With the development of surveillance cameras, more bandwidth is required to transmit surveillance videos. Since surveillance videos contain a large amount of redundant information, it causes a waste of bandwidth. Meanwhile, previous video compression methods with the fixed compression standards are unable to handle asymmetric information effectively. To address these problems, we propose Task-driven Semantic Communication with Unsupervised Semantic Segmentation (TSCUSS) for surveillance video assisted by Generative Artificial Intelligence (GAI), to improve efficiency. First, at the transmitter, we segment the videos into the foreground semantic and background models. Second, in the transmission side, we transmit the extracted semantic information in two-stage semantic communication, which greatly reduces redundant information. Third, at the receiver, we merge the foreground and background semantic models through the diffusion model to recover the original semantic content. Finally, our experiment shows that our method not only achieves 78.34% average video compression rate and improves bandwidth utilization, but also dominates in both semantic segmentation accuracy and generative foreground background merge similarity.

Wireless power transfer enhanced mobile edge computing (WPT-MEC) has emerged as a key technology to support low-latency and energy-efficient computation in wireless networks. With increasing network density, multi-base-station architectures emerge where wireless devices (WDs) offload tasks to distributed base stations (BSs), creating challenges in maintaining quality-of-service fairness during complex resource coordination in multi-BS WPT-MEC networks. To address these challenges, we investigate a non-orthogonal multiple access (NOMA)-enhanced WPT-MEC network comprising multiple WDs and BSs with finite computational capacities. For ensuring fairness, we formulate a max-min problem to maximize the minimum task computation amount by jointly optimizing offloading decisions, NOMA decoding orders, offloading powers and time resource allocation, which results in a challenging mixed integer, sequence and nonlinear programming (MISNLP). To tackle this problem, we propose a two-stage distributed multi-agent algorithm. In the first stage, each BS agent generates offloading preferences based on partial observations, guiding WDs' offloading decisions. In the second stage, given these offloading decisions, we develop an efficient convex-based algorithm to solve the per-BS resource allocation subproblem, jointly optimizing NOMA decoding order, offloading powers and time resource allocation. For effective training, we leverage off-policy training and the centralized training with decentralized execution (CTDE) paradigm with two key innovations: (1) a convex-based critic that evaluates the joint action without bias, and (2) a counterfactual baseline that isolates individual agent credit assignment. The proposed C3MA algorithm achieves six times faster convergence and at least 20% performance improvement when serving more than 20 WDs, compared with existing multi-agent schemes, while maintaining a near-optimal Jain's fairness index of 0.97. Moreover, it sustains an ultra-low execution delay below 5 milliseconds even with 40 WDs, confirming its efficiency and scalability.

In this paper, we propose a task-oriented semantic communication system based on the cloud-network-end (C-N-E) architecture to improve the energy efficiency of image transmission. Within the system, a cloud server provides storage and computation resources for image data collected by multiple cameras. The semantic information of an image is modeled as a scene graph, enabling the analysis of end-user interests. To reduce communication overhead, only useful semantic information relevant to user interests is transmitted. Considering the delay constraint, we formulate an optimization problem to minimize the total energy consumption by jointly selecting semantic information and allocating computation and communication resources. To solve this problem efficiently, an iterative algorithm based on optimal matching and sequential convex approximation is developed. Comparative simulations validate the efficacy of our algorithm.

In fully decoupled networks with separate uplink/downlink (UL/DL) base station (BS) deployments and high user mobility, ensuring efficient UL/DL user association remains a critical challenge. The dynamic environment and complex channel conditions necessitate state perception for optimal association strategies, while the densification of nodes demands scalable solutions to handle increased combinatorial complexity in UL and DL transmissions. This paper introduces a novel framework leveraging unmanned aerial vehicle (UAV) sensing-assisted to predict user mobility and channel dynamics, combined with multiple association mechanism to address dense node interactions. Accordingly, a joint optimization problem is formulated, where Kriging-based prediction is adopted to assist the user association for both UL and DL. To solve it, a hierarchical matching game is developed to decompose the joint problem into decoupled UL and DL games. Particularly, a low-complexity Kriging prediction-based hierarchical matching algorithm is designed to obtain the solution. Simulation results in dynamic network scenarios demonstrate that the effectiveness of proposed approach and the superiority is validated by comparisons.

Digital Twin (DT) and mobile edge computing are two promising solutions for achieving latency-sensitive and computing-intensive applications in Industrial Internet of Things (IIoT). However, existing task offloading schemes with DT empowerment are faced with challenges, such as the spatio-temporal heterogeneity of edge server (ES) resources, resource-constrained ESs, and the explosive growth of data in emerging applications. This paper investigates the issues of task offloading and resource allocation under the assistance of DT and multiple ESs parallel collaboration. One novel scheme, abbreviated as Mes-PCORA, is proposed. With comprehensive information within the digital space, the Mes-PCORA scheme dynamically adjusts task allocation ratios across multiple ESs to achieve collaborative task offloading. The offloading request is formulated as a non-convex problem. To make the non-convex problem solvable in polynomial time, the original problem transformed into a bilevel optimization problem. Then, a bilevel iterative optimization approach is proposed. Specifically, the upper-level optimization problem is formulated as a multi-agent Markov Decision Process, and a deep reinforcement learning-based resource allocation algorithm is designed to solve it. Subsequently, for the lower-level optimization problem, it is solved by the interior point method. Numerical results demonstrate that the proposed scheme reduces the average task completion latency by 27.16%–63.44% and decreases the task offloading failure rate by 35.83%–73.95%, compared to state-of-the-art baselines.

Integrated sensing and communication (ISAC)-enabled low-altitude Internet of Things (IoT) networks hold significant potential for applications in smart cities and emergency communication systems. However, achieving secure and energy-efficient communication under complex environments, particularly in the presence of the mobile full-duplex eavesdropper (MFDE), presents significant challenges. This study investigates the optimization of secure rate energy efficiency (SREE) in ISAC-enabled low-altitude IoT networks, where the problem is further complicated by the strong coupling between unmanned aerial vehicles (UAV) trajectory design, power allocation, and artificial noise (AN) generation, leading to an optimization issue marked by significant dimensionality and a lack of convexity. To tackle this challenge, a power cost factor-based Twin Delayed Deep Deterministic Policy Gradient (CTD3) algorithm is developed, which incorporates a game-theoretic power allocation strategy into the TD3 framework to efficiently handle the high-dimensional coupled optimization problem. The algorithm reformulates part of the high-dimensional continuous optimization process into a strategy interaction problem and introduces a power cost factor into the utility function, effectively reducing the dimensionality of optimization variables and the overall computational burden. Furthermore, an adaptive dynamic sensing mechanism is introduced to enhance resource utilization while effectively countering the dynamic behavior of eavesdroppers. The effectiveness of the proposed strategy in enhancing SREE performance amidst environmental uncertainties is validated through extensive simulations, where it consistently outperforms baseline methods.

In this work, we explore the use of active reconfigurable intelligent surfaces (A-RIS) to improve vehicle-to-everything (V2X) communication systems to address limitations in traditional vehicular communication. In particular, we formulate an optimization problem to maximize the uplink sum rate for vehicle-to-infrastructure (V2I) links by optimizing transmit precoders, phase-shift matrices, transmit power, and spectrum sharing for vehicle-to-vehicle (V2V) links. To handle complex hybrid control scenarios, we propose a mixed-action deep reinforcement learning (DRL) algorithm and compare it with conventional benchmark methods like deep deterministic policy gradient (DDPG) with discrete actions (DA) and alternating optimization (AO). We evaluate the proposed algorithm’s effectiveness under imperfect channel state information as well. Simulation results highlight the efficacy of our approach, demonstrating significant enhancement in vehicular communication quality through A-RIS. Furthermore, we illustrate the impact of various factors such as number of A-RIS elements, vehicle speed, loss, execution time, amplification power, and CSI error on the performance of the V2X system.

As an emerging field of communication, Vehicular Social Networks (VSNs) can reduce traffic congestion while enhancing road safety by sharing data among groups of commuters. In VSNs, Vehicular Cloud Server (VCS) based data sharing technology with encrypted primitives allows local users to outsource encrypted data for reducing the storage burden on the user side and sharing data without location restrictions. However, existing data encryption solutions that have been applied in VSNs environments still encounter weaknesses in efficiency, security, or privacy due to the following problems: (1) lack of effective access policies for flexible authorizing ciphertext to multiple data users; (2) data breaches caused by malicious data publishers; (3) necessity in hiding the private information of receivers. To date, no such solution has been available that securely enables one-to-many user authorization with privacy protection, while greatly resisting malicious data publishers. We propose a Sanitizable Access Control System with Policy-protection (SASP) for VSNs in this paper. Our SASP enables a sanitizer to test and sanitize encrypted data to defend against malicious data publishers, ensuring that the plaintext can only be recovered if an authorized user has a valid key. Furthermore, in our SASP system, the access policy is separated into attribute names and attribute values. Wherein, the attribute values contain a lot of private information, which is hidden in the ciphertext to guarantee data users’ privacy. Rigorous security analysis and performance evaluations demonstrate the practicality of SASP for VSNs.

The terahertz (THz) band (0.1-10 THz) is widely considered to be a candidate band for the sixth-generation mobile communication technology (6G). However, due to its short wavelength (less than 1 mm), scattering becomes a particularly significant propagation mechanism. In previous studies, we proposed a scattering model to characterize the scattering in THz bands, which can only reconstruct the scattering in the incidence plane. In this paper, a three-dimensional (3D) stochastic model is proposed to characterize the THz scattering on rough surfaces. Then, we reconstruct the scattering on rough surfaces with different shapes and under different incidence angles utilizing the proposed model. Good agreements can be achieved between the proposed model and full-wave simulation results. This stochastic 3D scattering model can be integrated into the standard channel modeling framework to realize more realistic THz channel data for the evaluation of 6G.

Increasing the energy efficiency of an Internet of Things (IoT) system is a major challenge for its successful implementation. To reduce the computation and storage burden and enhance the efficiency of traditional IoT, an energy-efficient diffusion-based algorithm for state estimation in multiagent networks is proposed in this article. In the proposed algorithm [referred to as the reduced-link diffusion Kalman filter (RL-diffKF)] the nodes (agents) can communicate only with a fraction of their neighbors and each node runs a local Kalman filter to estimate the state of a linear dynamic system. This algorithm results in a significant reduction in communication cost during both adaptation and aggregation processes albeit at the expense of possible degradation in the network performance. To justify the stability and convergence of the RL- diffKF algorithm, an in-depth analysis of the performance is reported. We also consider the problem of optimal selection of combination weights and use the idea of minimum variance estimation to analytically derive the adaptive combiners. The theoretical findings are verified through numerical simulations.

Demand-side management (DSM) involves a group of programs, initiatives, and technologies designed to encourage consumers to modify their level and pattern of electricity usage. This is performed following the methods such as financial incentives and behavioral change through education. While the objective of the DSM is to achieve a balance between energy production and demand, effective and efficient implementation of the program rests within effective use of emerging Internet-of-Things (IoT) concept for online interactions. Here, a novel DSM framework based on the diffusion and alternating direction method of multipliers (ADMM) strategies, repeated under a model predictive control (MPC) protocol, is proposed. On the demand side, the customers autonomously and by cooperation with their immediate neighbors estimate the baseline price in real time. Based on the estimated price signal, the customers schedule their energy consumption using the ADMM cost-sharing strategy to minimize their incommodity level. On the supply side, the utility company determines the price parameters based on the customer's real-time behavior to make a profit and prevent infrastructure overload. The proposed mechanism is capable of tracking drifts in the optimal solution resulting from the changes in supply/demand sides. Moreover, it considers all classes of appliances by formulating the DSM problem as a mixed-integer programming (MIP) problem. Numerical examples are provided to show the effectiveness of the proposed framework.

Machine-to-machine communication with autonomous data acquisition and exchange plays a key role in realizing the “control”-oriented tactile Internet applications such as industrial automation. In this paper, we develop a two-stage access control and resource allocation algorithm. In the first stage, we propose a contract-based incentive mechanism to motivate some delay-tolerant machine-type communication devices to postpone their access demands in exchange for higher access opportunities. In the second stage, a long-term cross-layer online resource allocation approach is proposed based on Lyapunov optimization, which jointly optimizes rate control, power allocation, and channel selection without prior knowledge of channel states. Particularly, the joint power allocation and channel selection problem is formulated as a two-dimensional matching problem, and solved by a pricing-based stable matching approach. Finally, the performance of the proposed algorithm is verified under various simulation scenarios.

This study addresses the tracking performance of the incremental augmented complex least mean square (IAC‐LMS) algorithm, operating in the presence of model non‐stationarities. The authors consider the mean‐square deviation and excess mean square error as performance metrics and use energy conservation argument to derive closed‐form expressions for the mentioned metrics. The expression describes how the IAC‐LMS algorithm performs under such non‐stationary conditions. The authors further find the step size range where the mean‐square stability of the IAC‐LMS algorithm is guaranteed. The authors provide some simulation results to support the theoretical derivations.

Adaptive estimation over distributed networks has received a lot of attention due to its broad range of applications. A useful estimation strategy is diffusion adaptive network, where the parameters of interest can be well estimated from noisy measurements through diffusion cooperation between nodes. The conventional diffusion algorithms exhibit good performance in the presence of Gaussian noise but their performance decreases in presence of impulsive noise. The aim of the present paper is to propose a robust diffusion based algorithm that alleviates the effect of impulsive noise. To this end, we move beyond mean squared error (MSE) criterion and recast the estimation problem in terms of the maximum correntropy criterion (MCC). We use stochastic gradient ascent and useful approximations to derive an adaptive algorithm which is appropriate for distributed implementation. The resultant algorithm has the computational simplicity of the popular LMS algorithm, along with the robustness that is obtained by using higher order moments. We present some simulations results which show that the proposed algorithm outperforms existing alternative that rely MSE criterion.

In this study, the effects of noisy links are investigated on the steady‐state performance of mobile adaptive networks with diffusion least mean‐squares strategies. The authors derive theoretical relations which explain how the steady‐state performance metrics, including the steady‐state network mean‐square deviation and steady‐state velocity mean‐square‐error is affected by noisy links. The provided analysis relies on the spatial–temporal energy conservation argument. The proposed simulation results reveal that although the noisy links degrade the performance of mobile adaptive networks; however, for suitably chosen combination coefficients the mobile adaptive network with noisy links provides a bounded estimation error. Finally, the proposed simulations verify that the derived theoretical analysis closely matches the actual steady‐state performance observed in a network.

In this paper the authors propose an adaptive estimation algorithm for in‐network processing of complex signals over distributed networks. In the proposed algorithm, as the incremental augmented complex least mean square (IAC‐LMS) algorithm, nodes of the network are allowed to collaborate via incremental cooperation mode to exploit the spatial dimension; while at the same time are equipped with LMS learning rules to endow the network with adaptation. The authors have extracted closed‐form expressions that show how IAC‐LMS algorithm performs in the steady‐state. The authors further have derived the required conditions for mean and mean‐square stability of the proposed algorithm. The authors use both synthetic benchmarks and real world non‐circular data to evaluate the performance of the proposed algorithm. Simulation results also reveal that the IAC‐LMS algorithm is able to estimate both second order circular (proper) and non‐circular (improper) signals. Moreover, IAC‐LMS algorithm outperforms the non‐cooperative solution.

The article studies the steady‐state performance of a diffusion least‐mean squares (LMS) adaptive network with imperfect communications where the topology is random (links may fail at random times) and the communication in the channels is corrupted by additive noise. Using the established weighted spatial–temporal energy conservation argument, the authors derive a variance relation which contains moments that represent the effects of noisy links and random topology. The authors evaluate these moments and derive closed‐form expressions for the mean‐square deviation, excess mean‐square error and mean‐square error to explain the steady‐state performance at each individual node. The mean stability analysis is also provided. The derived theoretical expressions have good match with simulation results. Nevertheless, the important result is that the noisy links are the main factor in performance degradation of a diffusion LMS algorithm running in a network with imperfect communications.

The performance of diffusion least-mean square adaptive networks, considerably deteriorates when communication links between nodes are subject to channel noise. One way to mitigate the effect of noisy links is to amplify the transmitted information among the nodes. Although this solution reduces the effect of channel noise, however, the price for this improvement is an increase in power consumption at the nodes. In this paper our goal is to find the minimum amplifying factor at node k such that the steady-state value of mean-square deviation (MSD) at node k becomes smaller than a desired (predefined) value. We derive an expression for the desired amplifying factor at node k and provide the simulation results to clarify the derived theoretical equations.

Analysis of adaptive networks in the simultaneous presence of noise and topology randomness is an important physical layer issue that has not been considered in previous work. Hence, in this paper, we study the steady-state performance of a diffusion least-mean square (LMS) adaptive network where the network topology is random and the communication channel is corrupted by additive noise. We use the weighted spatialtemporal energy conservation approach to derive closed-form expressions for the mean-square deviation (MSD), excess mea-square error (EMSE) and mean-square error (MSE) to explain the steady-state performance at each individual node. Simulations of the derived mathematical equations show that the main factor in performance degradation of the diffusion LMS algorithm is the presence of noise.

In this work, we provide a mathematical framework for estimating the expected value of the carrier to interference ratio and evaluating the probability of error in a wireless ad hoc network. Our approach differs from the earlier studies in that previous valuable work has been mostly based on simulations, on simplified regular patterns for node distribution, has been limited to single service networks, or has not integrated the medium access model in the network. In our analysis, we consider a multiservice interference limited wireless ad hoc network using a multiple access scheme with carrier sensing at the data link layer. The population of nodes in the network is considered as a number governed by a two-dimensional Poisson process. We derive explicit formulas for the carrier to interference ratio and probability of error experienced by nodes in the network. Numerical results are provided for the case of two service classes, where carrier to interference ratios and probabilities of packet loss experienced by nodes in the network are presented as functions of the node densities.