Blockchain Papers

Yilai Lian, X. Liu, Lanying Liang, Wei Ye · 10 authors

No abstract is available for this record.

Carvalho Tiago, Rijo Gon鏰lo, Gon鏰lves Lio, Amorim Vasco · 5 authors

Traditional electronic Kanban (eKanban) systems depend on manual scans and offer only discrete material visibility, limiting responsiveness and automation in lean manufacturing environments. These operational bottlenecks are magnified in high-mix contexts, where delayed replenishment signals degrade flow stability, increase work-in-progress, and hinder sustainable material handling. Furthermore, vendor-specific systems lack interoperability for scalable automation, constraining the development of intelligent manufacturing solutions. This work investigates whether zone-based replenishment automation can be enabled through real-time locating systems (RTLS) using open interoperability standards, addressing a gap in empirical validation of such approaches. A middleware architecture was developed that integrates ultra-wideband (UWB) positioning, an Omlox-compliant location middleware (DeepHub), and a cloud-based eKanban system to replace manual triggers with geofence-driven order creation. The novelty of this study lies in demonstrating a fully automated Kanban signaling loop built on the open Omlox standard, providing vendor-independent RTLS interoperability and eliminating human intervention in replenishment signaling. This contributes new knowledge on how continuous location data can be converted into actionable replenishment events in a standards-based, modular manner, enabling more intelligent and autonomous material-flow control. A controlled proof-of-concept experiment simulating shop-floor conditions showed that the system achieved a 100% detection success rate, zero duplicate orders, and an average trigger-to-action latency of 2.7 s, while automatically recovering from authentication and WebSocket failures. These results provide the first empirical evidence that Omlox-compliant RTLS middleware can reliably support zone-based eKanban automation. The findings have direct implications for intelligent and sustainable manufacturing by demonstrating a scalable pathway toward interoperable, real-time material-flow systems that reduce manual intervention, avoid unnecessary handling, and lower work-in-progress. More broadly, the work addresses the current lack of empirical validation of open-standard RTLS integration within lean and sustainable production environments.

Yiheng Jiang, Yuwei Le, Rui Jiang, Xiaoyang Zhou · 5 authors

No abstract is available for this record.

Mohsen Ahmadvand, Pedro Souto

Zero-knowledge rollups rely on provers to generate multi-step state transition proofs under strict finality and availability constraints. These steps require expensive hardware (e.g., GPUs), and finality is reached only once all stages complete and results are posted on-chain. As rollups scale, staying economically viable becomes increasingly difficult due to rising throughput, fast finality demands, volatile gas prices, and dynamic resource needs. We base our study on Halo2-based proving systems and identify transactions per second (TPS), average gas usage, and finality time as key cost drivers. To address this, we propose a parametric cost model that captures rollup-specific constraints and ensures provers can keep up with incoming transaction load. We formulate this model as a constraint system and solve it using the Z3 SMT solver to find cost-optimal configurations. To validate our approach, we implement a simulator that detects lag and estimates operational costs. Our method shows a potential cost reduction of up to 70\%.

Huang Rong, Cheng Fan

In today's world, the need for smart buildings along with smart interior design is quickly growing, requiring advanced AI-based methods for better area usage, power conservation as well as safety. Usual methods battle in continuing real-time adaptability, with several security concerns, and in sufficient data processing, making them inadequate for multidimensional architectural and interior design systems. For countering these challenges, this research introduces DesignTwin-SDN, a Digital Twin-based AI system in combination with CRL-Net, a Deep Learning (DL)-based clever model fusing CNN, ResNet-50, and LSTM for the creation of more precise spatial-temporal analysis and anomaly detection. Also, for possessing strong security, the system includes Adaptive Flow-Encrypted AES (AFEA), a dual-mode encryption strategy of combining Advanced Encryption Standard (AES) with Flow-Based Dynamic Encryption (FBDE). Furthermore, Starfish-Orangutan Optimization Algorithm (SOOA), which is a combination of the Starfish Optimization Algorithm (SFO) with Orangutan Optimization Algorithm (OOA), is proposed for the selection of the optimal secret key for purposes of maximizing that encryption efficiency. The Zero-Knowledge Proof (ZKP) system further secures authentication by precluding forbidden use in smart spaces. Experimental results show the suggested framework's effectiveness. These results show a 99.05% accuracy and a 98.51% recall, a large increase over the current method. DesignTwin-SDN merges DL and digital twins with SDN-IoT to change smart architecture and interior layout planning into more secure, efficient, and flexible systems with dynamic environments.

Guy Goren, Andrew Hariri, Timothy D. R. Hartley, Ravi Kappiyoor · 6 authors

Existing decentralized storage protocols fall short of the service required by real-world applications. Their throughput, latency, cost-effectiveness, and availability are insufficient for demanding workloads such as video streaming, large-scale data analytics, or AI training. As a result, Web3 data-intensive applications are predominantly dependent on centralized infrastructure. Shelby is a high-performance decentralized storage protocol designed to meet demanding needs. It achieves fast, reliable access to large volumes of data while preserving decentralization guarantees. The architecture reflects lessons from Web2 systems: it separates control and data planes, uses erasure coding with low replication overhead and minimal repair bandwidth, and operates over a dedicated backbone connecting RPC and storage nodes. Reads are paid, which incentivizes good performance. Shelby also introduces a novel auditing protocol that provides strong cryptoeconomic guarantees without compromising performance, a common limitation of other decentralized solutions. The result is a decentralized system that brings Web2-grade performance to production-scale, read-intensive Web3 applications.

Shahid Salim, Giovanni De Gasperis, Sante Dino Facchini

Distributed Ledgers, such as Blockchain and the IOTA Tangle, offer secure, transparent, and decentralized solutions that can streamline decision-making processes in agriculture. IOTA, in particular, stands out as a fee-less and scalable architecture designed to support real-time data transmission from Internet-of-Things sensors. This makes it a promising candidate for enhancing agricultural monitoring and management. This study systematically evaluates IOTA’s potential to meet the specific needs of agriculture using the Protocol Search Appraisal Synthesis Analysis Report (PSALSAR) methodology. The IOTA Tangle provides an efficient framework for precision farming by enabling seamless data exchange without the high transaction costs typical of traditional blockchain technologies. The research also highlights how this Distributed Ledger Technologies can enhance the monitoring of environmental factors, optimize resource usage, improve traceability of the production chain and ultimately contribute to more sustainable farming practices.

Tomaž Berlec, Marko Corn, Sergej Varljen, Primož Podržaj

The Fourth Industrial Revolution has introduced “shared manufacturing” 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.

Niels Agatz, Jan C. Fransoo, Elliot Rabinovich, Rui Sousa

Last mile operations (LMO), the processes involved in the critical last stage of delivering goods and services, have widespread relevance across major sectors of the economy, including retail, food services, healthcare, humanitarian services, energy distribution, telecommunications, public services, and others. These operations account for a significant portion of the costs, jobs, and economic output in these sectors. Global economic output involving last mile deliveries alone, for instance, is valued at $165 billion per year and is growing at about 10% per year (InsightAce Analytic 2024). Recent decades have witnessed an acceleration in the rate of evolution of LMO (Agatz et al. 2024; Boutilier and Chan 2022; Boyer and Hult 2005; Dreischerf and Buijs 2022; He and Goh 2022; Lyu and Teo 2022). Technology-driven innovations have catalyzed profound changes in the planning, design, and execution of LMO, with significant implications for the economics of these operations. Extending the last mile to the final user has increased convenience, accessibility, and reliability. Zipline, for example, has introduced drones to safely deliver lifesaving products in remote communities (Ackerman and Koziol 2019). An increasing number of pharmacies in Europe and Africa have been equipped with smart lockers to allow 24/7 access to critical medicines (Gobir et al. 2024). Some innovations leveraging platforms based on smartphone apps have given small corner stores in neighborhoods in cities across Latin America the means to sell and deliver daily groceries and other household staples to local residents (Escamilla et al. 2021). Other innovations, leveraging artificial intelligence, have found applications in vehicle routing tools and warehouse and fulfillment automation (such as Ocado's system (Mason 2019)), track-and-trace systems that provide real-time communications and visibility into delivery processes (such as Instacart and Uber Eats), anticipatory shipping algorithms to move inventories to specific areas ahead of realized demand (Chen and Graves 2021), and integration tools with third-party services (successfully deployed by ClickPost and ShipEngine). However, considerable challenges remain. For example, because of short time frames and high delivery volumes to many dispersed locations, LMO have little room for human error. Yet, since many firms tend to tap into low-skilled, temporary, or crowdsourced labor to provide these services, there is high variability in performance and worker availability. LMO are also expensive, due in part to rising labor costs, delivery failures, more demanding customers, and vehicle and parking restrictions. Although academic research in LMO has a long tradition in Operations Research (see e.g., Agatz et al. (2011), Otto et al. (2018), Boysen et al. (2019) and Reed et al. (2022)), LMO have barely been considered as an operations problem that requires process understanding and management within a sociotechnical system. The need for this is apparent, as increasing evidence points to managerial, economic, and sociotechnical challenges as major determinants of LMO success. Delivery workers have been noted to largely ignore the recommendations by routing algorithms in urban settings (Liu et al. (2023)); working conditions are an increasing societal and corporate concern; and customer experiences are less than satisfactory in many cases. Further, LMO are associated with negative externalities such as emissions, traffic congestion, and the abuse of public parking space. Operational costs are also very high—often up to a point where LMO are loss-making, such as in grocery home delivery. And, while there have been extensive technological innovations, many seem to fail in scaling at large, which could potentially be due to a poor understanding of the LMO from a process perspective. We need new research to better understand these challenges, as well as to propose new operational practices and business models based on the application of recent innovations. Such research requires a broadening of the phenomenological and theoretical scope of LMO research beyond traditional work in Operations Research. Theories on innovation applied to Operations Management can offer a valuable foundation to study research questions surrounding the scalability of technologies to support new business models in the last mile (Arthur 1994). Similarly, theoretical models examining technology, productivity, and employment can provide a foundation to understand how innovations can change the nature of work in last-mile settings (Autor et al. 2003; Autor 2015). Additional opportunities also exist to use transaction and information cost theories to understand how technological innovations may change organizational boundaries and the nature of organizations in the last mile (Afuah 2003). This confluence of innovations in the field, the multidimensional phenomena that determine performance, and the perspectives from theories from the operations management field provide an opportunity to shape a research program in LMO that will benefit from the Operations Management academic community. This was one of the main goals of our call for papers for the special issue on “Innovations, Technologies, and the Economics of Last-Mile Operations.” Another objective of this special issue was to formalize a research agenda and offer future directions for research to advance our understanding of LMO. To that end, in Section 2, we delve deeper into these operations, their functionalities, distinctive features, and challenges in the context of Operations Management. Then, in Section 3, we expand on research opportunities to tackle the most pressing challenges in LMO and identify knowledge gaps in Operations Management to be addressed in this endeavor. We close in Section 4 with conclusions, recommendations, and potential initiatives to build on the momentum created so far and further advance LMO as a knowledge area within Operations Management. In doing so, we introduce the several papers in the special issue as exemplars of research that can be done in the LMO domain. LMO are made of processes triggered by an agent (e.g., consumer, user, patient, worker, organization) that enable the provision of a service to this agent at the agent's selected location and time (or time period). LMO involve interactions with the agent—who participates in the process and co-creates value—and, by definition, comprise different service processes (Sampson and Froehle 2006). These processes are triggered by an agent's request for service and include the preparation and movement of goods and/or tangible resources (people, equipment) required for providing the service to the agent's selected location at the agent's selected time. A key trait of LMO is the fact that agents select the location and time of the provision of the service and that the provision of the service requires at least in part co-location with the agent. We submit that LMO can be classified into two main categories that differ significantly in the nature and extent of the associated customer co-creation activities (Sampson and Froehle 2006): goods-focused and agent-focused. Goods-focused LMO entails the provision of agent access to goods at a selected location and time, involving the preparation and movement of goods (e.g., groceries, meals) and resources (e.g., delivery vans, delivery people) to that location. A typical example would be e-commerce deliveries to consumer homes. Agent inputs are limited, primarily including information about the required goods (product selection and quantities) and delivery (time and location), as well as engaging in minor interactions with the provider during goods reception. The core value added is the movement of the goods to the agent's selected location and time. Goods-focused LMO correspond to “delivery services” and have received most research attention. Agent-focused LMO entail the provision of more general services to an agent at a selected location and time, involving the preparation and movement of service provision resources (e.g., people, equipment, inventory) to that location. A typical example would be performing repairs of equipment owned by the agent at its selected location, involving the movement of technicians, tools, and inventory (spare parts) to the agent's location. Another example would be an emergency ambulance service, which involves the movement of equipment (vehicle, medical instruments), medical staff, and inventory (medical supplies) to the agent's location. Agent inputs are substantial, including information about the required service, service delivery time and location, and agent's resources, as well as engaging in relevant service co-creation activities at the agent's location. The core value added is the transformation of the agent's inputs (e.g., agent-owned equipment, the agent self). Typically, the level of customization and agent co-creation increases from goods-focused to service-focused LMOs, while the transaction volumes decrease. LMO processes are characterized by a set of distinctive features that raise unique challenges for the management of operations. Based on our conceptualization of LMO and extant literature, we summarize LMO's distinctive features and associated challenges in Table 1. The remainder of the editorial will discuss LMO against this framework and address in more detail several of the distinctive features and challenges. The distinctiveness of LMO processes, their pervasiveness and widespread economic relevance, and the managerial challenges that remain unaddressed jointly motivate the development of a specific research program for LMO within the field of Operations Management. Need to cover very diverse geographical areas, with specific challenges: Reliance on a large number of independent resources (including subcontractors, crowdsourced labor, inventory, contracted or rented equipment, third-party platforms) has the following implications: The features and challenges presented in Section 2 provide a framework for the development of new LMO research that can broaden the scope of LMO subject knowledge, as well as strengthen the theoretical foundations supporting LMO research. This framework also serves as a reference for new research to inform about new technologies and business models in LMO and their implementation and execution. The remainder of this section expands on these research directions. Research on LMO has concentrated on goods-focused LMO, in particular the delivery of goods from a transportation hub or inventory location to an end consumer. 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A Suganya, P Nagarasu, Shankar Siva, M. Vignesh

AegisLibre is a novel decentralized storage algorithm designed to prioritize high security and efficiency for sensitive data like personal information, text, photos, and other private content. Drawing inspiration from blockchain technology, it combines dynamic encryption, smart contracts, zero-knowledge proofs (ZKP), and Proof of Storage (PoS) to offer a highly secure and verifiable storage system. This paper explores the foundations of AegisLibre, compares it with existing algorithms such as IPFS, discusses its key features, and presents an analysis of its security capabilities and performance.

Sunil Kumar, Karan Singh, Aadya Bubber

The healthcare industry grapples with numerous challenges related to the authenticity, traceability, and integrity of products, particularly pharmaceuticals, within its supply chain. Counterfeit drugs, diversion, and tampering are rampant issues that not only jeopardize patient safety but also undermine the credibility of the entire healthcare ecosystem. Blockchain technology, with its inherent characteristic of immutability and transparency, coupled with Non-Fungible Tokens (NFTs), offers a promising solution to combat these challenges. This paper primarily focuses on the registration process of pharmaceutical products. The proposed work of this paper contains multiple access controls for different smart contracts. By leveraging the blockchain's distributed ledger, each transaction and interaction within the supply chain can be recorded securely. NFTs, as unique digital assets, can be used to represent individual pharmaceutical products or batches, ensuring their authenticity and enabling seamless traceability. Moreover, the integration of smart contracts in a blockchain-based system can automate the compliance process, ensuring adherence to regulatory standards at each stage of the product's registration process with ownership details.

K. S. Chandrasekaran, V. Mahalakshmi, M. R. Anantha Padmanaban

Over the past ten years, blockchain technology has significantly captured interest in various application fields. Originally devised for the Bitcoin peer-to-peer cryptocurrency network, extensive research now explores integrating blockchain with various other service domains. The technology is celebrated for its decentralized structure, robust security, immutability, and transparency. In blockchain systems, consensus algorithms play a crucial role in establishing unanimous agreement among participants within a distributed computing environment, facilitating the addition of new blocks to the blockchain network. The effectiveness and security of the network largely hinge on the performance of these consensus algorithms. However, existing consensus algorithms face challenges with throughput, latency, and communication complexity. To address these issues, an enhanced consensus algorithm known as Intuitive Random Selection based Byzantine Fault Tolerant (BFTIRS) is introduced. This algorithm optimizes the consensus process by selecting a subset of nodes, thereby reducing network complexity and enhancing efficiency without sacrificing security. To tackle scalability issues in blockchains, a hierarchical BFTIRS algorithm that incorporates sharding is developed. This approach segments network participants into local and global consensus groups, each conducting the consensus process independently. Performance evaluations of this algorithm show improvements in both efficiency and security over existing solutions.

Kapil Hande, Manoj B. Chandak

Blockchain technology is proving to be a disruptive technology in many areas of supply chain, manufacturing, medical, agriculture, and so on. Warehouses are an inevitable part of the supply chain. Issues like space optimization, route optimization, quick item pick-up, demand forecasting, and transaction management are of importance to address in warehouse management systems (WMS). Traditional database systems have limitations of interoperability among different entities involved in warehouses. This paper presents an innovative application of blockchain technology and machine learning (ML) to build a smart warehouse management system in Web3 (SWMW3). We developed a decentralized application (DApp) using Web3.0 principles, integrating ReactJS for the frontend, express for the backend, and blockchain through smart contracts. This integration enhances security and transparency by storing WMS operational data in the blockchain and automating payments and verifications through smart contracts. Additionally, we implemented a ML model for predicting the total time from order receipt to delivery, leveraging historical data to optimize workflow, reduce delays, and improve overall efficiency. This combination of blockchain for secure transactions and ML for predictive analytics generates a robust, efficient, and optimized management system for the warehouse.

Oussema Gharsallaoui, Damien Nicolas, Marie-Laure Watrinet, Ion Turcanu

The integration of autonomous robots in last-mile delivery systems raises technological challenges such as communication, navigation, and cybersecurity. Blockchain offers a secure and transparent solution, but its scalability is a concern. In this paper, we present a co-simulation framework that combines the SUMO mobility simulator with the Ethereum Proof of Stake (POS) blockchain. We evaluate the impact of increasing the number of autonomous robots on blockchain performance, using Transaction Execution Time (TET) and Transaction Finality Time (TFT) as metrics. Our results demonstrate the feasibility and challenges of using blockchain in autonomous delivery systems.

Hongping Yuan, Li Zhang, Bing‐Bing Cao, Wenwen Chen

No abstract is available for this record.

Xin Chen

As electric vehicles (EV) become more prevalent and advances in electric vehicle electronics continue, vehicle-to-grid (V2G) techniques and large-scale scheduling strategies are increasingly important to promote renewable energy utilization and enhance the stability of the power grid. This study proposes a hierarchical multistakeholder V2G coordination strategy based on safe multi-agent constrained deep reinforcement learning (MCDRL) and the Proof-of-Stake algorithm to optimize benefits for all stakeholders, including the distribution system operator (DSO), electric vehicle aggregators (EVAs) and EV users. For DSO, the strategy addresses load fluctuations and the integration of renewable energy. For EVAs, energy constraints and charging costs are considered. The three critical parameters of battery conditioning, state of charge (SOC), state of power (SOP), and state of health (SOH), are crucial to the participation of EVs in V2G. Hierarchical multi-stakeholder V2G coordination significantly enhances the integration of renewable energy, mitigates load fluctuations, meets the energy demands of the EVAs, and reduces charging costs and battery degradation simultaneously.

Mohammed El‐Hajj, Bjorn Oude Roelink

This study builds on our previous systematic literature review (SLR) that assessed the applications and performance of zk-SNARK, zk-STARK, and Bulletproof non-interactive zero-knowledge proof (NIZKP) protocols. To address the identified research gaps, we designed and implemented a benchmark comparing these three protocols using a dynamic minimized multiplicative complexity (MiMC) hash application. We evaluated performance across four general-purpose programming libraries and two programming languages. Our results show that zk-SNARK produced the smallest proofs, while zk-STARK generated the largest. In terms of proof generation and verification times, zk-STARK was the fastest, and Bulletproof was the slowest. Interestingly, zk-SNARK proofs verified marginally faster than zk-STARK, contrary to other findings. These insights enhance our understanding of the functionality, security, and performance of NIZKP protocols, providing valuable guidance for selecting the most suitable protocol for specific applications.

Yifan Mao, Mengya Zhang, Shaileshh Bojja Venkatakrishnan, Zhiqiang Lin

Maximal extractable value (MEV) in which block proposers unethically gain profits by manipulating the order in which transactions are included within a block, is a key challenge facing blockchains such as Ethereum today. Left unchecked, MEV can lead to a centralization of stake distribution thereby ultimately compromising the security of blockchain consensus. To preserve proposer decentralization (and hence security) of the blockchain, Ethereum has advocated for a proposer-builder separation (PBS) in which the functionality of transaction ordering is separated from proposers and assigned to separate entities called builders. Builders accept transaction bundles from searchers, who compete to find the most profitable bundles. Builders then bid completed blocks to proposers, who accept the most profitable blocks for publication. The auction mechanisms used between searchers, builders and proposers are crucial to the overall health of the blockchain. In this paper, we consider PBS design in Ethereum as a game between searchers, builders and proposers. A key novelty in our design is the inclusion of future block proposers, as all proposers of an epoch are decided ahead of time in proof-of-stake (PoS) Ethereum within the game model. Our analysis shows the existence of alternative auction mechanisms that result in a better (more profitable) equilibrium to players compared to state-of-the-art. Experimental evaluations based on synthetic and real-world data traces corroborate the analysis. Our results highlight that a rethinking of auction mechanism designs is necessary in PoS Ethereum to prevent disruption.

Rishi Prakash Shukla, Satnam Singh, Pawan Kumar, Abhishek Chauhan

Step into the dynamic world of supply chain management, where a significant transformation is unfolding through the adoption of innovative technologies. This chapter is a guide through this exciting journey, exploring the impactful changes brought about by robo advisors. We'll dive into their practical uses, the hurdles faced, and what the future holds. Looking ahead, we'll gaze into the future of supply chain management. From decentralized autonomous supply chains to the integration of augmented reality, self-aware robotic systems, and even the potential of quantum computing, there's much to anticipate. This chapter is not just a look into the crystal ball; it's a call to action, helping organizations prepare for the next phase of innovation and efficiency.

Abdulrahman A. Alshdadi, Souad Kamel, Eesa Alsolami, Miltiadis D. Lytras · 5 authors

Cold supply chains are becoming more and more attractive due to the high demand induced by increased consumption. To fulfill standards and customers’ requirements regarding the conditions under which cold supply chain products (mainly foods and pharmaceuticals) are stored (in warehouses) and transported to the end-users, tracking those conditions is a necessity. To ensure a high level of visibility, fostering emerging technologies can improve the quality of service in supply chains in terms of delivery time, cost, and quality. In this paper, a global framework for monitoring the conditions of storage and transportation of cold products across the whole supply chain is designed and implemented practically. The proposed solution is built around low-cost and low-energy consumption devices such as sensors and microcontrollers which are connected to cloud storage to allow a high level of visibility in the supply chain allowing all parties, including the end-consumers, to follow the products during their transfer, providing a conceptual framework that monitors the performance on a real-time basis and enhances decision making. A prototype using an embedded temperature/humidity sensor, a tiny microcontroller equipped with a Wi-Fi connectivity device, and a mobile 4G/5G network is designed and implemented. The proposed system is connected to a cloud-storage platform continuously accessible by the main parties of the cold supply chain including the provider, the transporter, and the end-consumer. The proposed framework may be handled as a smart contract during which any party can assume its responsibility for the assurance of the best conditions of the supply chain operation. A small-scale real-life scenario conducted in Jeddah City, Saudi Arabia is introduced to show the feasibility of the proposed framework.

M. M. L. Arora, Vasim Ahmad, Rakesh Kumar, Nagendar Yamsani · 6 authors

Due to the integration of technology in the form of Artificial Intelligence and Internet of Things into the cold chain, the fourth industrial revolution, often known as “Industry 4.0,” evolves as a new technological paradigm. According to the Industry 4.0 vision, the smart cold chain emphasizes worldwide networks of autonomously communicating information along with efficient control systems. Autonomous operation of the smart cold network is made possible by using the cyber-physical system. Since the cooperation between suppliers, manufacturers, producers, and processors, as well as customers, is crucial to improve transparency of all processes from the point of origin to the point of consumption, it is imperative to look at the influence of Industry 4.0 on the entire cold chain. This research study analyzes the application of technology in cold chain and suggests the use of various technologies in the form of solutions to resolve various issues existing in the cold chain.

Koduru Manohar Reddy, Kowtharapu Kesava Sai, D. Deepa

The blockchain-based voting system is emerging efficient technology as it reduces voting costs and increases voter turnout. This technology lets people to vote from anywhere with an Internet connection by eliminating the need to visit a physical polling center. The primary challenge faced by internet voting is its security. To overcome this challenge, the decentralized and distributed blockchain technology is preferred. It provides tamper-proof and visible voting records, ensuring that the votes are dependable, accurate, and secure. This study has developed a blockchain-based e-ballot application to provide a secure electronic voting system that offers the same degree of equality and privacy as existing voting services but with the added transparency and flexibility that electronic voting systems provide. The purpose of employing blockchain as a service, in conjunction with distributed ledger technologies to construct an electronic voting system is to address the drawbacks faced by existing voting system while also making it more cost efficient. To summarize, blockchain-based voting systems may be able to overcome some of the challenges that now hinder political decision-making.

Zhu Kuang Lee, Jing Yu Wong, Rachel Roch, Xin Yi Tan

This paper draws on the hands-on experience of HSBC in implementing distributed ledger technology (DLT)-based solutions through building its own proprietary tokenisation platform, HSBC Orion, and participating in various industry initiatives such the Hong Kong Monetary Authority (HKMA)’s inaugural tokenised green bond issuance. The paper highlights key considerations for building DLT-based solutions and provides a view of the future state of digital assets as the ecosystem continues to grow and evolve.

Jae‐Hun Kim, Soo‐Mook Moon

블록체인에 대한 관심이 많아지고, 이더리움의 사용량이 증가함에 따라 블록체인 상태 데이터는 폭발적으로 증가하였는데, 이는 사용자의 네트워크 참여를 어렵게 만들었다. 본 논문에서는 상태 데이터의 상당수를 차지하는 스토리지 트라이를 과거 트랜잭션 데이터를 기반으로 최적화하는 방법을 제안하고자 한다. 본 연구에서는 1,400만 블록의 거대한 스토리지 트라이 중에서 100만 블록동안 한 번도 등장하지 않은 스토리지 트라이를 삭제하여 19.6%인 10.8GB의 저장공간을 줄였다. 본 논문의 연구 결과인 데이터 기반 스토리지 트라이 최적화를 기반으로 더욱 효과적인 스토리지 트라이 최적화를 제안할 수 있을 것으로 기대한다.