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Information-Centric Networking (ICN) has recently emerged as a prominent candidate for the Future Internet Architecture (FIA) that addresses existing issues with the host-centric communication model of the current TCP/IP-based Internet. Named Data Networking (NDN) is one of the most recent and active ICN architectures that provides a clean slate approach for Internet communication. NDN provides intrinsic content security where security is directly provided to the content instead of communication channel. Among other security aspects, Access Control (AC) rules specify the privileges for the entities that can access the content. In TCP/IP-based AC systems, due to the client-server communication model, the servers control which client can access a particular content. In contrast, ICN-based networks use content names to drive communication and decouple the content from its original location. This phenomenon leads to the loss of control over the content causing different challenges for the realization of efficient AC mechanisms. To date, considerable efforts have been made to develop various AC mechanisms in NDN. In this paper, we provide a detailed and comprehensive survey of the AC mechanisms in NDN. We follow a holistic approach towards AC in NDN where we first summarize the ICN paradigm, describe the changes from channel-based security to content-based security and highlight different cryptographic algorithms and security protocols in NDN. We then classify the existing AC mechanisms into two main categories: Encryption-based AC and Encryption-independent AC. Each category has different classes based on the working principle of AC (e.g., Attribute-based AC, Name-based AC, Identity-based AC, etc). Finally, we present the lessons learned from the existing AC mechanisms and identify the challenges of NDN-based AC at large, highlighting future research directions for the community., Comment: This paper has been accepted for publication by the ACM Computing Surveys. The final version will be published by the ACM

We prove that the implicit time Euler scheme coupled with finite elements space discretization for the 2D Navier-Stokes equations on the torus subject to a random perturbation converges in $L^2(\Omega)$, and describe the rate of convergence for an $H^1$-valued initial condition. This refines previous results which only established the convergence in probability of these numerical approximations. Using exponential moment estimates of the solution of the stochastic Navier-Stokes equations and convergence of a localized scheme, we can prove strong convergence of this space-time approximation. The speed of the $L^2(\Omega)$-convergence depends on the diffusion coefficient and on the viscosity parameter. In case of Scott-Vogelius mixed elements and for an additive noise, the convergence is polynomial.

Advances in deep neural networks (DNN) greatly bolster real-time detection of anomalous IoT data. However, IoT devices can hardly afford complex DNN models, and offloading anomaly detection tasks to the cloud incurs long delay. In this paper, we propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems to solve this problem, for both univariate and multivariate IoT data. First, we construct multiple anomaly detection DNN models with increasing complexity, and associate each model with a layer in HEC from bottom to top. Then, we design an adaptive scheme to select one of these models on the fly, based on the contextual information extracted from each input data. The model selection is formulated as a contextual bandit problem characterized by a single-step Markov decision process, and is solved using a reinforcement learning policy network. We build an HEC testbed, implement our proposed approach, and evaluate it using real IoT datasets. The demo shows that our proposed approach significantly reduces detection delay (e.g., by 71.4% for univariate dataset) without sacrificing accuracy, as compared to offloading detection tasks to the cloud. We also compare it with other baseline schemes and demonstrate that it achieves the best accuracy-delay tradeoff. Our demo is also available online: https://rebrand.ly/91a71, Comment: Accepted for presenting at IEEE International Conference on Distributed Computing Systems (ICDCS), Demo Track, July 2020

We consider a multicontinuum model in porous media applications, which is described as a system of coupled flow equations. The coupling between different continua depends on many factors and its modeling is important for porous media applications. The coefficients depend on particle deposition that is described in term of a stochastic process solution of an SDE. The stochastic process is considered to be faster than the flow motion and we introduce time-space scales to model the problem. Our goal is to pass to the limit in time and space and to find an associated averaged system. This is an averaging-homogenization problem, where the averages are computed in terms of the invariant measure associated to the fast motion and the spatial variable. We use the techniques developed in our previous paper to model the interactions between the continua and derive the averaged model problem that can be used in many applications., Comment: arXiv admin note: text overlap with arXiv:1810.07534, arXiv:1504.04845

Advances in deep neural networks (DNN) greatly bolster real-time detection of anomalous IoT data. However, IoT devices can barely afford complex DNN models due to limited computational power and energy supply. While one can offload anomaly detection tasks to the cloud, it incurs long delay and requires large bandwidth when thousands of IoT devices stream data to the cloud concurrently. In this paper, we propose an adaptive anomaly detection approach for hierarchical edge computing (HEC) systems to solve this problem. Specifically, we first construct three anomaly detection DNN models of increasing complexity, and associate them with the three layers of HEC from bottom to top, i.e., IoT devices, edge servers, and cloud. Then, we design an adaptive scheme to select one of the models based on the contextual information extracted from input data, to perform anomaly detection. The selection is formulated as a contextual bandit problem and is characterized by a single-step Markov decision process, with an objective of achieving high detection accuracy and low detection delay simultaneously. We evaluate our proposed approach using a real IoT dataset, and demonstrate that it reduces detection delay by 84% while maintaining almost the same accuracy as compared to offloading detection tasks to the cloud. In addition, our evaluation also shows that it outperforms other baseline schemes., Comment: To be published in the AAAI Workshop on Artificial Intelligence of Things (AIoT), Feb 2020

In this paper we introduce and study the notion of a graded nil-good ring which is graded by a group. We investigate extensions of graded nil-good rings to graded group rings, Further, we discuss graded matrix ring extensions and trivial extensions of graded nil-good rings. Furthermore, we show that the class of graded rings which are nil-good and the class of graded nil-good rings are not comparable. Moreover, we discuss the question of when the nil-good property of the component, which corresponds to the identity element the grading group, implies that the whole graded ring is graded nil-good is also treated., Comment: arXiv admin note: text overlap with arXiv:1904.02376 by other authors

We study the two-dimensional Euler equations, damped by a linear term and driven by an additive noise. The existence of weak solutions has already been studied; pathwise uniqueness is known for solutions that have vorticity in $L^\infty$. In this paper, we prove the Markov property and then the existence of an invariant measure in the space $L^\infty$ by means of a Krylov-Bogoliubov's type method, working with the weak$\star$ and the bounded weak$\star$ topologies in $L^\infty$., Comment: 22 pages. This is the version accepted for publication in Commun. Math. Phys

This paper investigates the longtime behavior of delayed 3D Navier-Stokes equations in terms of attractors. The study will strongly rely on the investigation of the linearized Navier-Stokes system, and the relationship between the discrete dynamical flow for the linearized system and the continuous flow associated to the original system. Assuming the viscosity to be sufficiently large, there exists a unique local attractor for the delayed 3D Navier-Stokes equations. Moreover, the local attractor reduces to a singleton set., Comment: 13 pages