Vision-Language-Action (VLA) models revolutionize robotic systems by enabling end-to-end perception-to-action pipelines that integrate multiple sensory modalities, such as visual signals processed by cameras and auditory signals captured by microphones. This multi-modality integration allows VLA models to interpret complex, real-world environments using diverse sensor data streams. Given the fact that VLA-based systems heavily rely on the sensory input, the security of VLA models against physical-world sensor attacks remains critically underexplored. To address this gap, we present the first systematic study of physical sensor attacks against VLAs, quantifying the influence of sensor attacks and investigating the defenses for VLA models. We introduce a novel ``Real-Sim-Real" framework that automatically simulates physics-based sensor attack vectors, including six attacks targeting cameras and two targeting microphones, and validates them on real robotic systems. Through large-scale evaluations across various VLA architectures and tasks under varying attack parameters, we demonstrate significant vulnerabilities, with susceptibility patterns that reveal critical dependencies on task types and model designs. We further develop an adversarial-training-based defense that enhances VLA robustness against out-of-distribution physical perturbations caused by sensor attacks while preserving model performance. Our findings expose an urgent need for standardized robustness benchmarks and mitigation strategies to secure VLA deployments in safety-critical environments.

This paper analyzes the security of Internet of Things (IoT) devices from the perspective of sensing, actuating, and communicating. Particularly, we discover a vulnerability in power supply modules and propose VoltSiren attacks. To launch a VoltSiren attack, attackers may compromise the power source and inject malicious signals through the power supply module, which is indispensable in most devices. Consequently, VoltSiren attacks can cause sensor measurements irrelevant to reality, maneuver actuators in a way disregarding the desired command, or disrupt communications. To understand VoltSiren, we systematically analyze the underlying principle of power supply signals affecting the electronic components, which are building blocks to constitute the sensors, actuators, or communication modules. Based on these findings, we implement and validate VoltSiren on off-the-shelf products: six sensors, three actuators, and two communication modules, which are used in applications ranging from automobile braking systems, industrial process control to robotic arms. The root cause of this vulnerability lies in the common belief that noises from the power line are unintentional, and our work aims to call for attention to enhancing the security of power supply modules and adding countermeasures to mitigate the attacks.

Multi-band transmission is a promising technical direction for spectrum and capacity expansion of existing optical networks. Due to the increase in the number of usable wavelengths in multi-band optical networks, the complexity of resource allocation problems becomes a major concern. Moreover, the transmission performance, spectrum width, and cost constraint across optical bands may be heterogeneous. Assuming a worst-case transmission margin in U, L, and C-bands, this paper investigates the problem of throughput maximization in multi-band optical networks, including the optimization of route, wavelength, and band assignment. We propose a low-complexity decomposition approach based on Column Generation (CG) to address the scalability issue faced by traditional methodologies. We numerically compare the results obtained by our CG-based approach to an integer linear programming model, confirming the near-optimal network throughput. Our results also demonstrate the scalability of the CG-based approach when the number of wavelengths increases, with the computation time in the magnitude order of 10 s for cases varying from 75 to 1200 wavelength channels per link in a 14-node network. Code of this publication is available at github.com/cchen000/CG-Multi-Band.

Hundreds of hours of audios are recorded and transmitted over the Internet for voice interactions such as virtual calls or speech recognitions. As these recordings are uploaded, embedded biometric information, i.e., voiceprints, is unnecessarily exposed. This paper proposes the first privacy-enhanced microphone module (i.e., MicPro) that can produce anonymous audio recordings with biometric information suppressed while preserving speech quality for human perception or linguistic content for speech recognition. Limited by the hardware capabilities of microphone modules, previous works that modify recording at the software level are inapplicable. To achieve anonymity in this scenario, MicPro transforms formants, which are distinct for each person due to the unique physiological structure of the vocal organs, and formant transformations are done by modifying the linear spectrum frequencies (LSFs) provided by a popular codec (i.e., CELP) in low-latency communications.

Personal video conferencing has become a new norm after COVID-19 caused a seismic shift from in-person meetings and phone calls to video conferencing for daily communications and sensitive business. Video leaks participants’ on-screen information because eyeglasses and other reflective objects unwittingly expose partial screen contents. Using mathematical modeling and human subjects experiments, this research explores the extent to which emerging webcams might leak recognizable textual and graphical information gleaming from eyeglass reflections captured by webcams. The primary goal of our work is to measure, compute, and predict the factors, limits, and thresholds of recognizability as webcam technology evolves in the future. Our work explores and characterizes the viable threat models based on optical attacks using multi-frame super resolution techniques on sequences of video frames. Our models and experimental results in a controlled lab setting show it is possible to reconstruct and recognize with over 75% accuracy on-screen texts that have heights as small as 10 mm with a 720p webcam. We further apply this threat model to web textual contents with varying attacker capabilities to find thresholds at which text becomes recognizable. Our user study with 20 participants suggests present-day 720p webcams are sufficient for adversaries to reconstruct textual content on big-font websites. Our models further show that the evolution towards 4K cameras will tip the threshold of text leakage to reconstruction of most header texts on popular websites. Besides textual targets, a case study on recognizing a closed-world dataset of Alexa top 100 websites with 720p webcams shows a maximum recognition accuracy of 94% with 10 participants even without using machine-learning models. Our research proposes near-term mitigations including a software prototype that users can use to blur the eyeglass areas of their video streams. For possible long-term defenses, we advocate an individual reflection testing procedure to assess threats under various settings, and justify the importance of following the principle of least privilege for privacy-sensitive scenarios.

This paper analyzes the security of Internet of Things (IoT) devices from the perspective of sensing and actuating. Particularly, we discover a vulnerability in power supply modules and propose Volttack attacks. To launch a Volttack attack, attackers may compromise the power source and inject malicious signals through the power supply module, which is indispensable in most devices. Eventually, Volttack attacks may cause the sensor measurement irrelevant to reality or maneuver the actuator in a way disregarding the desired command. To understand Volttack, we systematically analyze the underlying principle of power supply signals affecting the electronic components, which are building blocks to constitute the sensor or actuator modules. Derived from these findings, we implement and validate Volttack on off-the-shelf products: 6 sensors and 3 actuators, which are used in applications ranging from automobile braking systems, industrial process control to robotic arms. The consequences of manipulating the sensor measurement or actuation include doubled car braking distance and a natural gas leak. The root cause of such a vulnerability stems from the common belief that noises from the power line are unintentional, and our work aims to call for attention to enhancing the security of power supply modules and adding countermeasures to mitigate the attacks.

Flexible optical networks (FONs) are being adopted to accommodate the increasingly heterogeneous traffic in today’s Internet. However, in presence of high traffic load, not all offered traffic can be satisfied at all time. As carried traffic load brings revenues to operators, traffic blocking due to limited spectrum resource leads to revenue losses. In this study, given a set of traffic requests to be provisioned, we consider the problem of maximizing operator’s revenue, subject to limited spectrum resource and physical layer impairments (PLIs), namely amplified spontaneous emission noise (ASE), self-channel interference (SCI), cross-channel interference (XCI), and node crosstalk. In FONs, adaptive modulation, multiple FEC, and the tuning of power spectrum density (PSD) can be effectively employed to mitigate the impact of PLIs. Hence, in our study, we propose a universal bandwidth-related impairment evaluation model based on channel bandwidth, which allows a performance analysis for different PSD, FEC and modulations. Leveraging this PLI model and a piecewise linear fitting function, we succeed to formulate the revenue maximization problem as a mixed integer linear program. Then, to solve the problem on larger network instances, a fast two-phase heuristic algorithm is also proposed, which is shown to be near-optimal for revenue maximization. Through simulations, we demonstrate that using adaptive modulation enables to significantly increase revenues in the scenario of high signal-to-noise ratio (SNR), where the revenue can even be doubled for high traffic load, while using multiple FECs is more profitable for scenarios with low SNR.

In presence of a high traffic load, not all offered traffic can be satisfied at all time. Luckily, several kinds of services (e.g. video, file transfer) are elastic because they can be degraded and accepted with a lower bit-rate, corresponding to a lower quality of service (QoS) level. In this paper, we aim at maximizing network revenue leveraging elastic service provisioning. To tackle this problem, we propose an integer linear programming model as well as a decomposition method, which selects a QoS level and provisions a lightpath for elastic request. Meanwhile, in order to exploit the spectrum resources of elastic services under a progressive network load, we propose an auto-degrading provisioning scheme to trigger the network reconfiguration for existing lightpaths in an effort to increase the total network revenue. Simulation results validate the revenue improvement by supporting elastic service provisioning scheme in the scenarios of static and progressive network load.

Nonlinearity is one of the key issues that hinder the development of high-capacity optical short reach systems. This paper proposes three variants of kernel affine projection (KAP) algorithms, all of which combine kernel mapping and affine projection in a reproducing kernel Hilbert space for compensating nonlinear impairments in optical short reach systems. An intensity modulation/direct detection system with a single digital-to-analogue converter, a packaged externally modulated laser and a packaged InP photo-detector is used for experimental demonstration, achieving 238-Gbps (net rate 222-Gbps). Experimental results show that the KAP algorithms can mitigate nonlinear impairments in short-reach communications while maintaining low complexity in reproducing kernel Hilbert space.

Large-scale failures are critical issues for the survivability of elastic optical inter-data center networks (EO-DCNs). Nowadays, cloud services can be served by an alternative data center (DC) with replicated content once the original connection fails. In this paper, we investigate the disaster-resilient cloud services provisioning problem involving content placement, routing, protection of path and content, and spectrum allocation. Both dedicated end-to-content backup path protection (DEBPP) and shared end-to-content backup path protection (SEBPP) are studied. To minimize the spectrum usage, a column generation (CG) based decomposition approach is proposed. Numerical simulations are performed to study the spectrum usage of DEBPP and SEBPP with respect to the traffic amount and the number of DCs and content replicas. Results demonstrate that a reasonable number of DCs with efficient content replicas in network design enables to achieve efficient spectrum usage for disaster-resilient cloud services provisioning.

This paper develops and evaluates a new protection solution for pre-configured-cycle (p-cycle) design in Mixed-Line-Rate (MLR) optical networks. Conventional p-cycle approaches require enumerating candidate cycles in advance and screening p-cycles using heuristic algorithms. Our method generates p-cycles directly in one-step using an Integer Linear Programming (ILP) model. Cost-effective transponders and distance-adaptive line rates are provisioned for every p-cycle to minimize joint cost of transponders and spare capacity. The design problem is solved together with spectral clustering based graph partitioning, which permits to compute the optimal solution in independent sub-graphs concurrently. The results show that our protection method is cost-efficient for p-cycle design with mixed line rates and scalable for large optical networks.

In this paper, we study the problem of data gathering with compressive sensing (CS) in wireless sensor networks (WSNs). Unlike the conventional approaches, which require uniform sampling in the traditional CS theory, we propose a random walk algorithm for data gathering in WSNs. However, such an approach will conform to path constraints in networks and result in the non-uniform selection of measurements. It is still unknown whether such a non-uniform method can be used for CS to recover sparse signals in WSNs. In this paper, from the perspectives of CS theory and graph theory, we provide mathematical foundations to allow random measurements to be collected in a random walk based manner. We find that the random matrix constructed from our random walk algorithm can satisfy the expansion property of expander graphs. The theoretical analysis shows that a k-sparse signal can be recovered using `1 minimization decoding algorithm when it takes m = O(k log(n=k)) independent random walks with the length of each walk t = O(n=k) in a random geometric network with n nodes. We also carry out simulations to demonstrate the effectiveness of the proposed scheme. Simulation results show that our proposed scheme can significantly reduce communication cost compared to the conventional schemes using dense random projections and sparse random projections, indicating that our scheme can be a more practical alternative for data gathering applications in WSNs.

Compressive sensing (CS) provides a new paradigm for efficient data gathering in wireless sensor networks (WSNs). In this paper, with the assumption that sensor data is sparse we apply the theory of CS to data gathering for a WSN where n nodes are randomly deployed. We investigate the fundamental limitation of data gathering with CS for both single-sink and multi-sink random networks under protocol interference model, in terms of capacity and delay. For the single-sink case, we present a simple scheme for data gathering with CS and derive the bounds of the data gathering capacity. We show that the proposed scheme can achieve the capacity Θ(\frac{nW}{M}) and the delay Θ(M\sqrtfrac{nlog n}), where W is the data rate on each link and M is the number of random projections required for reconstructing a snapshot. The results show that the proposed scheme can achieve a capacity gain of Θ (\frac{n}{M}) over the baseline transmission scheme and the delay can also be reduced by a factor of Θ(\fracsqrt{n\log n}{M}). For the multi-sink case, we consider the scenario where n_d sinks are present in the network and each sink collects one random projection from n_s randomly selected source nodes. We construct a simple architecture for multi-session data gathering with CS. We show that the per-session capacity of data gathering with CS is Θ(\frac{n\sqrt{n}W}{M n_d \sqrt{n_s \log n}}) and the per-session delay is Θ(M\sqrtfrac{{n}{log n}}). Finally, we validate our theoretical results for the scaling laws of the capacity in both single-sink and multi-sink networks through simulations.

In this paper, we study data gathering with compressive sensing from the perspective of in-network computation in random networks, in which n nodes are uniformly and independently deployed in a unit square area. We formulate the problem of data gathering to compute multiround random linear function. We study the performance of in-network computation with compressive sensing in terms of energy consumption and latency in centralized and distributed fashions. For the centralized approach, we propose a tree-based protocol for computing multiround random linear function. The complexity of computation shows that the proposed protocol can save energy and reduce latency by a factor of Θ(√n= log n) for data gathering comparing with the traditional approach, respectively. For the distributed approach, we propose a gossip-based approach and study the performance of energy and latency through theoretical analysis. We show that our approach needs fewer transmissions than the scheme using randomized gossip.

Compressive sensing (CS) provides a new paradigm for efficient data gathering in wireless sensor networks (WSNs). The theory of CS allows to reconstruct all sensor data of the network, while only collecting a small number of measurements at a sink. In this paper, we consider a scenario where a sink collects spatially correlated sensor data from n sensor nodes randomly deployed in a region. We investigate the fundamental limitation of data gathering with CS in such a scenario, in terms of capacity and delay. We construct a scheduling and routing scheme based on CS for data gathering in WSNs. We show that the proposed scheme can achieve a per-node transport capacity of Θ(1/ log n) under physical interference model. Furthermore, we also study the delay performance of the proposed scheme and show that the delay for collecting a snapshot with CS is Θ(√n log n). In particular, our results demonstrate that the proposed scheme can achieve a capacity gain of Θ(n/log n) over the case without CS and the delay can also be reduced by a factor of Θ(√n/log n).

Compressed sensing is an emerging theory which provides a new framework for sampling and compressing a sparse signal simultaneously at a reduced sampling rate. Besides this, compressed sensing also provides a new approach for the task of detection. Detection from compressive measurements without reconstructing the signals remains as a challenging problem. In this paper, we investigate the performance of compressive detection and propose a sequential compressive detection scheme to reduce the number of measurements for target detection in wireless sensor networks. We derive the sequential compressive decision rules and analyze its detection performance in terms of the number of measurements. Simulations show that sequential compressive detection can save about 50 percents of the average number of measurements under a given detection performance requirement compared with that of compressive detection.

The integrated computing system optical network has been viewed as a promising platform to support real-time distributed computing applications. For such a system involving so many heterogeneous computing and network resources, faults seem to be inevitable. Therefore, a fault-tolerant scheme is necessary to improve the availability of the integrated system. However, existing joint task scheduling schemes for real-time distributed computing applications generally do not consider the application availability issues when making scheduling decisions. In this paper, we develop an availability-aware joint task scheduling (AAJTS) scheme, by both taking into account availability improvement and timing requirements. The proposed scheme iteratively enhances the application availability under deadline constraint to protect data communication tasks from network link failures. Extensive simulations demonstrate the effectiveness and the feasibility of the proposed scheme.