Neighborhood Failure Localization in All-Optical Networks via Monitoring Trails

Shared protection, such as failure dependent protection (FDP), is well recognized for its outstanding capacity efficiency in all-optical mesh networks, at the expense of lengthy restoration time due to multi-hop signaling mechanisms for failure localization, notification, and device configuration. This paper investigates a novel monitoring trail (m-trail) scenario, called Global Neighborhood Failure Localization (G-NFL), that aims to enable any shared protection scheme, including FDP, for achieving all-optical and ultra-fast failure restoration. We firstly define neighborhood of a node, which is a set of links whose failure states should be known to the node in restoration of the corresponding working lightpaths (W-LPs). By assuming every node can obtain the on-off status of traversing m-trails and W-LPs via lambda monitoring, the proposed G-NFL problem routes a set of m-trails such that each node can localize any failure in its neighborhood. Bound analysis is performed on the minimum bandwidth required for m-trails under the proposed G-NFL problem. Then a simple yet efficient heuristic approach is presented. Extensive simulation is conducted to verify the proposed G-NFL scenario under a number of different definitions of nodal neighborhood which concern the extent of dependency between the monitoring plane and data plane. The effect of reusing the spare capacity by FDP for supporting m-trails is examined. We conclude that the proposed G-NFL scenario enables a general shared protection scheme, toward signaling-free and ultra-fast failure restoration like p-Cycle, while achieving optimal capacity efficiency as FDP

Neighborhood Failure Localization in All-Optical Networks via Monitoring Trails

Shared protection, such as failure dependent protection (FDP), is well recognized for its outstanding capacity efficiency in all-optical mesh networks, at the expense of lengthy restoration time due to multi-hop signaling mechanisms for failure localization, notification, and device configuration. This paper investigates a novel monitoring trail (m-trail) scenario, called Global Neighborhood Failure Localization (G-NFL), that aims to enable any shared protection scheme, including FDP, for achieving all-optical and ultra-fast failure restoration. We firstly define neighborhood of a node, which is a set of links whose failure states should be known to the node in restoration of the corresponding working lightpaths (W-LPs). By assuming every node can obtain the on-off status of traversing m-trails and W-LPs via lambda monitoring, the proposed G-NFL problem routes a set of m-trails such that each node can localize any failure in its neighborhood. Bound analysis is performed on the minimum bandwidth required for m-trails under the proposed G-NFL problem. Then a simple yet efficient heuristic approach is presented. Extensive simulation is conducted to verify the proposed G-NFL scenario under a number of different definitions of nodal neighborhood which concern the extent of dependency between the monitoring plane and data plane. The effect of reusing the spare capacity by FDP for supporting m-trails is examined. We conclude that the proposed G-NFL scenario enables a general shared protection scheme, toward signaling-free and ultra-fast failure restoration like p-Cycle, while achieving optimal capacity efficiency as FDP

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

On Integrating Failure Localization with Survivable Design

In this thesis, I proposed a novel framework of all-optical failure restoration which jointly determines network monitoring plane and spare capacity allocation in the presence of either static or dynamic traffic. The proposed framework aims to enable a general shared protection scheme to achieve near optimal capacity efficiency as in Failure Dependent Protection(FDP) while subject to an ultra-fast, all-optical, and deterministic failure restoration process. Simply put, Local Unambiguous Failure Localization(L-UFL) and FDP are the two building blocks for the proposed restoration framework. Under L-UFL, by properly allocating a set of Monitoring Trails (m-trails), a set of nodes can unambiguously identify every possible Shared Risk Link Group (SRLG) failure merely based on its locally collected Loss of Light(LOL) signals. Two heuristics are proposed to solve L-UFL, one of which exclusively deploys Supervisory Lightpaths (S-LPs) while the other jointly considers S-LPs and Working Lightpaths (W-LPs) for suppressing monitoring resource consumption. Thanks to the ``Enhanced Min Wavelength Max Information principle'', an entropy based utility function, m-trail global-sharing and other techniques, the proposed heuristics exhibit satisfactory performance in minimizing the number of m-trails, Wavelength Channel(WL) consumption and the running time of the algorithm. Based on the heuristics for L-UFL, two algorithms, namely MPJD and DJH, are proposed for the novel signaling-free restoration framework to deal with static and dynamic traffic respectively. MPJD is developed to determine the Protection Lightpaths (P-LPs) and m-trails given the pre-computed W-LPs while DJH jointly implements a generic dynamic survivable routing scheme based on FDP with an m-trail deployment scheme. For both algorithms, m-trail deployment is guided by the Necessary Monitoring Requirement (NMR) defined at each node for achieving signaling-free restoration. Extensive simulation is conducted to verify the performance of the proposed heuristics in terms of WL consumption, number of m-trails, monitoring requirement, blocking probability and running time. In conclusion, the proposed restoration framework can achieve all-optical and signaling-free restoration with the help of L-UFL, while maintaining high capacity efficiency as in FDP based survivable routing. The proposed heuristics achieve satisfactory performance as verified by the simulation results

Fault Localization in All-Optical Mesh Networks

Fault management is a challenging task in all-optical wavelength division multiplexing (WDM) networks. However, fast fault localization for shared risk link groups (SRLGs) with multiple links is essential for building a fully survival and functional transparent all-optical mesh network. Monitoring trail (m-trail) technology is an effective approach to achieve the goal, whereby a set of m-trails are derived for unambiguous fault localization (UFL). However, an m-trail traverses through a link by utilizing a dedicated wavelength channel (WL), causing a significant amount of resource consumption. In addition, existing m-trail methods incur long and variable alarm dissemination delay. We introduce a novel framework of real-time fault localization in all-optical WDM mesh networks, called the monitoring-burst (m-burst), which aims at initiating a balanced trade-off between consumed monitoring resources and fault localization latency. The m-burst framework has a single monitoring node (MN) and requires one WL in each unidirectional link if the link is traversed by any m-trail. The MN launches short duration optical bursts periodically along each m-trail to probe the links of the m-trail. Bursts along different m-trails are kept non-overlapping through each unidirectional link by scheduling burst launching times from the MN and multiplexing multiple bursts, if any, traversing the link. Thus, the MN can unambiguously localize the failed links by identifying the lost bursts without incurring any alarm dissemination delay. We have proposed several novel m-trail allocation, burst launching time scheduling, and node switch fabric configuration schemes. Numerical results show that the schemes, when deployed in the m-burst framework, are able to localize single-link and multi-link SRLG faults unambiguously, with reasonable fault localization latency, by using at most one WL in each unidirectional link. To reduce the fault localization latency further, we also introduce a novel methodology called nested m-trails. At first, mesh networks are decomposed into cycles and trails. Each cycle (trail) is realized as an independent virtual ring (linear) network using a separate pair of WLs (one WL in each direction) in each undirected link traversed by the cycle (trail). Then, sets of m-trails, i.e., nested m-trails, derived in each virtual network are deployed independently in the m-burst framework for ring (linear) networks. As a result, the fault localization latency is reduced significantly. Moreover, the application of nested m-trails in adaptive probing also reduces the number of sequential probes significantly. Therefore, practical deployment of adaptive probing is now possible. However, the WL consumption of the nested m-trail technique is not limited by one WL per unidirectional link. Thus, further investigation is needed to reduce the WL consumption of the technique.1 yea

Scalable fault management architecture for dynamic optical networks : an information-theoretic approach

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.MIT Barker Engineering Library copy: printed in pages.Also issued printed in pages.Includes bibliographical references (leaves 255-262).All-optical switching, in place of electronic switching, of high data-rate lightpaths at intermediate nodes is one of the key enabling technologies for economically scalable future data networks. This replacement of electronic switching with optical switching at intermediate nodes, however, presents new challenges for fault detection and localization in reconfigurable all-optical networks. Presently, fault detection and localization techniques, as implemented in SONET/G.709 networks, rely on electronic processing of parity checks at intermediate nodes. If similar techniques are adapted to all-optical reconfigurable networks, optical signals need to be tapped out at intermediate nodes for parity checks. This additional electronic processing would break the all-optical transparency paradigm and thus significantly diminish the cost advantages of all-optical networks. In this thesis, we propose new fault-diagnosis approaches specifically tailored to all-optical networks, with an objective of keeping the diagnostic capital expenditure and the diagnostic operation effort low. Instead of the aforementioned passive monitoring paradigm based on parity checks, we propose a proactive lightpath probing paradigm: optical probing signals are sent along a set of lightpaths in the network, and network state (i.e., failure pattern) is then inferred from testing results of this set of end-to-end lightpath measurements. Moreover, we assume that a subset of network nodes (up to all the nodes) is equipped with diagnostic agents - including both transmitters/receivers for probe transmission/detection and software processes for probe management to perform fault detection and localization. The design objectives of this proposed proactive probing paradigm are two folded: i) to minimize the number of lightpath probes to keep the diagnostic operational effort low, and ii) to minimize the number of diagnostic hardware to keep the diagnostic capital expenditure low.(cont.) The network fault-diagnosis problem can be mathematically modeled with a group testing-over-graphs framework. In particular, the network is abstracted as a graph in which the failure status of each node/link is modeled with a random variable (e.g. Bernoulli distribution). A probe over any path in the graph results in a value, defined as the probe syndrome, which is a function of all the random variables associated in that path. A network failure pattern is inferred through a set of probe syndromes resulting from a set of optimally chosen probes. This framework enriches the traditional group-testing problem by introducing a topological structure, and can be extended to model many other network-monitoring problems (e.g., packet delay, packet drop ratio, noise and etc) by choosing appropriate state variables. Under the group-testing-over-graphs framework with a probabilistic failure model, we initiate an information-theoretic approach to minimizing the average number of lightpath probes to identify all possible network failure patterns. Specifically, we have established an isomorphic mapping between the fault-diagnosis problem in network management and the source-coding problem in Information Theory. This mapping suggests that the minimum average number of lightpath probes required is lower bounded by the information entropy of the network state and efficient source-coding algorithms (e.g. the run-length code) can be translated into scalable fault-diagnosis schemes under some additional probe feasibility constraint. Our analytical and numerical investigations yield a guideline for designing scalable fault-diagnosis algorithms: each probe should provide approximately 1-bit of state information, and thus the total number of probes required is approximately equal to the entropy of the network state.(cont.) To address the hardware cost of diagnosis, we also developed a probabilistic analysis framework to characterize the trade-off between hardware cost (i.e., the number of nodes equipped with Tx/Rx pairs) and diagnosis capability (i.e., the probability of successful failure detection and localization). Our results suggest that, for practical situations, the hardware cost can be reduced significantly by accepting a small amount of uncertainty about the failure status.by Yonggang Wen.Ph.D

Exploring Motion Signatures for Vision-Based Tracking, Recognition and Navigation

As cameras become more and more popular in intelligent systems, algorithms and systems for understanding video data become more and more important. There is a broad range of applications, including object detection, tracking, scene understanding, and robot navigation. Besides the stationary information, video data contains rich motion information of the environment. Biological visual systems, like human and animal eyes, are very sensitive to the motion information. This inspires active research on vision-based motion analysis in recent years. The main focus of motion analysis has been on low level motion representations of pixels and image regions. However, the motion signatures can benefit a broader range of applications if further in-depth analysis techniques are developed. In this dissertation, we mainly discuss how to exploit motion signatures to solve problems in two applications: object recognition and robot navigation. First, we use bird species recognition as the application to explore motion signatures for object recognition. We begin with study of the periodic wingbeat motion of flying birds. To analyze the wing motion of a flying bird, we establish kinematics models for bird wings, and obtain wingbeat periodicity in image frames after the perspective projection. Time series of salient extremities on bird images are extracted, and the wingbeat frequency is acquired for species classification. Physical experiments show that the frequency based recognition method is robust to segmentation errors and measurement lost up to 30%. In addition to the wing motion, the body motion of the bird is also analyzed to extract the flying velocity in 3D space. An interacting multi-model approach is then designed to capture the combined object motion patterns and different environment conditions. The proposed systems and algorithms are tested in physical experiments, and the results show a false positive rate of around 20% with a low false negative rate close to zero. Second, we explore motion signatures for vision-based vehicle navigation. We discover that motion vectors (MVs) encoded in Moving Picture Experts Group (MPEG) videos provide rich information of the motion in the environment, which can be used to reconstruct the vehicle ego-motion and the structure of the scene. However, MVs suffer from high noise level. To handle the challenge, an error propagation model for MVs is first proposed. Several steps, including MV merging, plane-at-infinity elimination, and planar region extraction, are designed to further reduce noises. The extracted planes are used as landmarks in an extended Kalman filter (EKF) for simultaneous localization and mapping. Results show that the algorithm performs localization and plane mapping with a relative trajectory error below 5:1%. Exploiting the fact that MVs encodes both environment information and moving obstacles, we further propose to track moving objects at the same time of localization and mapping. This enables the two critical navigation functionalities, localization and obstacle avoidance, to be performed in a single framework. MVs are labeled as stationary or moving according to their consistency to geometric constraints. Therefore, the extracted planes are separated into moving objects and the stationary scene. Multiple EKFs are used to track the static scene and the moving objects simultaneously. In physical experiments, we show a detection rate of moving objects at 96:6% and a mean absolute localization error below 3:5 meters

A Novel Energy Harvesting Aware Routing Protocol for Underwater Wireless Sensor Networks

Underwater wireless sensor networks (UWSNs) have the potential to empower smart ocean applications. However, the widespread use of UWSN applications has been limited due to the many daunting challenges incurred in underwater wireless acoustic communication. Moreover, underwater wireless communication is energy-hungry, which confines UWSN deployment to small-scale due to the risks and costs of missions for at sea replacement of the nodes' batteries. The energy harvesting capability of underwater sensor nodes is an important characteristic that has been overlooked in the literature. In this thesis, we study the data routing process in UWSNs with energy harvesting capabilities. We proposed a novel opportunistic routing protocol, named RELOR, that is the first in the literature to consider the energy harvesting capability of underwater sensor nodes during routing decisions. RELOR implements a learning framework for the best selection of the forwarder nodes based on the observed environment conditions. We conduct extensive simulations to compare the performance of the proposed protocol to the state-of-the-art solution. Obtained results show that RELOR outperforms the related work in terms of packet delivery ratio, end-to-end latency, and nodes’ energy consumption