Decentralized finance (DeFi) agents automate multi-transaction workflows such as swapping, lending, and vault management, but they also create process-level risk. A run can consist of individually valid calls while still becoming economically unsafe because an intermediate step leaves latent authority, weakens execution constraints, or accepts unverified external evidence. Existing defenses are often mismatched to this process-level risk. Off-chain preflight checks alone cannot protect against runtime deviations from the intended plan, and coarse on-chain allowlists are too weak to express the call-level intent that matters in DeFi. We present CheckpointAgent, a workflow-security architecture for checkpointed DeFi-agent execution. It composes manifest commitments, smart-account policy guards, post-state predicates, and attestation-gated advancement to constrain a run step by step and tie checkpoint advancement to verifiable evidence. Rather than judging safety only after a workflow finishes, CheckpointAgent checks whether each step remains consistent with the intended workflow and stops execution when the required conditions no longer hold. In the author-curated 27-scenario local-chain suite, the strongest evaluated setting preserves all 5 benign runs and prevents unsafe completion in all 22 adversarial runs, stopping them either through on-chain enforcement or through trusted-attestation advancement under the configured attester assumption. Under explicit trust assumptions and within the measured workflows and snapshots, checkpointed execution can materially reduce process-level risk without modifying target protocols.
Decentralized exchanges (DEXs) form a cornerstone of the decentralized finance (DeFi) ecosystem, processing token trades worth billions of dollars daily. Yet, a significant fraction of these trades are suboptimal: alternative routing paths could yield more target tokens. Addressing this inefficiency is both practically urgent and theoretically compelling. Building on the linear line-graph-based routing method of Zhang et al. (2025), we propose three key extensions that better capture real-world trading complexity. First, we introduce a breadth-first search (BFS) link iteration rule that reduces computational cost and average execution time without sacrificing profitability. Second, we design a route-splitting strategy that divides large trades into smaller ones, alleviating price slippage and increasing average trader profits, albeit at the cost of higher computational overhead. Third, we generalize the method beyond a single DEX to a multi-DEX aggregator setting, reflecting actual trading environments. Using empirical data from Uniswap V2 and Sushiswap V2, we demonstrate that these extensions substantially improve both computational efficiency and profitability, establishing a foundation for future routing enhancements.
David King Boison, Musah Osumanu Doumbia, Ahmed Antwi-Boampong, Frank Senyo Loglo ¡ 5 authors
This study explores the application of blockchain (BC) technology in enhancing terminal operations at West African seaports, with a specific focus on Tema Port in Ghana. The purpose is to address inefficiencies in cargo processing, traceability, and data integrity that often impede port performance. Using a multi-layered qualitative approach, including observation and value stream mapping, the study examines current operational challenges at Tema Port and proposes a BC adoption model tailored to ship operations, quay transfers, yard management, container freight stations, and receipt/delivery processes. The findings suggest that BC technology significantly improves transparency, operational efficiency, and data security across port processes, offering a unified ledger system accessible to all stakeholders. Based on these findings, the study recommends a phased BC implementation, beginning with targeted pilot programs to mitigate technological and infrastructural constraints common in developing regions. Implications for port managers, policymakers, and academics underscore BCâs potential to reduce operational costs, enhance real-time visibility, and improve compliance in port logistics. This study is limited by its focus on terminal operations at a single port; future research could explore BCâs impact on other areas of the maritime supply chain across multiple ports. The originality of this study lies in its contextualized BC model for West African ports, addressing specific challenges faced by developing regions and offering a foundational framework for future BC applications in logistics.
SATHISHKUMAR RANGANATHAN, Muralindran Mariappan, M. Karthigayan
Consensus mechanisms are essential in swarm robotics to maintain uniformity of decisions and states across distributed agents. Earlier methods often relied on approaches such as majority voting, averaging techniques, or leader election. In recent years, blockchain-based algorithms including Proof of Work (PoW), Proof of Stake (PoS), Practical Byzantine Fault Tolerance (PBFT), and Raft have been adopted for this purpose. While these methods provide certain advantages, their application to swarm robotics is restricted by issues such as limited computational power, energy constraints, scalability challenges caused by message complexity and latency, and weak protection against Byzantine agents. This study introduces a consensus approach specifically designed to address these gaps and to improve collaborative decision-making in swarm environments. The work outlines the motivation for the proposed solution, describes the simulation-based experimental design, and presents a detailed analysis of the observed results.
Proof-of-Work cryptocurrencies employ miners to sustain the system through algorithmic reward adjustments. We develop a stochastic model of the multicurrency mining market and identify conditions for stable transaction speeds. Bitcoin's algorithm requires hash supply elasticity $\le$ 1 for stability, while ASERT remains stable for any elasticity and can be interpreted as a form of stochastic gradient descent algorithm under a certain loss function. Interactions with other currencies can relax Bitcoin's stability requirements. Using a ``halving'' event, we estimate miners' hash supply elasticity and conduct counterfactual simulations. Our findings reveal Bitcoin's heavy reliance on low hash-supply elasticity and interactions with smaller cryptocurrencies, suggesting an algorithm upgrade is crucial for stability.
Peter KimemiahMwangi, Stephen TNjenga, Gabriel NdungâuKamau
Directed Acyclic Graph (DAG) based Distributed Ledger Technologies (DLTs) are being explored to address the scalability and energy efficiency challenges of traditional blockchain in IoT applications. The objective of this research was to gain insight into algorithms predicting how IoT-DAG DLT horizontal scalability changes with increasing node count in a heterogeneous ecosystem of full and light nodes. It specifically questioned how incorporating preferential attachment topology impacts IoT network scalability and performance, focusing on transaction throughput and energy efficiency. Using an AgentBased Modelling (ABM) simulation, the study evaluated a heterogeneous 1:10 full/light node network with BarabĂĄsi Albert Preferential Attachment (PA-2.3) across increasing node counts (100-6400). Performance was measured by Confirmed Transactions Per Second (CTPS) and Mean Transaction Latency (MTL). Results showed CTPS scales linearly with node count (R² â 1.000), exhibiting robust predictability. MTL increased logarithmically (R² â 0.970), becoming more predictable as the network grew. Horizontal scalability showed exponential decay. The study confirms that IoT-DAG DLTs with preferential attachment can achieve predictable, near-linear throughput horizontal scalability, highlighting that topology matters and optimising CTPS yields the highest throughput gains.
TomaĹž Berlec, Marko Corn, Sergej Varljen, PrimoĹž PodrĹžaj
The Fourth Industrial Revolution has introduced âshared manufacturingâ as a key concept that leverages digitalization, IoT, blockchain, and robotics to redefine the production and delivery of manufacturing services. This paper presents a novel approach to decentralized warehouse management integrating Large Language Models (LLMs) into the decision-making processes of autonomous agents, which serves as a proof of concept for shared manufacturing. A multi-layered system architecture consisting of physical, digital shadow, organizational, and protocol layers was developed to enable seamless interactions between parcel and warehouse agents. Shared Warehouse game simulations were conducted to evaluate the performance of LLM-driven agents in managing warehouse services, including direct and pooled offers, in a competitive environment. The simulation results show that the LLM-controlled agent clearly outperformed traditional random strategies in decentralized warehouse management. In particular, it achieved higher warehouse utilization rates, more efficient resource allocation, and improved profitability in various competitive scenarios. The LLM agent consistently ensured optimal warehouse allocation and strategically selected offers, reducing empty capacity and maximizing revenue. In addition, the integration of LLMs improves the robustness of decision-making under uncertainty by mitigating the impact of randomness in the environment and ensuring consistent, contextualized responses. This work represents a significant advance in the application of AI to decentralized systems. It provides insights into the complexity of shared manufacturing networks and paves the way for future research in distributed production systems.
Duarte Costa, JoĂŁo Ubiratan Moreira dos Santos, Tiago Dias, Miguel Correia
The real estate market, valued at trillions of euros, faces challenges such as illiquidity, inefficiencies, and the reliance on slow and costly processes with unnecessary intermediaries. This paper proposes a real estate marketplace built on Distributed Ledger Technology (DLT) to leverage its inherent decentralized properties. The system aims to accelerate processes with reduced costs, improved transparency, and a reduced level of fraud. The outcome is a secure decentralized finance (DeFi) platform involving transactions of Real-World Asset (RWA) tokens, providing a digital representation of the ownership rights of a real estate property.
Kemal Ihsan Kilic, Samir Maity, Inkyung Sung, Peter Nielsen
Maritime Search and Rescue (MSAR) operations face significant challenges due to high uncertainty, dynamic conditions, and resource constraints. Additionally, rigid organizational structures and hierarchical human-centered communication frameworks, fail to adapt to the challenging conditions of maritime environments. This paper provides a comprehensive review of the integration of Artificial Intelligence (AI) into MSAR operations, highlighting how AI can transform these systems through enhanced decision-making, real-time adaptability, decentralized autonomy, and resource optimization. Through analysis and synthesis, we identified and categorized key challenges in traditional SAR frameworks, such as inherent environmental and structural challenges. We discussed AI-driven solutions that offer efficient, autonomous, resilient, and decentralized coordination. Our thematic and statistical analysis of existing literature reveals significant research gaps, particularly regarding the holistic integration of AI across all SAR stages toward a decentralized fully autonomous paradigm shift. The paper also considers the technological challenges for the integration and adaptation of AI in SAR. By envisioning fully autonomous, AI-driven MSAR operations, this study sets the stage for future research and practical innovations, aiming to improve effectiveness and efficiency in maritime rescue efforts. ⢠Comprehensive Literature Review of AI over MSAR. ⢠Identification of Gaps and Key Challenges in MSAR. ⢠Proposed AI-Driven Solutions Strategies for MSAR. ⢠Trends and Future Directions through AI in MSAR.
Abstract With the rapid growth of hyperconnected devices and decentralized data architectures, safeguarding Internet of Things (IoT) transactions is becoming increasingly challenging. Blockchain presents a promising solution, yet its effectiveness depends on the underlying consensus algorithm. Conventional mechanisms, such as Proof of Work and Proof of Stake, are often impractical for resource-constrained IoT environments. To address these limitations, this work introduces a fair and lightweight hybrid consensus algorithm tailored for IoT. The proposed approach minimizes resource demands on the nodes while providing a fair and secure agreement process. Specifically, it utilizes a distributed lottery mechanism to ensure fair block proposals without requiring dedicated hardware. In addition, to enhance trust and establish finality, a reputation-based voting mechanism is incorporated. Finally, we experimentally validated the key features of the proposed consensus algorithm.
Alisha Roushan, Amrit Das, Anirban Dutta, Uttam Kumar Bera
Efficient supply chain models are crucial for ensuring swift medical intervention and the timely delivery of essential supplies in disaster management. This study focuses on optimizing disaster relief efforts in meteorological disasters , specifically flash floods triggered by cloudburst events. We propose a multi-objective supply chain model that minimizes both cost and time during emergencies by employing drones for rapid response and delivery to inaccessible areas. The model leverages Dijkstraâs algorithm to identify the shortest emergency routes and integrates Neutrosophic Compromise Programming (NCP) and the Weighted Sum Method (WSM) to optimize drone deployment for cost-effectiveness and timely intervention. Pentagonal Type-2 Fuzzy Variables (PT2FV) manage uncertainty and accurately represent real-world disasters. The study also introduces a smart contract framework to enhance transparency and accountability in logistics and rescue operations. These smart contracts govern the assignment of drone-based delivery tasks, ensuring that supplies are optimally allocated and transported via the most efficient routes. The system verifies task completion and maintains a transparent record of the logistics process . The robustness of the model is validated through sensitivity analysis, while the smart contract system is confirmed through unit testing, demonstrating its reliability under varied conditions. This work aligns with Industry 5.0 , integrating human-centric decision-making, drones, intelligent systems, and blockchain-based smart contracts to automate and effectively manage disaster, facilitating seamless collaboration between humans and machines.
Optimistic Rollup is a blockchain scalability solution. However, it assumes that transactions are correct unless challenged by a challenger, which leads to the concept of a challenge period. If there is no challenge during this time, the user needs to wait until the end of the challenge period, which is very inconvenient for the user. This is because challengers and task publishers are self-interested and aim to maximize their own benefits, leading to internal incentive issues within Optimistic Rollup. Although zero-knowledge proofs can effectively address this issue, ZK rollups face challenges such as high computational complexity and the need for transaction-specific proofs. Optimistic rollups have low computational requirements and simple design, making the design of an effective incentive mechanism a good choice. This paper proposes a reward-based incentive model to encourage rational workers to actively verify transactions based on game theory. The rewards in this incentive model are related to a time factor, where earlier verification yields greater rewards, motivating verifiers to verify transactions more quickly. We simulate under various proportions of false declarations and the results show that our method could incentivize the workers effectively to exert their best efforts.
The consensus problem in distributed ledger systems has two distinct dimensions that existing protocols systematically conflate. The first is the Byzantine fault-tolerance question: can a network reach agreement in the presence of arbitrary failures? The second â less formalised but no less fundamental â is the anti-cartel question: can the incentive structure of the consensus mechanism structurally resist the formation of cartels that reconstitute centralised authority under a nominally decentralised banner? Bitcoin's proof-of-work has produced a system where a small number of industrial mining pools control the majority of hash power. BitCell is a proposal that takes the anti-cartel question seriously as an engineering problem rather than an economic folk theorem. BitCell replaces hash-grinding and stake-weighting with cellular automaton tournaments as the computational substrate for block proposal rights. In each round, miners commit to a pattern in a bounded Conway's Game of Life grid, are verifiably randomly paired via a VRF-based pairing mechanism, and compete in a deterministic single-elimination tournament whose outcome depends on strategic pattern design rather than raw computational expenditure or capital size. Victory rights are not transferable and are not enhanced by pooling strategies: a cartel of sub-majority miners cannot coordinate to construct a jointly optimal pattern that dominates unilateral honest play, because the tournament's pairwise structure, hidden identities (via ring signatures), non-shareable rewards, and reputation-gated eligibility remove each of the primary economic motivations that make mining pools attractive. Under a simple Bayesian model of miner incentives, collusive strategies for sub-majority cartels yield strictly lower expected payoffs than unilateral honest participation. Tournament eligibility and reward weighting are governed by an Evidence-Based Subjective Logic (EBSL) reputation layer. All state transitions are proven using succinct zero-knowledge proofs, enabling a ZKVM-backed smart contract layer with native privacy. BitCell makes three primary contributions: (i) a proof-of-computation consensus mechanism whose computational task is verifiable, bounded, non-parallelisable by pooling, and intellectually non-trivial; (ii) a game-theoretic proof that the combination of pairwise tournaments, anonymised pairing, non-transferable victory rights, and reputation gating renders cartel coordination strictly dominated in a Bayesian Nash equilibrium; and (iii) a native ZKVM execution environment for privacy-preserving smart contracts.
In this paper, we provide an overview of a specific, but rather popular type of blockchain scaling solutions, which are usually referred to as Layer 2 blockchains.These solutions depend on the rollup scaling strategy.First, they all create batches of transactions known as rollups.Second, there are two primary approaches to rollup verifications.One approach is referred to as optimistic rollup, where it is assumed that fraudulent transactions rarely occur and should it happen, then some nodes could raise a challenge, which would lead to the resolution of the problem.The interaction between the asserter and the challenger is referred to as interactive fraud proof.The other approach is a conservative approach, where every rollup batch is associated with a validity proof.For all practical purposes, Ethereum is used as the Layer 1 blockchain because it is the first and only large-scale public blockchain that supports Turing-complete smart contracts.These Layer 2 blockchains typically deploy at least two smart contracts on the Layer 1 blockchain, one to receive rollups from the Layer 2 blockchain, and the other to verify the rollup batch.Furthermore, we provide technical details of two Layer 2 blockchains (i.e., Arbitrum and Optimism) that use the optimistic rollup mechanism, and two Layer 2 blockchains (i.e., Polygon and Starknet) that use validity rollup mechanism.Finally, we analyze the Layer 2 blockchain solutions in the framework of the blockchain trilemma theory.We show that all Layer 2 scaling solutions trade decentralization for better scalability.
We propose an extended demand response program, based on ancillary service for supplying flexible electricity demand. In our proposed scheme, we suggest a broader management model to control the scheduling and power consumption of Bitcoin mining machines. The main aspect that we focus on is suppressing the power ramping and related transient effects. We extend previous works on the subject, that study the impact of incorporating cryptocurrency mining machines into existing power grid, and explore the potential profit of exploiting this flexible load in the Israeli electricity market. We analyze a trend based on historical data, of increasing electricity prices and ramping costs due to the increasing penetration of renewable energy sources. We suggest an extension to the unit commitment problem from which we obtain the scheduling scheme of the Bitcoin mining machines. We use simulation and the real-world data acquired from the "Noga" grid operator to verify the proposed ancillary service and test its practical limits for reducing the ramping costs, under changing ratio of energy production from renewable sources. Out results suggests that the machine price and ratio of production from renewable sources plays a significant role in determining the profitability of the proposed demand-response program.
We show that for every polynomial q* there exist polynomial-size, constant-query, non-adaptive PCPs for NP which are perfect zero knowledge against (adaptive) adversaries making at most q* queries to the proof. In addition, we construct exponential-size constant-query PCPs for NEXP with perfect zero knowledge against any polynomial-time adversary. This improves upon both a recent construction of perfect zero-knowledge PCPs for #P (STOC 2024) and the seminal work of Kilian, Petrank and Tardos (STOC 1997).
In this study, we introduce a novel zero-knowledge identification scheme based on the hardness of the subgroup distance problem in the Hamming metric. The proposed protocol, named Subgroup Distance Zero Knowledge Proof (SDZKP), employs a cryptographically secure pseudorandom number generator to mask secrets and utilizes a Stern-type algorithm to ensure robust security properties.
Widespread use of lead acid batteries (LABs) is resulting in the generation of million tons of battery waste, globally. LAB waste contains critical and hazardous materials, which have detrimental effects on the environment and human health. In recent times, recycling of the LABs has become efficient but the collection of batteries in developing countries is not efficient, which led to the non-professional treatment and recycling of these batteries in the informal sector. This paper proposes a blockchain-enabled architecture for LAB circularity, which ensures authentic, traceable and transparent system for collection and treatment of batteries. The stakeholders-battery manufacturers, distributors, retailers, users, and validators (governments, domain experts, third party experts, etc.)-are integrated in the circular loop through a blockchain network. A mobile application user interface is provided to all the stakeholders for the ease of adoption. The batteries manufactured and supplied in a geographical region as well as the recycled materials at the battery end-of-life are traced authentically. This architecture is expected to be useful for the battery manufacturers to improve their extended producer responsibility and support responsible consumption and production.
Preserving privacy in blockchain-based systems is crucial for ensuring anonymity and confidentiality during transactions. While cryptographic solutions can address on-chain privacy concerns, their implementation on blockchains may introduce performance overhead, which remains unclear to researchers and practitioners. This paper investigates the performance impact of integrating zero-knowledge proofs (ZKPs) into the widely adopted permissioned blockchain framework called Hyperledger Fabric. The study focuses on evaluating the scalability and bottleneck aspects of blockchain platforms incorporating ZKPs. Through comprehensive experimentation and analysis, the study reveals that the integration of ZKPs compromises performance in terms of transaction rates and latency, while effectively safeguarding usersâ personal information. Implementing on-chain ZKP feature would result in a performance loss of 30% to 87.5% in various experimental configurations in Hyperledger Fabric. The findings presented in this paper are informative for the design and implementation of blockchain-based systems with strict privacy requirements.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques