Remote and resource-constrained Internet-of Things (IoT) deployments often lack terrestrial connectivity for task offloading, motivating non-terrestrial networks (NTNs) with onboard multiaccess edge computing (MEC) capabilities. Nevertheless, in the presence of malicious actors, authentication needs to be performed to avoid non-authorized nodes from draining the computing resources of the NTN nodes. As a solution, we propose a four-layer MEC-enabled NTN with unmanned aerial vehicles (UAVs) acting as access nodes, a high altitude platform station (HAPS) acting as coordinator and authenticator, and a constellation of low-Earth orbit satellites (LEOSats) acting as remote MEC servers. We consider a tag-based physical-layer authentication (PLA) scheme to authenticate legitimate users, and formulate a joint task offloading decision and resource allocation for the admitted tasks, which is solved via block coordinate descent. Numerical results show that the PLA scheme is efficient and performs better than the benchmark schemes. We also demonstrate that the proposed scheme is robust against malicious attacks even under relaxed false-alarm constraints.

peer reviewed

In this paper, we address the resource allocation problem for task offloading from Internet of Things (IoT) devices to a non-terrestrial network. The proposed architecture contains clusters of IoT devices that can either execute their computing tasks locally or offload them to a dedicated unmanned aerial vehicle (UAV) functioning as a multi-access edge computing (MEC) server. The UAV can process the tasks itself or further offload them to an available high-altitude platform station (HAPS) or to a low-earth orbit (LEO) satellite within line-of-sight for remote computing. We formulate an optimization problem that aims to minimize the weighted sum of the total task-execution delay and the energy consumption of the IoT devices. Due to non-convexity of the problem and the inherent complexity-performance trade-off in optimization algorithms, we propose a set of low-complexity solutions. These include optimal methods based on convex subproblem decomposition and a greedy heuristic guided by convex optimization criteria. The framework jointly optimizes the computing resources and transmission power of IoT devices, the digital precoders and combiners at the UAV, the computing resources at the remote nodes (UAV, HAPS, and LEO), as well as task offloading decisions and subchannel allocation through a one-shot block coordinate descent approach. Simulation results highlight the performance gains of the proposed methods, demonstrating the impact of algorithmic complexity on key system metrics and the benefits of incorporating multiple non-terrestrial nodes compared to architectures lacking such capabilities.

The employment of unmanned aerial vehicles (UAVs) aligned with multistatic sensing in integrated sensing and communication (ISAC) systems can provide remarkable performance gains in sensing, by taking advantage of the cell-free massive multiple-input multiple-output (mMIMO) architecture. Under these considerations, in this paper, the achievable sensing signal-to-noise-plus-interference ratio (SINR) of a cell-free mMIMO ISAC UAV-based network is evaluated for two different deployments of UAVs, namely, mobile and tethered. In both scenarios, a transmit precoder that jointly optimizes the sensing and communication requirements subjected to power constraints is designed. Specifically, for the scenario with mobile UAVs, beyond the transmit precoding, we also optimize the position of the transmit UAVs through particle swarm optimization (PSO). The results show that, although tethered UAVs have a more efficient power allocation, the proposed position control algorithm for the mobile UAVs can achieve a superior gain in terms of sensing SINR.

This paper proposes an unmanned aerial vehicle (UAV)-based distributed sensing framework that uses orthogonal frequency-division multiplexing (OFDM) waveforms to detect the position of a ground target. The area of interest, where the target is located, is sectioned into a grid of cells, where the radar cross-section (RCS) of every cell is jointly estimated by the UAVs, and a central node acts as a fusion center by receiving all the estimations and performing information-level fusion. A periodogram is employed for local estimation at each UAV, and a digital receive beamformer is assumed. The fused RCS estimates of the grid are used to estimate the cell containing the target. To evaluate the accuracy of the proposed framework, Monte Carlo simulations are carried out to obtain the detection probability, and our results show that the proposed framework attains a notable improved accuracy over a single mono-static UAV benchmark, due to the fusion from multiple sensing UAVs.

In this work, we investigate the impact of two cooperative unmanned aerial vehicle (UAV)-based jammers on the secrecy performance of a ground wireless network in the presence of an eavesdropper. For that purpose, we investigate the secrecy-area related metrics, Jamming Coverage and Jamming Efficiency. Moreover, we propose a hybrid metric, the so-called Weighted Secrecy Coverage (WSC) and a virtual distributed multiple-input-multiple-output (MIMO)-based zero-forcing precoding scheme to avoid the jamming effects on the legitimate receiver. For evaluating these metrics, we derive a closed-form position-based metric, the secrecy improvement. Our mathematical derivations and comparative simulations show that the proposed zero-forcing scheme leads to an improvement on the secrecy performance in terms of the WSC, and provides conditions for improvement of Jamming Efficiency. They also show positioning trends on the UAVs over a fixed orbit around the legitimate transmitter as well as power allocation trends for optimal secrecy.