arXiv subjects

Geng Sun

Publications and source records attributed to Geng Sun.

At least 19 recordsLinked to original sources

arXiv2026

RecoverFly: A Failure-Aware Reinforcement Learning Post-Training Framework for Aerial Vision-Language Navigation

Unmanned aerial vehicle vision-language navigation (UAV-VLN) requires agents to translate visual observations and language instructions into reliable flight actions in complex environments. Although recent end-to-end UAV vision-language-action (UAV-VLA) policies reduce reliance on separately designed perception, planning, and control modules, their behavior-cloning objectives provide limited corrective supervision for interactive closed-loop execution. Reinforcement learning (RL) offers a promising solution, while its effectiveness is constrained by inefficient use of samples, long-tailed scene distributions, and policy distribution shift during optimization. To this end, we propose RecoverFly, a failure-aware RL post-training framework for end-to-end UAV-VLA policies. Specifically, RecoverFly adapts token-level RL for stable optimization of grammar-constrained autoregressive UAV actions, revisits unresolved failure cases to strengthen corrective learning and sample utilization, and combines a two-stage long-tail scene curriculum with reference-policy regularization to improve scene adaptation while preserving acquired capabilities. Experiments on the TravelUAV benchmark demonstrate that RecoverFly achieves the best performance on the seen, unseen-map, and unseen-object splits. Moreover, compared to the AerialVLA initialization, RecoverFly improves success rate by 3.12 to 8.37 percentage points under a total rollout budget of about 30\% of the training-set size, validating its effectiveness, robustness, and generalization capabilities.

cs.CV↗

arXiv2026

Toward Intelligent Skies: Signal Processing and AI Foundations of Low-Altitude Wireless Networks

The rapid growth of low-altitude aerial services and applications, driven by uncrewed aerial vehicles (UAVs), calls for a new class of digital infrastructure beyond conventional terrestrial networks. The low-altitude wireless network (LAWN) has been proposed as dynamically reconfigurable three-dimensional architectures that integrate aerial and ground nodes to provide connectivity, sensing, and control in open, safety-critical airspace. This tutorial presents a comprehensive treatment of LAWNs from the joint perspectives of artificial intelligence (AI) and signal processing. We first review the historical evolution and architectural foundations of LAWNs, introducing altitude-based layers and functional planes, and summarizing the regulatory and standardization landscape. Building on this system view, we then discuss signal processing fundamentals for LAWNs, including 3D channel and system models, performance metrics, waveform and receiver design, localization and tracking, and multi-functionality co-design. Next, we survey AI techniques for LAWNs, covering discriminative and generative models for perception, control, resource management, and security, as well as emerging paradigms such as foundation models, large language models, and digital twins for mission planning and closed-loop optimization. To illustrate AI-signal processing integration in practice, we provide a case study of an AI-driven multi-tier LAWN with hybrid satellite, high-altitude, and ground nodes. The tutorial concludes by outlining key research challenges in architecture design, signal processing-AI co-design, safety and security, experimentation, and standardization, and by highlighting opportunities for LAWNs to evolve into dependable, AI-native infrastructure for the intelligent skies.

eess.SP↗

arXiv2026

Evolving Intelligent Complex Systems via Intellicise Networks: Architecture, Technologies, and Pathways

Future engineering infrastructures are evolving into large-scale, open, heterogeneous, and wirelessly interconnected complex systems. These systems present significant challenges in optimizing network resource utilization, managing high-dimensional information spaces, and accommodating diverse business requirements. Intellicise networks, characterized by Intent-driven operation, semantic-native capability, and distributed intelligence, offer a promising paradigm for enabling such intelligent complex systems. We provide a systematic exploration of future intelligent complex systems from the perspective of intellicise networks. Specifically, we propose a cross-domain intelligent communication network architecture based on intellicise networks, grounded in information theory, systems theory, game theory, and cybernetics. The architecture comprises a cross-layer organizational framework, multi-functional planes, and novel information flows. The cross-layer framework defines the vertical evolution from perception and cognition to decision, while the control, user, data, computation, intelligence, and security planes deliver horizontal intellicise capabilities. Moreover, data, knowledge, model, and task flows interconnect the various layers and planes, forming a closed-loop process that derives simplicity from high-level intelligene while concurrently pursuing enhanced. Building on this architecture, we review key enabling technologies, tracing their evolution from semantic extraction to intent understanding, from heterogeneous resource integration to self-configuration and self-optimization, from generative artificial intelligence (AI) to agentic AI, and from embodied AI to symbodied AI. Additionally, we present a case study on intellicise networks for embodied agent communications and discuss representative applications and services for intelligent complex systems.

eess.SP↗

arXiv2026

Towards Semantic-based Agent Communication Networks: Vision, Technologies, and Challenges

The International Telecommunication Union (ITU) identifies "Artificial Intelligence (AI) and Communication" as one of six key usage scenarios for 6G. Agentic AI, characterized by its ca-pabilities in multi-modal environmental sensing, complex task coordination, and continuous self-optimization, is anticipated to drive the evolution toward agent-based communication net-works. Semantic communication (SemCom), in turn, has emerged as a transformative paradigm that offers task-oriented efficiency, enhanced reliability in complex environments, and dynamic adaptation in resource allocation. However, comprehensive reviews that trace their technologi-cal evolution in the contexts of agent communications remain scarce. Addressing this gap, this paper systematically explores the role of semantics in agent communication networks. We first propose a novel architecture for semantic-based agent communication networks, structured into three layers, four entities, and four stages. Three wireless agent network layers define the logical structure and organization of entity interactions: the intention extraction and understanding layer, the semantic encoding and processing layer, and the distributed autonomy and collabora-tion layer. Across these layers, four AI agent entities, namely embodied agents, communication agents, network agents, and application agents, coexist and perform distinct tasks. Furthermore, four operational stages of semantic-enhanced agentic AI systems, namely perception, memory, reasoning, and action, form a cognitive cycle guiding agent behavior. Based on the proposed architecture, we provide a comprehensive review of the state-of-the-art on how semantics en-hance agent communication networks. Finally, we identify key challenges and present potential solutions to offer directional guidance for future research in this emerging field.

eess.SP↗

arXiv2026

Wireless Context Engineering for Efficient Mobile Agentic AI and Edge General Intelligence

Future wireless networks demand increasingly powerful intelligence to support sensing, communication, and autonomous decision-making. While scaling laws suggest improving performance by enlarging model capacity, practical edge deployments are fundamentally constrained by latency, energy, and memory, making unlimited model scaling infeasible. This creates a critical need to maximize the utility of limited inference-time inputs by filtering redundant observations and focusing on high-impact data. In large language models and generative artificial intelligence (AI), context engineering has emerged as a key paradigm to guide inference by selectively structuring and injecting task-relevant information. Inspired by this success, we extend context engineering to wireless systems, providing a systematic way to enhance edge AI performance without increasing model complexity. In dynamic environments, for example, beam prediction can benefit from augmenting instantaneous channel measurements with contextual cues such as user mobility trends or environment-aware propagation priors. We formally introduce wireless context engineering and propose a Wireless Context Communication Framework (WCCF) to adaptively orchestrate wireless context under inference-time constraints. This work provides researchers with a foundational perspective and practical design dimensions to manage the wireless context of wireless edge intelligence. An ISAC-enabled beam prediction case study illustrates the effectiveness of the proposed paradigm under constrained sensing budgets.

eess.SP↗

arXiv2026

Leveraging the Power of Ensemble Learning for Secure Low Altitude Economy

Low Altitude Economy (LAE) holds immense promise for enhancing societal well-being and driving economic growth. However, this burgeoning field is vulnerable to security threats, particularly malicious aircraft intrusion attacks. To address the above concerns, intrusion detection systems (IDS) can be used to defend against malicious aircraft intrusions in LAE. Whereas, due to the heterogeneous data, dynamic environment, and resource-constrained devices within LAE, current IDS face challenges in detection accuracy, adaptability, and resource utilization ratio. In this regard, due to the inherent ability to combine the strengths of multiple models, ensemble learning can realize more robust and diverse anomaly detection further enhance IDS accuracy, thereby improving robustness and efficiency of the secure LAE. Unlike single-model approaches, ensemble learning can leverage the collective knowledge of its constituent models to effectively defend the malicious aircraft intrusion attacks. Specifically, this paper investigates ensemble learning for secure LAE, covering research focuses, solutions, and a case study. We first establish the rationale for ensemble learning and then review research areas and potential solutions, demonstrating the necessities and benefits of applying ensemble learning to secure LAE. Subsequently, we propose a framework of ensemble learning-enabled malicious aircrafts tracking in the secure LAE, where its feasibility and effectiveness are evaluated by the designed case study. Finally, we conclude by outlining promising future research directions for further advancing the ensemble learning-enabled secure LAE.

cs.CR↗

arXiv2026

SIM-assisted Secure Mobile Communications via Enhanced Proximal Policy Optimization Algorithm

With the development of sixth-generation (6G) wireless communication networks, the security challenges are becoming increasingly prominent, especially for mobile users (MUs). As a promising solution, physical layer security (PLS) technology leverages the inherent characteristics of wireless channels to provide security assurance. Particularly, stacked intelligent metasurface (SIM) directly manipulates electromagnetic waves through their multilayer structures, offering significant potential for enhancing PLS performance in an energy efficient manner. Thus, in this work, we investigate an SIM-assisted secure communication system for MUs under the threat of an eavesdropper, addressing practical challenges such as channel uncertainty in mobile environments, multiple MU interference, and residual hardware impairments. Consequently, we formulate a joint power and phase shift optimization problem (JPPSOP), aiming at maximizing the achievable secrecy rate (ASR) of all MUs. Given the non-convexity and dynamic nature of this optimization problem, we propose an enhanced proximal policy optimization algorithm with a bidirectional long short-term memory mechanism, an off-policy data utilization mechanism, and a policy feedback mechanism (PPO-BOP). Through these mechanisms, the proposed algorithm can effectively capture short-term channel fading and long-term MU mobility, improve sample utilization efficiency, and enhance exploration capabilities. Extensive simulation results demonstrate that PPO-BOP significantly outperforms benchmark strategies and other deep reinforcement learning algorithms in terms of ASR.10.1109/TWC.2026.3658332

eess.SP↗

arXiv2026

Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework

The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges due to dynamic three-dimensional mobility patterns, distributed autonomous operations, and severe resource constraints. Traditional intrusion detection systems designed for static ground-based networks prove inadequate for tackling the unique characteristics of aerial IoT environments, including frequent topology changes, real-time detection requirements, and energy limitations. In this article, we analyze the intrusion detection requirements for LAE-IoT networks, complemented by a comprehensive review of evaluation metrics that cover detection effectiveness, response time, and resource consumption. Then, we investigate transformative potential of agentic artificial intelligence (AI) paradigms and introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT networks. This leads to our proposal of a novel multi-agent collaborative intrusion detection framework that leverages specialized LLM-enhanced agents for intelligent data processing and adaptive classification. Through experimental validation, our framework demonstrates superior performance of over 90\% classification accuracy across multiple benchmark datasets. These results highlight the transformative potential of combining agentic AI principles with LLMs for next-generation LAE-IoT security systems.

cs.CR↗

arXiv2026

UAV-Assisted Joint Data Collection and Wireless Power Transfer for Batteryless Sensor Networks

The development of wireless power transfer (WPT) and Internet of Things (IoT) offers significant potential but faces challenges such as limited energy supply, dynamic environmental changes, and unstable transmission links. This paper presents an unmanned aerial vehicle (UAV)-assisted data collection and WPT scheme to support batteryless sensor (BLS) networks in remote areas. In this system, BLSs harvest energy from the UAV and utilize the harvested energy to transmit the collected data back to the UAV. The goal is to maximize the collected data volume and fairness index while minimizing the UAV energy consumption. To achieve these objectives, an optimization problem is formulated to jointly optimize the transmit power and UAV trajectory. Due to the non-convexity and dynamic nature of the problem, a deep reinforcement learning (DRL)-based algorithm is proposed to solve the problem. Specifically, this algorithm integrates prioritized experience replay and the performer module to enhance system stability and accelerate convergence. Simulation results demonstrate that the proposed approach consistently outperforms benchmark schemes in terms of collected data volume, fairness, and UAV energy consumption.

cs.NI↗

arXiv2026

Low-altitude Multi-UAV-assisted Data Collection and Semantic Forwarding for Post-Disaster Relief

The low-altitude economy (LAE) is an emerging economic paradigm which fosters integrated development across multiple fields. As a pivotal component of the LAE, low-altitude uncrewed aerial vehicles (UAVs) can restore communication by serving as aerial relays between the post-disaster areas and remote base stations (BSs). However, conventional approaches face challenges from vulnerable long-distance links between the UAVs and remote BSs, and data bottlenecks arising from massive data volumes and limited onboard UAV resources. In this work, we investigate a low-altitude multi-UAV-assisted data collection and semantic forwarding network, in which multiple UAVs collect data from ground users, form clusters, perform intra-cluster data aggregation with semantic extraction, and then cooperate as virtual antenna array (VAAs) to transmit the extracted semantic information to a remote BS via collaborative beamforming (CB). We formulate a data collection and semantic forwarding multi-objective optimization problem (DCSFMOP) that jointly maximizes both the user and semantic transmission rates while minimizing UAV energy consumption. The formulated DCSFMOP is a mixed-integer nonlinear programming (MINLP) problem that is inherently NP-hard and characterized by dynamically varying decision variable dimensionality. To address these challenges, we propose a large language model-enabled alternating optimization approach (LLM-AOA), which effectively handles the complex search space and variable dimensionality by optimizing different subsets of decision variables through tailored optimization strategies. Simulation results demonstrate that LLM-AOA outperforms AOA by approximately 26.8\% and 22.9\% in transmission rate and semantic rate, respectively.

cs.NI↗

arXiv2026

Low-Altitude Satellite-AAV Collaborative Joint Mobile Edge Computing and Data Collection via Diffusion-based Deep Reinforcement Learning

The integration of satellite and autonomous aerial vehicle (AAV) communications has become essential for the scenarios requiring both wide coverage and rapid deployment, particularly in remote or disaster-stricken areas where the terrestrial infrastructure is unavailable. Furthermore, emerging applications increasingly demand simultaneous mobile edge computing (MEC) and data collection (DC) capabilities within the same aerial network. However, jointly optimizing these operations in heterogeneous satellite-AAV systems presents significant challenges due to limited on-board resources and competing demands under dynamic channel conditions. In this work, we investigate a satellite-AAV-enabled joint MEC-DC system where these platforms collaborate to serve ground devices (GDs). Specifically, we formulate a joint optimization problem to minimize the average MEC end-to-end delay and AAV energy consumption while maximizing the collected data. Since the formulated optimization problem is a non-convex mixed-integer nonlinear programming (MINLP) problem, we propose a Q-weighted variational policy optimization-based joint AAV movement control, GD association, offloading decision, and bandwidth allocation (QAGOB) approach. Specifically, we reformulate the optimization problem as an action space-transformed Markov decision process to adapt the variable action dimensions and hybrid action space. Subsequently, QAGOB leverages the multi-modal generation capacities of diffusion models to optimize policies and can achieve better sample efficiency while controlling the diffusion costs during training. Simulation results show that QAGOB outperforms five other benchmarks, including traditional DRL and diffusion-based DRL algorithms. Furthermore, the MEC-DC joint optimization achieves significant advantages when compared to the separate optimization of MEC and DC.

cs.NI↗

arXiv2025

Hierarchical Online Optimization Approach for IRS-enabled Low-altitude MEC in Vehicular Networks

In this paper, we propose an intelligent reflecting surface (IRS)-enabled low-altitude multi-access edge computing (MEC) architecture, where an aerial MEC server cooperates with a terrestrial MEC server to provide computing services, while hybrid IRSs (i.e., building-installed and UAV-carried IRSs) are deployed to enhance the air-ground connectivity under blockage. Based on this architecture, we formulate a multi-objective optimization problem (MOOP) to minimize the task completion delay and energy consumption by jointly optimizing task offloading, UAV trajectory control, IRS phase-shift configuration, and computation resource allocation. The considered problem is NP-hard, and thus we propose a hierarchical online optimization approach (HOOA) to efficiently solve the problem. Specifically, we reformulate the MOOP as a Stackelberg game, where MEC servers collectively act as the leader to determine the system-level decisions, while the vehicles act as followers to make individual decisions. At the follower level, we present a many-to-one matching mechanism to generate feasible discrete decisions. At the leader level, we propose a generative diffusion model-enhanced twin delayed deep deterministic policy gradient (GDMTD3) algorithm integrated with a Karush-Kuhn-Tucker (KKT)-based method, which is a deep reinforcement learning (DRL)-based approach, to determine the continuous decisions. Simulation results demonstrate that the proposed HOOA achieves significant improvements, which reduces average task completion delay by 2.5% and average energy consumption by 3.1% compared with the best-performing benchmark approach and state-of-the-art DRL algorithm, respectively. Moreover, the proposed HOOA exhibits superior convergence stability while maintaining strong robustness and scalability in dynamic environments.

cs.NI↗

arXiv2025

Encrypted Traffic Detection in Resource Constrained IoT Networks: A Diffusion Model and LLM Integrated Framework

The proliferation of Internet-of-things (IoT) infrastructures and the widespread adoption of traffic encryption present significant challenges, particularly in environments characterized by dynamic traffic patterns, constrained computational capabilities, and strict latency constraints. In this paper, we propose DMLITE, a diffusion model and large language model (LLM) integrated traffic embedding framework for network traffic detection within resource-limited IoT environments. The DMLITE overcomes these challenges through a tri-phase architecture including traffic visual preprocessing, diffusion-based multi-level feature extraction, and LLM-guided feature optimization. Specifically, the framework utilizes self-supervised diffusion models to capture both fine-grained and abstract patterns in encrypted traffic through multi-level feature fusion and contrastive learning with representative sample selection, thus enabling rapid adaptation to new traffic patterns with minimal labeled data. Furthermore, DMLITE incorporates LLMs to dynamically adjust particle swarm optimization parameters for intelligent feature selection by implementing a dual objective function that minimizes both classification error and variance across data distributions. Comprehensive experimental validation on benchmark datasets confirms the effectiveness of DMLITE, achieving classification accuracies of 98.87\%, 92.61\%, and 99.83\% on USTC-TFC, ISCX-VPN, and Edge-IIoTset datasets, respectively. This improves classification accuracy by an average of 3.7\% and reduces training time by an average of 41.9\% compared to the representative deep learning model.

cs.NI↗

arXiv2025

Agentic AI for ISAC: Analysis, Framework, and Case Study

Integrated sensing and communication (ISAC) has emerged as a key development direction in the sixth-generation (6G) era, which provides essential support for the collaborative sensing and communication of future intelligent networks. However, as wireless environments become increasingly dynamic and complex, ISAC systems require more intelligent processing and more autonomous operation to maintain efficiency and adaptability. Meanwhile, agentic artificial intelligence (AI) offers a feasible solution to address these challenges by enabling continuous perception-reasoning-action loops in dynamic environments to support intelligent, autonomous, and efficient operation for ISAC systems. As such, we delve into the application value and prospects of agentic AI in ISAC systems in this work. Firstly, we provide a comprehensive review of agentic AI and ISAC systems to demonstrate their key characteristics. Secondly, we show several common optimization approaches for ISAC systems and highlight the significant advantages of generative artificial intelligence (GenAI)-based agentic AI. Thirdly, we propose a novel agentic ISAC framework and prensent a case study to verify its superiority in optimizing ISAC performance. Finally, we clarify future research directions for agentic AI-based ISAC systems.

cs.AI↗

arXiv2025

Collaborative Charging Optimization for Wireless Rechargeable Sensor Networks via Heterogeneous Mobile Chargers

Despite the rapid proliferation of Internet of Things applications driving widespread wireless sensor network (WSN) deployment, traditional WSNs remain fundamentally constrained by persistent energy limitations that severely restrict network lifetime and operational sustainability. Wireless rechargeable sensor networks (WRSNs) integrated with wireless power transfer (WPT) technology emerge as a transformative paradigm, theoretically enabling unlimited operational lifetime. In this paper, we investigate a heterogeneous mobile charging architecture that strategically combines an automated aerial vehicle (AAV) and a ground smart vehicle (SV) in heterogeneous deployment scenarios to collaboratively exploit the superior mobility of the AAV and extended endurance of the SV for energy distribution. We formulate a multi-objective optimization problem that simultaneously addresses the dynamic balance of heterogeneous charger advantages, charging efficiency versus mobility energy consumption trade-offs, and real-time adaptive coordination under time-varying network conditions. This problem presents significant computational challenges due to its high-dimensional continuous action space, non-convex optimization landscape, and dynamic environmental constraints. To address these challenges, we propose the improved heterogeneous agent trust region policy optimization (IHATRPO) algorithm that integrates a self-attention mechanism for enhanced complex environmental state processing and employs a Beta sampling strategy to achieve unbiased gradient computation in continuous action spaces. Simulation results demonstrate that IHATRPO achieves a 51% performance improvement over the original HATRPO, significantly outperforming state-of-the-art baseline algorithms while substantially decreasing sensor node mortality rate and improving charging system efficiency.

cs.NI↗

arXiv2025

Secure Low-altitude Maritime Communications via Intelligent Jamming

Low-altitude wireless networks (LAWNs) have emerged as a viable solution for maritime communications. In these maritime LAWNs, unmanned aerial vehicles (UAVs) serve as practical low-altitude platforms for wireless communications due to their flexibility and ease of deployment. However, the open and clear UAV communication channels make maritime LAWNs vulnerable to eavesdropping attacks. Existing security approaches often assume eavesdroppers follow predefined trajectories, which fails to capture the dynamic movement patterns of eavesdroppers in realistic maritime environments. To address this challenge, we consider a low-altitude maritime communication system that employs intelligent jamming to counter dynamic eavesdroppers with uncertain positioning to enhance the physical layer security. Since such a system requires balancing the conflicting performance metrics of the secrecy rate and energy consumption of UAVs, we formulate a secure and energy-efficient maritime communication multi-objective optimization problem (SEMCMOP). To solve this dynamic and long-term optimization problem, we first reformulate it as a partially observable Markov decision process (POMDP). We then propose a novel soft actor-critic with conditional variational autoencoder (SAC-CVAE) algorithm, which is a deep reinforcement learning algorithm improved by generative artificial intelligence. Specifically, the SAC-CVAE algorithm employs advantage-conditioned latent representations to disentangle and optimize policies, while enhancing computational efficiency by reducing the state space dimension. Simulation results demonstrate that our proposed intelligent jamming approach achieves secure and energy-efficient maritime communications.

cs.CR↗

arXiv2025

Stackelberg Game-Driven Defense for ISAC Against Channel Attacks in Low-Altitude Networks

The increasing saturation of terrestrial resources has driven economic activities into low-altitude airspace. These activities, such as air taxis, rely on low-altitude wireless networks, and one key enabling technology is integrated sensing and communication (ISAC). However, in low-altitude airspace, ISAC is vulnerable to channel-access attacks, thereby degrading performance and threatening safety. To address this, we propose a defense framework based on a Stackelberg game. Specifically, we first model the system under attack, deriving metrics for the communication and the sensing to quantify performance. Then, we formulate the interaction as a three-player game where a malicious attacker acts as the leader, while the legitimate drone and ground base station act as followers. Using a backward induction algorithm, we obtain the Stackelberg equilibrium, allowing the defenders to dynamically adjust their strategies to mitigate the attack. Simulation results verify that the proposed algorithm converges to a stable solution and outperforms existing baselines, ensuring reliable ISAC performance for critical low-altitude applications.

eess.SP↗

arXiv2025

Security-Aware Joint Sensing, Communication, and Computing Optimization in Low Altitude Wireless Networks

As terrestrial resources become increasingly saturated, the research attention is shifting to the low-altitude airspace, with many emerging applications such as urban air taxis and aerial inspection. Low-Altitude Wireless Networks (LAWNs) are the foundation for these applications, with integrated sensing, communications, and computing (ISCC) being one of the core parts of LAWNs. However, the openness of low-altitude airspace exposes communications to security threats, degrading ISCC performance and ultimately compromising the reliability of applications supported by LAWNs. To address these challenges, this paper studies joint performance optimization of ISCC while considering secrecyness of the communications. Specifically, we derive beampattern error, secrecy rate, and age of information (AoI) as performance metrics for sensing, secrecy communication, and computing. Building on these metrics, we formulate a multi-objective optimization problem that balances sensing and computation performance while keeping the probability of communication being detected below a required threshold. We then propose a deep Q-network (DQN)-based multi-objective evolutionary algorithm, which adaptively selects evolutionary operators according to the evolving optimization objectives, thereby leading to more effective solutions. Extensive simulations show that the proposed method achieves a superior balance among sensing accuracy, communication secrecyness, and information freshness compared with baseline algorithms, thereby safeguarding ISCC performance and LAWN-supported low-altitude applications.

cs.CR↗