In this paper, we study a system in which a sensor forwards status updates to a receiver through an error-prone channel, while the receiver sends the transmission results back to the sensor via a reliable channel. Both channels are subject to random delays. To evaluate the timeliness of the status information at the receiver, we use the Age of Information (AoI) metric. The objective is to design a sampling policy that minimizes the expected time-average AoI, even when the channel statistics (e.g., delay distributions) are unknown. We first review the threshold structure of the optimal offline policy under known channel statistics and then reformulate the design of the online algorithm as a stochastic approximation problem. We propose a Robbins-Monro algorithm to solve this problem and demonstrate that the optimal threshold can be approximated almost surely. Moreover, we prove that the cumulative AoI regret of the online algorithm increases with rate$\mathcal {O}(\ln K)$, where$K$is the number of successful transmissions. In addition, our algorithm is shown to be minimax order optimal, in the sense that for any online learning algorithm, the cumulative AoI regret up to the$K$-th successful transmissions grows with the rate at least$\Omega (\ln K)$in the worst case delay distribution. Finally, we improve the stability of the proposed online learning algorithm through a momentum-based stochastic gradient descent algorithm. Simulation results validate the performance of our proposed algorithm.

We introduce the hybrid ageing problem in gossip networks, where each node has different ageing processes. This generalization brings two new issues: i) the traditional subset recursion method in [1] fails; ii) the existence of stationary average age penalty needs to be re-examined. To resolve issue i) and ii), we leverage a node-by-node SHSs analysis by introducing splitting Poisson processes to evaluate the average age penalty. We first analyze the hybrid ageing problem in two types of line networks, i.e., one-way and two-way gossip lines. In one-way gossip lines, we derive the closed-form expression of average age penalty in two different cases, where the ageing process of each node can be ordered or disordered (Definition 1). The closedform expressions of average age penalty are also derived in the two-way gossip line. Moreover, we show that the growth rate of age penalty is bounded by the arrival rates between gossip nodes in the one-way line to ensure the existence of average age penalty.

Face recognition service has been widely adopted across various domains, offering significant convenience and enhancing efficiency in numerous applications. However, once a user's facial data is transmitted to a service provider, the user will lose control over his/her biometric data. In recent years, there have been various security and privacy issues due to the leakage of facial data. Although many privacy enhancement methods have been proposed, they usually fail when they are not accessible to adversaries' strategies or the complete face recognition model. Therefore, in this work, we propose a Privacy Minimization Transformation (PMT) method, designed to address two common scenarios in practical face recognition systems: the uploading of facial images and facial features. This method can process the private facial data based on the shallow network of the face recognition model to obtain the obfuscated data. The obfuscated data cannot only maintain satisfactory performance on the authorized models (i.e., the models specified by the user) and restrict the performance on other unauthorized models (i.e., the models not specified by the user) but also prevent privacy data from leaking by AI methods and human visual theft. Additionally, since a service provider may execute preprocessing operations on the received data, we propose an enhanced perturbation method to improve the robustness of PMT. Besides, to authorize one facial image to multiple service models simultaneously, a multiple-restriction mechanism is proposed to improve the scalability of PMT. Finally, we conduct extensive experiments and evaluate the effectiveness of the proposed PMT against face reconstruction, function creep, and face attribute estimation attacks. Experimental results demonstrate that PMT performs well in preventing facial function creep and privacy leakage while maintaining high face recognition accuracy

In the field of gesture recognition, A-mode ultrasound (AUS) and surface electromyography (sEMG) exhibit distinct advantages and limitations. However, they synergistically compensate for each other’s drawbacks, thereby enhancing practical performance. To integrate these modalities effectively, we introduce MSMFNet, a novel multi-scale multi-modal fusion network. This network consists of two key stages: the feature extraction stage and the fusion stage. In the feature extraction stage, we utilize a dual-branch network design that is more suitable for heterogeneous data, allowing effective handling of different types of data and providing a richer feature representation for subsequent steps. Firstly, one branch constructs a multi-scale Conv-Transformer network. Utilizing a combination of convolutional neural networks (CNN) and Transformer, this branch extracts information at various scales from raw sEMG images, comprehensively capturing complex details and providing a global, multi-level feature representation for the task. The second branch utilizes multi-scale mixed convolution for feature extraction from stacked sEMG and AUS images, enabling simultaneous processing of information from two distinct sources. This facilitates the effective fusion of multi-modal information, enhancing the model’s overall understanding of the correlation between different data sources. In the fusion stage, we incorporate an innovative adaptive weight-learning fusion mechanism, departing from conventional fusion approaches. This design is crafted to forestall potential performance deterioration arising from feature conflicts. By dynamically learning weights, we guarantee a precise reflection of the importance of features extracted from diverse branches in the final outcome, thus significantly amplifying the network’s performance. MSMFNet achieves an average accuracy of 93.09% on our mixed dataset, surpassing current state-of-the-art multimodal fusion methods and validating its superiority and robustness.

In this work, we study a system with a sensor forwarding status update to the receiver through an error-prone channel, and the receiver sends the transmission results to the sensor via a reliable link. We assume both transmission links suffer from random delays. We use Age of Information (AoI) to measure the freshness of the status information at the receiver. Our goal is to design a sampling policy that minimizes the expected time average AoI when the channel statistics are unknown. The problem is reformulated into a renewal-reward process optimization, and an online algorithm based on the Robbins-Monro algorithm is proposed. We prove that when the forward and backward transmission delays are bounded, the AoI difference between the online algorithm and the optimal policy decays with rate$\mathcal{O}(\ln K/K)$, where$K$is the number of successful transmissions. Simulation results validate the performance of our proposed algorithm.

We consider the problem of minimizing the age of information when a source can transmit status updates over two heterogeneous channels. Our work is motivated by recent developments in 5 G mmWave technology, where transmissions may occur over an unreliable but fast (e.g., mmWave) channel or a slow reliable (e.g., sub-6 GHz) channel. The unreliable channel is modeled as a time-correlated Gilbert-Elliot channel at a high rate when the channel is in the “ON” state. The reliable channel provides a deterministic but lower data rate. The scheduling strategy determines the channel to be used for transmission in each time slot, aiming to minimize the time-average age of information (AoI). The optimal scheduling problem is formulated as a Markov Decision Process (MDP), which is challenging to solve because super-modularity does not hold in a part of the state space. We address this challenge and show that a multi-dimensional threshold-type scheduling policy is optimal for minimizing the age. By exploiting the structure of the MDP and analyzing the discrete time Markov chains (DTMCs) of the threshold-type policy, we devise a low-complexity bisection algorithm to compute the optimal thresholds. We compare different scheduling policies using numerical simulations.

In this paper, we aim to design an optimal sampler for a system in which fresh samples of a signal (source) are sent through an unreliable channel to a remote estimator, and acknowledgments are sent back over a feedback channel. Both the forward and feedback channels could have random transmission times due to time varying channel conditions. Motivated by distributed sensing, the estimator can estimate the real-time value of the source signal by combining the signal samples received through the channel and the noisy signal observations collected from a local sensor. We prove that the estimation error is a non-decreasing function of the Age of Information (AoI) for the received signal samples and design an optimal sampling strategy that minimizes the long-term average estimation error subject to a sampling rate constraint. The sampling strategy is also optimal for minimizing the long-term average of general non-decreasing functions of the AoI. The optimal sampler design follows a randomized threshold strategy: If the last transmission was successful, the source waits until the expected estimation error upon delivery exceeds a threshold and then sends out a new sample. If the last transmission fails, the source immediately sends out a new sample without waiting. The threshold is the root of a fixed-point equation and can be solved with low complexity (e.g., by bisection search). The optimal sampling strategy holds for general transmission time distributions of the forward and feedback channels. Numerical simulations are provided to compare different sampling policies.

In this paper, we study a sampling problem in which fresh samples of a signal (source) are sent through an unreliable channel to a remote estimator, and acknowledgments are sent back over a feedback channel. Both the forward and feedback channels are subject to random transmission times. Motivated by distributed sensing, the estimator can estimate the real-time value of the source signal by combining the signal samples received through the channel and noisy signal observations collected from a local sensor. We prove that the estimation error is a non-decreasing function of the Age of Information (AoI) for received signal samples and design an optimal sampling strategy that minimizes the long-term average estimation error. The optimal sampler design follows a threshold strategy: If the last transmission was successful, the source waits until the expected estimation error upon delivery exceeds a threshold and then sends out a new sample. If the last transmission fails, the source immediately sends out a new sample without waiting. The threshold is the unique root of a fixed-point equation and can be solved with low complexity (e.g., by bisection search). In addition, the proposed sampling strategy is also optimal for minimizing the long-term average of general non-decreasing functions of the AoI. Its optimality holds for general transmission time distributions of the forward and feedback channels.

In this paper, we study the problem of minimizing the age of information when a source can transmit status updates over two heterogeneous channels. Our work is motivated by recent developments in 5G mmWave technology, where transmissions may occur over an unreliable but fast (e.g., mmWave) channel or a slow reliable (e.g., sub-6GHz) channel. The unreliable channel is modeled as a time-correlated Gilbert-Elliot channel, where information can be transmitted at a high rate when the channel is in the "ON" state. The reliable channel provides a deterministic but lower data rate. The scheduling strategy determines the channel to be used for transmission with the aim to minimize the time-average age of information (AoI). The optimal scheduling problem is formulated as a Markov Decision Process (MDP), which in our setting poses some significant challenges because e.g., supermodularity does not hold for part of the state space. We show that there exists a multi-dimensional threshold-based scheduling policy that is optimal for minimizing the age. A low-complexity bisection algorithm is further devised to compute the optimal thresholds. Numerical simulations are provided to compare different scheduling policies.

Age of information, as a metric measuring the data freshness, has drawn increasing attention due to its importance in many data update applications. Most existing studies have assumed that there is one single channel in the system. In this work, we are motivated by the plethora of multi-channel systems that are being developed, and investigate the following question: how can one exploit multi-channel resources to improve the age performance? We first derive a policy-independent lower bound of the expected long-term average age in a multi-channel system. The lower bound is jointly characterized by the external arrival process and the channel statistics. Since direct analysis of age in multi-channel systems is very difficult, we focus on the asymptotic regime, when the number of users and number of channels both go to infinity. In the many-channel asymptotic regime, we propose a class of Maximum Weighted Matching policies that converge to the lower bound near exponentially fast. In the many-user asymptotic regime, we design a class of Randomized Maximum Weighted Matching policies that achieve a constant competitive ratio compared to the lower bound. Finally, we use simulations to validate the aforementioned results.

Core percolation is crucial in network controllability and robustness. Prior works are mainly based on single, non-interacting network where core nodes are obtained by a classic Greedy Leaf Removal (GLR) procedure that takes of leaf nodes along with their neighbors iteratively. We take a first look into core percolation in coupled networks with two fully-interdependent networks. To obtain core nodes in both networks, we propose a new algorithm, called Alternating GLR procedure, that recursively switches between networks in carrying out GLR for node removal. We prove that the proposed algorithm can guarantee the uniqueness in the sense that the final remaining nodes and edges in either of the coupled networks remain the same, and then present analytical solutions for the fraction of core nodes that will be ultimately left when the algorithm terminates. Our simulation demonstrates that the presence of core exhibits a jump at the critical point as a first order transition in coupled networks.