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

TL;DR

This work addresses securing integrated sensing, communication, and computation (ISCC) in low-altitude wireless networks (LAWNs) by explicitly incorporating secrecy constraints into the joint optimization of sensing_beampattern accuracy, secrecy_rate, and data_freshness (AoI). It derives $R_{B_{error}}$, $\gamma_{secure}$, and $AAoI$ and formulates a constrained multi-objective optimization problem over $P_{BS}$, $P_{sens}$, $\mu_{BS}$, and $\mu_{UAV}$ under power and QoS limits. To solve the CMOP, it introduces a Deep Q-Network (DQN) based MOEA that adaptively selects evolutionary operators guided by population convergence, diversity, and feasibility, with four sub-objectives and a custom operator set. Extensive simulations show the proposed approach achieves superior trade-offs among sensing accuracy, secrecy, and information freshness compared with GA and IMODE baselines, under varying antenna counts and noise conditions. These results demonstrate a viable path to secure and reliable LAWNs for applications like urban air mobility, where secrecy, latency, and sensing performance are jointly critical.

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.

Figures (10)

• Figure 1: System model of the security-aware sensing, communication, and computing in low altitude wireless networks. In the considered LAWNs, the BS provides sensing, communication, and computing services to low-altitude applications such as air taxis in a time-division manner. Given the openness of the low-altitude airspace, LAWNs inject noise into the transmitted communication signals to reduce the probability of being detected by eavesdropper, thereby enhancing the network security.
• Figure 2: The computation-oriented average AoI model.
• Figure 3: Diagram of the proposed DRL-based MOEA. The Evolution process uses evolutionary operators chosen by the DQN agent to modify the population. The interaction process shows how the agent controls MOEA evolution and receives rewards that reflect the impact of its actions. The Learning process then uses the resulting state-action-reward data to update the Q-values, improving its decision-making over time. The Decision process uses the current state of the population to inform the DQN's action selection.
• Figure 4: The objective function value under different antenna configurations.
• Figure 5: Performance of sub-objectives under various BS antenna configurations. (a) provides the beam-pattern error of sensing, (b) presents the secrecy rate of communications, (c) shows the average AoI, and (d) illustrated the computing energy consumption of the UAV.