IEEE Transactions on Signal and Information Processing over Networks

Migrating computational intensive tasks from mobile devices to more resourceful cloud servers is a promising technique to increase the computational capacity of mobile devices while saving their battery energy. In this paper, we consider an MIMO multicell system where multiple mobile users (MUs) ask for computation offloading to a common cloud server. We formulate the offloading problem as the joi... View full abstract»

Vehicular ad hoc network (VANET) is an enabling technology in modern transportation systems for providing safety and valuable information, and yet vulnerable to a number of attacks from passive eavesdropping to active interfering. Intrusion detection systems (IDSs) are important devices that can mitigate the threats by detecting malicious behaviors. Furthermore, the collaborations among vehicles i... View full abstract»

This paper is concerned with the problem of joint distributed attack detection and distributed secure estimation for a networked cyber-physical system under physical and cyber attacks. The system is monitored by a wireless sensor network, in which a group of sensors is spatially distributed and the sensors' measurements are broadcast to remote estimators via a wireless network medium. A malicious ... View full abstract»

Securing Internet of Things (IoT) applications is complicated by the limited power and processing capabilities of a typical sensor node. This paper proposes a protocol and a method of spectrum management that can guard against common types of security threats despite the limitations of the local processing. With a hierarchical cognitive IoT architecture, this paper incorporates the strengths of co... View full abstract»

We consider a private distributed online optimization problem where a set of agents aim to minimize the sum of locally convex cost functions while each desires that the local cost function of individual agent is kept differentially private. To solve such problem, we propose differentially private distributed stochastic subgradient online optimization algorithm over time-varying directed networks. ... View full abstract»

Cyber-physical systems have recently emerged in several practical engineering applications where security and privacy are of paramount importance. This motivated the paper and a recent surge of interest in development of innovative and novel anomaly and intrusion detection technologies. This paper proposes a novel distributed blind intrusion detection framework by modeling sensor measurements as t... View full abstract»

In this paper, we propose the primal-dual method of multipliers (PDMM) for distributed optimization over a graph. In particular, we optimize a sum of convex functions defined over a graph, where every edge in the graph carries a linear equality constraint. In designing the new algorithm, an augmented primal-dual Lagrangian function is constructed which smoothly captures the graph topology. It is s... View full abstract»

Networked radar systems are vulnerable to different types of attacks, including electronic countermeasure (ECM) jamming and false data injection (FDI) attack. Substantial research has concentrated on ECM jamming, which interferes with radar echoes between a radar and targets. However, FDI attack in which an attacker somehow replaces or modifies radars' measurements, has rarely been considered. FDI... View full abstract»

We propose a Bayesian method for distributed sequential localization of mobile networks composed of both cooperative agents and noncooperative objects. Our method provides a consistent combination of cooperative self-localization (CS) and distributed tracking (DT). Multiple mobile agents and objects are localized and tracked using measurements between agents and objects and between agents. For a d... View full abstract»

The joint task of detecting attacks and securely monitoring the state of a cyber-physical system is addressed over a cluster-based network wherein multiple fusion nodes collect data from sensors and cooperate in a neighborwise fashion in order to accomplish the task. The attack detection-state estimation problem is formulated in the context of random set theory by representing joint information on... View full abstract»

It is really popular to detect hot topics, which can benefit many tasks including topic recommendations, the guidance of public opinions, and so on. However, in some cases, people may want to know when to rehot a topic, i.e., make the topic popular again. In this paper, we address this issue by introducing a temporal user topic participation (UTP) model, which models users' behaviors of posting me... View full abstract»

In this paper, we propose an adaptive cyber-enabled parametric feedback linearization (PFL) control scheme for transient stability of smart grids. Based on feedback linearization control theory, the distributed PFL controller utilizes a distributed energy storage system to modify the dynamics of the power system during transients. We consider cyber attacks on data integrity and availability in the... View full abstract»

The aim of this paper is to propose a least mean squares (LMS) strategy for adaptive estimation of signals defined over graphs. Assuming the graph signal to be band-limited, over a known bandwidth, the method enables reconstruction, with guaranteed performance in terms of mean-square error, and tracking from a limited number of observations over a subset of vertices. A detailed mean square analysi... View full abstract»

Mobile cloud computing enables the offloading of computationally heavy applications, such as for gaming, object recognition or video processing, from mobile users (MUs) to cloudlet or cloud servers, which are connected to wireless access points, either directly or through finite-capacity backhaul links. In this paper, the design of a mobile cloud computing system is investigated by proposing the j... View full abstract»

We propose filter banks in the graph spectral domain, where each filter is defined by a sum of sinusoidal waves. The main advantages of these filter banks are that (a) they have low approximation errors even if a lower-order shifted Chebyshev polynomial approximation is used, (b) the upper bound of the error after the pth order Chebyshev polynomial approximation can be calculated rigorously withou... View full abstract»

We address the problem of identifying the structure of an undirected graph from the observation of signals defined on its nodes. Fundamentally, the unknown graph encodes direct relationships between signal elements, which we aim to recover from observable indirect relationships generated by a diffusion process on the graph. The fresh look advocated here leverages concepts from convex optimization ... View full abstract»

We consider the problem of distributed soft decision fusion in a bandwidth-constrained spatially uncorrelated wireless sensor network (WSN). The WSN is tasked with the detection of an intruder transmitting an unknown signal over a fading channel. Existing distributed consensus-based fusion rules algorithms only ensure equal combining of local data and in the case of band width constrained WSNs, we... View full abstract»

We introduce a novel framework for an approximate recovery of data matrices which are low rank on graphs, from sampled measurements. The rows and columns of such matrices belong to the span of the first few eigenvectors of the graphs constructed between their rows and columns. We leverage this property to recover the nonlinear low-rank structures efficiently from sampled data measurements, with a ... View full abstract»

This paper investigates the problem of resilient consensus under malicious attacks for multiagent systems. Compared with most of existing works, a more general attack model is considered, where malicious agents can neighbor and collude with each other and the number of tolerable attacks is not limited by the network connectivity, which makes the problem more challenging. To solve this problem, we ... View full abstract»

Deregulated electricity markets with time-varying electricity prices and opportunities for consumer cost mitigation makes energy storage, such as a battery, an attractive proposition. Sharing a large capacity battery across a group of homes in a community can not only alleviate the economic deterrents but also exploit the fact that users' activity patterns do not necessarily overlap. However, batt... View full abstract»

Neural node embeddings have recently emerged as a powerful representation for supervised learning tasks involving graph-structured data. We leverage this recent advance to develop a novel algorithm for unsupervised community discovery in graphs. Through extensive experimental studies on simulated and real-world data, we demonstrate that the proposed approach consistently improves over the current ... View full abstract»

We propose an efficient solution to peer-to-peer localization in a wireless sensor network that works in two stages. At the first stage the optimization problem is relaxed into a convex problem, given in the form recently proposed by Soares et al. The convex problem is efficiently solved in a distributed way by an alternating direction method of multipliers approach, which provides a significant i... View full abstract»

This paper considers the problem of distributed detection in the presence of Byzantines who seek to degrade detection performance by falsifying data. This paper proposes a novel mechanism to mitigate Byzantine attacks by partitioning sensors into groups. Local decisions from sensors in each group are sent to the Fusion Center (FC) via multiple paths, which enable the FC to assess (i.e., to audit) ... View full abstract»

In this paper, we investigate energy-efficient joint subcarrier pairing, subcarrier allocation, and power allocation algorithms for improving the network energy efficiency (EE) in multiuser amplify-and-forward (AF) relay networks while ensuring the desired quality-of-service (QoS) requirement for the users through the concept of “network price.” Further, we introduce a network price ... View full abstract»

Recent advances of hardware design and radio technologies have opened the way for an emerging category of network-enabled smart physical devices as a result of convergence in computing and wireless communication capabilities. Inspired by biological interactions, distributed processing of data collected by individual devices is now becoming crucial to let the nodes self-learn relevant network-state... View full abstract»

We propose two algorithms based on the Primal-Dual Method of Multipliers (PDMM) to be used in distributed network optimization: Function Split PDMM (FS-PDMM) and Quadratically Approximated PDMM (QA-PDMM). Our approaches simplify the local subproblems that must be solved for each node, at each update iteration, improving computational efficiency at distributed processors. FS-PDMM allows for simplif... View full abstract»

This paper presents a graph signal denoising method with the trilateral filter defined in the graph spectral domain. The original trilateral filter (TF) is a data-dependent filter that is widely used as an edge-preserving smoothing method for image processing. However, because of the data-dependency, one cannot provide its frequency domain representation. To overcome this problem, we establish the... View full abstract»

We introduce a measure of causality that captures the functional dependencies in dynamical systems and subsequently, define anew type of graphical model, functional dependency graph, to encode such dependencies. We study the relationship between this type of graphical model and other graphical models such as directed information graphs and linear dynamical graphs that have been proposed to capture... View full abstract»

Dense networks call for inter-connectivity of a large number of devices, and in this context, the diversity of interconnected devices introduces challenges to be considered for management and coordination of the network. Synchronization of timing (TO) and carrier frequency offsets (CFO) are critical aspects to be considered to guarantee the proper network interconnectivity. This paper addresses a ... View full abstract»

Recent years have seen a rapid growth in computational methods for a better understanding of functional connectivity brain networks constructed from neuroimaging data. Most of the current work has been limited to static functional connectivity networks (FCNs), where the relationships between different brain regions is assumed to be stationary. Recent work indicates that functional connectivity is ... View full abstract»

We consider an energy-harvesting sensor network, possibly a subnetwork of a larger system, with a central unit that acts as information sink, and also recharges periodically the sensors. At each stage, a system supervisor allows the central unit to manage a certain amount of energy, which is to be apportioned amongst the sensors for their sensing tasks. How should energy be apportioned? And how su... View full abstract»

This work proposes a decentralized, iterative, sparse Bayesian learning algorithm for in-network estimation of multiple joint-sparse vectors by a network of nodes, using noisy and underdetermined linear measurements. The proposed algorithm, called consensus-based distributed sparse Bayesian learning, exploits the network wide joint sparsity of the unknown sparse vectors to recover them from signif... View full abstract»

In this work, we study a generic network cost minimization problem, in which every node has a local decision vector to determine. Each node incurs a cost depending on its decision vector and each link also incurs a cost depending on the decision vectors of its two end nodes. All nodes cooperate to minimize the overall network cost. The formulated network cost minimization problem has broad applica... View full abstract»

In this paper, we develop a 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... View full abstract»

In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks, such as tracking and/or monitoring of one or more dynamic targets. A remote fusion center fuses the information provided by these sensors so that a final estimate of certain target characteristics-such as the position-is expected to possess much improved quality. In this work, we p... View full abstract»

Information analysis of data often boils down to properly identifying their hidden structure. In many cases, the data structure can be described by a graph representation that supports signals in the dataset. In some applications, this graph may be partly determined by design constraints or predetermined sensing arrangements. In general though, the data structure is not readily available nor easil... View full abstract»

In this paper, information sharing over adversarial adaptive networks is considered. Defense against adversarial attacks is enforced by an attack detection mechanism that guides the diffusion strategy in the parameter estimation task and the transmission decisions of agents. The latter are taken to maximize long-term rewards, expressed in terms of estimation performance, communication cost, and il... View full abstract»

Voltage control in power distribution networks has been greatly challenged by the increasing penetration of volatile and intermittent devices. These devices can also provide limited reactive power resources that can be used to regulate the network-wide voltage. A decentralized voltage control strategy can be designed by minimizing a quadratic voltage mismatch error objective using gradient-project... View full abstract»

License plate recognition (LPR) sensors are embedded camera systems that monitor road traffic. When a vehicle passes by a sensor, the vehicle's license plate, the location, and the time of observation are recorded. Given a stream of such observations from a collection of sensors spread around the road network, our goal is to detect convoys: groups of two or more vehicles traveling with highly corr... View full abstract»

How neurons influence each other's firing depends on the strength of synaptic connections among them. Motivated by the highly interconnected structure of the brain, in this study, we propose a computational model to estimate the relationships among voxels and employ them as features for cognitive state classification. We represent the sequence of functional Magnetic Resonance Imaging (fMRI) measur... View full abstract»

Reconstruction in compressed sensing relies on knowledge of a sparsifying transform. In a setting where a sink reconstructs a field based on measurements from a wireless sensor network, this transform is tied to the locations of the individual sensors, which may not be available to the sink during reconstruction. In contrast to previous works, we do not assume that the sink knows the position of e... View full abstract»

In this paper, we consider the problem of sparse signal detection based on partial support set estimation with compressive measurements in a distributed network. Multiple nodes in the network are assumed to observe sparse signals, which share a common but unknown support. While in the traditional compressive sensing framework, the goal is to recover the complete sparse signal, in sparse signal det... View full abstract»

This paper investigates the effect of relay randomness on the end-to-end throughput in multi-hop wireless networks using stochastic geometry. We model the nodes as Poisson Point Processes and calculate the spatial average of the throughput over all potential geometrical patterns of the nodes. More specifically, for problem tractability, we first start with the simple nearest neighbor (NN) routing ... View full abstract»

We address the problem of centralized detection of a binary event in the presence of fraction falsifiable sensor nodes (SNs) (i.e., controlled by an attacker) for a bandwidthconstrained under 􀀀attack spatially uncorrelated distributed wireless sensor network (WSN). The SNs send their one-bit test statistics over orthogonal channels to the fusion center (FC), which linearly combines them t... View full abstract»

This paper builds theoretical foundations for the recovery of a newly proposed class of smooth graph signals, approximately bandlimited graph signals, under three sampling strategies: uniform sampling, experimentally designed sampling, and active sampling. We then state minimax lower bounds on the maximum risk for the approximately bandlimited class under these three sampling strategies and show t... View full abstract»

This paper considers the problem of signal detection in distributed networks in the presence of data falsification (Byzantine) attacks. Detection approaches considered in the paper are based on fully distributed consensus algorithms, where all of the nodes exchange information only with their neighbors in the absence of a fusion center. For such networks, we first characterize the negative effect ... View full abstract»

Magnetic resonant coupling (MRC) is an efficient method for realizing the near-field wireless power transfer (WPT). Although the MRC-enabled WPT (MRC-WPT) with a single pair of transmitter and receiver has been thoroughly studied in the literature, there is limited work on the general setup with multiple transmitters and/or receivers. In this paper, we consider a point-to-multipoint MRC-WPT system... View full abstract»

A key challenge in wireless networking is the management of interference between transmissions. Identifying which transmitters interfere with each other is a crucial first step. In this paper, we cast the task of estimating the wireless interference environment as a graph learning problem. Nodes represent transmitters and edges represent the presence of interference between pairs of transmitters. ... View full abstract»

In this paper, the focus is on subsampling as well as reconstructing the second-order statistics of signals residing on nodes of arbitrary undirected graphs. Second-order stationary graph signals may be obtained by graph filtering zero-mean white noise and they admit a well-defined power spectrum whose shape is determined by the frequency response of the graph filter. Estimating the graph power sp... View full abstract»

This paper characterizes hierarchical clustering methods that abide by two previously introduced axioms-thus, denominated admissible methods-and proposes tractable algorithms for their implementation. We leverage the fact that, for asymmetric networks, every admissible method must be contained between reciprocal and nonreciprocal clustering, and describe three families of intermediate methods. Gra... View full abstract»