Remote Patient Monitoring systems (RPMs) are becoming increasingly important as the aging populationstruggles to manage chronic illnesses and secure consistent healthcare access. While these systems excel in tracking physical metrics, integrating emotion recognition can bridge the often-overlooked connection between emotional and physical well-being. By identifying emotional responses such as discomfort, distress, or contentment, RPM systems can provide immediate feedback for care adjustments and reveal long-term trends. Subtle shifts in emotional patterns may act as early indicators of mental health conditions linked to chronic diseases or transient emotional stress. Expanding RPMs to include emotion awareness makes care more adaptive and holistic. This review explores how emotion recognition can enhance RPM systems by addressing both physical and emotional health. It examines methods that leverage physiological and behavioral responses to capture emotional states, assessing the advantages, limitations, and applicability of these modalities in RPM settings. By incorporating emotion-aware tools, RPMs have the potential to deliver more comprehensive, responsive, and personalized care, revolutionizing healthcare delivery for diverse patient groups.

Mental health disorders are often comorbid, highlighting the need for predictive models that can address multiple outcomes simultaneously. Multi-task learning (MTL) provides a principled approach to jointly model related conditions, enabling shared representations that improve robustness and reduce reliance on large disorder-specific datasets. In this work, we present a tri-modal speech-based framework that integrates text transcriptions, acoustic landmarks, and vocal biomarkers within a large language model (LLM)-driven architecture. Beyond static assessments, we introduce a longitudinal modeling strategy that captures temporal dynamics across repeated clinical interactions, offering deeper insights into symptom progression and relapse risk. Our MTL design simultaneously predicts depression relapse, suicidal ideation, and sleep disturbances, reflecting the comorbid nature of adolescent mental health. Evaluated on the Depression Early Warning (DEW) dataset, the proposed longitudinal trimodal MTL model achieves a balanced accuracy of 70.8%, outperforming unimodal, single-task, and non-longitudinal baselines. These results demonstrate the promise of combining MTL with longitudinal monitoring for scalable, noninvasive prediction of adolescent mental health outcomes.

Smart grids are critical cyber-physical systems that are vital to our energy future. Smart grids’ fault resilience is dependent on the use of advanced protection systems that can reliably adapt to changing grid conditions. The vast amount of operational data generated and collected in smart grids can be used to develop these protection systems. However, given the safety-criticality of protection, the algorithms used to analyze this data must be stable, transparent, and easily interpretable to ensure the reliability of the protection decisions. Additionally, the protection decisions must be fast, selective, simple, and reliable. To address these challenges, this article proposes a data-driven protection strategy, based on Gaussian Discriminant Analysis, for fault detection and isolation. This strategy minimizes the communication requirements for time-inverse relays, facilitates their coordination, and optimizes their settings. The interpretability of the protection decisions is a key focus of this article. The method is demonstrated by showing how it can protect the medium-voltage CIGRE network as it transitions between islanded and grid-connected modes and radial and mesh topologies.

Industrial cyber-physical systems (CPSs) are increasingly facing sophisticated threats. As such, understanding the evolving strategies of cyberattacks is vital for proactive defense. CPS honeypots represent an effective tool for distracting cyberattackers from actual targets, while gleaning insights into their strategies. This article proposes a novel CPS honeypot framework that leverages rich physical models to deceive threat actors, extend their engagement, and glean valuable data. We propose the use of safety critical control to adaptively resist attackers luring them into further engagement providing deeper insights into their tactics. A microgrid-based case study demonstrates the honeypot’s ability to handle intelligent attack scenarios, ensuring prolonged engagement without compromising covertness, as well as the potential of CPS honeypots to enhance threat intelligence.

With the rapid integration of electronically interfaced renewable energy resources and loads into smart grids, there is increasing interest in power quality disturbances (PQD) classification to enhance the security and efficiency of these grids. This paper introduces a new approach to PQD classification based on the Vision Transformer (ViT) model. When a PQD occurs, the proposed approach first converts the power quality signal into an image and then utilizes a pre-trained ViT to accurately determine the class of the PQD. Unlike most previous works, which were limited to a few disturbance classes or small datasets, the proposed method is trained and tested on a large dataset with 17 disturbance classes. Our experimental results show that the proposed ViT-based approach achieves PQD classification precision and recall of 98.28% and 97.98%, respectively, outperforming recently proposed techniques applied to the same dataset.

Cooperative adaptive cruise control (CACC) is a smart transportation solution to alleviate traffic congestion and enhance road safety. The performance of CACC systems can be remarkably affected by communication time delays, and traditional control methods often compromise control performance by adjusting control gains to maintain system stability. In this article, we present a study on the stability of a CACC system in the presence of time delays and highlight the tradeoff between control performance and tuning controller gains to address increasing delays. We propose a novel approach incorporating a neural network module called the deep time delay filter (DTDF) to overcome this limitation. The DTDF leverages the assumption that time delays primarily originate from the communication layer of the CACC network, which can be subject to adversarial delays of varying magnitudes. By considering time-delayed versions of the car states and predicting the present (un-delayed) states, the DTDF compensates for the effects of communication delays. The proposed approach combines classical control techniques with machine learning, offering a hybrid control system that excels in explainability and robustness to unknown parameters. We conduct comprehensive experiments using various deep learning architectures to train and evaluate the DTDF models. Our experiments utilize a robot platform consisting of MATLAB, Simulink, the Optitrack motion capture system, and the Qbot2e robots. Through these experiments, we demonstrate that when appropriately trained, our system can effectively mitigate the adverse effects of constant time delays and outperforms a traditional CACC baseline in control performance. This experimental comparison, to the best of the authors’ knowledge, is the first of its kind in the context of a hybrid machine learning CACC system. We thoroughly explore initial conditions and range policy parameters to evaluate our system under various experimental scenarios. By providing detailed insights and experimental results, we aim to contribute to the advancement of CACC research and highlight the potential of hybrid machine learning approaches in improving the performance and reliability of CACC systems.

Internet of Things (IoT) systems cannot successfully realize the notion of ubiquitous connectivity of everything if they are not capable to truly include 'multimedia things'. However, the current research and development activities in the field do not ...

The ongoing modernization of the power system, involving new equipment installations and upgrades, exposes the power system to the introduction of malware into its operation through supply chain attacks. Supply chain attacks present a significant threat to power systems, allowing cybercriminals to bypass network defenses and execute deliberate attacks at the physical layer. Given the exponential advancements in machine intelligence, cybercriminals will leverage this technology to create sophisticated and adaptable attacks that can be incorporated into supply chain attacks. We demonstrate the use of reinforcement learning for developing intelligent attacks incorporated into supply chain attacks against generation control devices. We simulate potential disturbances impacting frequency and voltage regulation. The presented method can provide valuable guidance for defending against supply chain attacks.

Connected Autonomous Vehicle (CAV) platoons have been extensively studied to protect against cyber and physical vulnerabilities. Faults can occur in all layers of the platoon system or could be introduced by impaired human drivers. Since different types of faults may require different fault resolution methods, identifying the fault class facilitates the selection of the best mitigation strategy. This paper introduces a Multi-Head Attention Machine Learning (MHA-ML) approach to classify a set of five different faults and abnormalities in mixed autonomous and human-driven vehicle platoons. Autonomous vehicles can face actuator faults, False Data Injection (FDI) attacks, and Denial-of-Service (DoS) attacks, while abnormalities such as drunk or distracted human drivers could occur. MHA-ML is developed to identify faulty vehicle behavior over long sequences of sensor measurements. MHA-ML is trained on a mixed platoon simulation model and then tested on mobile laboratory robots. The experiment classifies the five fault categories with 90% accuracy and outperforms a baseline recurrent neural network approach.

The increased push for green transportation has been apparent to address the alarming increase in atmospheric${CO}_{2}$levels, especially in the last five years. The success and popularity of Electric Vehicles (EVs) have led many carmakers to shift to developing clean cars in the next decade. Moreover, many countries around the globe have set aggressive EV target adoption numbers, with some even aiming to ban gasoline cars by 2050. Unlike their gasoline-based counterparts, EVs comprise many sensors, communication channels, and decision-making components vulnerable to cyberattacks. Hence, the unprecedented demand for EVs requires developing robust defenses against these increasingly sophisticated attacks. In particular, recently proposed cyberattacks demonstrate how malicious owners may mislead EV charging networks by sending false data to unlawfully receive higher charging priorities, congest charging schedules, and steal power. This paper proposes a learning-based detection model that can identify deceptive electric vehicles. The model is trained on an original dataset using real driving traces and a malicious dataset generated from a reinforcement learning agent. The Reinforcement Learning (RL) agent is trained to create intelligent and stealthy attacks that can evade simple detection rules while also giving a malicious EV high charging priority. We evaluate the effectiveness of the generated attacks compared to handcrafted attacks. Moreover, our detection model trained with RL-generated attacks displays greater robustness to intelligent and stealthy attacks.

Cooperative adaptive cruise control (CACC) is a smart transportation solution that can mitigate traffic jams and improve road safety. CACC performance is heavily impacted by communication time delay; moreover, control theory solutions generally compromise control performance by tuning control gains in order to maintain plant stability. We propose a control-machine learning hybrid approach called deep time delay filter (DTDF). DTDF predicts the present (un-delayed) car states given time delayed versions. We successfully train a neural network for the DTDF method and use a physical testbed to show that DTDF can mitigate the effects of constant time delays as large as 5s while maintaining superior control performance compared to that of a baseline control algorithm.

Load redistribution (LR) attacks have proven to be hard-detectable and damaging, which require effective corrective schemes to mitigate the impact on power grid operations. Traditional game-theoretic methods and corrective dispatches employing static line rating (SLR) have been studied for attack mitigation based on specific attack objectives but have high dispatch cost and limited performance of attack mitigation. This is because the power transfer capacity of the existing transmission network is underestimated with SLR, and in practical operations, the specific objective of the adversary is not available to the defender, which would introduce uncertainties to the design of corrective schemes. As such, this article incorporates the dynamic line rating (DLR) technology, which enhances the power transfer capability of the existing network, to develop the cost-effective corrective dispatch for mitigating LR attacks with unknown objectives. Specifically, a DLR-based robust corrective (DRC) dispatch model is presented, which guarantees the system security as well as the economic performance. A methodology utilizing the robust counterpart technique and column constraint generation (CCG) algorithm is proposed to solve the dispatch model in a decomposition framework. Case studies based on the IEEE 14- and 118-bus systems verify the performance of the proposed DRC dispatch in enhancing the cyber–physical security of power grids.

In this paper, we consider periodic communication delays within the connected autonomous vehicles platoon. Periodic signals are fundamentally simple to create, and in this study we analyze whether certain amplitude or frequencies can cause instability. This is important as we discover in this study, the classical method of replacing time-varying delays with constant delays does not capture the complex stability boundary of periodic time delays which could be exploited by attackers to cause instability in the vehicle platoon. We use the semi-discretization method to obtain plant stability. Then, we take the average value of the time delay functions to characterize the maximal admissible delay region such that the time-delayed system remains stable and provide string stability analysis. Through numerical simulations, we verify the analytical results. We construct stability charts in different parameter spaces to explore the effects of the model parameters on the system stability. A specific range of frequencies was found that could destabilize the connected autonomous vehicle platoon.

An autonomous vehicle platoon is a network of autonomous vehicles that communicate together to move in a desired way. One of the greatest threats to the operation of an autonomous vehicle platoon is the failure of either a physical component of a vehicle or a communication link between two vehicles. This failure affects the safety and stability of the autonomous vehicle platoon. Transmissibility-based health monitoring uses available sensor measurements for fault detection under unknown excitation and unknown dynamics of the network. After a fault is detected, a sliding mode controller is used to mitigate the fault. Different fault scenarios are considered including vehicle internal disturbances, cyber attacks, and communication delays. We apply the proposed approach to a bond graph model of the platoon and an experimental setup consisting of three autonomous robots.

Smart microgrids are rapidly being developed and deployed, even as concerns over their cyber-physical security are increasing. The high penetration of these power electronic-interfaced energy resources has resulted in weaker power grids and an increase in cyberattack surface. The implementation of frequency regulation in these new resources—particularly in wind generators—is on the rise. This article investigates how malicious controllable loads can threaten the integrity of frequency-regulating wind generators. Adopting a user-centric approach and benefiting from small-signal analyses, the article shows for the first time how these wind generators can be the target of attackers. Effective methods to enhance system resilience are sought by mitigating the attack risk in the extended end-users, wind generators. The article models and explores how proper tuning and design of the physical system can improve cyber-physical security. The work also extends the user-centric method to the physical layer of smart grids. Detailed time-domain simulations verify the results of the analyses.

Summary The ever increasing number of Android malware has always been a concern for cybersecurity professionals. Even though plenty of anti‐malware solutions exist, we hypothesize that the performance of existing approaches can be improved by deriving relevant attributes through effective feature selection methods. In this paper, we propose a novel two‐step feature selection approach based on Rough Set and Statistical Test named as RSST to extract refined system calls, which can effectively discriminate malware from benign apps. By refined set of system call, we mean the existence of highly relevant calls that are uniformly distributed thought target classes. Moreover, an optimal attribute set is created, which is devoid of redundant system calls. To address the problem of higher dimensional attribute set, we derived suboptimal system call space by applying the proposed feature selection method to maximize the separability between malware and benign samples. Comprehensive experiments conducted on three datasets resulted in an accuracy of 99.9%, Area Under Curve (AUC) of 1.0, with 1% False Positive Rate (FPR). However, other feature selectors (Information Gain, CFsSubsetEval, ChiSquare, FreqSel, and Symmetric Uncertainty) used in the domain of malware analysis resulted in the accuracy of 95.5% with 8.5% FPR. Moreover, the empirical analysis of RSST derived system calls outperformed other attributes such as permissions, opcodes, API, methods, call graphs, Droidbox attributes, and network traces.

This article focuses on characterizing the impact of communication on distributed control performance in smart grid systems. Using a cyber-physical model of the smart grid and utilizing electromechanical wave propagation in transmission systems, a holistic approach is proposed to unify cyber-physical coupling representation. This is facilitated by establishing an event-propagation paradigm relating measurements, distributed control and the physical power systems network characteristics. We investigate how power system characteristics impose limitations on distributed control performance using the proposed approach. The proposed approach is then used to derive fundamental communication delay limits for effective distributed control, and comparative analysis of distributed control performance is investigated for example distributed control scenarios.

The impact and mitigation of false data injection (FDI) attacks with nonlinear dynamics targeting actuators in cyber-physical mechatronic systems (CPMSs) is investigated in this paper. Actuators in mechatronic systems exhibit vulnerabilities to inputs with well-known nonlinearities (e.g., backlash, deadzone, and saturation), where the nonlinear dynamics can affect the actuators' performance. A mitigation approach is proposed based on the retrospective cost-based adaptive control to stabilize and regulate the CPMS under such FDI cyberattack. Since mechatronic systems are implemented with actuators of different dynamical properties, this paper considers systems of linear and nonlinear dynamics. Simulation results demonstrate how the proposed adaptive control system achieves internal model control with the dynamics of the actuator systems and the nonlinearities of the backlash, deadzone, and saturation attacks. Results further show that the controller inverts and rejects the effects of attacks with unknown nonlinearities.

A novel framework for the distributed control is proposed in this paper for transient stability of smart power transmission systems in the face of disturbances. The proposed framework seamlessly integrates the traditional governor-based power control with energy storage system (ESS)-based controls. The design objectives also address practical challenges unaccounted for in former work, including limited ESS capacity and availability, and absent, delayed, or corrupt sensor measurements. The IEEE 68-bus test power system is used to demonstrate the merits of the control strategy in enhancing the transient stability of power systems. The results of this paper suggest that integrating different types of control can lead to an enhanced transient stabilization process in the face of different practical limitations.

In this work we propose an adaptive cyber-enabled control scheme for transient stability of smart grids. Based on feedback linearization control theory, the proposed distributed scheme utilizes a distributed energy storage system (ESS) to modify the dynamics of the power system during transients. However, the controller's design parameter is adapted to the cyber state of the smart grid. Specifically, the distributed control scheme adapts to the level of noise, communication latency, interference, and data injection attacks. Further, depending on the severity of the physical disturbance, the controller adjusts its design parameter in order to speed up the stabilization process. The performance of the proposed control scheme is validated on the IEEE 68-bus test power system where the controller is shown to efficiently stabilize the power system during physical and cyber disturbances.

Cyber enabling infrastructures such as the smart grid transforms many of the it's aspects. A key to many of the promised benefits is the performance of the communication network, mainly in terms of delay and quality. In one area of development, distributed control that rely on received system measurements have shown enhanced system performance against disturbances. We establish a propagation of events framework between the communication system and the physical power system based on electromechanical waves. The proposed framework is used to arrive at fundamental limits of communication latency beyond which distributed control would not be able to properly address disturbances.

In this paper, we study a distributed control strategy that harnesses the highly granular data available in future power systems in order to improve system resilience to disturbances. Specifically, we investigate the role of external energy storage systems (ESSs) in stabilizing the dynamics of power systems during periods of disruption. We consider an information-rich multiagent framework and focus on ESS output control via linear feedback optimal (LFO) control to achieve transient stability. The LFO control scheme relies on receiving timely state information to actuate distributed ESSs in order to drive the synchronous generators to stability. We evaluate the performance of the LFO control on the 39-bus 10-generator New England test power system in the presence of ideal and nonideal conditions including communication latency, finite sampling rate, and sensor noise. The LFO controller is found to have a simple structure, be tunable, and to have fast response to achieving transient stability while being sensitive to information latency and data rate.

Security of smart power systems is a sound concern as more cyber elements are added to the power grid. In this paper, we focus on cyber attacks that target data integrity in smart grid systems. We specifically investigate the impact of false data injection (FDI) attacks on distributed transient stability control schemes. As an example, we focus on the parametric feedback linearization (PFL) controller, and we derive closed-form expressions for the errors in rotors' speed and angle as a result of cyber attacks on data integrity. Furthermore, we investigate adaptive control strategies to eliminate or minimize the impact of FDI attacks on system dynamics. The IEEE 68-bus test power system is used to numerically evaluate the impact of example attacks and to draw valuable insights.

Power demands are rising at an exponential pace due to the increasing proliferation of high-energy consuming devices such as plug-in hybrid electric vehicles. It is well known that scaling traditional power generation systems to accommodate these soaring demands will be excessively costly and may lead to negative environmental ramifications. One approach to supplement increasing energy needs involves diversifying the generation mix to incorporate a large number of local distributed generators (DGs) for economical and sustainable operation. However, such an approach remains an open challenge due to the inherent generation variability of DGs. In this paper, we propose a distributed generation dispatch strategy that can effectively coordinate a large number of DGs to meet consumer demand in real time. Through theoretical analysis based on population games and simulation studies, we demonstrate that our dispatch strategy is scalable and allows for the seamless integration of alternative energy resources into the grid in a robust and an optimally cost-effective manner.

We study the modeling of communication routing strategies in wide area monitoring systems based on flocking theory. We assert that analogies exist between the flocking principles of behavioral transitions, collision avoidance and obstacle avoidance and the routing goals of adaptability, buffer overflow management and re-routing in the presence of changing network conditions. Our model is dynamic and can easily be incorporated in existing flocking-based models of power system operation to provide an overall hierarchical cyber-physical model for a smart grid. Through simulations we show how our model can provide insight on effective routing strategies to promote the transient stabilization of faulted power systems in the presence of denial-of-service attacks on communications infrastructure.

Wireless multimedia sensor networks (WMSNs) emerge as a solution in wireless unattended video surveillance scenarios. The G-E-M methodology was recently introduced by Czarlinska et al. to address the problem providing protection to visual wireless surveillance systems in the presence of another hostile sensing system. This work builds upon the G-E-M framework by introducing pre- and post-processing stages that reduce decoding errors and visual noise as well as allowing the effective control of bitrate.

The goal of preventative steganalysis is to offer a proactive solution against steganography by increasing the steganalyst's knowledge of the cover-media therefore emphasizing the presence of hidden messages. This paper presents the concept of preventative steganalysis applied to wireless visual sensor networks. By means of the entropy, the uncertainty of the data captured by the network's camera is reduced, hence reducing the potential embedding capacity and discouraging the use of steganography.

There has been recent interest in the development of untethered sensor nodes that communicate directionally via free space optical communications for mission critical settings in which high-speed link guarantees in hostile environments are needed. Directional wireless optical sensor networks have the potential to provide gigabits per second speeds for relatively low power consumption enabling bursty traffic and longer network lifetimes. In randomly deployed sensor settings, the crucial steps of ad hoc route setup and node localization are not only nontrivial, but also vulnerable to security attacks. In response to these challenges, this paper proposes a lightweight security-aware integrated routing and localization approach that exploits the benefits of link directionality inherent to wireless optical sensor networks. The circuit-based algorithm that makes use of directional routing loops, called SIRLoS, leverages the resources of the base station and a hierarchical network structure to identify topological information and detect security violations in neighborhood discovery and routing mechanisms. We study the performance of the SIRLoS algorithm demonstrating that reduced localization error, routing overhead, and likelihood of attack in various contexts are possible within lightweight computational constraints.

This paper studies the problem of achieving watermark semifragility in watermark-based authentication systems through a composite hypothesis testing approach. Embedding a semifragile watermark serves to distinguish legitimate distortions caused by signal-processing manipulations from illegitimate ones caused by malicious tampering. This leads us to consider authentication verification as a composite hypothesis testing problem with the watermark as side information. Based on the hypothesis testing model, we investigate effective embedding strategies to assist the watermark verifier to make correct decisions. Our results demonstrate that quantization-based watermarking is more appropriate than spread-spectrum-based methods to achieve the semifragility tradeoff between two error probabilities. This observation is confirmed by a case study of an additive Gaussian white noise channel with a Gaussian source using two figures of merit: 1) relative entropy of the two hypothesis distributions and 2) the receiver operating characteristic. Finally, we focus on common signal-processing distortions, such as JPEG compression and image filtering, and investigate the discrimination statistic and optimal decision regions to distinguish legitimate and illegitimate distortions. The results of this paper show that our approach provides insights for authentication watermarking and allows for better control of semifragility in specific applications.

In designing wireless multihop sensor networks, determining system parameters that guarantee a reasonably connected network is crucial. In this paper, we investigate node isolation in wireless optical sensor networks (WOSNs) as a topology attribute for network connectivity. Our results pertain to WOSNs modeled as random-scaled sector graphs that employ directional broad-beamed free space optics for point-to-point communication. We derive a generalized analytical expression relating the probability that no node is isolated to the physical layer parameters of node density, transmitter radius, and angular beam width. Through simulations, we demonstrate that for probability values close to 1, dense networks, and increasing beam width, the probability that the WOSN is connected is tightly upper bounded by the probability that no isolated node exists. In addition, our study demonstrates conditions for probabilistic K-connectivity guarantees and provides empirical insights on the impact of clustering on connectivity by employing simulations to validate analytical derivations. Our analysis provides a methodology of practical importance to choosing physical layer parameter values for effective network level design.

In this work we consider an event-driven wireless visual sensor network (WVSN) where each camera node transmits a frame to the cluster-head only if an event of interest was captured in the frame for energy and bandwidth conservation. Specifically, we consider the scenario where each camera node receives decision support from an independent but possibly attacked (and hence error-prone) scalar-sensor regarding the presence or absence of an event. We study the overall detection performance achieved by various techniques that utilize the scalar and image-based decisions. We conclude that in image sequences involving extraneous lighting and background changes (such as in the case of outdoor surveillance), the combination techniques generally achieve a lower total probability of error.

There is a critical need to provide privacy assurances for distributed vision-based sensor networking in applications such as building surveillance and healthcare monitoring. To effectively address protection and reliability issues, secure networking and processing must be considered from system inception. This paper presents attacks that affect the data privacy in visual sensor networks and proposes privacy-promoting security solutions based on opponent detection via game-theoretic analysis and keyless encryption.

We present an approach for the development of lightweight analog cognition for autonomous navigation in unknown environments. We consider a hypothetical differential drive robot, equipped with a limited sensor suite that includes one front and one rear monocular obstacle sensor and a target (bearing and distance) sensor. A robotic motion control scheme is developed for obstacle avoidance and target-seeking using an innovative dynamical systems architecture incorporating a nonlinear controller derived using Lyapunov techniques. We claim that the avoidance-steering behavioral property of the agent is a weakly emergent characteristic. Simulations are provided and discussed.

Event-driven visual sensor networks (VSNs) rely on a combination of camera nodes and scalar sensors to determine if a frame contains an event of interest that should be transmitted to the cluster head. The appeal of event-driven VSNs stems from the possibility of eliminating non-relevant frames at the source thus implicitly minimizing the amount of energy required for coding and transmission. The challenges of the event-driven paradigm result from the vulnerability of scalar sensors to attack or error and from the lightweight image processing available to the camera nodes due to resource constraints. In this work we focus on the reliability issues of VSNs in the case of global actuation attacks on the scalar sensors. We study the extent to which various utility functions enable an attacker to increase the average expected number of affected nodes with a relatively small penalty in the loss of stealth. We then discuss tradeoffs between different attack detection strategies in terms of the cost of processing and the required information at the cluster head and nodes.

Event-driven visual sensor networks consist of collaborating camera nodes and scalar sensors which aid in the detection of events of interest in the environment. This collaboration is significant since the camera nodes generally utilize lightweight image processing in order to determine if a frame is relevant to the given application. The reliability of the supporting scalar sensor however may be compromised by an actuation attack which perturbs the sensor's measurements. In this work we examine the achievable actuation of hostile nodes that are not globally coordinated and that may be selfish or untrustworthy in their preferences. We compare our findings with existing research which assumes that all the hostile nodes are coordinated to actuate with the same parameter. We determine that given certain conditions, local optimization may actually result in a stronger stealthy attack than the global coordination case.

There is a critical need to provide privacy and security assurances for distributed multimedia sensor networking in applications including military surveillance and healthcare monitoring. Such guarantees enable the widespread adoption of such information systems, leading to large-scale societal benefit. To effectively address protection and reliability issues, secure communications and processing must be considered from system inception. Due to the emerging nature of broadband sensor systems, this provides fertile research ground for proposing more paradigm-shifting approaches. This paper discusses issues in designing for security and privacy in distributed multimedia sensor networks. We introduce the heterogeneous lightweight sensornets for trusted visual computing framework for distributed multimedia sensor networks. Protection issues within this architecture are analyzed, leading to the development of open research problems including secure routing in emerging free-space optical sensor networks and distributed privacy for vision-rich sensor networking. Proposed solutions to these problems are presented, demonstrating the necessary interaction among signal processing, networking, and cryptography.

This correspondence studies the distributed secret sharing problem which is a twist of the classical secret sharing problem. In this new problem, each user needs to encode his or her own unique secret message without collaboration with other users and without the use of any common secret materials or cryptographic keys. The goal is to ensure that an adversary without access to all of the encoded messages learns as little as possible about the secret messages, while a legitimate joint decoder with all the encoded messages can decode all of them without cryptographic keys. Furthermore the users do not know the channels that will be compromised ahead of time, and thus must protect all channels. Specifically, we study two related variants of this problem. The first problem deals with source coding and secrecy, while the second problem deals with channel coding and secrecy. From the results of these two problems, we conclude that interference is necessary for unconditional secrecy.

In this work we consider an event-driven wireless visual sensor network (WVSN) comprised of untethered camera nodes and scalar sensors deployed in a hostile environment. In the event-driven paradigm, each camera node transmits a surveillance frame to the cluster-head only if an event of interest was captured in the frame, for energy and bandwidth conservation. We thus examine a simple image processing algorithm at the camera nodes based on difference frames and the chi-squared detector. We show that the test statistic of the chi-squared detector is equivalent to that of a robust (non-parametric) detector and that this simple algorithm performs well on indoor surveillance sequences and some, but not all, outdoor sequences. In outdoor sequences containing significant changes in background and lighting, this simple detector may produce a high probability of error and benefits from the inclusion of scalar sensor decisions. The scalar sensor decisions are, however, prone to attack and may exhibit errors that are arbitrarily frequent, pervasive throughout the network and difficult to predict. To achieve attack prediction and mitigation given an attacker whose actions are not known a priori, we employ game-theoretic analysis. We show that the scalar sensor error can be controlled through cluster-head checking and appropriate selection of cluster size n. Given this attack mitigation, we employ real-life sequences to determine the total probability of error when individual and combined decisions are utilized and we discuss the ensuing ramifications and performance issues.

Wireless Sensor Networks (WSNs) deployed in hostile environments are susceptible to various attacks directed at their data. In this work we focus on an emerging and largely unexplored issue arising from the presence of actuator (or actor) nodes in the form of Wireless Sensor Actuator Networks (WSANs). Specifically, we consider the case where hostile WSAN nodes belonging to a foreign network directly perturb the readings of WSN nodes during sensing. The attack is modeled as affecting the decision that a WSN node reports about the presence or absence of a phenomenon to its cluster head. To assess the potential loss of sensing fidelity due to the opposing network, we employ a game theoretic analysis. We focus on determining the probability that the WSN cluster head becomes alerted to such an attack given some statistical information about the phenomenon. Our results show that an actuation attack may go unnoticed even if such an attack is not coordinated among the hostile WSAN nodes. Importantly, the number of WSN nodes in a cluster affects the probability of WSAN attack success. For clusters consisting of only a few nodes, the hostile WSAN may achieve a stealthy attack with a wider range of attack parameters. We also determine that natural phenomena with certain characteristics are more susceptible to the attack and require further sensing-verification mechanisms.

In traditional secret sharing, a central trusted authority must divide a secret into multiple parts, called shares, such that the secret can only be recovered when a certain number of shares are available for reconstruction [1], [2]. In this paper, we consider a secret sharing problem in which each share must be created separately by independent entities such that no collaboration or shared cryptographic keys are required; we call this the distributed keyless secret sharing problem. For this problem, general tradeoffs between compression and secrecy are characterized yielding the impossibility result that perfect secrecy is unachievable. In response to this impossibility, we define a practical measure of secrecy and design a low-cost solution based on this measure of secrecy.

This paper studies distributed joint secrecy and compression suitable for sensor networks. A capacity region that characterizes the tradeoff between compression and secrecy is derived. We demonstrate for the two-node case that under the restriction of separate enciphering (i.e., no inter-node collaboration) unconditional secrecy by both parties cannot be achieved simultaneously. A fundamental design rule for lightweight encoder implementation critical for secrecy based on distributed source coding using syndromes and Reed-Solomon codes is presented highlighting practical feasibility.

This paper provides a timely review of influential work in the area of peer-to-peer (P2P) content search. We begin with a survey of text-based P2P search mechanisms and continue with an exposition of content-based approaches followed by a discussion of future directions.

Wireless ad-hoc sensor networks (WSNs) consist of randomly and densely deployed nodes which self-organize to cooperatively maintain multi-hop network connectivity [1]. The nodes act as both environmental sensors and network routers. The ability to set up distributed sensor networks inexpensively, in large-scale, quickly, and without fixed infrastructure makes them a promising candidate for a host of applications, including military surveillance and disaster relief.

In this paper, we investigate the fundamental property of connectivity in a hierarchical ad-hoc optical sensor network (OSN) communicating with broad beam lasers. The OSN is modeled as a random sector graph, in which the randomly deployed sensors can send data within a contiguous and randomly oriented sector of communication. Hierarchy is achieved by employing passive and active free space optical devices. Connectivity analysis in this hierarchical network model is vital in order to assess the feasibility of routing, security and network protocols. To this end, this paper provides probabilistic bounds on network parameters for hierarchical connectivity in OSNs, using a novel approach that directly considers directionality. In particular, we prove probabilistic bounds on the jointly minimum radius of communication as well as angle of the communication sector (i.e., beam width) required for network connectivity.

In this paper we introduce OPSENET, a novel and efficient protocol that facilitates secure routing in directional optical sensor networks. We show that even though the uni- directionality of links in an optical sensor network (OSN) complicates the design of efficient routing, link directionality actually helps security in our network setup. In particular, we leverage the naturally-occurring clustering that results from passive (bi-directional) communication of select cluster head nodes with the base station, to improve overall network performance. This paper presents two main contributions: (1) We introduce OPSENET, a novel secure cluster-based routing algorithm for base station circuit discovery in OSNs. In order to support the efficient utilization of a nodes' resources, we employ symmetric cryptography in the design of OPSENET, using efficient oneway hash functions and pre-deployed keying. OPSENET achieves base station broadcast authentication, per-hop authentication, and cluster group secrecy, without requiring any time synchronization. (2) We analyze the relevance of traditional routing attacks on OSNs, and show that OPSENET is robust against uncoordinated (non-smart) insider routing attacks, amongst other compromises. An important performance metric of OPSENET is its low byte overhead, and graceful degradation with the number of compromised nodes in the network. To the best of our knowledge, this is the first paper to consider secure routing in an OSN network scenario.

This paper provides a brief overview of recent progress of peer-to-peer (P2P) technologies for multimedia applications. We provide an overview of the technical challenges, creative solutions and results related to P2P file sharing and content search, and P2P media streaming

In this paper we evaluate the energy and security consideration for a security-aware routing protocol proposed for uni-directional, hierarchical optical sensor network. We bootstrap the unconstrained resources of the base station to design GORA, a greedy optimized routing algorithm, in which the base station is responsible for network route optimization and updates. This paper extends our recent work on OPSENET, a novel and efficient protocol that facilitates secure routing in directional optical sensor networks. We evaluate security and energy metrics for our scheme proposed scheme. Analysis and simulation results are used to show the performance of our algorithm, compared with other hierarchical bi-directional clustering routing schemes

In this paper, we present effective steganalysis techniques for digital video sequences based on interframe collusion that exploits the temporal statistical visibility of a hidden message. Steganalysis is the process of detecting, with high probability, the presence of covert data in multimedia. Present image steganalysis algorithms when applied directly to video sequences on a frame-by-frame basis are suboptimal; we present methods that overcome this limitation by using redundant information present in the temporal domain to detect covert messages embedded via spread-spectrum steganography. Our performance gains are achieved by exploiting the collusion attack that has recently been studied in the field of digital video watermarking and pattern recognition tools. Through analysis and simulations, we evaluate the effectiveness of the video steganalysis based on linear collusion approaches. The proposed steganalysis methods are successful in detecting hidden watermarks bearing low energy with high accuracy. The simulation results also show the improved performance of the proposed temporal-based methods over purely spatial methods

This paper focuses on a coding approach for effective analysis and design of secure watermark-based multimedia authentication systems. We provide a design framework for semi-fragile watermark-based authentication such that both objectives of robustness and fragility are effectively controlled and achieved. Robustness and fragility are characterized as two types of authentication errors. The authentication embedding and verification structures of the semi-fragile schemes are derived and implemented using lattice codes to minimize these errors. Based on the specific security requirements of authentication, cryptographic techniques are incorporated to design a secure authentication code structure. Using nested lattice codes, a new approach, called MSB-LSB decomposition, is proposed which we show to be more secure than previous methods. Tradeoffs between authentication distortion and implementation efficiency of the secure authentication code are also investigated. Simulations of semi-fragile authentication methods on real images demonstrate the effectiveness of the MSB-LSB approach in simultaneously achieving security, robustness, and fragility objectives.

For very low power, high bandwidth applications, free space optical sensor networks (FSOSN) have shown potential. They promise increasing node functionality, lower energy consumption, lower cost and smaller sizes. However, the new optical communication architecture yields new routing challenges. The objective of our paper is to introduce novel routing protocols for FSOSN that take into account the line-of-sight requirement for optical communications. Our network is modeled as a directed hierarchical random sector geometric graph, in which sensors route their data via multi-hop paths, to a powerful base station, through a cluster head. Following the dominant communication pattern in sensor networks, we propose a new efficient routing algorithm for local neighborhood discovery and a base station (up-link and down-link) discovery algorithm. We show that our routing protocols require Olog(n) storage at each node, versus O(n) seen in the literature, and present analytical and simulation results to evaluate the proposed protocols

We present a theoretical framework for the linear collusion analysis of watermarked digital video sequences, and derive a new theorem equating a definition of statistical invisibility, collusion-resistance, and two practical watermark design rules. The proposed framework is simple and intuitive; the basic processing unit is the video frame and we consider second-order statistical descriptions of their temporal inter-relationships. Within this analytical setup, we define the linear frame collusion attack, the analytic notion of a statistically invisible video watermark, and show that the latter is an effective counterattack against the former. Finally, to show how the theoretical results detailed in this paper can easily be applied to the construction of collusion-resistant video watermarks, we encapsulate the analysis into two practical video watermark design rules that play a key role in the subsequent development of a novel collusion-resistant video watermarking algorithm discussed in a companion paper.

We develop a novel video watermarking framework based on the collusion-resistant design rules formulated in a companion paper. We propose to employ a spatially-localized image dependent approach to create a watermark whose pairwise frame correlations approximate those of the host video. To characterize the spread of its spatially-localized energy distribution, the notion of a watermark footprint is introduced. Then we explain how a particular type of image dependent footprint structure, comprised of subframes centered around a set of visually significant anchor points, can lead to two advantageous results: pairwise watermark frame correlations that more closely match those of the host video for statistical invisibility, and the ability to apply image watermarks directly to a frame sequence without sacrificing collusion-resistance. In the ensuing overview of the proposed video watermark, two new ideas are put forward: synchronizing the subframe locations using visual content rather than structural markers and exploiting the inherent spatial diversity of the subframe-based watermark to improve detector performance. Simulation results are presented to show that the proposed scheme provides improved resistance to linear frame collusion, while still being embedded and extracted using relatively low complexity frame-based algorithms.

This paper provides a tutorial and survey of digital fingerprinting and video scrambling algorithms based on partial encryption. Necessary design tradeoffs for algorithm development are highlighted for multicast communication environments. We also propose a novel architecture for joint fingerprinting and decryption that holds promise for a better compromise between practicality and security for emerging digital rights management applications.

In this letter, we propose a multiplicative watermarking method operating in the ridgelet domain. We employ the directional sensitivity and the anisotropy of the ridgelet transform (RT) in order to obtain a sparse image representation, where the most significant coefficients represent the most energetic direction of an image with straight edges. Therefore, given a natural image, the associated edge image is obtained by means of a filter bank designed using the circular harmonic functions, then the edge image is partitioned into small blocks in order to deal with straight edges. Finally, the RT is performed for each block, the most relevant coefficients are selected, and eventually the watermark is embedded. Robustness and transparency are proven by experimental results.

We study the performance of robust digital watermarking approaches in the presence of lossy compression by introducing practical analysis methodologies. Correlation expressions between the embedded watermark and the extracted watermark are derived to determine the optimal watermarking domain to maximize data hiding rates for spread spectrum and quantization watermarking. It is determined both theoretically and through simulations that the embedding strategy, in addition to the transform used for lossy compression, dictate the optimal transform for watermarking. Through analytic comparisons, we develop a novel hybrid watermarking algorithm that exploits the best of both approaches for greater resilience to JPEG compression.

This paper proposes an approach for the combined image authentication and compression of color images by making use of a digital watermarking and data hiding framework. The digital watermark is comprised of two components: a soft-authenticator watermark for authentication and tamper assessment of the given image, and a chrominance watermark employed to improve the efficiency of compression. The multipurpose watermark is designed by exploiting the orthogonality of various domains used for authentication, color decomposition and watermark insertion. The approach is implemented as a DCT-DWT dual domain algorithm and is applied for the protection and compression of cultural heritage imagery. Analysis is provided to characterize the behavior of the scheme under ideal conditions. Simulations and comparisons of the proposed approach with state-of-the-art existing work demonstrate the potential of the overall scheme.

This paper presents a novel robust watermarking approach called FuseMark based on the principles of image fusion for copy protection or robust tagging applications. We consider the problem of logo watermarking in still images and employ multiresolution data fusion principles for watermark embedding and extraction. A human visual system model based on contrast sensitivity is incorporated to hide a higher energy hidden logo in salient image components. Watermark extraction involves both characterization of attacks and logo estimation using a rake-like receiver. Statistical analysis demonstrates how our extraction approach can be used for watermark detection applications to decrease the problem of false negative detection without increasing the false positive detection rate. Simulation results verify theoretical observations and demonstrate the practical performance of FuseMark.

This paper presents a novel video watermarking algorithm based on two key ideas: statistical invisibility and content-synchronized placement. We argue that statistical invisibility is essential to protect video watermarks from statistical collusion, and present a natural way to induce this property using a content-dependent spatially localized watermarking framework. We introduce the notion of a watermark footprint, the spatial locations over which its energy is spread. By defining localized footprints with regular structures, e.g., a set of subframes within each frame, current image watermarking techniques can immediately be applied at the subframe-level. We address the issue of reduced spatial redundancy by proposing an attack model based on bilinear interpolation, and embedding the watermark into regions with lower expected distortions. Results are presented to demonstrate the effectiveness of the algorithm.

In this work, we take an information theoretic approach to analyze the watermark communication problem in the presence of perceptual coding. Our effective watermark channel is modeled as a set of parallel independent zero-mean uniformly distributed additive noise channels. Energy allocation principles are identified to maximize the capacity results. Our findings are compared to the traditional water-filling solution and shed light on strategies to maximize the data hiding rate in the presence of compression.

In this paper, we present two novel approaches for the classification of blurry images. It is assumed that the blur is linear and space invariant, but that the exact blurring function is unknown. The proposed fusion-based approaches attempt to perform the simultaneous tasks of blind image restoration and classification. We call such a problem blind image fusion. The techniques are implemented using the nonnegativity and support constraints recursive inverse filtering (NAS-RIF) algorithm for blind image restoration and the Markov random field (RIRF)-based fusion method for classification by Schistad-Solberg et al. Simulation results on synthetic and real photographic data demonstrate the potential of the approaches. The algorithms are compared with one another and to situations in which blind blur removal is not attempted.

We present and analyze an approach to improve the performance of a broad class of watermarking schemes through attack characterization. Traditional robust watermarking methods use little information concerning the way in which the image is tampered to estimate the embedded watermark. In our novel scheme we propose adding two types of watermarks: reference and robust. The reference watermark is used to assess the way in which the marked image has been modified so that the robust watermark can be optimally extracted to maximize security. Analysis and simulation results are provided to demonstrate the significant performance improvement when the proposed approach is employed in an existing watermarking scheme.

In this paper, we consider the problem of digital watermarking to ensure the credibility of multimedia. We specifically address the problem of fragile digital watermarking for the tamper proofing of still images. Applications of our problem include authentication for courtroom evidence, insurance claims, and journalistic photography. We present a novel fragile watermarking approach which embeds a watermark in the discrete wavelet domain of the image by quantizing the corresponding coefficients. Tamper detection is possible in localized spatial and frequency regions. Unlike previously proposed techniques, this novel approach provides information on specific frequencies of the image that have been modified. This allows the user to make application-dependent decisions concerning whether an image, which is JPEG compressed for instance, still has credibility. Analysis is provided to evaluate the performance of the technique to varying system parameters. In addition, we compare the performance of the proposed method to existing fragile watermarking techniques to demonstrate the success and potential of the method for practical multimedia tamper proofing and authentication.

We present a novel technique for the digital watermarking of still images based on the concept of multiresolution wavelet fusion. The algorithm is robust to a variety of signal distortions. The original unmarked image is not required for watermark extraction. We provide analysis to describe the behaviour of the method for varying system parameter values. We compare our approach with another transform domain watermarking method. Simulation results show the superior performance of the technique and demonstrate its potential for the robust watermarking of photographic imagery.

In this paper we present a novel fragile watermarking scheme for the tamper-proofing of multimedia signals. Unlike previously proposed techniques, the novel approach provides spatial and frequency domain information on how the signal is modified. We call such a technique a telltale tamper-proofing method. Our design embeds a fragile watermark in the discrete wavelet domain of the signal by quantizing the corresponding coefficients with user-specified keys. Tamper detection is possible in the localized spatial and frequency regions of the given signal. We provide analysis, simulations and comparisons with two other tamper-proofing methods to show the potential of the proposed approach in detecting and characterizing the distortion imposed on the signal.

We present an approach for still image watermarking in which the watermark embedding process employs multiresolution fusion techniques and incorporates a model of the human visual system (HVS). The original unmarked image is required to extract the watermark. Simulation results demonstrate the high robustness of the algorithm to such image degradations as JPEG compression, additive noise and linear filtering.

We propose a novel method for the robust classification of blurred and noisy images that incorporates ideas from data fusion. The technique is applicable to blind situations in which the exact blurring function is unknown. The approach treats differently deblurred versions of the same image as distinct correlated sensor readings of the same scene. The images are fused during the classification process to provide a more reliable result. We show analytically that the various restorations can be treated as images acquired from different but correlated sensor readings. Experimental results demonstrate the potential of the method for robust classification of imagery.

Classical linear image restoration techniques assume that the linear shift invariant blur, also known as the point-spread function (PSF), is known prior to restoration. In many practical situations, however, the PSF is unknown and the problem of image restoration involves the simultaneous identification of the true image and PSF from the degraded observation. Such a process is referred to as blind deconvolution. This paper presents a novel blind deconvolution method for image restoration. The method is flexible for incorporating different constraints on the true image. An example of the method is given for situations in which the imaged scene consists of a finite support object against a uniformly grey background. The only information required are the nonnegativity of the true image and the support size of the original object. For situations in which the exact object support is unknown, a novel support-finding algorithm is proposed.

In this paper, the authors present a convergence analysis for the NAS-RIF (nonnegativity and support constraints recursive inverse filtering) algorithm used in blind image restoration. A novel approach is presented to determine sufficient conditions for the global convergence of the technique. The approach is general to many signal processing algorithms and incorporates Lyapunov's direct method used commonly in nonlinear system analysis. The sufficient conditions for convergence are determined to be in the form of constraints on the blurred image pixels which can be tested for prior to the use of the NAS-RIF algorithm. An apparent trade-off between the quality of the restoration and the uniqueness of the solution is found.