<![CDATA[ IEEE Transactions on Signal and Information Processing over Networks - new TOC ]]>
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TOC Alert for Publication# 6884276 2018February 22<![CDATA[Table of Contents]]>41C1C4128<![CDATA[IEEE Transactions on Signal and Information Processing over Networks publication information]]>41C2C267<![CDATA[Guest Editorial Distributed Signal Processing for Security and Privacy in Networked Cyber-Physical Systems]]>4113193<![CDATA[Differentially Private Distributed Online Algorithms Over Time-Varying Directed Networks]]>41417356<![CDATA[Mitigation of Byzantine Attacks on Distributed Detection Systems Using Audit Bits]]>411832954<![CDATA[Two-Tier Device-Based Authentication Protocol Against PUEA Attacks for IoT Applications]]>4133471500<![CDATA[Distributed Attack Detection and Secure Estimation of Networked Cyber-Physical Systems Against False Data Injection Attacks and Jamming Attacks]]>4148591173<![CDATA[Resilient Consensus with Mobile Detectors Against Malicious Attacks]]>416069650<![CDATA[A Distributed Control Paradigm for Smart Grid to Address Attacks on Data Integrity and Availability]]>4170811471<![CDATA[Privacy Aware Stochastic Games for Distributed End-User Energy Storage Sharing]]>418295759<![CDATA[Distributed Joint Attack Detection and Secure State Estimation]]>4196110981<![CDATA[Secure Information Sharing in Adversarial Adaptive Diffusion Networks]]>411111241416<![CDATA[A Novel Data Fusion Algorithm to Combat False Data Injection Attacks in Networked Radar Systems]]>411251361011<![CDATA[Distributed-Graph-Based Statistical Approach for Intrusion Detection in Cyber-Physical Systems]]>41137147677<![CDATA[Distributed Privacy-Preserving Collaborative Intrusion Detection Systems for VANETs]]>41148161750<![CDATA[Cooperative Localization in WSNs: A Hybrid Convex/Nonconvex Solution]]>et al. The convex problem is efficiently solved in a distributed way by an alternating direction method of multipliers approach, which provides a significant improvement in speed with respect to the original solution. In the second stage, a soft transition to the original, nonconvex, nonrelaxed formulation is applied in such a way to force the solution toward a local minimum. The algorithm is built in such a way to be fully distributed, and it is tested in meaningful situations, showing its effectiveness in localization accuracy and speed of convergence, as well as its inner robustness.]]>411621721257<![CDATA[Distributed Optimization Using the Primal-Dual Method of Multipliers]]>$O(1/K)$ (where $K$ denotes the iteration index) for general closed, proper, and convex functions. Other properties of PDMM such as convergence speeds versus different parameter-settings and resilience to transmission failure are also investigated through the experiments of distributed averaging.]]>41173187724<![CDATA[Localization in Mobile Networks via Virtual Convex Hulls]]>distributed algorithm to localize an arbitrary number of agents moving in a bounded region of interest. We assume that the network contains at least one agent with known location (hereinafter referred to as an anchor), and each agent measures a noisy version of its motion and the distances to the nearby agents. We provide a geometric approach, which allows each agent to (i) continually update the distances to the locations where it has exchanged information with the other nodes in the past; and (ii) measure the distance between a neighbor and any such locations. Based on this approach, we provide a linear update to find the locations of an arbitrary number of mobile agents when they follow some convexity in their deployment and motion. Since the agents are mobile, they may not be able to find nearby nodes (agents and/or anchors) to implement a distributed algorithm. To address this issue, we introduce the notion of a virtual convex hull with the help of the aforementioned geometric approach. In particular, each agent keeps track of a virtual convex hull of other nodes, which may not physically exist, and updates its location with respect to its neighbors in the virtual hull. We show that the corresponding localization algorithm, in the absence of noise, can be abstracted as a linear time-varying system, with nondeterministic system matrices, which asymptotically tracks the true locations of the agents. We provide simulations to verify the analytical results and evaluate the performance of the algorithm in the presence of noise on the motion as well as on the distance measurements.]]>411882011386<![CDATA[When to Make a Topic Popular Again? A Temporal Model for Topic Rehotting Prediction in Online Social Networks]]>412022161199<![CDATA[IEEE Transactions on Signal and Information Processing over Networks Edics]]>4121721750<![CDATA[IEEE Transactions on Multimedia information for authors]]>4121821962<![CDATA[IEEE Transactions on Signal and Information Processing over Networks]]>41C3C349