We explore the feasibility of deploying Bitcoin as the shared monetary standard between Earth and Mars, accounting for physical constraints of interplanetary communication. We introduce a novel primitive, Proof-of-Transit Timestamping (PoTT), to provide cryptographic, tamper-evident audit trails for Bitcoin data across high-latency, intermittently-connected links. Leveraging Delay/Disruption-Tolerant Networking (DTN) and optical low-Earth-orbit (LEO) mesh constellations, we propose an architecture for header-first replication, long-horizon Lightning channels with planetary watchtowers, and secure settlement through federated sidechains or blind-merge-mined (BMM) commit chains. We formalize PoTT, analyze its security model, and show how it measurably improves reliability and accountability without altering Bitcoin consensus or its monetary base. Near-term deployments favor strong federations for local settlement; longer-term, blind-merge-mined commit chains (if adopted) provide an alternative. The Earth L1 monetary base remains unchanged, while Mars can operate a pegged commit chain or strong federation with 1:1 pegged assets for local block production. For transparency, if both time-beacon regimes are simultaneously compromised, PoTT-M2 (and PoTT generally) reduces to administrative assertions rather than cryptographic time-anchoring.
The construction supply chain often faces challenges such as contract disputes, inefficient payments, and difficult claim management due to its complexity and the involvement of multiple parties. Most existing solutions focus on optimizing contract terms or improving local processes, but they lack systemization, automation, and transparency. Therefore, this study proposes a claim and payment process management model based on blockchain and smart contracts, aiming to achieve digital and automated governance of the construction supply chain. The study constructs three types of smart contracts: The supply chain decomposition smart contract automatically divides engineering projects into independent billing cycles. The billing unit smart contract monitors the compliance of construction. The negotiation and settlement smart contract automatically handles disputes and payments. These three types of smart contracts work together to form a decentralized dynamic management framework. Through simulation experiments comparing the traditional process with the smart contract model, the results show that in scenarios with a high probability of claims and a large proportion of construction defects, the capital flow efficiency of the smart contract model is increased by more than 20%, and it shows stronger stability under high claim risks. The contribution of this study lies in combining blockchain technology with the logic of supply chain decomposition, proposing a smart contract system applicable to dynamic engineering projects, thus providing a digital processing method for the integrated management of claims and payments in the construction supply chain. This digital dynamic management method can ultimately systematically solve the island problem in supply chain claim research. Its automated and transparent characteristics help reduce dispute costs and enhance trust among multiple parties, which has important practical significance for improving the overall efficiency of the industry.
The article explores the evolution of marketing innovations in the retail sector through the lens of technological development and the transformation of consumer expectations. Five key stages of innovation development are identified—traditional, network based, digital, omnichannel, and innovation-technological—each characterized by specific challenges, opportunities, and influencing factors. The traditional stage was marked by a focus on product policy and individual promotions within the physical store. The network-based stage introduced the integration of IT solutions into logistics, CRM systems, and initial customer segmentation. The digital stage was distinguished by the emergence of online stores, mobile marketing, and personalized communication. The omnichannel stage involved the full synchronization of online and offline channels to ensure a holistic customer experience. The innovation-technological stage includes the extensive implementation of artificial intelligence, AR/VR, blockchain, and emotional analytics. The study draws conclusions about the patterns of transition between stages and the role of innovation in transforming business models in retail. Key directions for further development of marketing innovations are identified, including the technologization of customer experience, intelligent marketing automation, a sustainable approach, Web3 integration, the growth of social commerce, and the use of emotional analytics. However, the implementation of these directions is accompanied by a number of challenges related to the rapid pace of technological change, increasing consumer expectations, and the need to adapt business models to new ethical and environmental standards. In Ukraine, these challenges are further intensified by martial law conditions, market instability, limited resources, and the urgent need for rapid transformation of the retail sector to fit the new realities. It is noted that the development vectors of marketing innovations in retail form a complex yet high-potential system of change that requires strategic thinking, flexibility, and readiness to implement new formats of customer interaction. The article has practical significance for marketing professionals, retail business managers, and researchers working on adapting business practices to the evolving digital economy.
Emma Verónica Ramos Farroñán, Gary Christiam Farfán Chilicaus, Luís Edgardo Cruz Salinas, Liliana Correa Rojas · 8 authors
This systematic review synthesizes evidence on economic instruments that mobilize renewable-energy investment in emerging economies, analyzing 50 peer-reviewed studies published between 2015 and 2025 under PRISMA 2020. We advance an Institutional Capacity Integration Framework that ties instrument efficacy to regulatory, market, and coordination capabilities. Green bonds have mobilized roughly USD 500 billion yet work only where robust oversight and liquid markets exist, offering limited gains for decentralized access. Direct subsidies cut renewable electricity costs by 30–50% and connect 45 million people across varied contexts, but pose fiscal–sustainability risks. Carbon pricing schemes remain rare given their administrative complexity, while multilateral climate funds show moderate effectiveness (coefficients 0.3–0.8) dependent on national coordination strength. Bibliometric mapping with Bibliometrix reveals three fragmented paradigms—market efficiency, state intervention, and international cooperation—and highlights geographic gaps: sub-Saharan Africa represents just 16% of studies despite acute financing barriers. Sixty-eight percent of articles employ descriptive designs, constraining causal inference and reflecting tensions between SDG 7 (affordable energy) and SDG 13 (climate action). Our framework rejects one-size-fits-all prescriptions, recommending phased, context-aligned pathways that progressively build capacity. Policymakers should tailor instrument mixes to institutional realities, and researchers must prioritize causal methods and underrepresented regions through focused initiatives for equitable global progress.
Jocelyn Aracelia Kusuma, Meyliana Meyliana, Kevin Deniswara
Innovative solutions are needed to reduce greenhouse gas emissions and promote sustainability in the face of climate change, a global problem. Although carbon markets often face problems such as centralization, lack of transparency, and high costs, they are intended to address these issues. A new way to improve carbon markets is offered by decentralized finance (DeFi) powered by blockchain technologies, such as tokenization and smart contracts. These technologies make carbon credit trading more efficient, transparent, and accessible. In this paper, a Systematic Literature Review (SLR) was used to examine 35 studies published between 2018 and 2024. These results are generated using the Technology-Organization-Environment (TOE) framework, which identifies fifteen critical components that influence the adoption of decentralized finance in carbon markets. Scalability and opaque regulation are issues that need further research, although DeFi may be able to address many of these issues. Additionally, the study highlights the role of decentralized finance in supporting the shift towards a greener economy by promoting sustainability and inclusivity. This study advances the understanding of how decentralized finance can aid carbon reduction efforts and improve the way carbon markets function.
The concept of Decentralized Autonomous Organizations (DAOs) has introduced a novel paradigm in organizational governance, characterized by more collaborative decision-making. However, the lack of established organizational frameworks for DAOs presents significant challenges to their constitution, stability, and longevity. Aiming to address this shortcoming, this paper presents a conceptual framework to guide the design of the community governance structure of DAOs. To achieve this aim, we employed two complementary methods: firstly, we conducted a systematic literature review about DAOs and community governance; secondly, we conducted an analysis of the governance methods employed by five DAOs operating in public blockchain ecosystems. The proposed framework provides a valuable tool for DAO founders, developers, and community members to design and implement effective governance structures and contributes to the understanding of DAO governance and further research.
Paper define innovative approach to unify authentication across Web2 and Web3 ecosystems by using biometric-driven decentralized identifiers (DIDs). The framework employs zero-knowledge attestations (ZKPs) to ensure privacy during verification processes [7], [11] and utilizes Chainlink's Cross-Chain protocol related toInteroperability(CCIP) for flawless operation across multiple blockchains [17]. To enhance liveness detection, we incorporate federated learning to eliminate centralized storage of sensitive biometric data [19]. A novel contribution is the Biometric Soulbound Token (BST), a non-transferable NFT that securely stores hashed facial data [5]. Also, quantum-resistant ZKPs are used to verify biometric matches without exposing raw inputs [14]. The DIDs function cohesively across Ethereum, Polygon, and Solana. Experimental results demon- strate a 99.2% authentication accuracy, a 1.3 -second latency, and full compliance with GDPR. By empowering users with control over their biometric data, this framework bridges centralized and decentralized platforms, enabling secure and efficient identity management.
Youwei Huang, Jianwen Li, Bin Hu, Sen Fang · 6 authors
Malicious developer intents in smart contracts constitute significant security threats to decentralized applications, leading to substantial economic losses. Prior work introduced SmartIntentNN, a deep learning model for detecting unsafe developer intents. By combining the Universal Sentence Encoder, a K-means clustering-based intent highlighting mechanism, and a Bidirectional Long Short-Term Memory (BiLSTM) network, the model achieved an F1 score of 0.8633 on an evaluation set of 10,000 real-world smart contracts across ten distinct intent categories. This paper presents SmartIntentV2 (Smart Contract Intent Neural Network Version 2). The primary enhancement is the integration of a BERT-based pre-trained programming language model, which we domain-adaptively pre-train on a dataset of 16,000 real-world smart contracts using a Masked Language Modeling objective. SmartIntentV2 retains the BiLSTM-based multi-label classification network for intent detection. On the same evaluation set of 10,000 smart contracts, it achieves superior performance with an accuracy of 0.9789, precision of 0.9090, recall of 0.9476, and an F1 score of 0.9279, substantially outperforming its predecessor and other baseline models. Notably, SmartIntentV2 also delivers a 65.5% relative improvement in F1 score over GPT-4.1 on this specialized task. These results establish SmartIntentV2 as a new state-of-the-art model for smart contract intent detection.
Agentification serves as a critical enabler of Edge General Intelligence (EGI), transforming massive edge devices into cognitive agents through integrating Large Language Models (LLMs) and perception, reasoning, and acting modules. These agents collaborate across heterogeneous edge infrastructures, forming multi-LLM agentic AI systems that leverage collective intelligence and specialized capabilities to tackle complex, multi-step tasks. However, the collaborative nature of multi-LLM systems introduces critical security vulnerabilities, including insecure inter-LLM communications, expanded attack surfaces, and cross-domain data leakage that traditional perimeter-based security cannot adequately address. To this end, this survey introduces zero-trust security of multi-LLM in EGI, a paradigmatic shift following the ``never trust, always verify'' principle. We begin by systematically analyzing the security risks in multi-LLM systems within EGI contexts. Subsequently, we present the vision of a zero-trust multi-LLM framework in EGI. We then survey key technical progress to facilitate zero-trust multi-LLM systems in EGI. Particularly, we categorize zero-trust security mechanisms into model- and system-level approaches. The former and latter include strong identification, context-aware access control, etc., and proactive maintenance, blockchain-based management, etc., respectively. Finally, we identify critical research directions. This survey serves as the first systematic treatment of zero-trust applied to multi-LLM systems, providing both theoretical foundations and practical strategies.
Rahanatu Suleiman, Akshita Maradapu Vera Venkata Sai, Wei Yu, Chenyu Wang
Digital Twins (DTs) have become essential tools for improving efficiency, security, and decision-making across various industries. DTs enable deeper insight and more informed decision-making through the creation of virtual replicas of physical entities. However, they face privacy and security risks due to their real-time connectivity, making them vulnerable to cyber attacks. These attacks can lead to data breaches, disrupt operations, and cause communication delays, undermining system reliability. To address these risks, integrating advanced security frameworks such as blockchain technology offers a promising solution. Blockchains’ decentralized, tamper-resistant architecture enhances data integrity, transparency, and trust in DT environments. This paper examines security vulnerabilities associated with DTs and explores blockchain-based solutions to mitigate these challenges. A case study is presented involving how blockchain-based DTs can facilitate secure, decentralized data sharing between autonomous connected vehicles and traffic infrastructure. This integration supports real-time vehicle tracking, collision avoidance, and optimized traffic flow through secure data exchange between the DTs of vehicles and traffic lights. The study also reviews performance metrics for evaluating blockchain and DT systems and outlines future research directions. By highlighting the collaboration between blockchain and DTs, the paper proposes a pathway towards building more resilient, secure, and intelligent digital ecosystems for critical applications.
Chigozie Athanasius Nnadiekwe, Collins Izuchukwu Okafor, Ikechi Saviour Igboanusi, Jae Min Lee · 5 authors
SoldierCare is a real-time, blockchain-enabled Internet of Medical Things (IoMT) framework designed to enhance military personnel safety through integrated health monitoring and cyberattack detection. The system employs a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model trained on the WUSTL-EHMS-2020 dataset, combining physiological sensor data and network traffic to identify both health anomalies and malicious activities with high accuracy. A smart contract-enabled Ethereum blockchain ensures the integrity and traceability of alerts by immutably logging metadata, while detailed data is stored off-chain in IPFS to reduce on-chain overhead. The architecture supports edge deployment, enabling low-latency inference and autonomous operation in mission-critical environments. Experimental results demonstrate a detection accuracy of 98.6%, with efficient scaling and minimal false detections. Optimizations such as transaction batching enhance blockchain performance under increasing load. SoldierCare represents a secure, scalable solution that fuses AI, fog computing, and blockchain to provide resilient operational support in dynamic battlefield scenarios.
This chapter proposes a formal alternative to blockchain-based ledgers by reconstructing the logic of bilateral exchange relationships using projective geometry and categorical methods. We show that the normative identity of a financial contract can be faithfully embedded into a projective elliptic curve, yielding an algebraic structure isomorphic to double-entry bookkeeping. This geometric realization enables compositional transaction modeling through the elliptic group law and supports structured reasoning about contract compliance, reversibility, and balance. In contrast to distributed ledger technologies, which often fail to preserve bilateral symmetry and internal control logic, our framework enforces normative integrity by construction. We analyze the limitations of blockchain systems in supply chain transparency and auditing and present a category-theoretic model that resolves these deficiencies through local contract verification and structured composition. The resulting framework extends naturally to multi-agent reasoning, tiered supply chains, and digital audit systems, offering a mathematically rigorous foundation for trustworthy and scalable accountability infrastructures.
Although supply chain finance is essential for modern business, it is also prone to fraud, mistakes, and inefficiencies. This paper presents a blockchain-based smart contract audit system to improve supply chain finance’s security, openness, and efficiency. The system uses distributed architecture, smart contracts and blockchain technology to automate and audit financial transactions. In terms of scalability, convergence speed, and accuracy, experimental results reveal quite excellent performance of the proposed system. The proposed method achieved in auditing financial transactions an accuracy of 95.2%. Showing a convergence speed of 41.2 s for 30 nodes, the system confirmed its ability to manage large-scale datasets. With 30 nodes, the system shown scalability proving it could control difficult supply chain financial circumstances. Promising supply chain finance solution the blockchain-based smart contract audit system shown a clear decrease in audit time and cost.
Toshiki Takakubo, Yinfeng Cao, Ruidong Li, Jiannong Cao
Sharding is a key technology for enhancing blockchain scalability by splitting the network into multiple shards, enabling parallel transaction (TX) processing. However, frequent cross-shard TXs can significantly limit the performance of sharding. Existing approaches reduce cross-shard TXs by modeling account relationships as a graph, but these methods only focus on efficiently processing normal transfer TXs between two parties. In practice, most blockchain TXs are generated by smart contracts, which involve multiple parties simultaneously, thus increasing graph complexity and prolonging shard partitioning time. In this paper, we propose UnionChain, a novel sharding protocol that reduces cross-shard TXs while efficiently handling smart contract TXs. Specifically, UnionChain first models the relationship between accounts and smart contracts as a weighted graph. By applying a community detection algorithm to partition this graph, it groups closely related entities into the same shard, thereby reducing cross-shard TXs and supporting efficient smart contract execution. To further address the graph complexity issue, UnionChain introduces an efficient vertex merging mechanism. When TXs occur between smart contracts, the corresponding contract vertices are merged into a single vertex. This mechanism significantly reduces graph size and shortens partitioning time. We implement a UnionChain prototype and evaluate its performance using a real Ethereum TX dataset. Compared to state-of-the-art sharding protocols, UnionChain shortens the partitioning time by up to 27% while maintaining a low cross-shard TX ratio, demonstrating its effectiveness in improving blockchain scalability.
This chapter proposes a Trustworthy Federated Identity Management (TFIM) framework developed to overcome user identity management challenges across multiple blockchain networks. The research work focuses on solving key issues, such as the lack of unified trust frameworks, insufficient cross-chain identity verification, and limited privacy-preserving mechanisms for cross-chain data sharing. TFIM allows secure DApp interoperability by assessing participant trustworthiness across blockchain networks with the integration of zero-knowledge proofs and federated learning techniques. Performance evaluation shows that TFIM processes 44,041 transactions/s with 15% degradation under real-world conditions, supports 1000 concurrent users, and handles 100 cross-chain authentications/s across up to 15 interconnected networks. Although TFIM provides better privacy protection and more advanced cross-chain capabilities than the Blockchain-Based Federated Identity Framework (BFIF), it comes at the cost of computational overhead and registration/authorization speed. The results suggest potential directions for future optimization while maintaining TFIM’s robust cross-chain functionality and privacy features.
Mayank Arora, M V Gururaj, Ankush Sharma, Naveen Chilamkurti
The transition towards decentralized energy systems has spurred the need for innovative consensus mechanisms to facilitate efficient and transparent energy trading among prosumers. In response to this challenge, we propose a novel Proof of Energy Authentication and Contribution (PoEAC) consensus mechanism tailored for decentralized energy trading systems. PoEAC integrates cryptographic authentication and contribution verification to empower authenticated prosumers in the energy market. Prosumers authenticate themselves by proving ownership of energy-producing assets or storage devices, while demonstrating their contribution to the energy system through verifiable evidence of energy production or storage capacity. Leveraging cryptographic techniques such as zero-knowledge proofs and digital signatures, prosumers generate proofs of their authenticated status and contribution, which are evaluated by the consensus algorithm to validate energy transactions. The proposed model was simulated in MATLAB, with four prosumers over a 24hour horizon. Simulation results confirm that PoEAC successfully validates all legitimate energy transactions while rejecting 100 % of invalid or unauthorized trades. This paper presents the design and implementation of PoEAC, highlighting its advantages in enhancing trust, transparency, and incentivized participation in decentralized energy trading systems.
Hope Leticia Nakayiza, Love Allen Chijioke Ahakonye, Dong‐Seong Kim, Jae Min Lee
The growing deployment of unmanned aerial vehicles (UAVs) in military operations necessitates a secure, scalable, and decentralized approach to airspace management. This paper introduces MilChain-UAV, a blockchain-based traffic control framework tailored for military UAV networks. Built on PureChain, a custom permissioned blockchain network, MilChain-UAV supports autonomous mission governance, real-time path validation, and decentralized collision avoidance. To optimize blockchain efficiency while ensuring auditability, telemetry data is stored off-chain using IPFS, with only the cryptographic hashes anchored on-chain. By replacing centralized controllers with a distributed ledger, the framework enhances resilience against jamming and spoofing while enabling dynamic routing and verifiable behavior logging. Experimentation results demonstrate MilChain- UA V's effectiveness in improving efficiency and scalability in critical military operations, providing a robust solution for autonomous and secure management of military UAV traffic.
Over 1.7 billion people lack basic sanitation, and 2 billion rely on contaminated drinking water, predominantly in low- and middle-income countries. Decentralized solutions offer a viable alternative to centralized systems but face barriers in governance, finance, and the Product Development Process (PDP). This paper examines challenges across seven PDP stages—Function, Assembly, Deployment, Maintenance, Upscaling, Disassembly, and Transfer—through a two-step qualitative sequential design approach. Findings reveal critical gaps, including insufficient funding for maintenance, fragmented regulatory frameworks, and neglect of end-of-life management. Humanitarian markets prioritize speed over sustainability, poor markets demand low-cost designs but lack institutional support, and emerging markets face regulatory complexity and uneven scalability. Practical recommendations include simplifying funding, adopting user-centered modular designs, and strengthening local capacity through partnerships. Future research should focus on governance reforms, sustainable maintenance, and scaling pathways to ensure innovations address the urgent needs of underserved populations. Theoretically, this paper advances understanding of how PDP challenges intersect with market typologies in resource-constrained contexts, offering a framework to analyze and address systemic barriers to innovation. This study contributes to the field of innovation for resource-constrained markets by providing actionable insights for technology providers, financers, and local implementers to address systemic governance, financial, and operational gaps in the PDP.
This paper proposes a cryptocurrency portfolio trading system (CPTS) that optimizes trading performance in the cryptocurrency futures market by leveraging reinforcement learning and timeframe analysis. By employing the advantage actor–critic (A2C) algorithm and analysis of variance (ANOVA) portfolios are constructed over multiple timeframes. Data corresponding to the trade of 18 major cryptocurrencies on Binance Futures––between January 2022 and December 2023––are used to show that trading strategies can be effectively categorized into those with high-frequency (10, 30, and 60 min) and low-frequency (daily) timeframes. Empirical results demonstrate statistically significant differences in returns between these timeframe groups, with major cryptocurrencies (e.g., Bitcoin and Ethereum) exhibiting higher returns in high-frequency trading (16–17%) than in daily trading (6–7%) during training. Performance evaluation during the test period revealed that the low-frequency group achieved a 43.06% average return, significantly outperforming the high-frequency group (5.68%). The ANOVA results confirm that both the frequency type and portfolio selection significantly influence trading performance at the 5% significance level. This study offers a novel approach to cryptocurrency trading that considers the distinct characteristics of different timeframes. The effectiveness of combining reinforcement learning with statistical analysis for portfolio optimization in highly volatile cryptocurrency markets is demonstrated.
We examine how Bitcoin and Ethereum volatilities react to macroeconomic data releases from the US, Germany, and Japan before, during, and after their official announcements. Analyzing 5-minute observations from 2016 to 2023, we find that volatility responds significantly to select news categories, particularly in the pre-announcement period. US monetary policy news consistently drives volatility across all phases, with a heightened impact during the pandemic. Ethereum shows greater sensitivity to US announcements than Bitcoin but remains unresponsive to non-US news, especially before the pandemic. Our findings highlight the need to account for both pre- and post-announcement periods when evaluating the intraday price impact of macroeconomic news on cryptocurrencies. • We examine the response of Bitcoin and Ethereum volatilities to macroeconomic figures. • We show that volatility reacts only to a few news categories. • US monetary policy news consistently affects volatility before, during, and after its release. • Ethereum volatility is more sensitive to US announcements compared to Bitcoin. • Ethereum exhibits less pre-announcement volatility and less sensitivity to non-US news.
This paper surveys the academic literature concerning the bubble periods in the cryptocurrency market. This study aims to understand the historical and developmental trajectory of the cryptocurrency market through its various bubble periods. This study also identifies the factors contributing to bubble formation. The study is based on the PRISMA framework for literature review. Based on the review, the cryptocurrency market experienced four major bubbles in 2011, 2013, 2017, and 2021. The enthusiasm for cryptocurrency innovation triggered the 2011 bubble. The 2013 bubble was influenced by the economic crisis that channeled funds to the cryptocurrency market due to their centralized nature. In 2017, the possibilities of Web 3.0 and altcoins increased the enthusiasm of crypto investors. The crypto winter of 2017 subsided with the rise of non-fungible tokens (NFTs), stimulating interest and driving prices in the cryptocurrency market. Specifically, speculation, media coverage, investor sentiment, herding, volatility, and coexplosivity are significant factors that trigger bubble development. Moreover, government policies and regulations can be crucial in sustaining and bursting the bubbles. This review offers a comprehensive view of academic studies on bubble periods in the cryptocurrency market. This study also provides a chronological overview of major bubble periods that have significantly influenced the market development. This study is one of the first reviews conducted to understand the development of the cryptocurrency market through bubble periods and the factors contributing to bubble formation. The study also follows the PRISMA framework for structuring the review, as the literature lacks reviews on bubble periods on the basis of this framework.
The increasing complexity of computational problems across many scientific and technological domains often challenges traditional centralized computing resources. As the internet evolves toward Web 3.0, blockchain technology is emerging as a foundational infrastructure for decentralization, transparency, and distributed collaboration. Applications like the metaverse, which demand real-time responsiveness and high computational throughput, further underscore the need for scalable and resilient computing frameworks. In this regard, we propose a novel framework for crowdsourcing computationally intensive tasks using blockchain and smart contracts. Operating atop existing blockchain networks, a Master (or Requester) node defines a computational problem, decomposes it into subtasks, and deploys a smart contract to manage task distribution. Worker nodes perform the computations off-chain using local resources and submit their results via on-chain transactions. The smart contract aggregates these results and finally, the Master validates them and automatically distributes rewards in cryptocurrency. A proof-of-concept simulation using Ganache, Solidity, and off-chain scripts demonstrates the feasibility of this approach, showcasing key features such as task decomposition, off-chain computation, and automated result handling. These findings underscore blockchain’s potential to enable transparent, automated, and scalable coordination of distributed computing tasks.
The cold-chain supply of perishable fruits continues to face challenges such as fuel wastage, fragmented stakeholder coordination, and limited real-time adaptability. Traditional solutions, based on static routing and centralized control, fall short in addressing the dynamic, distributed, and secure demands of modern food supply chains. This study presents a novel end-to-end architecture that integrates multi-agent reinforcement learning (MARL), blockchain technology, and generative artificial intelligence. The system features large language model (LLM)-mediated negotiation for inter-enterprise coordination, Pareto-based reward optimization balancing spoilage, energy consumption, delivery time, and climate and emission impact. Smart contracts and Non-Fungible Token (NFT)-based traceability are deployed over a private Ethereum blockchain to ensure compliance, trust, and decentralized governance. Modular agents-trained using centralized training with decentralized execution (CTDE)-handle routing, temperature regulation, spoilage prediction, inventory, and delivery scheduling. Generative AI simulates demand variability and disruption scenarios to strengthen resilient infrastructure. Experiments demonstrate up to 50% reduction in spoilage, 35% energy savings, and 25% lower emissions. The system also cuts travel time by 30% and improves delivery reliability and fruit quality. This work offers a scalable, intelligent, and sustainable supply chain framework, especially suitable for resource-constrained or intermittently connected environments, laying the foundation for future-ready food logistics systems.