To observe and control a networked system, especially in failure-prone circumstances, it is imperative that the underlying network structure is robust against node or link failures. A common approach for increasing network robustness is redundancy: deploying additional nodes and establishing new links between nodes, which could be prohibitively expensive. This paper addresses the problem of improving structural robustness of networks without adding extra links. The main idea is to ensure that a small subset of nodes, referred to as the trusted nodes, remain intact and function correctly at all times. We extend two fundamental metrics of structural robustness with the notion of trusted nodes, network connectivity and $r$-robustness, and then show that by controlling the number and location of trusted nodes, any desired connectivity and robustness can be achieved without adding extra links. We study complexity of finding trusted nodes and construction of robust networks with trusted nodes. Finally, we present a resilient consensus algorithm with trusted nodes and show that, unlike existing algorithms, resilient consensus is possible in sparse networks containing few trusted nodes.

Local interaction rules for consensus and synchronization are vital for many applications in distributed control of cyber-physical systems. However, most research in this area assumes all nodes (or agents) in the networked system cooperate. This paper considers local interaction rules for resilient first-order consensus and weakly stable, higher order synchronization whenever some of the agents in the network are Byzantine-like adversaries defined in a continuous-time setting. The normal agents have identical dynamics modeled by continuous-time, linear, time-invariant, weakly stable systems. Agents in the networked system influence one another by sharing state or output information according to a directed, time-varying graph. We present a resilient consensus protocol as well as dynamic state and output feedback control laws for the normal agents, to achieve the resilient consensus and synchronization objectives, respectively. We characterize the required network topologies using the property of network robustness. We demonstrate the results in simulation examples to illustrate the resilient synchronization output feedback control law.

Among many structural assessment methods, the change of modal characteristics is considered a well‐accepted damage detection method. However, the presence of environmental or operational variations may pollute the baseline and prevent a dependable assessment of the change. In recent years, the use of machine learning algorithms gained interest within structural health community, especially due to their ability and success in the elimination of ambient uncertainty. This paper proposes an end‐to‐end architecture to detect damage reliably by employing machine learning algorithms. The proposed approach streamlines (a) collection of structural response data, (b) modal analysis using system identification, (c) learning model, and (d) novelty detection. The proposed system aims to extract latent features of accessible modal parameters such as natural frequencies and mode shapes measured at undamaged target structure under temperature uncertainty and to reconstruct a new representation of these features that is similar to the original using well‐established machine learning methods for damage detection. The deviation between measured and reconstructed parameters, also known as novelty index, is the essential information for detecting critical changes in the system. The approach is evaluated by analyzing the structural response data obtained from finite element models and experimental structures. For the machine learning component of the approach, both principal component analysis (PCA) and autoencoder (AE) are examined. While mode shapes are known to be a well‐researched damage indicator in the literature, to our best knowledge, this research is the first time that unsupervised machine learning is applied using PCA and AE to utilize mode shapes in addition to natural frequencies for effective damage detection. The detection performance of this pipeline is compared to a similar approach where its learning model does not utilize mode shapes. The results demonstrate that the effectiveness of the damage detection under temperature variability improves significantly when mode shapes are used in the training of learning algorithm. Especially for small damages, the proposed algorithm performs better in discriminating system changes.

Wireless sensor networks consist of small, inexpensive devices which interact with the environment, communicate with each other, and perform distributed computations in order to monitor spatio-temporal phenomena. These devices are ideally suited for a variety of applications including object tracking, environmental monitoring, and homeland security. At present, sensor network technologies do not provide off-the-shelf solutions to users who lack low-level network programming experience. Because of limited resources, ad hoc deployments, and volatile wireless communication links, the development of distributed applications require the combination of both application and system-level logic. Programming frameworks and middleware for traditional distributed computing are not suitable for many of these problems due to the resource constraints and interactions with the physical world. To address these challenges we have developed OASiS, a programming framework that provides abstractions for object-centric, ambient-aware, service-oriented sensor network applications. OASiS uses a well-defined model of computation based on globally asynchronous locally synchronous dataflow, and is complemented by a user-friendly modeling environment. Applications are realized as graphs of modular services and executed in response to the detection of physical phenomena. We have also implemented a suite of middleware services that support OASiS to provide a layer of abstraction shielding the low-level system complexities. A tracking application is used to illustrate the features of OASiS. Our results demonstrate the feasibility and the benefits of a service-oriented programming framework for composing and deploying applications in resource-constrained sensor networks.

In this paper, we study resilient distributed diffusion for multi-task estimation in the presence of adversaries where networked agents must estimate distinct but correlated states of interest by processing streaming data. We show that in general diffusion strategies are not resilient to malicious agents that do not adhere to the diffusion-based information processing rules. In particular, by exploiting the adaptive weights used for diffusing information, we develop time-dependent attack models that drive normal agents to converge to states selected by the attacker. We show that an attacker that has complete knowledge of the system can always drive its targeted agents to its desired estimates. Moreover, an attacker that does not have complete knowledge of the system including streaming data of targeted agents or the parameters they use in diffusion algorithms, can still be successful in deploying an attack by approximating the needed information. The attack models can be used for both stationary and non-stationary state estimation. In addition, we present and analyze a resilient distributed diffusion algorithm that is resilient to any data falsification attack in which the number of compromised agents in the local neighborhood of a normal agent is bounded. The proposed algorithm guarantees that all normal agents converge to their true target states if appropriate parameters are selected. We also analyze trade-off between the resilience of distributed diffusion and its performance in terms of steady-state mean-square-deviation (MSD) from the correct estimates. Finally, we evaluate the proposed attack models and resilient distributed diffusion algorithm using stationary and non-stationary multi-target localization.