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.
Non-Fungible Tokens (NFTs) have emerged as a revolutionary method for managing digital assets, providing transparency and secure ownership records on a blockchain. In this paper, we present a theoretical framework for leveraging NFTs to manage UAV (Unmanned Aerial Vehicle) flight data. Our approach focuses on ensuring data integrity, ownership transfer, and secure data sharing among stakeholders. This framework utilizes cryptographic methods, smart contracts, and access control mechanisms to enable a tamper-proof and privacy-preserving management system for UAV flight data.
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.
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.
The separation of the control plane from the data plane in a blockchain-enabled software-defined network (SDN) has emerged as a promising solution in the industry for optimising network traffic management. This approach provides network administrators with enhanced flexibility and efficiency in managing network traffic. However, SDN also presents new security challenges, such as denial-of-service (DoS) attacks and a single point of failure. To mitigate these attacks in real-time, blockchain uses a distributed network of nodes to verify transactions, which are then added to an immutable ledger. However, deploying SDN blockchain in resource-constrained devices with issues such as efficient energy utilisation, huge data processing, and minimised end-to-end delay is challenging. In this work, we discuss a hierarchical architecture for secure and efficient deployment, as well as a task offloading strategy that aims to improve the efficiency of resource-constrained edge devices by utilising a centralised algorithm for low latency, secure, and reliable decision-making. To maintain the confidentiality of the information and prevent unauthorised access, it is recommended to encrypt data before storing it in blocks. Finally, enforcing flow rules in the network at a granular level provides a secure and tamper-proof record of the rules enforced in the switches. These methods can enhance the adaptability of blockchain-based SDN in resource-constrained network devices compared with existing methods.
As applications based on Distributed Ledger Technology (DLT) gain popularity, the wide range of vulnerabilities that have affected existing blockchains (most notably Ethereum and Solidity-based applications) has motivated renewed interest in the design of programming languages capable of providing more adequate abstractions for managing digital assets and their access control policies. These mechanisms are crucial to certify that applications are safe and secure before deploying them on the target blockchains.Venturing into this challenge, we focus on Move, currently one of the most promising programming languages for resources and digital assets management with the aim to investigate its effectiveness in the realm of general-purpose smart contract development, and the long-term goal to identify the design principles and language-based techniques for the safe and secure development of asset-based DLT applications. As a first step in that direction, in the present paper, we develop ALGOMOVE, a Move embedding on Algorand. In addition to providing new insight into the nature of digital assets, the embedding is noteworthy in its own right. It provides Algorand/TEAL developers with a friendly API that aligns with their familiar programming patterns, while at the same time leveraging Move’s mechanisms of static typing and security verification to offer certified, language-level protection against double spending and other resource-related pitfalls commonly encountered in DLT applications.
João Henrique Faes Battisti, Vitor Emanuel Batista, Guilherme Koslovski, Maurício A. Pillon · 8 authors
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.
Julio César Úbeda Ortega, Jesús Rodríguez-Molina, Margarita Martínez, Juan Garbajosa
Livestock monitoring often requires human supervision to guide farm animals to a specific point and the displacement of workers to the places where these animals are, which is likely to be several kilometers away, thus resulting in a repetitive task that requires a significant amount of time and demands the usage of land vehicles capable of moving swiftly through the countryside. In addition to that, data collection about animal behaviour with such procedures is often insufficient and cannot be shared in a secure enough manner. This paper describes how Using Unmanned Aerial Vehicles (UAVs) tailored for this kind of task, when combined with other protocols and software technologies, can provide a useful to mitigate these issues. To prove this end, a functional prototype has been designed, built and tested, offering the operator accurate monitoring of farm facilities and animals. Additionally, security has been conceived as a cornerstone of the presented system from the very beginning. Not only the communication protocols used for this purpose have built-in security layers, but also InterPlanetary File System (IPFS) and blockchain have been used as the technologies that enhance data storage among peers in a network.
M. Rajesh, T. Archana Acharya, Hafis Hajiyev, E. Laxmi Lydia · 8 authors
Recently, Internet of Things (IoT) has been developed into a field of research and it purposes at linking many sensors enabling devices mostly to data collection and track applications. Wireless sensor network (WSN) is a vital element of IoT paradigm since its inception and has developed into one of the chosen platforms for deploying many smart city application regions such as disaster management, intelligent transportation, home automation, smart buildings, and other such IoT-based application. The routing approaches were extremely-utilized energy efficient approaches with an initial drive that is, for balancing the energy amongst sensor nodes. The clustering and routing procedures assumed that Non-Polynomial (NP) hard problems but bio-simulated approaches are utilized to a recognized time for resolving such problems. With this motivation, this paper presents a new blockchain with Enhanced Hunger Games Search based Route Planning (BCEHGS-RP) scheme for IoT assisted WSN. The presented BCEHGS-RP model majorly employs BC technology for secure communication in the IoT supported WSN environment. In addition, an effective multihop route planning approach was designed by the use of EHGS technique. The proposed EHGS technique is derived from the concept of Hill Climbing strategy (HCS) and HGS algorithm. Moreover, a fitness function with two parameters namely residual energy (RE) and inter-cluster distance to elect optimal routes. The performance validation of the BCEHGS-RP model is experimented with under diverse number of nodes. Extensive experimental outcomes highlighted the better performance of the BCEHGS-RP technique on recent approaches.
Roman Overko, Rodrigo Ordóñez-Hurtado, Sergiy Zhuk, Robert Shorten
In this paper, we describe an approach to guide drivers searching for a parking space (PS). The proposed system suggests a sequence of routes that drivers should traverse in order to maximise the expected likelihood of finding a PS and minimise the travel distance. This system is built on our recent architecture SPToken, which combines both Distributed Ledger Technology (DLT) and Reinforcement Learning (RL) to realise a system for the estimation of an unknown distribution without disturbing the environment. For this, we use a number of virtual tokens that are passed from vehicle to vehicle to enable a massively parallelised RL system that estimates the best route for a given origin-destination (OD) pair, using crowdsourced information from participant vehicles. Additionally, a moving window with reward memory mechanism is included to better cope with non-stationary environments. Simulation results are given to illustrate the efficacy of our system.
Ahmad Reza Cheraghi, Abdelrahman Abdelgalil, Kálmán Graffi
This research introduces the design and implementation of a universal systematic 2-dimensional terrain marking and coverage solution. Real world applications such as lawn mowing, mine detection, chemical spill clean-up, and humanitarian search and rescue missions can be automated by employing swarms of autonomous mobile robots to complete the task. In most of these real world applications, efficiency is of utmost importance especially when human lives are involved. The solution proposed builds up on two graph traversal algorithms; Depth First Search (DFS) and Breadth First Search (BFS), where the algorithms are adapted and modified to be utilized for coverage of 2-dimensional isometric grid-like representations of terrains. The solution is developed so that each robot in the swarm would be fully capable of covering and marking any terrain by itself. The efficiency of the solution is optimized by increasing the swarm size, as robots benefit from data sharing in their path planning and self organization within the terrain. Communication between robots enable them to perform on a higher level by benefiting from the collaborative distributed behavior of the swarm as a whole. The communication between particles is decentralized and is carried out at a local level with no need for a central guidance mechanism. Robot abilities in this research are limited, where a robot can see and move only to locations that are adjacent to its current location. A simulation based evaluation is conducted in this research to assess the robots' area coverage and marking performance. The results show that the simulated robot swarm systems are suited for efficient flat area coverage, allowing for redundancy in data collection, and tolerating individual robot errors and shortcomings as the number of robots becomes more abundant.
The blockchain is a secure and trustworthy distributed transaction management system that is being extensively researched and developed for various applications and use cases. This study introduces a novel distributed control system using the Blockchain. A multi-robot path planning application is developed and deployed to benchmark a blockchain platform, the Hyperledger Fabric. Blockchain technology has the reputation of being sufficiently dilatory that it is inappropriate for time-sensitive applications. This research demonstrates how the enterprise-grade blockchain solutions overcome this shortcoming and investigates their potential for enabling secure and trusted distributed control systems for IoT.
Ladan Rabieekenari, Kamran Sayrafian, John S. Baras
Public safety organizations increasingly rely on wireless technology for their mission critical communication during disaster response operations. In such situations, a communication network could face much higher traffic demands compared to its normal operation. Given the limited capacity of base stations in the network, such peak traffic scenarios could lead to high blocking probability or equivalently service interruptions during critical communications. At the same time, networking infrastructure can breakdown during a disaster. Proper deployment of mobile cells - Cells on Wheels - can help to enhance the network coverage or accommodate excess traffic in areas with high concentration of users. In addition, an intelligent relocation strategy can be used to efficiently adapt the cell locations to match variations in the spatial distribution of the traffic. In practical scenarios, these mobile base stations may not be able to relocate to all positions within the target field. Such prohibited areas introduce additional constraints on designing an intelligent relocation strategy. In this paper, we propose a decentralized relocation algorithm that enables mobile cells to adapt their positions in response to potentially changing traffic patterns in a field with prohibited areas. Extensive simulations show considerable improvement in supporting spatially variable traffic throughout the target field.
Nicolas Dousse, Grégoire Heitz, Felix Schill, Dario Floreano
Semi- or fully autonomous personal aerial vehicles (PAVs) are currently studied and developed by public and private organizations as a solution for traffic congestion. While optimal collision-free navigation algorithms have been proposed for autonomous robots, trajectories and accelerations for PAVs should also take into account human comfort. In this letter, we propose a reactive decentralized collision avoidance strategy that incorporates passenger physiological comfort based on the optimal reciprocal collision avoidance strategy. We study in simulation the effects of increasing PAV densities on the level of comfort, on the relative flight time and on the number of collisions per flight hour and demonstrate that our strategy reduces collision risk for platforms with limited dynamic range. Finally, we validate our strategy with a swarm of ten quadcopters flying outdoors.
Toshio Fukuda, Go Iritani, T. Ueyama, Fumihito Arai
This paper deals with a self-organizing robotic system that consists of a number of autonomous robots. We describe the idea of society of robots, which is based on decentralized autonomous systems such as human being or insects. As one of the self-organizing robotic systems, cellular robotic system (CEBOT) was proposed by T. Fukuda. The cellular robotic system is one of the distributed robotic systems, which is composed of a number of autonomous robotic units called cells. The cell is a fundamental unit that has a simple function. To carry out given tasks, the cells connect each other and cooperate mutually. This paper denotes the concept of the cellular robotic system (CEBOT), the social organization of robotic systems, and evolution of group systems.
Decentralized control methods are appealing in coordination of multiple vehicles due to their low demand for long-range communication and their robustness to single-point failures. In this paper we explore a decentralized approach to path generation for a group of vehicles in a battlefield scenario. The mission is to maneuver the vehicles to cover a target area while avoiding obstacles and threats during the maneuver. Each vehicle makes its moving decision by minimizing a potential function that encodes information about its neighbours, obstacles, threats and the target. Preliminary analysis of vehicle behaviors is conducted. Simulation has shown that this approach leads to interesting emergent behaviors, and the behaviors can be varied by adjusting the weighting coefficients of different potential function terms.
As a flexible and robust intelligent robot system, we have been developing an autonomous and decentralized robot system called ACTRESS, which is composed of multiple robotic agents who have various kinds of functionalities. In order to achieve a given mission in multirobot environment, cooperative behaviors by autonomous agents are essential. In this paper, we focus on the case that multiple robots execute a task cooperatively with functional complement, especially for sensing function. We discuss cooperation of multiple robots using communication in a multi-agent robotic system, and propose a method of functional complement by multiple agents, including group organization and cooperative motion control. Finally, we show the experimental result to verify the proposed method.>
Toshio Fukuda, Go Iritani, Fumihito Arai, Koji Yamada
In this research, we address the organization of group behavior on decentralized autonomous robotic systems. Collective group behavior is exhibited in the natural world, such as by ants and fish, in teamwork in sports and by the human society. Therefore, research on group behavior of decentralized autonomous robotic systems can be regarded as one the research fields of Artificial Life. Decentralized autonomous robotic systems refer to multiple robotic systems including many autonomous robots, such as the Cellular Robotic System (CEBOT). The CEBOT, which has been studied by the authors, consists of a number of robotic units called cells. In research on the CEBOT, it is necessary to evolve a cooperative group behavior effectively in the system, since a well-organized group behavior is required to carry out given tasks efficiently and influences its performance ability. In order to organize the behavior in a dynamic environment, we proposed a concept of the self-recognition for the decision making of the behavior in a robotic group. In addition to the proposed concept, this paper will show a construction mechanism of group behavior using the character of the attractor. Based on this idea, we present the behavioral evolution of a group robotic system.
In this research, we address the organization of group behavior on decentralized autonomous robotic systems. Collective group behavior is exhibied in the natural world, such as by ants and fish, in teamwork in sports and by the human society. Therefore, research on group behavior of decentralized autonomous robotic systems can be regarded as one of the research fields of Artificial Life. Decentralized autonomous robotic systems refer to multiple robotic systems including many autonomous robots, such as the Cellular Robotic System (CEBOT). The CEBOT, which has been studied by the authors, consists of a number of robotic units called "cells". In the research on the CEBOT, it is necessary to evolve a cooperative group behavior effectively in the system, since a well-organized group behavior is required to carry out given tasks efficiently and influences its perfor-mance ability. In order to organize the behavior in a dynamic environment, we proposed a concept of "self-recognition" for decision making of the behavior in a robotic group. In this paper, in addition to the proposed concept, we will show the organization and adaptation of group behavior with the coordination of intention, and represent some simulation results with the coordination of intention.