Low-altitude wireless networks (LAWNs) are expected to play a pivotal role in sixth-generation (6G) systems by enabling flexible, on-demand, and infrastructure-light connectivity for communication, sensing, and security-critical applications. However, the highly dynamic propagation conditions, frequent blockages, and evolving security threats inherent to LAWNs pose fundamental challenges to conventional static network architectures. In this article, we investigate flying reconfigurable intelligent surfaces (FRIS) as a transformative enabler for cell-free integrated sensing and communication (CF-ISAC) in LAWNs. By jointly exploiting aerial mobility and programmable electromagnetic reflections, FRIS introduces a new degree of freedom for three-dimensional and environment-aware control of wireless propagation. We present a comprehensive overview of enabling technologies, highlight physical layer security benefits, and provide a representative case study demonstrating the performance gains and sensing-communication trade-offs of FRIS-assisted CF-ISAC systems. Finally, we discuss key research challenges and future directions toward practical and secure 6G low-altitude deployments.
Shanza Shakoor, Quang Nhat Le, Minh-Hien T. Nguyen et al.· IEEE Communications Magazine· 0 citations
Vehicular crowdsensing (VCS) is a paradigm that exploits vehicle mobility, on-board sensing capabilities, and drivers' smartphone sensors to collect large-scale, distributed information to provide intelligent, location-based services. Satellite-assisted VCS architecture can complement terrestrial networks by enabling wide-area and infrastructure-independent data collection. Incentivizing vehicles to participate in satellite-assisted VCS campaign remains a major challenge due to associated sensing and communication costs, requiring each vehicle to optimize their sensing level to maximize their received reward. Moreover, unlike conventional assumptions where all vehicles participate simultaneously, practical VCS scenarios are asynchronous, as vehicle may start and complete sensing tasks at different times. To capture this realistic setting, we propose an asynchronous multi-agent proximal policy optimization (A-MAPPO) algorithm within a centralized training and decentralized execution (CTDE) framework to optimize the sensing strategies of individual vehicles in a satellite-assisted VCS setting. A dynamic social network effect among vehicles is also incorporated to encourage vehicle participation driven by social benefits. Extensive numerical experiments are conducted to evaluate the performance of the proposed approach, demonstrating that A-MAPPO achieves superior performance compared with MASAC, DQN, Greedy-Q, and Random baselines.
Arbil Chakma, Jingrong Wang, Quang Nhat Le et al.· IEEE Transactions on Network...· 1 citation
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