Integrated sensing and communication (ISAC)-enabled low-altitude Internet of Things (IoT) networks hold significant potential for applications in smart cities and emergency communication systems. However, achieving secure and energy-efficient communication under complex environments, particularly in the presence of the mobile full-duplex eavesdropper (MFDE), presents significant challenges. This study investigates the optimization of secure rate energy efficiency (SREE) in ISAC-enabled low-altitude IoT networks, where the problem is further complicated by the strong coupling between uncrewed aerial vehicles’ (UAVs) trajectory design, power allocation, and artificial noise (AN) generation, leading to an optimization issue marked by significant dimensionality and a lack of convexity. To tackle this challenge, a power cost factor-based Twin Delayed Deep Deterministic Policy Gradient (CTD3) algorithm is developed, which incorporates a game-theoretic power allocation strategy into the TD3 framework to efficiently handle the high-dimensional coupled optimization problem. The algorithm reformulates part of the high-dimensional continuous optimization process into a strategy interaction problem and introduces a power cost factor into the utility function, effectively reducing the dimensionality of optimization variables and the overall computational burden. Furthermore, an adaptive dynamic sensing mechanism is introduced to enhance resource utilization while effectively countering the dynamic behavior of eavesdroppers. The effectiveness of the proposed strategy in enhancing SREE performance amidst environmental uncertainties is validated through extensive simulations, where it consistently outperforms baseline methods.

To cope with climate change and other environmental problems, countries and regions around the world have begun to pay attention to the development of renewable energy under the drive of achieving the global carbon emission peak and carbon neutrality goal. The distributed photovoltaic (PV) power grid is an effective solution that can utilize solar energy resources to provide clean a energy supply. However, with the continuous grid connection of distributed energy, it poses great challenges to the power supply stability and security of the grid. Therefore, it is particularly important to promote the local consumption of distributed energy and the construction of the energy internet. This paper aims to study the cooperative operation and energy optimization scheduling problem among distributed PV power grids, and proposes a new scheme to reduce the electricity cost under the constraint of power supply and demand balance. The optimization problem is modeled as a Markov decision process (MDP), and a Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is used to solve the MDP problem. Simulation results show that the proposed algorithm outperforms other benchmark algorithms in terms of reducing electricity cost, convergence and stability, and verifies its effectiveness.

Integrated sensing and communication (ISAC) in cloud radio access networks (C-RANs) provides a promising architecture for cooperative sensing and secure transmission in future sixth-generation (6G) networks. However, achieving covert communication in such systems is challenging due to stringent covertness requirements, severe multi-user interference, multi-static sensing constraints, and finite fronthaul capacity. In this paper, we propose a rate-splitting multiple access (RSMA)-enabled integrated direct localization and covert communication framework for fronthaul-constrained C-RAN. In the proposed design, the RSMA common stream acts as an overt public signal, while the private streams convey covert information to legitimate users. A dedicated sensing/artificial-noise beamformer is jointly designed to support multi-static direct localization and shape the received energy profile at the warden. We formulate a sum covert rate maximization problem by jointly optimizing common-rate allocation, BS-stream scheduling, communication precoding, and sensing beamforming under covertness, sensing accuracy, transmit power, and fronthaul constraints. To solve the resulting mixed-integer non-convex problem, we develop an iterative algorithm based on continuous relaxation, semidefinite relaxation, linear matrix inequalities, and successive convex approximation. We further establish the convergence behavior of the proposed algorithm and characterize its computational burden. Numerical results demonstrate that the proposed scheme outperforms SDMA and fixed-scheduling baselines, especially under stringent fronthaul and covertness constraints.