Abstract The rise of decentralized technologies introduces challenges in fairness, efficiency, and scalability within distributed ledger protocols. The Internet of Things Applications (IOTA) Tangle, a directed acyclic graph (DAG)-based structure, addresses these challenges by enabling scalable, feeless transactions for IoT applications. This study presents a novel Partially Observable Markov Decision Process (POMDP)-based Tip Selection Algorithm (TSA) to optimize fairness in the IOTA Tangle. The proposed TSA reduces orphaned transactions to as low as 0.003% and eliminates lazy tip selection under medium network loads. Extensive simulations demonstrate that the POMDP-based TSA confirms up to 107 transactions at optimal lambda values, outperforming existing algorithms like Weighted TSA by 328% in efficiency. This algorithm offers significant scalability, fairness, and adaptability, making it a robust solution for IoT-based decentralized applications. These findings advance DAG-based distributed ledger systems by addressing orphaned transactions and lazy behavior, ensuring secure and efficient operations under diverse network conditions.
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.
Carlo Beltracchi, Ahmed Elmaraghy, Pierpaolo Ruttico, S. Maccagnan
This contribution proposes a way to broaden access to computational design by combining: (1) an agentic workflow where AI micro-agents translate natural-language prompts into executable, self-verified parametric graphs; (2) a data-driven economy in which each reuse of logic triggers automatic micropayments; and (3) a decentralised network that stores versions, rights and transactions on-chain. Assessor, provider and validator agents assemble, check and publish sub-graphs serialised as semi-fungible tokens; a blockchain ledger tracks lineage and redistributes royalties. The platform merges open-source principles with Web3 incentives: newcomers gain ready-to-use solutions, experienced designers monetise know-how, and the community governs parameters via on-chain voting. Supported by robotic 3D-printing partners, the framework targets XR adoption: tokenised parametric graphs power virtual configurators for (1:1) design alternatives; users and curators vary parameters within constraints and record reuse on-chain, supporting an inclusive creator economy across the generative process.
Navigation in unstructured, GPS-denied environments, such as forests and agricultural fields, poses persistent challenges for heterogeneous robotic teams. While visual homing and Wide Area Visual Navigation (WAVN) enable lightweight, map-free operation, their effectiveness in large-scale, decentralized settings can be constrained by the absence of a coordination mechanism that accounts for varying reliability across robots. This article examines the innovative combination of blockchain techniques with WAVN to tackle visual navigation issues in diverse mobile robots used in unstructured sectors like agriculture and forestry. It addresses GPS reliance, adapts to environmental shifts, and reduces computational burdens by integrating RoboStake, a novel blockchain Proof-of-Stake (PoS) mechanism, into the WAVN system. This solution seeks to bolster cooperative navigation by assessing the reliability of each robot’s navigational input. With methods including a stake weight function, a PoS consensus score, and a navigability function, this strategy confronts the computational hurdles of coordinating robots and verifying data. Lastly, we showcase how the proposed approach upholds critical navigability features of the WAVN system and present results from scalable simulation experiments to highlight the improved efficiency achieved through enhanced cooperation.
The Agentic Service Ecosystem consists of heterogeneous autonomous agents (e.g., intelligent machines, humans, and human-machine hybrid systems) that interact through resource exchange and service co-creation. These agents, with distinct behaviors and motivations, exhibit autonomous perception, reasoning, and action capabilities, which increase system complexity and make traditional linear analysis methods inadequate. Swarm intelligence, characterized by decentralization, self-organization, emergence, and dynamic adaptability, offers a novel theoretical lens and methodology for understanding and optimizing such ecosystems. However, current research, owing to fragmented perspectives and cross-ecosystem differences, fails to comprehensively capture the complexity of swarm-intelligence emergence in agentic contexts. The lack of a unified methodology further limits the depth and systematic treatment of the research. This paper proposes a framework for analyzing the emergence of swarm intelligence in Agentic Service Ecosystems, with three steps: measurement, analysis, and optimization, to reveal the cyclical mechanisms and quantitative criteria that foster emergence. By reviewing existing technologies, the paper analyzes their strengths and limitations, identifies unresolved challenges, and shows how this framework provides both theoretical support and actionable methods for real-world applications.
We present Free-Delete, a censorship-resistant group-chat protocol whose membership is validated by non-fungible-token (NFT) ownership while user privacy ranges across five selectable modes. A single Groth16 circuit, anchored in a sparse Merkle tree of verifiable commitments, realises (i) Fully Anonymous messaging, (ii) Linkable Anonymous reputation building, (iii) Publicly Identified disclosure, (iv) Confidential end-to-end encryption, and (v) Rate-Limited Accountability that revokes keys on spam—all without moderator involvement or economic deposits. A black-paper prototype written in TypeScript, Circom 2, and Solidity achieves 0.35 s for register and 1.38 s for postMessage on consumer hardware; on-chain verification costs 3–5.5×105 gas per proof on Polygon. These results demonstrate that NFT-gated, stake-free, privacy-preserving communication can be deployed today on any EVM chain.
Mariia Deinega Mariia Deinega, Theodoros Dounas, Daniel Hall Daniel Hall, Hico McDonald Hico McDonald · 6 authors
Decentralized project delivery in architecture faces challenges related to transparency, accountability, and role definition. This paper explores the application of Soulbound Tokens (SBTs) as a governance and record-keeping mechanism within decentralized autonomous organizations (DAOs). Using a systematic review of practice, the paper identifies five opportunities for SBTs, proposes an operating framework for SBTs with respect to record-keeping (e.g., skills, contributions) and project governance (e.g, voting power, reputation), and describes one case of technical implementation of SBTs. Future research can improve on this technical implementation or develop additional decentralised applications for SBT skills verification and governance mechanisms.
Luis Blanco-Cocom, Selva Vía, Cristian J. Vaca-Rubio, Engin Zeydan · 26 authors
This paper proposes a highly sustainable and scalable integrated AI-native architecture defining UNified archITecture for Open RAN-enabled Distributed, Scalable and SustainabilitY-enhanced 6G Networks (UNITY-6G) project that can support the diverse requirements of 6G networks by relying on advanced technologies, such as distributed ledger technology, semantic communications, digital network twinning to enhance the performance, cost-efficiency and trustworthiness of integrated 6G network services and applications. The focus is on scalability and sustainability for integrated networks (Non-Terrestrial and Terrestrial Networks, xHaul, Open RAN, Non-Public Networks, Edge, Core and Cloud). Furthermore, we aim to evolve to realtime distributed and network state-aware Open RAN that can leverage the integration of distributed applications in the integrated architecture. This will enable fine-grained data-driven management and control via incorporating dApps, distributed applications that complement existing xApps/rApps and use cases with stricter timing requirements in an integrated network. Common interfaces and protocols will be defined so that different heterogenous domains can communicate seamlessly. To better guide the design, This paper also use the principles of service based architecture for integrated networks and leverage digital twins for network evaluation and considers four use cases targeting: i) Sustainable networks for disaster handling, (ii) Immersive Experience with Real-time XR/holographic communications, (iii) Digital Twin for Integrated 6G Network Evaluation, (iv) MultiRAT O-RAN enabled NPN for supporting time sensitive applications for Industry 4.0.
Peter KimemiahMwangi, Stephen TNjenga, Gabriel Ndung’uKamau
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 Barabási 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 ≈ 1.000), exhibiting robust predictability. MTL increased logarithmically (R² ≈ 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.
We describe the design and implementation of MicroNova, a folding-based recursive argument for producing proofs of incremental computations of the form$y=F^{(\ell)}(x)$, where$F$is a possibly non-deterministic computation (encoded using a constraint system such as R1CS),$x$is the initial input,$y$is the output, and$\ell > 0$The proof of an$e$-step computation is produced step-by-step such that the proof size nor the time to verify it depends on$e$. The proof at the final iteration is then compressed, to achieve further succinctness in terms of proof size and verification time. Compared to prior folding-based arguments, a distinguishing aspect of MicroNova is the concrete efficiency of the verifier-even in a resource-constrained environment such as Ethereum's blockchain. In particular, the compressed proof consists of O(log N) group elements and it can be verified with O(log N) group scalar multiplications and two pairing operations, where$N$is the number of constraints for a single invocation of$F$MicroNova requires a universal trusted setup and can employ any existing setup material created for the popular KZG univariate polynomial commitment scheme. Finally, we implement and experimentally evaluate MicroNova. We find that MicroNova's proofs can be efficiently verified on the Ethereum blockchain with ≈2.2M gas. Furthermore, MicroNova's prover incurs minimal overheads atop its baseline Nova's prover.
This work develops a novel two-phase control framework that enables a swarm of compact spacecraft (agents), such as CubeSats and Nanosats, to autonomously capture tumbling and uncooperative targets. By leveraging decentralized, bio-inspired swarm behavior control and distributed coordination strategies, the proposed system enables fully interchangeable agents to achieve robust, leaderless self-organization. During the capture, flocking behavior guides agents towards the target, while anti-flocking behavior enforces uniform dispersion of agents around it to provide full surface coverage and effective encapsulation prior to capture. A consensus-based protocol synchronizes the capture action among agents by allowing all agents to agree on a common action time. In this process, each agent autonomously identifies available capture points and participates in an auction-based allocation algorithm to collectively allocate optimal capture positions among agents. Simulation results validate the effectiveness of the proposed framework in autonomously capturing targets of various shapes, sizes and motion patterns, and demonstrate scalability across different swarm sizes. Overall, the proposed approach shows significant potential for coordinated, efficient, and robust swarm-based capture of uncooperative targets in space, offering benefits in scalability, adaptability, robustness, and cost-effectiveness.
Robotic swarm intelligence is a rapidly evolving field that leverages principles of decentralized control, self-organization, and emergent behavior to enable effective coordination and collaboration in multi-robot systems. Inspired by biological swarms, such as ant colonies and bird flocks, swarm robotics focuses on the collective performance of simple agents interacting locally to achieve complex tasks. This approach enhances scalability, robustness, and adaptability in dynamic and unpredictable environments. Key applications include search and rescue, environmental monitoring, industrial automation, and military operations. Recent advancements in artificial intelligence, machine learning, and communication technologies have further improved swarm decision-making, task allocation, and formation control. This paper explores the fundamental principles, coordination strategies, and challenges in robotic swarm intelligence, highlighting future directions for optimizing collaboration in autonomous multi-robot systems.
The Hedera hashgraph algorithm has been shown to be Asynchronous Byzantine Fault Tolerant (ABFT) for achieving consensus on adding a transaction into local copies of a hash-graph distributed database. The ABFT result is theoretically the best result that can be achieved for distributed ledger technology (DLT) regarding trusting that the data in each local copy of a distributed global database has not been tampered with during each transaction process to add data into the global distributed database. The hashgraph algorithm ensures that each transaction in each local copy of the global database can be trusted to be a true copy of the data submitted by each node in the set of peer nodes as long as no more than 1/3 of the peer nodes in the peer-to-peer network of hashgraph nodes have been compromised. The Aquaticus, capture the flag (CTF) force-on-force free-play competition between Artificial Intelligence (AI)/Machine Learning (ML) agents enables use of a variety of ML algorithms to build AI/ML agents to play and win the CTF game in a maritime environment by employing the MOOS-IvP autonomy stack. This paper explores the integration of Hedera hashgraph DLT into the MOOS-IvP Aquaticus testbed for efficient and secure data transfer in collaborative autonomy scenarios. The study focuses on developing a multi-node Hedera network to support decentralized, real-time, and tamper-proof communication among autonomous agents in adversarial maritime environments. A detailed network setup using Docker and solo-compose is outlined, including transitioning from single-node to multi-node configurations. The system's application is evaluated in the context of the Aquaticus capture-the-flag (CTF) environment, highlighting its role in synchronizing flag positions and tagging status among unmanned surface vehicles (USVs), Initial findings indicate that the Hedera network can enhance data integrity and scalability while reducing latency in distributed systems. Challenges in scaling and resource optimization are discussed, along with proposed future work to deploy physical nodes using Raspberry Pi and integrate reinforcement learning frameworks like PyQuaticus. This research provides a foundation for advancing decentralized communication in autonomous robotics, emphasizing its potential for secure and robust multi-agent collaboration.
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.
According to its “technology neutrality” principle, MiCA applies to all cryptoassets unless they qualify under other EU financial law, except for e-money tokens (EMTs) for which both MiCA and the E-money Directive (EMD) apply. Thus, Section 4.1 starts with the definition of ‘cryptoassets’, and Section 4.2 covers the exemptions from MiCA’s scope: exempted entities, the exemption for non-fungible tokens (NFTs), exemptions for assets covered by other EU financial law, and the exemption for fully decentralised services. Section 4.3 discusses MiCA’s geographical scope, and Section 4.4 provides an in-depth analysis of the difficult interface towards other EU financial law. Section 4.5 explains MiCA’s approach to addressing this difficult scope issue, and Section 4.6 concludes.
The transition from fifth-generation (5 G) to sixth-generation (6 G) networks is driving significant advancements in network slicing, fueled by the growing demand for next-generation applications and services. However, managing these advancements within the constraints of finite resources creates the opportunity for open resource marketplaces, which introduces technical and business challenges. To address these, we propose TokenNet, the first blockchain-based architecture that represents network resources as non-fungible tokens (NFTs) in the context of network slicing. TokenNet facilitates secure, decentralized resource trading, ownership traceability, and management, optimizing resource allocation through auctioning, brokering, and trust mechanisms. It offers more granular, flexible, and trustworthy control over network resources compared to existing state-of-the-art systems. Our prototype implementation demonstrates its effectiveness, outperforming baseline models in cost efficiency, reducing delays, and improving minting performance. These advantages position TokenNet as a promising solution for future network management.
The collaboration among service robots operated by multiple vendors is in demand, but it is difficult to achieve due to issues such as the lack of interoperability among vendor-specific operating systems. To tackle this problem, we propose a crowdsourcing platform leveraging distributed ledger technology (DLT), where users can request services by flexibly combining multiple tasks assigned to robots. We also present a solution to avoid collisions of mobile robots by employing a universal space identification technology, called Spatial-ID, and defining a Space of Attention (SoA) as a location that requires attention for navigating robots. Our approach does not require the sharing of navigation maps among vendors unlike conventional methods. As a case study, we compared the transaction approval latency of the Ethereum blockchain and IOTA Tangle using their test networks through computer simulations. We then evaluated the risk of transaction conflicts, concluding that IOTA Tangle is a promising technology for implementing the proposed platform.
In the era of Web3, decentralized technologies have emerged as the cornerstone of a new digital paradigm. Backed by a decentralized blockchain architecture, the Web3 space aims to democratize all aspects of the web. From data-sharing to learning models, outsourcing computation is an established, prevalent practice. Verifiable computation makes this practice trustworthy as clients/users can now efficiently validate the integrity of a computation. As verifiable computation gets considered for applications in the Web3 space, decentralization is crucial for system reliability, ensuring that no single entity can suppress clients. At the same time, however, decentralization needs to be balanced with efficiency: clients want their computations done as quickly as possible. Motivated by these issues, we study the trade-off between decentralization and efficiency when outsourcing computational tasks to strategic, rational solution providers. Specifically, we examine this trade-off when the client employs (1) revelation mechanisms, i.e. auctions, where solution providers bid their desired reward for completing the task by a specific deadline and then the client selects which of them will do the task and how much they will be rewarded, and (2) simple, non-revelation mechanisms, where the client commits to the set of rules she will use to map solutions at specific times to rewards and then solution providers decide whether they want to do the task or not. We completely characterize the power and limitations of revelation and non-revelation mechanisms in our model.
This work examines the innovative combination of blockchain techniques with Wide Area Visual Navigation (WAVN) to tackle visual navigation issues in a diverse group of mobile robots used in unstructured sectors like agriculture and forestry. It focuses on addressing GPS reliance, adapting to environmental shifts, and reducing computational burdens by integrating RoboStake, a novel blockchain Proof of Stake (PoS) mechanism into the WAVN system. This solution seeks to bolster cooperative navigation by assessing the reliability of each robot's navigational input. With methods including a stake weight function, a PoS consensus score, and a navigability function, this strategy confronts the computational hurdles of coordinating robots and verifying data. Lastly, we showcase how the proposed approach upholds critical navigability features of the WAVN system and present results from scalable simulation experiments to highlight the improved efficiency achieved through enhanced cooperation.
Ayush M. Patil, Atharv S. Pakmode, Rishi Jain, Rupali D. Rode · 5 authors
Swarm robotics is a broad area of study that looks at the collective behavior of many autonomous robots to complete challenging tasks. Swarm robotics attempts to take advantage of the power of decentralization and self-organization to achieve adaptability, scalability, and robustness in robotic systems by taking inspiration from the collective behavior of natural swarms, such as ants, bees, and schools of fish.
The objective of nature-inspired robotics is to augment the functionalities of robotic systems by imitating the intricate behavioral systems that are present in the natural world. By deriving inspiration from a wide range of ecosystems, this discipline applies biomechanical, ethological, and evolutionary principles to the development of robotics that exhibit efficient and adaptable actions. The concept of nature-inspired robotics originates from the observation that natural organisms navigate their environments with unparalleled adaptability, efficiency, and resilience. Researchers strive to emulate the complexities of biological systems in artificial agents through the process of decoding them, with the ultimate goal of improving the functionalities of robotic platforms. Nature-inspired robotics encompasses a wide range of domains, including soft robotics, swarm robotics, and bio-inspired algorithms. By incorporating knowledge from the study of ecosystem dynamics, animal behavior, and plant morphology, scientists intend to create robotic systems that can adapt autonomously to challenging and dynamic environments. The task involves effectively conveying the intricacies of natural behaviors through algorithmic frameworks and tangible manifestations. It is of utmost importance to surmount the intrinsic constraints of conventional robotics, including inflexible architectures and predetermined code, in order to develop robots capable of dynamically adapting to evolving circumstances, imitating collaborative intelligence, and seamlessly interacting with their environment. The principal aim is to design and construct robotic systems that replicate, and ideally exceed, the adaptability and efficiency of their biological counterparts. By amalgamating insights from various academic disciplines, the objective is to develop autonomous systems that can perform a wide array of duties, including environmental monitoring, search, and rescue, with minimal reliance on explicit human intervention. Nature-inspired robotic behavioral systems have exhibited encouraging progressions in the domains of self-organization, decentralized decision-making, and swarm intelligence. By imitating the behaviors of natural ecosystems, these robots demonstrate the capacity to revolutionize domains including precision agriculture, environmental exploration, and disaster response by adapting to unanticipated obstacles.
Blockchain technology has become a research hotspot in distributed systems, aiming to sustain a decentralized ledger via consensus. Traditional consensus solutions exhibit slow processing speed and response time, resulting in poor performance. To address this issue, several consensus protocols have been proposed. One such popular protocol is HotStuff, a Byzantine fault-tolerant consensus (BFT) that achieves high throughput at the cost of latency. However, its throughput suffers from a proportional decrease with the increase in latency, posing a significant challenge. In this paper, we propose a new protocol called Dolphin that builds upon HotStuff. It operates in a partially synchronous network with$n$replicas, up to$f$byzantine faults, where$n \ge 3f+1$, and achieves higher throughput in high-latency environments by leveraging non-blocking concurrent block generation. Specifically, we formalize our strategy as a generic Asynchronization Procedure Patch and prove that it does not affect the execution process of the original protocol. Theoretical analysis validates that Dolphin preserves the safety, liveness, and responsiveness properties while enhancing the throughput. The evaluation demonstrates that Dolphin typically achieves more than 10x higher throughput in Wide Area Network (WAN) environments with lower latency compared to HotStuff and its variants, and exhibits similar bandwidth utilization to DAG-based protocols such as Narwhal.