Fundamental limits of failure identifiability by Boolean Network Tomography

Boolean network tomography is a powerful tool to infer the state (working/failed) of individual nodes from path-level measurements obtained by egde-nodes. We consider the problem of optimizing the capability of identifying network failures through the design of monitoring schemes. Finding an optimal solution is NP-hard and a large body of work has been devoted to heuristic approaches providing lower bounds. Unlike previous works, we provide upper bounds on the maximum number of identifiable nodes, given the number of monitoring paths and different constraints on the network topology, the routing scheme, and the maximum path length. The proposed upper bounds represent a fundamental limit on the identifiability of failures via Boolean network tomography. This analysis provides insights on how to design topologies and related monitoring schemes to achieve the maximum identifiability under various network settings. Through analysis and experiments we demonstrate the tightness of the bounds and efficacy of the design insights for engineered as well as real network

How to Survive Targeted Fiber Cuts: A Game Theoretic Approach for Resilient SDON Control Plane Design

Software-defined optical networking (SDON) paradigm enables programmable, adaptive and application-aware backbone networks via centralized network control and management. Aside from the manifold advantages, the control plane (CP) of an SDON is exposed to diverse security threats. As the CP usually shares the underlying optical infrastructure with the data plane (DP), an attacker can launch physical-layer attacks to cause severe disruption of the CP. This paper studies the problem of resilient CP design under targeted fiber cut attacks, whose effectiveness depends on both the CP designer\u27s and the attacker\u27s strategies. Therefore, we model the problem as a non-cooperative game between the designer and the attacker, where the designer tries to set up the CP to minimize the attack effectiveness, while the attacker aims at maximizing the effectiveness by cutting the most critical links. We define the game strategies and utility functions, conduct theoretical analysis to obtain the Nash Equilibrium (NE) as the solution of the game. Extensive simulations confirm the effectiveness of our proposal in improving the CP resilience to targeted fiber cuts

Network-wide localization of optical-layer attacks

Optical networks are vulnerable to a range of attacks targeting service disruption at the physical layer, such as the insertion of harmful signals that can propagate through the network and affect co-propagating channels. Detection of such attacks and localization of their source, a prerequisite for securenetwork operation, is a challenging task due to the limitations in optical performance monitoring, as well as the scalability and cost issues. In this paper, we propose an approach for localizing the source of a jamming attack by modeling the worst-case scope of each connection as a potential carrier of a harmful signal. We define binary words called attack syndromes to model the health of each connection at the receiver which, when unique, unambiguously identify the harmful connection. To ensure attack syndrome uniqueness, we propose an optimization approach to design attack monitoring trails such that their number and length is minimal. This allows us to use the optical network as a sensor for physical-layer attacks. Numerical simulation results indicate that our approach obtains network-wide attack source localization at only 5.8% average resource overhead for the attackmonitoring trails

A Swarm Robotic Approach to Inspection of 2.5 D Surfaces in Orbit

Robotic inspection offers a robust, scalable, and flexible alternative to deploying fixed sensor networks or humaninspectors. While prior work has mostly focused on single robot inspections, this work studies the deployment of a swarm ofinspecting robots on a simplified surface of an in-orbit infrastructure. The robots look for points of mechanical failure and inspectthe surface by assessing propagating vibration signals. In particular, they measure the magnitude of acceleration they sense ateach location on the surface. Our choice for sensing and analyzing vibration signals is supported by the established position ofvibration analysis methods in industrial infrastructure health assessment. We perform simulation studies in Webots, a physicsbased robotic simulator, and present a distributed inspection algorithm based on bio-inspired particle swarm optimization andevolutionary algorithm niching techniques to collectively localize an a priori unknown number of mechanical failure points. Toperform the vibration analysis and obtain realistic acceleration data, we use the ANSYS multi-physics simulation software andmodel mechanical failure points as vibration sources on the surface. We deploy a robot swarm comprising eight robots of 10-cmsize that use a bio-inspired inchworming locomotion pattern. The swarm is deployed on 2.5D (that is curved 2D) cylindricalsurfaces with and without obstacles to investigate the robustness of the algorithm in environments with varying geometric complexity. We study three performance metrics: (1) proximity of the localized sources to their ground truth locations, (2) time tolocalize each source, and (3) time to finish the inspection task given an 80% surface coverage threshold. Our results show thatthe robots accurately localize all the failure sources and reach the coverage threshold required to complete the inspection. Thiswork demonstrates the viability of deploying robot swarms for inspection of potentially complex 3D environments.<br/

A Swarm Robotic Approach to Inspection of 2.5 D Surfaces in Orbit

Robotic inspection offers a robust, scalable, and flexible alternative to deploying fixed sensor networks or humaninspectors. While prior work has mostly focused on single robot inspections, this work studies the deployment of a swarm ofinspecting robots on a simplified surface of an in-orbit infrastructure. The robots look for points of mechanical failure and inspectthe surface by assessing propagating vibration signals. In particular, they measure the magnitude of acceleration they sense ateach location on the surface. Our choice for sensing and analyzing vibration signals is supported by the established position ofvibration analysis methods in industrial infrastructure health assessment. We perform simulation studies in Webots, a physicsbased robotic simulator, and present a distributed inspection algorithm based on bio-inspired particle swarm optimization andevolutionary algorithm niching techniques to collectively localize an a priori unknown number of mechanical failure points. Toperform the vibration analysis and obtain realistic acceleration data, we use the ANSYS multi-physics simulation software andmodel mechanical failure points as vibration sources on the surface. We deploy a robot swarm comprising eight robots of 10-cmsize that use a bio-inspired inchworming locomotion pattern. The swarm is deployed on 2.5D (that is curved 2D) cylindricalsurfaces with and without obstacles to investigate the robustness of the algorithm in environments with varying geometric complexity. We study three performance metrics: (1) proximity of the localized sources to their ground truth locations, (2) time tolocalize each source, and (3) time to finish the inspection task given an 80% surface coverage threshold. Our results show thatthe robots accurately localize all the failure sources and reach the coverage threshold required to complete the inspection. Thiswork demonstrates the viability of deploying robot swarms for inspection of potentially complex 3D environments.<br/

A Swarm Robotic Approach to Inspection of 2.5 D Surfaces in Orbit

Robotic inspection offers a robust, scalable, and flexible alternative to deploying fixed sensor networks or humaninspectors. While prior work has mostly focused on single robot inspections, this work studies the deployment of a swarm ofinspecting robots on a simplified surface of an in-orbit infrastructure. The robots look for points of mechanical failure and inspectthe surface by assessing propagating vibration signals. In particular, they measure the magnitude of acceleration they sense ateach location on the surface. Our choice for sensing and analyzing vibration signals is supported by the established position ofvibration analysis methods in industrial infrastructure health assessment. We perform simulation studies in Webots, a physicsbased robotic simulator, and present a distributed inspection algorithm based on bio-inspired particle swarm optimization andevolutionary algorithm niching techniques to collectively localize an a priori unknown number of mechanical failure points. Toperform the vibration analysis and obtain realistic acceleration data, we use the ANSYS multi-physics simulation software andmodel mechanical failure points as vibration sources on the surface. We deploy a robot swarm comprising eight robots of 10-cmsize that use a bio-inspired inchworming locomotion pattern. The swarm is deployed on 2.5D (that is curved 2D) cylindricalsurfaces with and without obstacles to investigate the robustness of the algorithm in environments with varying geometric complexity. We study three performance metrics: (1) proximity of the localized sources to their ground truth locations, (2) time tolocalize each source, and (3) time to finish the inspection task given an 80% surface coverage threshold. Our results show thatthe robots accurately localize all the failure sources and reach the coverage threshold required to complete the inspection. Thiswork demonstrates the viability of deploying robot swarms for inspection of potentially complex 3D environments.<br/