142 research outputs found
Blockchain-based secure Unmanned Aerial Vehicles (UAV) in network design and optimization
Unmanned Aerial Vehicles (UAVs) have emerged as transformative technologies with wide ranging applications, including surveillance, mapping, remote sensing, search and rescue, and disaster management. As sophisticated Unmanned Aerial Vehicle (UAV) increasingly operate in collaborative swarms, joint optimization challenges arise, such as flight trajectories, scheduling, altitude, Aerial Base Stations (ABS), energy harvesting, power transfer, resource allocation, and power consumption. However, the widespread adoption of UAV networks has been hindered by challenges related to optimal Three-Dimensional (3D) deployment, trajectory optimization, wireless and computational resource allocation, and limited flight durations when operating as ABSs. Crucially, the broadcast nature of UAV-assisted wireless networks renders them susceptible to privacy and security threats such as Distributed Denial-of-Service (DDoS) replay, impersonation, message injection, spoofing, malware infection, eavesdropping, and line of-interference attacks.
This study aims to address these privacy and security challenges by leveraging blockchain technology’s potential to secure data and delivery in UAV communication networks. With amalgamation of blockchain, this study seeks to harness its inherent immutability and cryptographic properties to ensure secure and tamper-proof data transmission, promote trust and transparency among stakeholders, enable automated Smart Contract (SC) for secure delivery, and facilitate standardization and interoperability across platforms. Specifically, blockchain can secure UAV network privacy and security through data privacy and integrity, secure delivery and tracking, access control, identity management, and resilience against cyber-attacks.
Furthermore, this study explores the synergies among blockchain, UAV networks, and Federated Learning (FL) for privacy-preserving intelligent applications in healthcare and wireless networks. FL enables collaborative training of Machine Learning (ML) models without sharing raw data, ensuring data privacy. By integrating FL with blockchain-assisted UAV networks, this study aims to revolutionize future intelligent applications, particularly in time-sensitive and privacy-critical domains. Overall, this thesis contributes to the field by providing a comprehensive analysis of integrating blockchain, FL, and UAV networks, beyond Fifth-Generation (5G) communication networks. It addresses privacy and security concerns related to data and delivery, thereby enabling secure, reliable, and intelligent applications in various sectors
Adoption of vehicular ad hoc networking protocols by networked robots
This paper focuses on the utilization of wireless networking in the robotics domain. Many researchers have already equipped their robots with wireless communication capabilities, stimulated by the observation that multi-robot systems tend to have several advantages over their single-robot counterparts. Typically, this integration of wireless communication is tackled in a quite pragmatic manner, only a few authors presented novel Robotic Ad Hoc Network (RANET) protocols that were designed specifically with robotic use cases in mind. This is in sharp contrast with the domain of vehicular ad hoc networks (VANET). This observation is the starting point of this paper. If the results of previous efforts focusing on VANET protocols could be reused in the RANET domain, this could lead to rapid progress in the field of networked robots. To investigate this possibility, this paper provides a thorough overview of the related work in the domain of robotic and vehicular ad hoc networks. Based on this information, an exhaustive list of requirements is defined for both types. It is concluded that the most significant difference lies in the fact that VANET protocols are oriented towards low throughput messaging, while RANET protocols have to support high throughput media streaming as well. Although not always with equal importance, all other defined requirements are valid for both protocols. This leads to the conclusion that cross-fertilization between them is an appealing approach for future RANET research. To support such developments, this paper concludes with the definition of an appropriate working plan
A Blockchain-Enabled Framework of UAV Coordination for Post-Disaster Networks
Emergency communication is critical but challenging after natural disasters
when ground infrastructure is devastated. Unmanned aerial vehicles (UAVs) offer
enormous potential for agile relief coordination in these scenarios. However,
effectively leveraging UAV fleets poses additional challenges around security,
privacy, and efficient collaboration across response agencies. This paper
presents a robust blockchain-enabled framework to address these challenges by
integrating a consortium blockchain model, smart contracts, and cryptographic
techniques to securely coordinate UAV fleets for disaster response.
Specifically, we make two key contributions: a consortium blockchain
architecture for secure and private multi-agency coordination; and an optimized
consensus protocol balancing efficiency and fault tolerance using a delegated
proof of stake practical byzantine fault tolerance (DPoS-PBFT). Comprehensive
simulations showcase the framework's ability to enhance transparency,
automation, scalability, and cyber-attack resilience for UAV coordination in
post-disaster networks.Comment: 6 pages, 4 figures,IEEE 99th Vehicular Technology Conference:
VTC2024-Spring, Singapor
Adaptive and learning-based formation control of swarm robots
Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation
How Physicality Enables Trust: A New Era of Trust-Centered Cyberphysical Systems
Multi-agent cyberphysical systems enable new capabilities in efficiency,
resilience, and security. The unique characteristics of these systems prompt a
reevaluation of their security concepts, including their vulnerabilities, and
mechanisms to mitigate these vulnerabilities. This survey paper examines how
advancement in wireless networking, coupled with the sensing and computing in
cyberphysical systems, can foster novel security capabilities. This study
delves into three main themes related to securing multi-agent cyberphysical
systems. First, we discuss the threats that are particularly relevant to
multi-agent cyberphysical systems given the potential lack of trust between
agents. Second, we present prospects for sensing, contextual awareness, and
authentication, enabling the inference and measurement of ``inter-agent trust"
for these systems. Third, we elaborate on the application of quantifiable trust
notions to enable ``resilient coordination," where ``resilient" signifies
sustained functionality amid attacks on multiagent cyberphysical systems. We
refer to the capability of cyberphysical systems to self-organize, and
coordinate to achieve a task as autonomy. This survey unveils the cyberphysical
character of future interconnected systems as a pivotal catalyst for realizing
robust, trust-centered autonomy in tomorrow's world
Robotic Wireless Sensor Networks
In this chapter, we present a literature survey of an emerging, cutting-edge,
and multi-disciplinary field of research at the intersection of Robotics and
Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor
Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system
that aims to achieve certain sensing goals while meeting and maintaining
certain communication performance requirements, through cooperative control,
learning and adaptation. While both of the component areas, i.e., Robotics and
WSN, are very well-known and well-explored, there exist a whole set of new
opportunities and research directions at the intersection of these two fields
which are relatively or even completely unexplored. One such example would be
the use of a set of robotic routers to set up a temporary communication path
between a sender and a receiver that uses the controlled mobility to the
advantage of packet routing. We find that there exist only a limited number of
articles to be directly categorized as RWSN related works whereas there exist a
range of articles in the robotics and the WSN literature that are also relevant
to this new field of research. To connect the dots, we first identify the core
problems and research trends related to RWSN such as connectivity,
localization, routing, and robust flow of information. Next, we classify the
existing research on RWSN as well as the relevant state-of-the-arts from
robotics and WSN community according to the problems and trends identified in
the first step. Lastly, we analyze what is missing in the existing literature,
and identify topics that require more research attention in the future
ESTADO DEL ARTE EN REDES VANET Y CADENAS DE MARKOV
El presente artículo resume el estado del arte de las redes VANET como una extensión de las redes MANET, por tener características particulares en cuanto a la forma de construir topologías que cambian dinámicamente en el tiempo, en tal sentido, se proponen los argumento para que en futuros trabajos de investigación, se haga un análisis no desde el punto vista de simulación, sino desde el punto de vista matemático para que se desarrolle a través de cadenas de Markov, ya que la construcción de redes VANET corresponde o puede verse como un proceso estocástico discreto, que puede ser analizado matemáticamente por medio de dichas cadenas, con el fin de garantizar una entrega confiable de paquetes de información entre vehículos los cuales están entrando y saliendo constantemente dentro de la topología de red
Ant-inspired Interaction Networks For Decentralized Vehicular Traffic Congestion Control
Mimicking the autonomous behaviors of animals and their adaptability to changing or foreign environments lead to the development of swarm intelligence techniques such as ant colony optimization (ACO) and particle swarm optimization (PSO) now widely used to tackle a variety of optimization problems. The aim of this dissertation is to develop an alternative swarm intelligence model geared toward decentralized congestion avoidance and to determine qualities of the model suitable for use in a transportation network.
A microscopic multi-agent interaction network inspired by insect foraging behaviors, especially ants, was developed and consequently adapted to prioritize the avoidance of congestion, evaluated as perceived density of other agents in the immediate environment extrapolated from the occurrence of direct interactions between agents, while foraging for food outside the base/nest. The agents eschew pheromone trails or other forms of stigmergic communication in favor of these direct interactions whose rate is the primary motivator for the agents\u27 decision making process.
The decision making process at the core of the multi-agent interaction network is consequently transferred to transportation networks utilizing vehicular ad-hoc networks (VANETs) for communication between vehicles. Direct interactions are replaced by dedicated short range communications for wireless access in vehicular environments (DSRC/WAVE) messages used for a variety of applications like left turn assist, intersection collision avoidance, or cooperative adaptive cruise control. Each vehicle correlates the traffic on the wireless network with congestion in the transportation network and consequently decides whether to reroute and, if so, what alternate route to take in a decentralized, non-deterministic manner. The algorithm has been shown to increase throughput and decrease mean travel times significantly while not requiring access to centralized infrastructure or up-to-date traffic information
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