To simultaneously address the traffic congestion and computational load imbalance among roadside units (RSUs) in vehicular networks caused by uneven vehicles distribution, this paper proposes a Cluster-based Collaborative Route Guidance Strategy (CCRGS) by jointly taking into account the road congestion and RSU load. The overall framework comprises a personalized initial route planning method and a dynamic rerouting strategy. Specifically, a multi-factor road weight model is established by integrating travel time, RSU computational resource utilization, and road popularity. Based on this model, a bi-objective A* algorithm is developed to generate personalized routes that align with user preferences while balancing network load. By integrating a dynamic road weight update mechanism, the algorithm enables implicit inter-vehicle coordination. The density-based spatial clustering of applications with noise (DBSCAN) algorithm is adopted to group vehicles and construct cluster-specific subgraphs, enabling rational, localized decision-making. In addition, a periodic rerouting strategy is designed, combining status monitoring, adaptive vehicle selection, and route updates to timely cope with possible road congestion and RSU overload. Extensive simulations on a real-world urban road network demonstrate that, compared to the shortest path algorithm, CCRGS reduces the average travel time by 14.76%–46.17% and decreases the number of vehicles with unmet computational demand by 67.23%–95.04%, while significantly improves the load balance across both roads and RSUs. These results validate the effectiveness of CCRGS in enhancing traffic efficiency, service continuity, and the overall system stability in vehicular networks.

The rapid development of the Industrial Internet of Things (IIoT) has driven an urgent demand for efficient and reliable communication in complex industrial scenarios. While existing industrial networks suffer from low transmission efficiency and inadequate robustness against dynamic channel conditions in low signal-to-noise ratio (SNR) regimes. To overcome these limitations, we propose the SNR-adaptive deep compressed sensing (SNR-ADCS) network, an industrial semantic communication framework that integrates deep compressed sensing with end-to-end joint source-channel coding. An SNR-adaptive module and deep reconstruction cascade block are developed to dynamically optimize transmission parameters in response to fluctuating channel conditions, thereby enhancing both robustness and semantic efficiency while preserving highfidelity signal reconstruction. Extensive experimental results demonstrate that our SNR-ADCS outperforms baseline models (ADJSCC, DeepJSCC-V, and SwinJSCC w/SA) under challenging low SNR conditions (SNR = 0) and a high compression ratio (CR = 1/12), achieving peak SNR improvements of 7.94%, 5.12%, and 14.51%, respectively

Vehicular edge computing (VEC) server allocates computing resource blocks (CRBs) with different CPU frequencies to process tasks with different low latency requirements. During task processing, some task vehicles (TaVs) and CRBs may finish processing tasks in advance, and their computing resources are idle. However, the existing work has not taken into account reusing these idle computing resources to handle other ongoing tasks, causing low computing resource utilization efficiency. To address this, considering the dynamic nature of the VEC networks, a task re-scheduling problem is formulated to minimize the task completion delay to realize dynamic task offloading and resource allocation (DTORA). To solve the problem, we divide it into a joint task offloading and computing resource allocation problem and a joint subtask re-scheduling and power allocation problem. Dealing with the first problem, a joint task offloading and computing resource allocation (JTOCA) algorithm is proposed. Given the computing resource allocation decision, a deep deterministic policy gradient (DDPG)-based joint subtask re-scheduling and power allocation (DDPG-JSRPA) algorithm is proposed to solve the second problem. Simulation results demonstrated that the proposed DTORA algorithm can reduce the system delay by 28% compared to the full partial offloading (FPO) algorithm.

Asynchronous federated edge learning (Asy-FEEL) has drawn intensive attention due to its ability to effectively address the straggler issue caused by the heterogeneity of the participated mobile devices (MDs). The quality of Asy-FEEL depends highly on the number of participating MDs. Since the local training of federated learning consumes computation resources of MDs, it inevitably reduces the resources that can be devoted to their own tasks (OTs). Therefore, there is always a decreased incentive of MDs to participate in Asy-FEEL, subsequently reducing the amount of Asy-FEEL tasks executed by MDs, thereby failing in achieving satisfied Asy-FEEL performance. How to effectively utilize the limited resources and schedule Asy-FEEL tasks and OTs to satisfy the quality of service requirements on the OTs and at the same time encourage MDs to participate to execute Asy-FEEL tasks is of vital importance. To this end, a joint tasks scheduling and resource allocation problem is formulated and investigated within a dynamic multitasks mobile edge computing (MEC) network, where Asy-FEEL tasks and OTs coexist. Since the problem is a dynamic stochastic optimization problem, a Lyapunov-based dynamic joint tasks scheduling and resources allocation (Lya-DJTR) algorithm is proposed to determine tasks scheduling, computational resource and bandwidth allocation, and transmit power control at MDs simultaneously. Simulation results demonstrate the superiority of the proposed algorithm in improving the efficiency of Asy-FEEL while ensuring the real-time processing of OTs when compared to baseline algorithms.

Federated learning (FL) has emerged as a promising paradigm for enabling distributed model training in vehicular networks while keeping raw data local. However, the dynamic mobility of vehicles and the limited spectrum resources create critical challenges for efficient FL execution. In particular, due to the high mobility of vehicles, the candidate set of participating vehicles keeps changing, which makes fixed-threshold selection strategies difficult to be applied effectively. Moreover, vehicle mobility also causes time-varying channel status, and if resource allocation is performed only once at the beginning of each training round, it might not match the varying channels, resulting in imprecise resource allocation and accordingly low resource utilization. To address these issues, we propose a dynamic vehicular FL framework where the long time duration is discretized into fine-grained time slots. A long time sequence two-timescale optimization problem is then formulated to jointly conduct vehicle selection and slot-level communication bandwidth allocation. To solve it, we design a hierarchical Markov Decision Process (H-MDP) framework, and then develop a hierarchical Proximal Policy Optimization-based vehicle selection and bandwidth allocation (HPPO-VSBAFL) strategy, consisting of two cooperative agents: a vehicle selection agent (VSA) for round-level participant selection, and a bandwidth allocation agent (BAA) for slot-level spectrum allocation. Extensive experimental results based on CIFAR-10 with ResNet-18 demonstrate that the proposed HPPO-VSBAFL framework significantly improves FL accuracy compared to baseline schemes, and can effectively adapt to the highly dynamic vehicular environments.

The integration of integrated sensing and communication (ISAC) with backscatter communication (B-ISAC) presents promising applications in the field of internet of things (IoT), enabling low-power communication and improved system sensing performance. This paper proposes a novel reconfigurable intelligent surface (RIS) enabled B-ISAC system, wherein the RIS facilitates the backscattering of its own information to the base station (BS) and dynamically adjusts signal reflection paths to optimize communication and sensing performance at a low cost. An optimization problem is formulated to maximize the system communication mutual information (CMI) and sensing mutual information (SMI), and a deep unfolding algorithm based on the projected gradient ascent (DUPGA) is proposed. Specifically, inspired by the iterative process of the projected gradient ascent (PGA) algorithm, we design an interpretable neural network to achieve the BS hybrid beamforming and the RIS phase-shift design. Simulation results verify that the proposed DUPGA algorithm offers significant reductions in computational complexity compared to traditional alternating algorithms, while achieving similar CMI and SMI performance.

This paper focuses on the energy-efficient uncrewed aerial vehicles (UAVs) assisted vehicular networks, where multiple rotary-wing UAVs are deployed to provide uplink service to numerous ground vehicles. The objective is to maximize the long-term system energy efficiency (EE) over the entire service period, through jointly optimizing the vehicle-UAV association, the sub-carrier assignment, the power control of vehicles and the trajectory design of UAVs. The formulated problem involves the limitations on the transmit power of vehicles and the propulsion power of UAVs, the quality of service (QoS) requirements of vehicles, the UAV movement constraints, and co-channel interference among vehicles. This makes the problem a mixed integer non-convex fractional programming problem accompanied by a mass of variables and diverse constraints, which is difficult to be solved within the polynomial time through traditional optimization methods. To cope with the timely decision-making requirement and dynamic moving scenario, we opt to the deep reinforcement learning (DRL) approach. To do so, the formulated problem is first transformed to a Markov decision process (MDP). Then, an improved k-means proximal policy optimization (IKPP) algorithm is proposed to solve the MDP problem. The proposed algorithm involves action reconstruction, the improved k-means algorithm, and proximal policy optimization-clip algorithm, which can help obtain the solutions with low complexity. Simulation results demonstrate the convergence, scalability and real-time of the proposed algorithm, along with its performance advantage over other benchmark algorithms.

Integrated sensing and communication (ISAC) improves spectrum utilization by enabling joint sensing and communication functionalities, but its performance in multi-user scenarios is limited by severe path loss and multi-user interference. To address these challenges, this paper proposes an active reconfigurable intelligent surface (RIS)-assisted rate-splitting multiple access (RSMA)-ISAC system, where an active RIS is employed to mitigate reflection-link attenuation and RSMA is adopted at the dual-functional base station to manage multi-user interference. We formulate a coupled non-convex optimization problem to maximize the received-echo signal-to-noise ratio (SNR) by jointly optimizing the BS precoding, active RIS beamforming, and rate-splitting variables under communication quality-of-service and active RIS power constraints. To solve this problem, an alternating optimization algorithm is developed by reformulating the original problem into tractable convex subproblems, thereby reducing redundant iterations and ensuring efficient convergence. Simulation results show that the proposed system improves radar sensing performance while maintaining the userrate requirements, and achieves superior radar SNR, detection probability, system sum rate, and energy efficiency compared with the passive RIS-assisted, spatial division multiple access (SDMA)-assisted, and non-orthogonal multiple access (NOMA)-assisted ISAC benchmarks.

Collaborative machine learning has been a key technology to enhance the development of intelligent transportation systems with the advantage of privacy protection and communication efficiency. However, model training on vehicles requires a large amount of computation resource especially that the machine learning (ML) models are massive nowadays. In this paper, we shed light on the situation that a part of the vehicles may have low computational capability and not able to participate in the training process. To address this problem, we propose a Vehicular Edge Hybrid Federated Split Learning (VEHFSL) paradigm considering the data privacy protection and resource constraints in Internet of Vehicles (IoV). Firstly, we select the vehicles to participate in the training process by sorting the weighted sum of the expected sojourn time and training data volume of each vehicle. Then, we formulate a joint optimization problem to minimize the training latency of one round by jointly optimizing the training mode selection, computation frequency and transmit power allocation. Given that the formulated optimization problem is mixed-integer nonlinear programming (MINLP), which is typically NP-hard, we design a joint mode selection and resource allocation algorithm (JMSRA) by performing block coordinate descent (BCD) technique to decompose it into three sub-problems and iteratively solving them to achieve a near-optimal solution efficiently with the balance of the computational complexity and optimality of the solution. Finally, extensive simulations are performed to validate the superiority of our proposed VEHFSL paradigm in resource-constrained vehicular networks.

Due to the reliance of task processing on specific service models, service caching has emerged as a promising technology for enhancing low-latency performance in vehicular edge computing (VEC) networks. When vehicular long-term service caching mismatches real-time task requirements, the mobility and autonomy of service vehicles enable them to fetch service models from providers actively. However, highly dynamic topology and resource constraints of VEC networks call for a more adaptable approach for jointly designing vehicular service fetching and task offloading. To this end, this paper aims at jointly optimizing active service fetching, task offloading, and communication and computing resource allocation to boost low-latency performance in two-tier caching-assisted VEC networks. In doing so, a total task completion delay minimization problem is formulated, where the social-mobility-aware network topology, vehicle classification, delay tolerance requirements, and resource constraints are taken into account. Since it is a mixed-integer non-linear programming (MINLP) problem, it is typically NP-hard. To solve it effectively, we decompose it into three subproblems, which can be efficiently solved by an iterative algorithm based on block coordinate descent (BCD) with low complexity. Extensive simulation results demonstrate that our proposed algorithm achieves fast convergence and reduces the task completion delay by 9.27%-59.77% compared to five representative baselines.

Semantic communication emerges as a pivotal technology for realizing the 6G vision. However, existing semantic-aware reconstruction systems rely on syntactic-level loss functions for optimization, failing to focus on the precise recovery of semantic information and resulting in suboptimal semantic fidelity. Meanwhile, the autoregressive decoding architecture adopted in text semantic communication introduces prohibitive high latency. To address these two issues, we propose a robust information bottleneck (RIB) guided non-autoregressive semantic communication (NASC) scheme, named RIB-NASC. First, we pioneer the integration of the RIB criterion into semanticaware reconstruction systems, formulating a direct optimization objective tailored for underlying semantic recovery and establishing an informativeness-robustness trade-off. Second, we derive a tractable variational lower bound for the RIB objective via variational approximation and a novel synonymous mapping-based semantic posterior estimation strategy. Third, we design a lightweight non-autoregressive semantic decoder architecture based on Transformer encoder, enabling high-speed parallel semantic decoding during inference. Extensive simulation results demonstrate that the RIB-NASC scheme significantly outperforms baseline schemes in terms of semantic recovery performance (BLEU score and sentence similarity) and achieves a decoding delay reduction of nearly 96.7% compared to traditional autoregressive decoding.

With the explosion of computation-intensive and latency-sensitive vehicular applications, multi-tier edge computing systems consisting of vehicular edge computing (VEC) server, server vehicles (SeVs) and task vehicles (TaVs) have been proposed to provide task offloading services for resource-limited TaVs. Since normally both edge servers (VEC server and SeVs) and TaVs are selfish and care about only their own benefits, reasonable pricing strategies and task offloading decisions should be carefully designed to motivate them to help TaVs. To this end, we propose a multi-leader multi-follower Stackelberg game-based joint task offloading and pricing strategy for multi-tier VEC systems by assuming edge servers (ESs) to be leaders and TaVs to be the followers. With the game, ESs maximize their own utility by optimizing the pricing strategies of their computing resources. According to the computing resources’ prices set by ESs, the TaVs as the followers make task offloading decisions to maximize their individual utility by jointly designing user association, bandwidth purchasing volume and computing resources purchasing volume. In solving the optimization problem, we prove there exists a unique Stackelberg equilibrium (SE) solution. The task offloading problem of the followers is decomposed into a resource purchasing sub-problem and a user association sub-problem, which are first solved by a joint optimization of bandwidth and computing (JOBC) algorithm and a Gale-Shapley-based matching game bilateral selection (GSMB) algorithm, respectively. Then, based on the followers’ task offloading decisions, the pricing problem is solved by a bisection search method. Finally, a joint optimization task offloading and pricing strategy (JOTPS) algorithm is established to obtain the SE. Simulation results show that the proposed scheme can maximize the individual utility of ESs and TaVs compared to those existing benchmark schemes.

Caching 360 degrees videos at the network edge can reduce user content request latency and mitigate transmission congestion in backbone networks. Given the fact that user only views a part of content of 360 degrees scope at any time, caching the entire video is resource inefficient. To address this, we focus a multiaccess edge computing (MEC)-based 360 degrees video service system, where the edge server only caches a portion of each video, that is, most likely falling in the Field of View (FoV) of users. To minimize the average video request latency of all users in the system, we formulate a large-scale {0-1} knapsack problem, which is NP-hard. To tackle it, we proposed a heuristic algorithm where the user viewing patterns extracted from the historical request information are taken into account. Specifically, we first design a cascading cache space allocation method to assign the total cache space of edge server to each segment of videos. After that, the original problem is decomposed into several small-scale yet individual tile caching subproblems with compressed solution space. Then, they are solved by using the dynamic programming algorithm with moderate complexity. To further enhance the caching performance, the PSO-based algorithm is designed to fine tune the parameters involved in the proposed caching algorithm. In addition, we introduce a content-based method to calculate the request probability of the newly generated videos. The effectiveness of the proposed algorithm is evaluated through simulations based on a real world dataset, where the results demonstrate a substantial improvement in both video request latency and cache hit rate compared to the benchmark methods.

Vehicle-to-everything (V2X) underlaid cellular networks in underlaid mode suffer catastrophic co-channel interference caused by spectrum sharing, results in a reduced system sum-rate. To cope with this, this work studies a social-mobility-aware nonorthogonal multiple access (NOMA)-enabled V2X underlaid cellular network to mitigate the co-channel interference and improve the sum rate. By jointly optimizing vehicle pairing and resources, a sum-rate maximization problem is formulated under the diverse quality of service requirements of both cellular and vehicular users. The formulated problem is proved to be a nondeterministic polynomial-time (NP)-hard problem and is difficult to solve. As an alternative, we propose a NOMA-based joint vehicle pairing, spectrum assignment, and power control algorithm (NOMA-JVP-SA-PCA), with which the original problem is decomposed into two disjoint subproblems, i.e., 1) joint vehicle pairing and spectrum assignment subproblem and 2) power control subproblem. Dealing the first subproblem, we propose a heuristic social-mobility-aware vehicle pairing algorithm (HSMA-VPA) and a revised Kuhn-Munkres-based spectrum assignment algorithm (KM-SAA) to acquire the vehicle pairing and spectrum assignment solutions. Then, solving the second subproblem, a closed-form power solution is obtained utilizing a 3-D geometric power control approach (3D-PCA). Finally, we solve the original problem through an iterative method. Simulation results show that the proposed NOMA-JVP-SA-PCA effectively enhances the sum rate and outperforms the baseline algorithms around 24%-53% within a specific range.

Semantic communications have demonstrated significant advantages in image transmission compared to conventional bit-based communication systems. Most existing semantic communication systems for image transmission focus only on the average reconstruction quality rather than the reconstruction quality of individual images. Moreover, these systems require prior knowledge of the signal-to-noise ratio (SNR) at both the transmitter and receiver for SNR-adaption, limiting their practical applicability in dynamic environments. To address the aforementioned problems, this letter proposes a Semantic Pilot Signal enabled Semantic Communication (SPS-SC) framework. In the framework, the semantic pilot signals are dynamically selected by semantic classification of images, enabling the receiver to quantify channel-induced semantic distortion via pilot signal analysis. By combining this distortion assessment with user-defined reconstruction quality requirements, the receiver adaptively determines the optimal compression ratio via codebook lookup, which is then fed back to the transmitter to achieve efficient image transmission. Simulation results demonstrate that the proposed SPS-SC framework is of outstanding effectiveness and robustness under varying channel conditions.

Significance The edge intelligence-enhanced sixth-generation(6G)mobile networks aim to build an integrated architecture that combines sensing,communication,and computation,continuing the trend of 5G's rapid expansion into vertical industries.Looking ahead,Industry 5.0-defined by human-centric design and large-scale personalized customization-requires 6G-enabled industrial networks to simultaneously meet the demands of sensing,transmission,and control.The efficient interconnection,communication,and intelligent management of large-scale Industrial Internet of Things(IIoT)devices remains fundamental to the digital,networked,and intelligent transformation of the manufacturing sector and its high-quality development.However,limited device resources,complex industrial environments,and the fragmented design of sensing,transmission,and control systems present major challenges.These include limited capability for comprehensive and accurate information sensing,inefficient interaction among heterogeneous devices and systems,and difficulties in achieving intelligent closed-loop collaboration across sensing,transmission,and control.Integrating Intelligent Sensing,Transmission,and Control(ISTC)is essential to enabling intelligent communications in industrial scenarios,facilitating the intelligent interconnection of humans,machines,objects,and environments,and enhancing intelligent management and control across production lines. Progress Achieving semantic interoperability across heterogeneous industrial systems is the core barrier to the integrated design of sensing,transmission,and control,and is also critical to enabling agile interaction between diverse systems,reducing subsystem development and deployment costs,and building autonomous,self-managing industrial networks.Modern IIoT systems typically integrate parallel subsystems across Information Technology(IT)and Operational Technology(OT)domains,each with independent data models and semantic specifications,resulting in natural interoperability barriers.These barriers restrict efficient interaction and expected collaborative operation across vendors and platforms,significantly limiting large-scale interconnection and data sharing.Therefore,comprehensive and accurate information sensing,reliable and efficient transmission,and responsive feedback control have become key requirements for future IIoT networks.Specifically:(1)Intelligent Sensing:Overcoming the limitations of the Nyquist sampling theorem through interpretable intelligent sensing is a prerequisite for ISTC.(2)Semantic Transmission:The effective extraction and unified representation of industrial semantics,combined with intelligent semantic-level interaction,are critical to ensuring interoperability in heterogeneous systems while maintaining operational efficiency and sustainable performance.(3)Integrated ISTC:Joint design of edge-intelligent sensing,efficient transmission,and optimal control enables streamlined workflows in industrial scenarios,reducing system response time,improving control accuracy,and optimizing energy efficiency. Conclusions This paper proposes an intelligent collaborative architecture for IIoT networks comprising edge nodes or terminals,intelligent gateways,and industrial cloud platforms.The focus is placed on three key technologies within Integrating ISTC:(1)Intelligent sensing methods for IIoT networks:These methods enhance sensing efficiency and accuracy by applying interpretable,physics-informed deep compressed sensing approaches to IIoT devices and systems.(2)Robust Industrial Semantic Communications(ISC)driven by cognitive intelligence:This technology combines industrial knowledge graphs with semantic communication mechanisms to improve semantic interoperability and transmission efficiency across heterogeneous industrial systems.(3)Joint design of edge-intelligent sensing,efficient semantic transmission,and optimal control:By clarifying the intrinsic coupling among sensing,transmission,and control processes,this approach optimizes the collaborative service capability of heterogeneous industrial networks and systems. Prospects Despite progress,ISTC still faces considerable challenges.Future research may focus on the following directions:(1)Industrial large models and intelligent agents:The development of specialized AI models remains essential,particularly in core industrial domains where implicit knowledge is concentrated.(2)Industry 5.0:Achieving efficient,semantic-level human-machine collaborative interaction will be a key breakthrough for future industrial scenarios.(3)Industrial cross-modal collaborative interaction:Integrating data across modalities and mining knowledge from diverse sources present significant challenges but are essential for enabling advanced collaborative interaction in IIoT networks.(4)Industrial digital twins:For complex industrial environments and physical systems,continued advances in digital twin technology—particularly in high-precision semantic perception,real-time efficient interaction,and adaptive fault-tolerant control,will play a critical role in accelerating ISTC development.

Deep learning methods have shown significant potential in tool wear lifecycle analysis. However, there are fewer open source datasets due to the high cost of data collection and equipment time investment. Existing datasets often fail to capture cutting force changes directly. This paper introduces QIT-CEMC, a comprehensive dataset for the full lifecycle of titanium (Ti6Al4V) tool wear. QIT-CEMC utilizes complex circumferential milling paths and employs a rotary dynamometer to directly measure cutting force and torque, alongside multidimensional data from initial wear to severe wear. The dataset consists of 68 different samples with approximately 5 million rows each, includes vibration, sound, cutting force and torque. Detailed wear pictures and measurement values are also provided. It is a valuable resource for time series prediction, anomaly detection, and tool wear studies. We believe QIT-CEMC will be a crucial resource for smart manufacturing research.

Deep learning (DL)-based semantic communications have gained significant attention for enhancing communication efficiency. However, existing DL-based schemes typically operate with a fixed semantic coding rate (SCR), limiting flexibility across varying channel environments and diverse user demands. To address this limitation, this letter proposes a novel bidimensional joint mask learning enabled multi-rate semantic communication (BiMSC) scheme. Specifically, an effective bidimensional joint mask mechanism is first designed to achieve fine-grained importance distinction among semantic code symbols. Then, a feature-aware network (FANet) is developed to dynamically determine the optimal mask length at different dimensions by jointly considering SCR, channel conditions, and data contents. Through end-to-end learning, arbitrary SCRs with high semantic recovery performance can be achieved. Simulation results demonstrate that the proposed BiMSC scheme exhibits robustness across different channel conditions, and outperforms other multi-rate semantic communication schemes in terms of BLEU score.

Reconfigurable intelligent surface (RIS) is widely recognized as one of the key technologies for 6G due to its ability to enhance communication signal coverage and quality. To fully explore the potential of RIS, its element phase shift must be optimized. Traditional schemes dedicated to the parameter design of RIS, such as finite element methods (FEM) and ray-tracing, are not suitable for handling dynamic scenarios due to their high computational complexity. Therefore, the digital twin has been adopted, but it is still far from working in real time. To address this, we introduce and combine physics informed neural networks (PINN) with digital twin to build digital replicas of RIS in virtual space for dynamic channel conditions. The proposed physics informed digital twin architecture integrates physical and data information, consisting of sensing, modeling, real-time interaction, prediction, and phase shift optimi-zation. In this work, the details on how to integrate PINN with digital twin to model RIS-assisted wireless communications are described, and a use case performance is analyzed. In addition, possible research directions and challenges are discussed. Simulation results show that the proposed scheme greatly reduces the prediction time compared to FEM and achieves better accuracy.

In this paper, we investigate an integrated communication and sensing (ISAC) framework with reconfigurable intelligent surface (RIS), where a base station (BS) simultaneously facilitates the wireless transmission for a single legitimate user and senses the potential threat of the eavesdropper. The purpose of the framework is to maximize the secure data transmission rate through the joint optimization of the BS's beamforming, RIS phase configuration, and radar receive filter. Under the constraints of BS transmit power, radar sensing threshold, and RIS phase shift, a trade-off between communication and sensing is investigated to optimize the security rate performance. We have developed an alternating optimization method that combines the semi-definite relaxation (SDR) technology and successive convex approximation (SCA) approach to solve this non-convex optimization problem. Numerical results validate the effectiveness of the proposed algorithm and demonstrate the achievable performance enhancement through RIS deployment in ISAC systems for security-critical applications.