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.
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.
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.
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.
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.
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 proliferation of smart devices, sensors, autonomous robots, drones, and other similar instruments have profoundly changed the way of implementing and deploying systems in industrial and home environments, for diverse scenarios such as smart agriculture, healthcare, or manufacturing. Devices in these settings are not limited to simply observe and acquire data for monitoring, but they are also equipped with actuation capabilities, as well as the possibility of autonomously processing the incoming data through various techniques. However, given their intrinsic limitations regarding the capacity to store and process computations, it is often necessary to delegate some of these processing tasks to intermediary edge nodes in the network. These nodes, given their unique position can act as orchestrators guiding the decentralized work of the interconnected autonomous devices. Beyond static and pre-defined organization structures, in this work we propose the usage of agent and multi-agent-based models for designing and implementing swarms of edge nodes, conceived to dynamically orchestrate other devices, while meeting quality of service conditions. Allowing the control of intelligent edge nodes as conveyors and orchestrators on swarms of devices, we aim at providing intelligence to the self-organization of edge nodes, which may interchange streaming data, and represent their own capabilities through semantic models. Swarm-inspired behavioral patterns would guide the collaborative distribution of their computational tasks. Finally, we will implement and demonstrate the proposed technologies in an elderly home environment powered with a host of edge computing, sensing, and actuating devices.
Darcy Camargo, Andreas Penzkofer, Sebastian Müller, William H. Sanders
The robust construction of the ledger data structure is an essential ingredient for the safe operation of a distributed ledger. While in traditional linear blockchain systems, permission to append to the structure is leader-based, in Directed Acyclic Graph-based ledgers, the writing access can be organised leaderless. However, this leaderless approach relies on fair treatment of non-referenced blocks, i.e. tips, by honest block issuers. We study the impact of a deviation from the standard tip selection by a subset of block issuers with the aim of halting the confirmation of honest blocks entirely. W e provide models on this so-called orphanage of blocks and validate these through open-sourced simulation studies. A critical threshold for the adversary issuance rate is shown to exist, above which the tip pool becomes unstable, while for values below the orphanage decrease exponentially. We study the robustness of the protocol with an expiration time on tips, also called garbage collection, and modification of the parent references per block.
Distributed ledger technologies, together with AI, smart systems and robotics could provide a scalable and robust platform for the smart underwater and surface marine infrastructures of the close future. They will be harbours or ports, marine farms or remote tourism facilities - only accessible through robotic avatars - for protected areas or for the elders. Autonomous Marine Vehicles, IoT networks, and humans will coexist in highly heterogeneous multivendor multiplatform environments where market transactions and complex administrative procedures will be ubiquitous. Some blockchains such as the Ethereum network are able to provide distributed scalable computing and trustable functionalities, capable of managing both technical interactions and market transactions among very diversified autonomous agents. For these reasons they seem to provide a valuable backbone for the smart marine infrastructure of the coming decades. In this paper we outline our research and innovation strategy and present our results showing the potential benefits of a subsidiary architecture integrating distributed ledger technologies with swarms of autonomous surface robots implemented by a Belief Space Planning approach.
Empowered by blockchain and Web3 technologies, decentralized autonomous organizations (DAOs) are able to redefine resources, production relations, and organizational structures in a revolutionary manner. This article aims to reanalyze DAOs from the perspectives of organization and operation, and provide a more precise definition of DAOs as Decentralized Autonomous Organizations and Operations. Based on this, the fundamental principles and requirements of DAOs are explained, while the infrastructure based on cyber–physical–social system (CPSS) and parallel intelligence, as well as the supporting technologies, such as digital twins, metaverse, and Web3, are discussed. Besides, a five-layer intelligent architecture is presented, and the closed-loop equation and new function-oriented intelligent algorithms are also proposed. Moreover, the governance mechanisms from the individual, organizational and social perspectives are discussed, and the incentive mechanisms for the human, robot, and digital human are analyzed. This article can be regarded as a stepping stone for further research and developments of DAOs.
Xiujuan Wang, Mengzhen Kang, Hequan Sun, Philippe De Reffye · 5 authors
Briefing: The demand for food is tremendously increasing with the growth of the world population, which necessitates the development of sustainable agriculture under the impact of various factors, such as climate change. To fulfill this challenge, we are developing Metaverses for agriculture, referred to as AgriVerse, under our Decentralized Complex Adaptive Systems in Agriculture (DeCASA) project, which is a digital world of smart villages created alongside the development of Decentralized Sciences (DeSci) and Decentralized Autonomous Organizations (DAO) for Cyber-Physical-Social Systems (CPSSs). Additionally, we provide the architectures, operating modes and major applications of DeCASA in Agri-Verse. For achieving sustainable agriculture, a foundation model based on ACP theory and federated intelligence is envisaged. Finally, we discuss the challenges and opportunities.
Sahar Salimpour, Farhad Keramat, Jorge Peña Queralta, Tomi Westerlund
Multiple opportunities lie at the intersection of multi-robot systems and distributed ledger technologies (DLTs). In this work, we investigate the potential of new DLT solutions such as IOTA, for detecting anomalies and byzantine agents in multi-robot systems in a decentralized manner. Traditional blockchain approaches are not applicable to real-world networked and decentralized robotic systems where connectivity conditions are not ideal. To address this, we leverage recent advances in partition-tolerant and byzantine-tolerant collaborative decision-making processes with IOTA smart contracts. We show how our work in vision-based anomaly and change detection can be applied to detecting byzantine agents within multiple robots operating in the same environment. We show that IOTA smart contracts add a low computational overhead while allowing to build trust within the multi-robot system. The proposed approach effectively enables byzantine robot detection based on the comparison of images submitted by the different robots and detection of anomalies and changes between them.
Abstract Offshore wind farms will play a vital role in the global ambition of net zero energy generation. Future offshore wind farms will be larger and further from the coast, meaning that traditional human‐based operations and maintenance approaches will become infeasible due to safety, cost, and skills shortages. The use of remotely operated or autonomous robotic assistants to undertake these activities provides an attractive alternative solution. This paper presents an autonomous multirobot system which is able to transport, deploy and retrieve a wind turbine blade inspection robot using an unmanned aerial vehicle (UAV). The proposed solution is a fully autonomous system including a robot deployment interface for deployment, a mechatronic link‐hook module (LHM) for retrieval, both installed on the underside of a UAV, a mechatronic on‐load attaching module installed on the robotic payload and an intelligent global mission planner. The LHM is integrated with a 2‐DOF hinge that can operate either passively or actively to reduce the swing motion of a slung load by approximately 30%. The mechatronic modules can be coupled and decoupled by special maneuvers of the UAV, and the intelligent global mission planner coordinates the operations of the UAV and the mechatronic modules for synchronous and seamless actions. For navigation in the vicinity of wind turbine blades, a visual‐based localization merged with the location knowledge from Global Navigation Satellite System has been developed. A proof‐of‐concept system was field tested on a full‐size decommissioned wind‐turbine blade. The results show that the experimental system is able to deploy and retrieve a robotic payload onto and from a wind turbine blade safely and robustly without the need for human intervention. The vicinity localization and navigation system have shown an accuracy of 0.65 and 0.44 m in the horizontal and vertical directions, respectively. Furthermore, this study shows the feasibility of systems toward autonomous inspection and maintenance of offshore windfarms.
Farhad Keramat, Jorge Peña Queralta, Tomi Westerlund
With the increasing ubiquity of autonomous robotic solutions, the interest in their connectivity and in the cooperation within multi-robot systems is rising. Two aspects that are a matter of current research are robot security and secure multi-robot collaboration robust to byzantine agents. Blockchain and other distributed ledger technologies (DLTs) have been proposed to address the challenges in both domains. Nonetheless, some key challenges include scalability and deployment within real-world networks. This paper presents an approach to integrating IOTA and ROS 2 for more scalable DLT-based robotic systems while allowing for network partition tolerance after deployment. This is, to the best of our knowledge, the first implementation of IOTA smart contracts for robotic systems, and the first integrated design with ROS 2. This is in comparison to the vast majority of the literature which relies on Ethereum. We present a general IOTA+ROS 2 architecture leading to partition-tolerant decision-making processes that also inherit byzantine tolerance properties from the embedded blockchain structures. We demonstrate the effectiveness of the proposed framework for a cooperative mapping application in a system with intermittent network connectivity. We show both superior performance with respect to Ethereum in the presence of network partitions, and a low impact in terms of computational resource utilization. These results open the path for wider integration of blockchain solutions in distributed robotic systems with less stringent connectivity and computational requirements.
The Ph.D research project aims to explore the potential of the Decentralized Autonomous Organization paradigm in conjunction with classic software architectures for Artificial Intelligence applications. The intended goal is to investigate and formalize a possible integration path between Multi-agent System architectures and Decentralized Autonomous Organizations. Starting from the Foundation for Intelligent Physical Agents standards, we will extend basic primitives to integrate Multi-agent Systems on Distributed Ledger Technology networks. Possible deployment of services and applications in the Internet-of-Things, Artificial Intelligence and Distributed Machine Learning areas will be tested. Application of Data Analysis techniques on datasets built on such a framework will be also addressed.
Blockchains can be either permissioned or permissionless. Existing blockchain consensus algorithms used to create permissioned blockchains to not allow new validators to be dynamically added to the blockchain during runtime. This can be a problem when blockchain is used in swarm robotics, where redundancy is required. Besides, popular algorithms such as proof of work (PoW) that offer redundancy consume a lot of resources, making them unsuitable to run on the powerless robots used in swarm robotics. In addition, such algorithms are also vulnerable to threats such as the 51% attack. This research proposes a novel proof-of-identity (PoI) blockchain consensus algorithm to create a dynamically permissioned blockchain to be used in swarm robotics. This algorithm uses a swarm controller to generate a private-public key pair that is used to authorise validators. Tests carried out showed that the PoI algorithm is immune to the 51% attack threat and can run on low-powered devices such as Raspberry Pi 3. Moreover, the consensus time was also shown to be shorter in comparison to PoW.
A random beacon provides a continuous public source of randomness and its applications range from public lotteries to zero-knowledge proofs. Existing random beacon protocols sacrifice either the fault tolerance or the communication complexity for security, or ease of reconfigurability. This work overcomes the challenges with the existing works through a novel communication efficient combination of state machine replication and (Publicly) Verifiable Secret Sharing (PVSS/VSS).
Eduardo Castelló Ferrer, Ernesto Jiménez, José Luis López-Presa, Javier Martín-Rueda
Interest in multirobot systems is rising rapidly both in academia and in industry. The use of multiple robots working in a coordinated way, rather than a single robot, has several advantages in a diverse range of applications. However, few studies have focused on operations in which robots can behave maliciously and alter the outcome of the collective mission. In this article, we present a set of Byzantine Follow The Leader (BFTL) problems, in which a subset of robots in the system shows unintended or inconsistent behavior (i.e., Byzantine robots). In the BFTL problems,leadersdiscover routes from their starting positions to specific destinations and guide thefollowers, whileByzantinerobots try to hinder the leaders and mislead the followers. In this research, blockchain technology is used as a communication tool within multirobot systems, forleadersto broadcast directions to the whole group. We propose algorithms to tackle the BFTL problems, prove their correctness, and validate them in simulated experiments of realistic scenarios. Results show that the proposed algorithms mitigate the impact of Byzantine robots in multirobot systems conducting a BFTL mission. Our analysis provides minimum and maximum boundary calculations for important metrics including number of robots reaching their destination, number of steps taken, and weight requirements of the chain used during the mission. Our results provide a path toward the deployment of byzantine-resistant real-world multirobot systems.