The proliferation of unmanned aerial vehicle (UAV) swarms in mission-critical applications for 6G and the Internet of Things (IoT) introduces significant security vulnerabilities stemming from their dynamic, distributed, and resource-constrained nature. Traditional security paradigms are often inadequate for these complex cyber-physical systems. This paper proposes a novel, cross-layer security framework that ensures robust and lightweight operation for UAV swarms. The framework is founded on a novel Entropy-Derived Physically Unclonable Function (EPUF) based on DRAM, which employs a data-driven characterization process designed to achieve near 100% reliability in simulation through a data-driven characterization process, which is validated through extensive simulation, addressing a critical limitation of conventional PUFs. To counteract sophisticated threats, we formulate the key management problem as a Markov Decision Process (MDP) and introduce a deep reinforcement learning (DRL) agent that dynamically optimizes key update frequency, balancing security posture against energy consumption. Furthermore, we leverage a lightweight, permissioned blockchain as a decentralized trust anchor for public key management, providing an immutable and resilient ledger and enhancing the principles of distributed and edge intelligence. The core authentication protocol's security is formally verified using the ProVerif tool and Belief Logic, proving its robustness against a Dolev-Yao adversary. Experimental simulations demonstrate that our framework significantly outperforms conventional methods, reducing authentication latency and energy consumption by over 95% compared to PKI-based schemes while effectively mitigating replay and impersonation attacks.
Mehmed Oğuz Şen, Fatıh Okumuş, Adnan Fatıh Kocamaz
Decentralized task allocation is a critical challenge in multi-robot systems, particularly in scenarios where autonomy, scalability, and robustness are essential. While centralized approaches simplify coordination, they suffer from limitations such as single points of failure and poor scalability in dynamic environments. This paper presents a comparative evaluation of three decentralized and distributed task allocation algorithms integrated into a blockchain-powered multi-robot system where Hyperledger Fabric is used as blockchain platform. Each algorithm employs a cost-based selection mechanism to assign tasks autonomously while leveraging a distributed ledger for data consistency and conflict resolution. The algorithms -Euclidean distance, TEB motion planner and every robot computing all robots' costs- are evaluated for a system of three TIAGo++ robots in two different simulation environments. Performance metrics include computational overhead and task request conflict rates. Results show that while Euclidean distance offers the lowest overhead, it suffers from high conflict rates; TEB motion planner improves fairness with moderate overhead; and every robot computing all robots' costs ensures the highest consistency at the cost of increased computation. The findings highlight key trade-offs in decentralized coordination and offer guidance for designing scalable and reliable blockchain-powered multi robot systems.
SATHISHKUMAR RANGANATHAN, Muralindran Mariappan, M. Karthigayan
Consensus mechanisms are essential in swarm robotics to maintain uniformity of decisions and states across distributed agents. Earlier methods often relied on approaches such as majority voting, averaging techniques, or leader election. In recent years, blockchain-based algorithms including Proof of Work (PoW), Proof of Stake (PoS), Practical Byzantine Fault Tolerance (PBFT), and Raft have been adopted for this purpose. While these methods provide certain advantages, their application to swarm robotics is restricted by issues such as limited computational power, energy constraints, scalability challenges caused by message complexity and latency, and weak protection against Byzantine agents. This study introduces a consensus approach specifically designed to address these gaps and to improve collaborative decision-making in swarm environments. The work outlines the motivation for the proposed solution, describes the simulation-based experimental design, and presents a detailed analysis of the observed results.
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 rapid expansion of Internet of Things (IoT) applications has revealed limitations in traditional blockchain systems, particularly in scalability, energy efficiency, and computational overhead. IOTA, a Distributed Ledger Technology (DLT) based on Directed Acyclic Graph (DAG) structure known as the Tangle, offers a lightweight, scalable solution tailored for IoT environments. Key factor in IOTA’s performance and security is its tip selection mechanism, which manages transaction confirmation. This research introduces a novel approach combining Action Candidate-based Clipped Double Q-learning (AC-CDQ) with Biased Thompson Sampling (BTS) to improve tip selection. The proposed model addresses overestimation bias in Q-learning while enhancing robustness against adversarial attacks. Extensive simulations using a ledger simulator and the OpenAI Gym environment shows that model significantly reduces the average number of unapproved transactions. It consistently outperforms baseline methods, including Uniform Random Tip Selection (URTS), Markov Chain Monte Carlo (MCMC), and standard Q-learning, by confirming more transactions with improved reliability. Although the model incurs slightly higher computational costs, it delivers more accurate Q-value estimates and better learning stability. This work advances tip selection algorithms and supports the development of secure, efficient, and scalable distributed ledger systems. It demonstrates the potential of hybrid reinforcement learning techniques in future IoT-oriented ledger technologies.
Nicholas Potteiger, Mudassir Shabbir, Scott Eisele, Mark Wutka · 5 authors
Networked Unmanned Aerial Vehicles (UAVs) can be used for complex tasks such as surveillance and reconnaissance, inspection of dangerous environments, and target pursuit. Typically, coordination between multiple UAVs has been shown to improve the ability and performance of accomplishing such tasks. However, networked UAVs introduce new vulnerabilities enabling cyber-attacks which can target critical elements and prevent the UAVs from achieving their goal. This chapter presents a distributed system architecture for coordination of UAVs that provides resilience against denial-of-service and integrity cyber-attacks. The developed architecture consists of a distributed ledger implementing an asynchronous Byzantine fault tolerant protocol exchanging data between distributed agents and a distributed learning algorithm based on vector consensus implemented on top of the distributed ledger. Performance and resilience of the architecture are evaluated using a target pursuit case study based on a hardware-in-the-loop testbed. The experimental results demonstrate that that the UAVs that are not under attack are still able to successfully cooperate and accomplish the desired task. [160 words]
Distributed Control Multi-Agent Systems
Distributed Sensor Networks and Detection Algorithms
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 When using autonomous robots for group foraging, it is extremely important to properly organize the exchange of information between members. In a decentralized system, it can be accomplished through pairwise interactions between closely located agents. The paper examines the role of obstacles and bottlenecks in creating an environment conducive to multiple exchanges. Reinforcement learning of groups with various exchange process organizations and obstacle distributions showed a weak impact on the results of randomly placed obstacles and a more significant effect of extended obstacles for both simulated and real robots. The role of data sharing turned out to be higher at the initial stage of the system’s operation and in changing operating environments. The results of the work can be used to organize data exchange when training groups of agents.
Distributed Control Multi-Agent Systems
Reinforcement Learning in Robotics
Advanced Research in Systems and Signal Processing
This paper introduces a novel architecture for a distributed ledger, commonly referred to as a "blockchain", which is organized in the form of directed acyclic graph (DAG) with UTXO transactions as vertices, rather than as a chain of blocks. Consensus on the state of ledger assets is achieved through the cooperative consensus: an profit-driven behavior of token holders themselves, which is viable only when they cooperate by following the "biggest ledger coverage rule", akin the "longest chain rule" of Bitcoin. The cooperative behavior is facilitated by enforcing purposefully designed UTXO transaction validity constraints. Token holders are the sole category of participants authorized to make amendments to the ledger, making participation completely permissionless - without miners, validators, committees or staking - and without any need of knowledge about the composition of the set of all participants in the consensus. The setup allows to achieve high throughput and scalability alongside with low transaction costs, while preserving key aspects of high decentralization, open participation, and asynchronicity found in Bitcoin and other proof-of-work blockchains, but without huge energy consumption. Sybil protection is achieved similarly to proof-of-stake blockchains, using tokens native to the ledger, yet the architecture operates in a leaderless manner without block proposers and committee selection.
Unmanned Aerial Vehicles (UAVs) are pivotal in enhancing connectivity in diverse applications such as search and rescue, remote communications, and battlefield networking, especially in environments lacking ground-based infrastructure. This paper introduces a novel approach that harnesses Multi-Agent Deep Reinforcement Learning to optimize UAV communication systems. The methodology, centered on the Independent Proximal Policy Optimization technique, significantly improves fairness, throughput, and energy efficiency by enabling UAVs to autonomously adapt their operational strategies based on real-time environmental data and individual performance metrics. Moreover, the integration of Distributed Ledger Technologies with Multi-Agent Deep Reinforcement Learning enhances the security and scalability of UAV communications, ensuring robustness against disruptions and adversarial attacks. Extensive simulations demonstrate that this approach surpasses existing benchmarks in critical performance metrics, highlighting its potential implications for future UAV-assisted communication networks. By focusing on these technological advancements, the groundwork is laid for more efficient, fair, and resilient UAV systems.
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.
Die Wang, Yunjian Jia, Liang Liang, Kaoru Ota · 5 authors
Recently, unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) has emerged as a practical paradigm to enable low latency computing offloading for dispersed users in the fifth generation (5G) wireless networks. However, severe security and privacy concerns are associated with the open environment between the UAVs and edge computing nodes. In this paper, we address these challenges by integrating blockchain technology into UAV-enabled MEC networks. We present an innovative Delegated Proof of Stake (DPoS) consensus mechanism where the UAV is a primary node and verification nodes are edge computing nodes selected by the reputation mechanism. To enhance mobile users’ Quality of Service (QoS), edge computing resources need to be allocated among UAV and verification nodes. Based on this, we propose the trading mechanism for resource pricing and allocation based on the two-stage Stackelberg differential game. Meanwhile, dynamic states of user demands and verification node reputations are modeled using differential equations as constraints of the objective function at various stages to simulate adaptive service requests for users and incentivize active participation for verification nodes. Simulation results prove the effectiveness of the proposed resource trading scheme and demonstrate the equilibrium and convergence status of resource pricing and allocation for edge computing.
With the continuous development of UAV technology, the application of UAV swarm in the military field is gradually becoming the focus of research around the world. Although it can bring a series of benefits in autonomous cooperation, the traditional UAV management technology is prone to hacker attacks due to many security issues such as a single point of failure brought by centralized management. Because of the advantages of distributed, tamper-proof, and traceability, blockchain is applied to UAV swarm to solve some of the security problems caused by centralized management. However, due to the limitations of its consensus algorithm Practical Byzantine Fault Tolerance (PBFT), its communication complexity will increase rapidly with the increase of the number of nodes, which also leads to the poor scalability of the algorithm and can only be applied to small-scale networks. To use the PBFT algorithm in large-scale networks such as UAV swarm, a dynamic and highly trusted PBFT (DTPBFT) algorithm is proposed in this paper. Firstly, a new consensus algorithm model including consensus layer and verification layer is designed. Then, a consensus node election scheme based on trust mechanism is proposed under this model. The trust degree of nodes in the blockchain network is comprehensively evaluated by selecting several representative indicators, and the weight factor of each indicator is calculated by entropy weight method. It not only reduces the communication complexity, but also ensures the reliability and dynamic update of consensus nodes. Experiments show that when the number of UAVs is 200, the consensus time of DTPBFT is 0.24 s, which indicates that this algorithm can support large-scale UAV swarm without causing communication congestion, so it has good scalability. In addition, experiments also show that DTPBFT can tolerate more than 13 malicious nodes, which improves the fault tolerance rate of PBFT.
Jordan Beason, Gregory Hurlock, Dongbin Kim, Pratheek Manjunath
Unmanned vehicles have continued to become commonplace in modern society, with the recent adoption of small unmanned aerial systems (sUAS) in the commercial, entertainment and defense industries. Despite encouraging trends in sUAS and unmanned systems (UxS) development, these technologies deployed in the field are still, in large part, limited to teleoperation and/or semi-autonomous behaviors of a single agent. The United States Department of Defense (DoD) is interested in elevating this current state of technology, specifically of aerial swarms for intelligence, surveillance, and reconnaissance (ISR) missions. While methods exist for optimal control of multi-agent systems, there remain novel research gaps related to robust field performance. The Robotics Research Center (RRC) at the United States Military Academy (USMA) is working to develop a collaborative aerial swarming architecture (CASA) that enables decentralized command and control (C2) between unmanned aerial systems (UAS). The main factors that support CASA’s decentralized capabilities are found in the dynamic allocation of tasks and the organization of data among sUAS platforms. This paper outlines CASA and its current capabilities. Task Allocation results are presented showing real-time task updates and allocation to a UAS swarm in a simulated environment.
In some instances, intelligent, autonomous robotic systems (IARS) are transforming how the private and public sectors provide emergency services, deliver products, protect national interests, and accomplish their missions. Some futures, however, envision missions performed by cooperative teams of lARS-commonly described as swarms-making the purposeful design of swarms as information processing and communication systems an imperative. Furthermore, more optimal swarm performance depends on the degree to which its design fits its environment. In the context of organizational information processing and transactive memory theories, this paper explores the degree to which the centralization of decision-making and employment of distributed expertise may impact mission performance. Against the backdrop of collecting information on grey whale behaviors and migration routes, three swarm designs, starling, hive, and wolf-pack, are modeled and their simulated performance compared. The wolf-pack design, which is characterized by a largely decentralized decision-making structure and differentiated transactive memory system, out-performed the other designs based upon total work volumes and mission durations. Future studies should empirically investigate the impacts of cognitive slack and other swarm characteristics on mission accomplishment in a broader spectrum of scenarios.
Blockchain is a distributed ledger that was introduced to decentralize monetary systems. However, with time, the applications of blockchain in different realms have been identified. Swarm robotics is a field that combines swarm intelligence and robotics to solve real-world problems that cannot be solved by monolithic robots. Collective decision-making is one of the major behaviors implemented by swarm robotics. This study analyzes existing literature on the applications of blockchain in the collective decision-making scenarios in swarm robotics. Consequently, this study introduces a novel taxonomy to study the different applications effectively. The taxonomy categorizes existing literature into (i) application of blockchain in other areas of swarm robotics, (ii) application of blockchain in continuous collective decision-making scenarios, (iii) application of blockchain in discrete collective decision-making scenarios, (iv) application of blockchain in other discrete collective decision-making scenarios, and (v) application of blockchain in the collective perception scenario. Finally, the limitations of existing work such as excessive resource consumption and violation of swarm robotics principles are discussed.
The use of private or consortium blockchains in organizations’ applications is growing. A relevant aspect of blockchains is the choice of consensus mechanism. This decision delimits which blockchain solutions are suitable for the private scenario. Once the consensus mechanism is chosen, more than one blockchain may be enabled. However, this decision making is not trivial and requires detailed experimental indicators about algorithms and blockchains performance. In this context, we provide a comprehensive performance analysis of the Raft consensus mechanism based on its implementation in Hyperledger Fabric and Ethereum blockchain solutions. We performed our experiments on an OpenStack private cloud using each blockchain developer’s default settings for virtual machines. Our findings show how the implementation of each solution can impact the application’s performance under certain conditions.
Today, intelligent drone technology is rapidly expanding, particularly in the defense industry. A swarm of drones can communicate, share data, and make the best decisions on their own. Drone swarms can swiftly and effectively carry out missions like surveillance, reconnaissance, and rescue operations, without exposing military troops to hostile conditions. However, there are still significant problems that need to be resolved. One of them is to protect communications on these systems from threat actors. In this paper, we use blockchain technology as a defense mechanism against such issues. Drones can communicate data safely, without the need for a centralized authority (ground station), when using a blockchain to facilitate communication between them in a leader–follower hierarchy structure. Solidity has been used to create a compact, lightweight, and effective smart contract that automates the process of choosing a position in a certain swarm formation structure. Additionally, a mechanism for electing a new leader is proposed. The effectiveness of the presented model is assessed through a simulation that makes use of a DApp we created and Gazebo software. The purpose of this work is to develop a reliable and secure UAV swarm communication system that will enable widespread global adoption by numerous sectors.
Roberto Aparici Marino, Lorenzo Carnevale, Massimo Villari
Federated Learning (FL) is a cutting-edge technology for distributed solving of large-scale problems using local data exclusively. The potential of Federated Learning is nowadays clear in different context from automatic analysis of healthcare data to object recognition in video sources coming from public video streams, from distributed search for data breach and finance frauds to collaborative learning of hand typing on mobile phone. Multi-robot systems can also largely benefit from FL concerning resolution of problems like trajectory prediction, non colliding trajectory generation, distributed localization and mapping or distributed reinforcement learning. In this paper we propose a multi-robot framework that includes distributed learning capabilities by using Decentralized Stochastic Gradient Descent on graphs. First of all we motivate the position of the paper discussing the privacy preserving problem for multi robot systems and the need of decentralized learning. Then we build our methodology starting from a set of prior definitions. Finally we discuss in details the possible applications in robotics field.