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

2025-11-03

AI Summary

To address security threats arising from open airspace in Low-Altitude Wireless Networks (LAWNs), which degrade the performance of integrated sensing, communication, and computing (ISCC), this paper proposes the first multi-objective joint optimization framework incorporating beam-pattern error, secrecy rate, and Age of Information (AoI). We innovatively integrate Deep Q-Networks (DQN) with a multi-objective evolutionary algorithm to design an adaptive secure-sensing optimization scheme, enabling concurrent optimization under physical-layer beamforming, secrecy communication, and timeliness constraints. Experimental results demonstrate that the proposed method achieves superior trade-offs among sensing accuracy, communication secrecy, and information freshness—significantly enhancing service quality and security for critical applications such as Urban Air Mobility (UAM). This work establishes a scalable, security-aware optimization paradigm for ISCC-integrated systems in LAWNs.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

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.

Problem

• Optimizing integrated sensing, communication, and computing performance in low-altitude networks
• Addressing security threats from open airspace that degrade system reliability
• Balancing sensing accuracy with communication secrecy and information freshness

Innovation

• Deep Q-network optimizes sensing, communication, and computing
• Evolutionary algorithm adapts to multi-objective optimization goals
• Balances sensing accuracy, secrecy rate, and information freshness