1,319 research outputs found
FairLedger: A Fair Blockchain Protocol for Financial Institutions
Financial institutions are currently looking into technologies for
permissioned blockchains. A major effort in this direction is Hyperledger, an
open source project hosted by the Linux Foundation and backed by a consortium
of over a hundred companies. A key component in permissioned blockchain
protocols is a byzantine fault tolerant (BFT) consensus engine that orders
transactions. However, currently available BFT solutions in Hyperledger (as
well as in the literature at large) are inadequate for financial settings; they
are not designed to ensure fairness or to tolerate selfish behavior that arises
when financial institutions strive to maximize their own profit.
We present FairLedger, a permissioned blockchain BFT protocol, which is fair,
designed to deal with rational behavior, and, no less important, easy to
understand and implement. The secret sauce of our protocol is a new
communication abstraction, called detectable all-to-all (DA2A), which allows us
to detect participants (byzantine or rational) that deviate from the protocol,
and punish them. We implement FairLedger in the Hyperledger open source
project, using Iroha framework, one of the biggest projects therein. To
evaluate FairLegder's performance, we also implement it in the PBFT framework
and compare the two protocols. Our results show that in failure-free scenarios
FairLedger achieves better throughput than both Iroha's implementation and PBFT
in wide-area settings
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
Application of reinforcement learning for security enhancement in cognitive radio networks
Cognitive radio network (CRN) enables unlicensed users (or secondary users, SUs) to sense for and opportunistically operate in underutilized licensed channels, which are owned by the licensed users (or primary users, PUs). Cognitive radio network (CRN) has been regarded as the next-generation wireless network centered on the application of artificial intelligence, which helps the SUs to learn about, as well as to adaptively and dynamically reconfigure its operating parameters, including the sensing and transmission channels, for network performance enhancement. This motivates the use of artificial intelligence to enhance security schemes for CRNs. Provisioning security in CRNs is challenging since existing techniques, such as entity authentication, are not feasible in the dynamic environment that CRN presents since they require pre-registration. In addition these techniques cannot prevent an authenticated node from acting maliciously. In this article, we advocate the use of reinforcement learning (RL) to achieve optimal or near-optimal solutions for security enhancement through the detection of various malicious nodes and their attacks in CRNs. RL, which is an artificial intelligence technique, has the ability to learn new attacks and to detect previously learned ones. RL has been perceived as a promising approach to enhance the overall security aspect of CRNs. RL, which has been applied to address the dynamic aspect of security schemes in other wireless networks, such as wireless sensor networks and wireless mesh networks can be leveraged to design security schemes in CRNs. We believe that these RL solutions will complement and enhance existing security solutions applied to CRN To the best of our knowledge, this is the first survey article that focuses on the use of RL-based techniques for security enhancement in CRNs
Distributed Robotic Systems in the Edge-Cloud Continuum with ROS 2: a Review on Novel Architectures and Technology Readiness
Robotic systems are more connected, networked, and distributed than ever. New
architectures that comply with the \textit{de facto} robotics middleware
standard, ROS\,2, have recently emerged to fill the gap in terms of hybrid
systems deployed from edge to cloud. This paper reviews new architectures and
technologies that enable containerized robotic applications to seamlessly run
at the edge or in the cloud. We also overview systems that include solutions
from extension to ROS\,2 tooling to the integration of Kubernetes and ROS\,2.
Another important trend is robot learning, and how new simulators and cloud
simulations are enabling, e.g., large-scale reinforcement learning or
distributed federated learning solutions. This has also enabled deeper
integration of continuous interaction and continuous deployment (CI/CD)
pipelines for robotic systems development, going beyond standard software unit
tests with simulated tests to build and validate code automatically. We discuss
the current technology readiness and list the potential new application
scenarios that are becoming available. Finally, we discuss the current
challenges in distributed robotic systems and list open research questions in
the field
Coordination and Self-Adaptive Communication Primitives for Low-Power Wireless Networks
The Internet of Things (IoT) is a recent trend where objects are augmented with computing and communication capabilities, often via low-power wireless radios. The Internet of Things is an enabler for a connected and more sustainable modern society: smart grids are deployed to improve energy production and consumption, wireless monitoring systems allow smart factories to detect faults early and reduce waste, while connected vehicles coordinate on the road to ensure our safety and save fuel. Many recent IoT applications have stringent requirements for their wireless communication substrate: devices must cooperate and coordinate, must perform efficiently under varying and sometimes extreme environments, while strict deadlines must be met. Current distributed coordination algorithms have high overheads and are unfit to meet the requirements of today\u27s wireless applications, while current wireless protocols are often best-effort and lack the guarantees provided by well-studied coordination solutions. Further, many communication primitives available today lack the ability to adapt to dynamic environments, and are often tuned during their design phase to reach a target performance, rather than be continuously updated at runtime to adapt to reality.In this thesis, we study the problem of efficient and low-latency consensus in the context of low-power wireless networks, where communication is unreliable and nodes can fail, and we investigate the design of a self-adaptive wireless stack, where the communication substrate is able to adapt to changes to its environment. We propose three new communication primitives: Wireless Paxos brings fault-tolerant consensus to low-power wireless networking, STARC is a middleware for safe vehicular coordination at intersections, while Dimmer builds on reinforcement learning to provide adaptivity to low-power wireless networks. We evaluate in-depth each primitive on testbed deployments and we provide an open-source implementation to enable their use and improvement by the community
Trusted UAV Network Coverage using Blockchain, Machine Learning and Auction Mechanisms
The UAV is emerging as one of the greatest technology developments for rapid network
coverage provisioning at affordable cost. The aim of this paper is to outsource network coverage of a specific
area according to a desired quality of service requirement and to enable various entities in the network to
have intelligence to make autonomous decisions using blockchain and auction mechanisms. In this regard,
by considering a multiple-UAV network where each UAV is associated to its own controlling operator,
this paper addresses two major challenges: the selection of the UAV for the desired quality of network
coverage and the development of a distributed and autonomous real-time monitoring framework for the
enforcement of service level agreement (SLA). For a suitable UAV selection, we employ a reputation-based
auction mechanism to model the interaction between the business agent who is interested in outsourcing
the network coverage and the UAV operators serving in closeby areas. In addition, theoretical analysis
is performed to show that the proposed auction mechanism attains a dominant strategy equilibrium. For
the SLA enforcement and trust model, we propose a permissioned blockchain architecture considering
Support Vector Machine (SVM) for real-time autonomous and distributed monitoring of UAV service. In
particular, smart contract features of the blockchain are invoked for enforcing the SLA terms of payment
and penalty, and for quantifying the UAV service reputation. Simulation results confirm the accuracy of
theoretical analysis and efficacy of the proposed model
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