamics are given by jump processes of controlled intensity, with mean-field interaction via the controls and affecting the jump intensities. We establish the existence of MFG equilibria in a general discrete-time setting, and prove a limit theorem as the time discretization goes to zero, establishing equilibria in the continuous-time setting for a class of MFGs of intensity control. This motivates numerical schemes that involve directly solving discrete-time games as opposed to coupled Hamilton-Jacobi-Bellman and Kolmogorov equations. As an example of the general theory, we consider cryptocurrency mining competition, modeled as an MFG both in continuous and discrete time, and illustrate the effectiveness of the discrete-time algorithm to solve it.
Deng Zilong, Mustafa Muwafak Alobaedy, Mohd Nurul Hafiz Bin Ibrahim, Xiaocun Huang
This research proposes a blockchain-centered framework designed to address ongoing challenges related to data security, privacy, and interoperability in Electronic Healthcare Records (EHRs).As the healthcare sector increasingly relies on digitalized records, centralized EHR systems face significant risks, including data breaches, privacy violations, and limited compatibility across platforms.This study introduces a decentralized blockchain-based framework, built on Hyperledger Fabric, that leverages cryptographic methods, smart contracts, and distributed ledger technology to ensure secure data sharing, enhanced privacy, and structured access control.The research focuses on the theoretical development and practical evaluation of this framework.Using a design-based approach, the study incorporates a comprehensive literature review, framework development, and empirical testing to assess the framework's effectiveness.Key performance indicators include data integrity, security, system efficiency, and interoperability across diverse healthcare platforms.In conclusion, this framework offers a promising solution for securely managing healthcare data and ensuring interoperability, with potential applications extending beyond the healthcare sector.
Jiaxin Wang, Qian’ang Mao, Hongliang Sun, Jiaqi Yan
With the development of blockchain technology, crypto gambling has gained popularity due to its high level of anonymity. However, similar to traditional casinos, crypto casinos are controlled by a few internal Delegatees, making it impossible for them to achieve complete transparency and fairness. These delegatees are hidden among gamblers and are difficult to identify and distinguish in anonymous and large-scale blockchain transaction networks. This paper proposes an unsupervised dual-stage role identification method to adaptively identify key roles and hidden delegatees in label-sparse crypto casinos. Specifically, inspired by voting-style transaction patterns, we propose a novel voting influence metric for key node identification. This metric is based on one-dimensional structural entropy to capture global dissemination capability. Subsequently, we develop a multi-view graph neural network framework enhanced with two-dimensional global structural entropy minimization and self-supervised contrastive learning to improve the robustness and interpretability of hidden role partitioning. Experiments on real-world cases of the most mainstream blockchains-Ethereum, TRON, and Arbitrum-demonstrate that our proposed method effectively reveals distinct role compositions and collusion patterns, distinguishing between gamblers and delegatees. Our results achieve a higher match with identities confirmed by judicial authorities than existing methods, indicating the effectiveness and generalizability of our approach in enhancing security and regulation oversight.
Ruonan Chen, Ye Dong, Yizhong Liu, Tingyu Fan · 8 authors
Federated Learning (FL) is a distributed machine learning paradigm that allows multiple clients to train models collaboratively without sharing local data. Numerous works have explored security and privacy protection in FL, as well as its integration with blockchain technology. However, existing FL works still face critical issues. i) It is difficult to achieving poisoning robustness and data privacy while ensuring high model accuracy. Malicious clients can launch poisoning attacks that degrade the global model. Besides, aggregators can infer private data from the gradients, causing privacy leakages. Existing privacy-preserving poisoning defense FL solutions suffer from decreased model accuracy and high computational overhead. ii) Blockchain-assisted FL records iterative gradient updates on-chain to prevent model tampering, yet existing schemes are not compatible with practical blockchains and incur high costs for maintaining the gradients on-chain. Besides, incentives are overlooked, where unfair reward distribution hinders the sustainable development of the FL community. In this work, we propose FLock, a robust and privacy-preserving FL scheme based on practical blockchain state channels. First, we propose a lightweight secure Multi-party Computation (MPC)-friendly robust aggregation method through quantization, median, and Hamming distance, which could resist poisoning attacks against up to <50% malicious clients. Besides, we propose communication-efficient Shamir's secret sharing-based MPC protocols to protect data privacy with high model accuracy. Second, we utilize blockchain off-chain state channels to achieve immutable model records and incentive distribution. FLock achieves cost-effective compatibility with practical cryptocurrency platforms, e.g. Ethereum, along with fair incentives, by merging the secure aggregation into a multi-party state channel. In addition, a pipelined Byzantine Fault-Tolerant (BFT) consensus is integrated where each aggregator can reconstruct the final aggregated results. Lastly, we implement FLock and the evaluation results demonstrate that FLock enhances robustness and privacy, while maintaining efficiency and high model accuracy. Even with 25 aggregators and 100 clients, FLock can complete one secure aggregation for ResNet in 2 minutes over a WAN. FLock successfully implements secure aggregation with such a large number of aggregators, thereby enhancing the fault tolerance of the aggregation.
James Evans, Davide Cassanmagnago, Tathagata Chatterji, Andrew Irvin · 10 authors
Urban innovation has emerged as a priority to address global climate and sustainability goals. As the world continues to urbanise, global organisations are encouraging cities to spearhead innovation to meet global carbon reduction targets and reduce inequalities (UN Habitat, 2022). Finding new ways to build, manage and live in cities is critical to provide all humans with adequate nutrition, shelter, access to products and services including mobility, leisure, health, energy, and education (UN, 2023). Focusing on the potential role of urban innovation is logical -cities drive innovation by bringing diverse people, knowledge and resources together (Florida et al., 2018). In many ways urbanisation represents the manifestation of new technologies and forms of social organisation, from the hydraulic cities of Mesopotamia 7000 years ago through to industrial and post-industrial cities today (Jacobs, 1969;Athey et al., 2008). However, while the link between cities and innovation is longstanding, the idea of 'urban innovation' as a specific activity to discover new ways to develop, manage and inhabit cities in more sustainable ways is recent, distinct and less familiar. This framing of urban innovation reflects established approaches to governance for sustainability, layered over the distinctive characteristics and capabilities of cities. In this paper we define urban innovation as a directed activity that takes place in and is driven by cities as a way to address local challenges that will contribute to the delivery of global climate and sustainability goals. The emergence of the urban innovation as an influential global policy agenda pushes cities once more to the front of the battle against climate change. This paper examines the emergence of urban innovation as a discrete and influential policy goal to deliver global climate and sustainability goals and outlines a research agenda to help achieve this.The paper is authored with leading global organisations in this space -the United Nations Climate Change Global Innovation Hub and the Global Covenant of Mayors. The United Nations is the leading global organisation coordinating international action on key challenges including climate change. The United Nations Global Innovation Hub for Climate Change promotes innovation as a catalyst for achieving global climate and sustainability targets. Their first global dialogue series focused on cities specifically because of their potential to integrate technological, social, and policy innovations. The Global Innovation Hub emphasizes systemic approaches to urban innovation, fostering cross-sectoral collaboration and community engagement to create sustainable, climate resilient development in urban environments. The Global Covenant of Mayors (GCoM) represents an alliance of more than 13,000 cities combatting climate change. Through its Innovate4Cities initiative, launched in 2019 in response to the Edmonton Cities and Climate Change Science Conference (Oke et al., 2022), the GCoM alliance outlines the knowledge gaps and action priorities for urban innovation, research and implementation, sharing data and best practices, and unlocking financing for scalable urban innovation projects. Both work closely with the Mission Innovation Urban Transitions Mission, which launched at COP 26 in 2021. This organisation empowers cities worldwide in their transition towards net-zero, resilient, and people-centred cities, mobilizing decision-makers across all levels of government to prioritize pathways enabled by clean energy and systemic innovation across all sectors and in urban governance (Urban Transitions Mission, 2024). The Urban Transitions Mission develops innovation systems capable of transforming cities to address climate change, including a focus on coordinating city level research and innovation challenges (European Commission, 2022).These organisations form part of a Global Innovation Alliance (GIA), which is engaging cities and relevant partners to build a worldwide urban innovation policy agenda for climate and sustainability goals. This policy agenda is complemented by the Global Research and Action Agenda on Cities and Climate Change Science, which provides a cross-sectoral, systems-based foundation for knowledge to enable urban innovation. This document launched at the Cities and Climate Change Science Conference in Edmonton, Canada in 2018, and in its latest iteration following the 2024 Innovate4Cities Conference, serves as an evidence base for the knowledge and innovation outputs being co-created by researchers, governments, businesses, and civil society (Global Covenant of Mayors for Climate and Energy, UN-Habitat & University of Melbourne, 2024). These initiatives demonstrate the growing ambition for science to inform action and facilitates the exchange of knowledge to support stronger and more ambitious urban solutions and partnerships.The priorities and initiatives of these international organisations individually and collectively show that urban innovation has become central to the delivery of global climate and sustainability goals at multiple levels of governance. Urban innovation is being promoted as a way to develop solutions to challenges in sectors ranging from energy and transport to housing and social justice. By cutting across domains, and more closely engaging economic and knowledge production, urban innovation represents an exciting new way to create more sustainable cities. However, as Bai (2024) notes in relation to the potential of cities to deliver the SDGs, action on the ground requires clarity of roles. The term 'urban innovation' is used widely now in policy and research, but in different ways by different groups to mean different things, not necessarily with sustainability or justice and equity as core values. Realising the potential of urban innovation to deliver climate and sustainability goals requires greater clarity in defining what exactly it is, how it should be done in practice, and who is supposed to be doing it. Given the political weight and resource behind it, researchers have an important role to play in helping to ensure that urban innovation is an equitable and effective. Addressing rather than exacerbating inequalities and making sure successful innovations are actually transformative lie at the forefront of this challenge. This Grand Challenge article builds upon the high levels of current ambition and activity associated with key international initiatives like the Global Innovation Alliance and Global Research and Action Agenda on Cities and Climate Change Science to frame a broad and inclusive research agenda for sustainable cities and urban innovation.Innovation relates to the development of new services, products or processes that generate value through being of use to customers or users. The concept of innovation was arguably first framed as a place-based agenda in scholarship and policies relating to regional innovation clusters. These often developed near universities, most famously in the case of the Silicon Valley innovation cluster in California that grew up around Stanford University. The focus was on supply-side technology development though, with little consideration of the demands of cities and their residents. The emergence of the idea of smart cities in the early Twenty-First Century dramatically changed this, positioning innovation as a key element of place-making. Urban innovation became an activity focusing on how to deploy digital technologies to improve cities and urban services (Angelidou, 2015). Successful examples include the replacement of traditional incandescent bulbs with LED for street lighting, and the adoption of digital information and payment platforms for citizens to engage with municipal authorities. Evidence suggests that cities investing in smart city projects tend to generate economic benefits associated with traditional innovation, gauged through measures such as numbers of patent filings (Caragliu and Del Bo, 2019). A rapidly growing body of work from China suggests a correlation between the innovative capacity of cities and their environmental performance (see for example Tan, et al., 2022;Yang, et al., 2022;Guo, et al., 2023). Work in this context has also identified a positive relationship between the existence of a digital economy and the achievement of low carbon transitions in larger Chinese cities (Liu et al., 2024). However, it is the distinct idea of urban innovation as a directed process that brings different stakeholders together to develop solutions to problems in their own cities that has gained traction beyond smart cities. In relation to environmental policy, the need for practical action to complement international commitments on climate change and sustainable development, coupled with the emergence of world cities as major political actors (Bulkeley, 2013), has provided fertile ground for urban innovation to emerge as a potential driver of societal change. Urban innovation represents the culmination of a longer-term metamorphosis of cities from being framed as sources of sustainability problems to sources of sustainability solutions (Angelo and Wachsmuth, 2020). In principle, urban innovation offers a way to situate and address the United Nation Sustainable Development the context of cities and 2024). 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Urban innovation is also cross-sectoral rather than focused on the of a specific These urban innovation to develop the of solutions to be sustainable and but more The and use of energy, and housing stakeholders for these urban innovation different to forms of innovation, and the to which urban innovation be like innovation and innovation, the to address with the of the urban innovation research community is of how urban innovation takes place in practice, the of urban innovation that are successful in urban problems and societal and the as and that are to successful urban policy agenda for urban innovation around widely and capacity The first of these how to achieve sustainability goals over the but specific in the urban The has emerged from specific challenges urban innovation have in through in urban The represents current around how to that enable urban innovation. The of the paper the key of and research priorities to help forms the focus of the urban innovation policy local urban innovation are with engagement to ensure innovation processes include all groups et al., 2018). The iteration of the Global Research and Action Agenda for Cities and Climate Change Science that and between all levels of government and stakeholders are for the Sustainable Development and change (Global Covenant of & University of Melbourne, 2024). collaboration with businesses, and stakeholders to develop solutions to the and of specific and for in more the of urban innovation and benefits are more Innovation that and benefits all is a priority for urban innovation, but a series of it is for cities to who should be in the of problems and how to to and the of different of community engagement in different et al., 2018). These the political context in which cities and local The of political and adoption of which tend to over it more to stakeholders in actors are on the urban as traditional municipal are to or et al., and play important in policy in smart cities et al., The as knowledge between research and practice, often helping to create of cities that work the of these new have for et al., 2022), the to which the and of urban innovation has little economic innovation as a of the role of and societal challenges as et al., 2024). This is as urban innovation for climate and sustainability goals takes place through governance and associated political and of different et al., 2023). the of governance are and cities have often to beyond for governance to how to change on the & This has to the emergence of new forms of governance that are more and to local Urban and innovation for with policy, and services that traditional forms of governance not et al., 2018). place-based to the need to stakeholders together to urban innovation. technologies play a transformative citizens to in urban governance in and ways that drive more and place forms of innovation & 2024). A has emerged around urban and but are major gaps in of how place-based of innovation integrate or social, environmental and economic and and drive in different urban et al., problems that most cities in the SDGs, are more but less like adequate housing and with which innovation. research on urban sustainability et al., research on innovation to focus on cities in the Global actors and the and the of the urban live in cities in the Global the of urban knowledge and systems in urban innovation are less in of their and In practice, this that while the context for urban innovation is different in the Global approaches and policy around urban sustainability challenges tend to of the Global et al., of innovation, civil society often innovation and the a less role has by development for in relation to the and achievement of the et al., and is but less in the urban innovation in knowledge and provide solutions to sustainability have developed sustainable like which and to or used traditional to to the of climate change et al., innovation is and solutions local resources et al., but in relation to transport include with in to greater or with for transport in In the context of urban city often provide like and energy for in and ways This urban innovation is to and such as innovation not associated with innovation on new solutions that like the of urban in of urban et al., and their in like 2024). The need for innovation that and benefits social groups is in Global and Global et al., in to include citizens in the of regional innovation challenges et al., urban innovation to address global climate and sustainability goals more and researchers help the of what innovation by its governance and and that of innovation are to be an frame for of inclusive the of this be to for the of innovations the actors and their the of and the in which a work is to and how forms of innovation, like innovation, or et al., A priority has identified in the on urban the of what as innovation is critical to new forms of governance and on more and processes of innovation and while policy and have to focus on innovation as a way to create new cities are a leading role in the of technologies including and that and from technologies and services be as governance innovations in as as ways to drive innovation to the that is and 2020). include cities that have to transport technologies et al., Work on the governance of 'urban or the of technologies in cities, is but represents an important research urban innovation for climate and sustainability need for a societal change to achieve climate and sustainability goals has a policy focus on how successful initiatives up to drive projects address challenges in a specific place in a up but the solutions are to more widely as the governance of cities to of different In of the challenges of place-based approaches is successful local initiatives beyond the to its by or its it more and et al., et al., social and support and fostering a of cross-sectoral in these is in and research as across that are including the and are the role of and knowledge in urban innovation and This the between and fostering inclusive forms of innovation. on environmental change the of the in including the and that how define or systems and as as their and through with and to urban innovation in more inclusive and researchers this beyond the focus on and policy to with less innovation actors and requires new for governance and policy on the and of innovation governance et al., 2024). and on action are challenges in the climate research and response and policy are key at the city and levels to create for urban innovations to deliver climate and sustainability goals et al., and Global Covenant of Mayors for Climate and Energy, UN-Habitat & University of Melbourne, 2024). change requires innovation to deliver value across a of including the and as as civil and over a than achieve this, development and their to support initiatives that prioritize sustainability rather than and financing to the achievement of specific sustainability In the case of for payment to upon the energy through In the urban policies and innovation and their like and innovation have a role to These are not as for knowledge and development, the for to and for innovative solutions to Their role in urban innovation for sustainability, in relation to and is but less economy including services and for urban et al., 2022). and the and of or products et al., In and for that for and and reduce carbon and in the et al., 2022). urban innovations is the of societal challenges the of and often for local benefits that in et al., 2022). This process requires at the of innovation processes that also be through specific like innovation urban innovation policy focus relates to the of key including and to build the capacity of cities to et define policy capacity as the and that are used to and has a in relation to environmental et (2024) show how key international on climate change and sustainability, the first on and Urban Agenda in research on urban climate and capacity has towards the of stakeholders and across cities and local to The Climate Innovation launched by the Global Covenant of Mayors (2024) in with a The to regional and of climate innovation at their urban and urban climate innovation as of urban stakeholders to and new or products or processes to to the challenges by climate local at the of this process the policy of urban innovation, while engagement from civil and in the The Climate Innovation is to from focused on governance and systems the on how their work upon systems and of climate resilient development across the their The Cities Mission represents influential and important governance innovation to help cities drive the energy climate transition by for to et al., The ways in which such capacity including its and and to change as are critical research digital technologies data and digital of urban that the capabilities of cities to problems and potential solutions 2018). cities a of problems and the of innovations to address but be resource to et al., or not to cities in ways that use 2018). in many cities the most transport data is by like forms of and environmental data are and to this, major of urban sustainability have little data and are important helping to and urban The potential of digital and to improve urban is but to be from in urban research help ensure technologies to the priorities and of and new forms and of governance that are more to local and et al., 2024). also the of cities to and use which represents a in urban governance et al., in which like these have transformative sustainability benefits important for capacity of cities to also on their to from their innovation and what In relation to the and a that and the local of innovation including its on and Evidence how are are to how to goals like innovation with sustainability and social justice et al., et al., 2022). measures the of the on the This such as or to the of the from Evidence the by different in urban innovation is to help cities to their capacity the process including and resource et al., 2019). This help and improve more from urban is and tend to focus on such as the role of or policy in specific for rather than the between different and their on innovation for climate and sustainability goals et al., 2023). and of not the and but of the for urban innovation et al., as that that requires organisations to not their but also their and political and urban engage with the processes of change and governance 2023). The concept of transformative which is the to and is not a but a of the relationship between the and actors et al., 2024). urban innovation projects are a of and to and drive this process & their of transformative innovation policy more et up the capacity of policy to and transformative as a key be by with actors a of critical in the the in groups became for the local by with the and through to deliver and et al., 2022). of or is but to what and et al., urban innovation capacity global to evidence and successful of data and evidence the and of initiatives has as being of critical to the capacity of cities to address sustainability and climate challenges across a of from to and local solutions to across and to be in diverse such as the Cities and or the Urban Innovation Hub in for how urban innovation be at and These are in the platforms for cities to and build across However, the growing of city focused on global challenges such as climate change, of the of these is often to policy such as to the delivery of carbon reduction goals et al., city are to play a role in global urban we need more process of how and for systemic are in innovative capacity and innovation has become central to global policy to sustainability and climate change. as a way to societal policy on urban innovation around the of and capacity This paper has identified a series of how these play in specifically urban with research priorities to support urban innovation for global sustainability and climate of innovation. Urban sustainability researchers are to contribute to this Urban innovation for climate and sustainability goals across all of how we manage and live in cities, requires that how we innovation and in urban Urban innovation the role of cities in societal change, for cities as key in global climate and sustainability The urban innovation agenda to be and that urban sustainability A community of researchers in making initiatives to improve cities, and as and have an important role to play in more inclusive and forms of place-based urban innovation et al., innovation is a to research and is by The of and to urban innovation for sustainability and climate change up a greater role for and research in urban innovation towards sustainability and climate goals. systems approaches that and data to deliver and equitable action in climate resilient development of In approaches that integrate multiple case including the of and challenges a and of practical and The to which urban innovation be like innovation and innovation, In many the of urban innovation are being the and of urban governance. In urban governance not with this Work on urban provides a of and but is more to in innovation policy and but to goals like cross-sectoral and change of research to be the urban innovation In with the of the to Action on Climate Research and this agenda research that is with stakeholders and to cities, and access to and case in Sustainable Cities was on a to and sharing knowledge to help cities become more Cities are around the world but not have the or resources to the role as a global research community is to evidence and that help urban innovation as and as
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Smart Cities and Technologies
Innovative Approaches in Technology and Social Development
The Engineering, Procurement, and Construction (EPC) industry faces significant financial management challenges due to the complexity of project financing, milestone-based payments, and multi-stakeholder collaboration. Traditional on-premise ERP financial systems are often inefficient, leading to delays in financial reporting, security vulnerabilities, and regulatory compliance difficulties. This study explores the development of cloud-based financial solutions tailored to the EPC industry, examining the benefits, challenges, and applicability of existing models such as Software as a Service (SaaS), Platform as a Service (PaaS), and Blockchain-based decentralized finance (DeFi). A Hybrid Cloud-Based Financial Framework is proposed, integrating SaaS for accounting, PaaS for customization, and Blockchain for secure transactions. Experimental validation demonstrates that cloud adoption reduces financial processing time by 87.5%, enhances cash flow visibility, improves security, and increases regulatory compliance efficiency by 40%. This paper highlights the importance of AI-driven predictive analytics, automated compliance, and hybrid cloud models in modern EPC finance and proposes strategies for overcoming integration challenges, cybersecurity risks, and workforce adoption barriers. Future research should focus on scaling hybrid cloud solutions globally and integrating AI-powered risk assessment tools.
Sharding blockchain networks face significant scalability challenges due to high frequencies of cross-shard transactions and uneven workload distributions among shards. To address these scalability issues, account migration offers a promising solution. However, existing migration solutions struggle with the high computational overhead and insufficient capture of complex transaction patterns. We propose AERO, a deep reinforcement learning framework to facilitate efficient account migration in sharding blockchains. AERO employs a prefix-based grouping strategy to enable group-level migration decisions and capture complex transaction patterns and relationships between accounts. We also implement a sharding blockchain system called AEROChain, which integrates AERO and aligns with the blockchain decentralization principle. Extensive evaluation with real Ethereum transaction data demonstrates that AERO improves the system throughput by 31.77% compared to existing solutions, effectively reducing cross-shard transactions and balancing shard workloads.
Bitcoin telah menunjukkan volatilitas harga yang tinggi. Hal ini membawa risiko besar bagi investor, tetapi juga memberikan peluang besar bagi investor. Salah satu metode yang menarik untuk prediksi harga Bitcoin yang memiliki volatilitas tinggi adalah algoritma LSTM, sebuah varian RNN yang dapat memproses data deret waktu serta mengingat informasi jangka panjang dan pendek secara efektif. Penelitian ini merupakan pembaruan dari penelitian-penelitian sebelumnya yang umumnya hanya menggunakan satu lapisan LSTM. Dalam penelitian ini, dilakukan perbandingan antara dua skenario model LSTM untuk melihat sejauh mana arsitektur dan konfigurasi model mempengaruhi performa prediksi harga Bitcoin. Berdasarkan hasil penelitian yang sudah dilakukan pada Skenario 1 dengan menggunakan algoritma LSTM Double Layer 128, 64 neuron, 100 epoch, 32 batch size, data latih 80%, data uji 20%, dan optimasi adam, lebih baik dibandingkan Skenario 2 yang menggunakan algoritma LSTM Single Layer 50 neuron, 100 epoch, 32 batch size, data latih 80%, data uji 20%, dan optimasi adam. Skenario 1 menunjukkan tingkat akurasi yang cukup baik dalam memprediksi harga Bitcoin. Performa model algoritma Skenario 1 dievaluasi menggunakan metrik MSE dengan nilai 0.00044, RMSE dengan nilai 0.02119, MAE dengan nilai 0.01586, MAPE dengan nilai 2.51% dan R2 dengan nilai 0.98. Hubungan antara prediksi harga dan evaluasi kinerja ini penting, karena prediksi harga yang akurat menjadi dasar untuk menghitung potensi keuntungan dan risiko dari investasi Bitcoin. Dari hasil prediksi harga Bitcoin yang dihasilkan dari model Skenario 1, kemudian digunakan untuk mengevaluasi kinerja aset Bitcoin selama periode 2018–2024. Rata-rata Return pertahun Bitcoin sebesar 83.07%. Volatilitas sebesar 82.837. Sharpe Ratio sebesar 1.003 menunjukkan bahwa return yang diperoleh relatif sebanding dengan risiko yang diambil, yang dianggap cukup baik dalam konteks investasi berisiko tinggi.