As a paradigm to recover unknown entries of a matrix from partial observations, low-rank matrix completion (LRMC) has generated a great deal of interest. Over the years, there have been lots of works on this topic, but it might not be easy to grasp the essential knowledge from these studies. This is mainly because many of these works are highly theoretical or a proposal of new LRMC technique. In this paper, we give a contemporary survey on LRMC. In order to provide a better view, insight, and understanding of potentials and limitations of the LRMC, we present early scattered results in a structured and accessible way. Specifically, we classify the state-of-the-art LRMC techniques into two main categories and then explain each category in detail. We next discuss the issues to be considered when one considers using the LRMC techniques. These include intrinsic properties required for the matrix recovery and how to exploit a special structure in the LRMC design. We also discuss the convolutional neural network (CNN)-based LRMC algorithms exploiting the graph structure of a low-rank matrix. Furthermore, we present the recovery performance and the computational complexity of state-of-the-art LRMC techniques. Our hope is that this paper will serve as a useful guide for practitioners and non-experts to catch the gist of the LRMC.

Named Data Networking (NDN) is a disruptive yet promising architecture for the future Internet, in which the content diffusion mechanisms are shifted from the conventional host-centric to content-centric ones so that the data delivery can be significantly improved. After a decade of research and development, NDN and the related NDN Forwarding Deamon (NFD) implementations are now mature enough to enable stakeholders, such as telcos, to consider them for a real deployment. Consequently, NDN and IP will likely cohabit, and the Future Internet may be formed of isolated administrative domains, each deploying one of these two network paradigms. The security question of the resulting architecture naturally arises. In this paper, we consider the case of Denial of Service. Even though the Interest Flooding Attack (IFA) has been largely studied and mitigated through NACK packets in pure NDN networks, we demonstrate in this paper through experimental assessments that there are still some ways to mount such an attack, and especially in the context of coupling NDN with IP, that can hardly be addressed by current solutions. Subsequently, we leverage hypothesis testing theory to develop a Generalized Likelihood Ratio Test (GLRT) adapted to evolved IFA attacks. Simulations show the relevance of the proposed model for guaranteeing the prescribed Probability of False Alarm (PFA) and highlights the trade-off between detection power and delay. Finally, we consider a real deployment scenario where NDN is coupled with IP to carry HTTP traffic. We show that the model of IFA attacks is not very accurate in practice and further develops a sequential detector to keep a high detection accuracy. By considering data from the testbed, we show the efficiency of the overall detection method.

Location awareness, providing ability to identify the location of sensor, machine, vehicle, and wearable device, is a rapidly growing trend of hyper-connected society and one of key ingredients for internet of things (IoT) era. In order to make a proper reaction to the collected information from things, location information of things should be available at the data center. One challenge for the IoT networks is to identify the location map of whole nodes from partially observed distance information. An aim of this paper is to present an algorithm to recover the Euclidean distance matrix (and eventually the location map) from partially observed distance information. By casting the low-rank matrix completion problem into the unconstrained minimization problem in a Riemannian manifold in which a notion of differentiability can be defined, we solve the low-rank matrix completion problem using a modified conjugate gradient algorithm. From the convergence analysis, we show that LRM-CG converges linearly to the original Euclidean distance matrix under the extended Wolfe's conditions. From the numerical experiments, we demonstrate that the proposed method, called localization in Riemannian manifold using conjugate gradient (LRM-CG), is effective in recovering the Euclidean distance matrix.

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