It is crucial that communication between an unmanned aerial vehicle (UAV) and the ground station (GS) be secure, and both devices should mutually authenticate each other to ensure that an adversary cannot obtain communicated information. Moreover, dynamically adapting the session time of an authenticated session can decrease the idle time of a session and consequently reduce the window of opportunity for an adversary to interfere with the communication link. In light of these considerations, we design aphysically unclonable function (PUF)andfuzzy extractor-based UAV-GS authentication mechanism calledUAV Authentication with Adaptive Session (UAAS). In UAAS, both UAV-GS and UAV-UAV authentication are two-way. We use aThompson Sampling (TS)-based approach to intelligently adapt the duration of a session. Both formal and informal security proofs are presented along with a computation and communication cost analysis to analyze the performance of UAAS. It is noted that UAAS is secure against various well-known attacks and has a lower communication cost than several baseline mechanisms. Due to the noise reduction that occurs in PUFs, the computational cost of UAAS is higher than that of baselines that ignore noise. Also, our simulation shows that UAAS significantly outperforms baselines when it comes to network performance.

Unmanned aerial vehicles (UAVs)-aided mobile-edge computing (MEC) systems face several challenges that hinder their practical implementation. First, the broadcast nature of wireless communications can cause security issues. Second, UAVs have constrained onboard power. Finally, the UAV should be able to serve a maximum number of ground users (GUs). It is also crucial to maintain fairness such that all GUs get equal opportunities to securely offload tasks to UAVs. We seek to address the aforementioned challenges by designing an intelligent mechanism,FairLearn, which maximizes the fairness in secure MEC services by controlling the UAV 3D trajectory, transmission power, and scheduling time for task offloading by mobile GUs. To this end, we formulate a maximization problem and solve it using adeep neural network (DNN)-based model, where the UAVs collaboratively learn the model by utilizing afederated learning (FL)approach. Each UAV uses areinforcement learning (RL)-based approach to individually generate the training dataset, making the training data span different network scenarios. Our model is based on UAV pairs, where one UAV executes the GUs' offloaded tasks, while the other is a jammer that suppresses eavesdroppers. The simulation evaluation of FairLearn shows that it significantly improves the performance of UAV-enabled MEC systems.

Unmanned aerial vehicles (UAVs) are operated remotely without the presence of a unified system of identity authentication, and wireless communications in untrusted environments can cause the loss of valuable data carried by UAVs. Traditional UAV authentication mechanisms are centralized approaches, which suffer from a single point of failure problem and may incur high complexity computations. Therefore, it is crucial to establish a distributed authentication mechanism between the ground station controller (GSC) and a UAV. Moreover, in case of UAV swarms, the high mobility of the UAVs affects the stability of UAV communications, which leads to the degradation of the UAV authentication performance. Addressing these challenges, we design a blockchain-based distributed authentication mechanism, known asSwarmAuth, for UAV swarms, where the GSC and UAVs follow a mutual authentication approach using physical unclonable functions (PUFs), and the K-means clustering-based intelligent approach is used to dynamically create location-based clusters. The blockchain helps store UAVs’ authentication information in an immutable storage and the associated smart contracts provide a convenient access control model. The security analysis of SwarmAuth is carried out through both formal and informal proofs considering general attacks. Experimental evaluation shows that SwarmAuth can assure trustworthy communications and improve the network performance.

Interference management is a key challenge for cellular-connected Internet of Drones (IoD) networks that employ multiple cellular-connected hovering drones for data acquisition in surveillance and monitoring applications. This article proposes a novel resource optimization framework for managing interference in cellular-connected IoD networks. Specifically, the envisioned system divides the set of transmitting drones into distinct drone pairs, where the paired drones simultaneously transmit over the same radio resource blocks (RRBs). Each drone pair is assigned a set of orthogonal RRBs for data transmission, where these RRBs are shared with the terrestrial cellular network as well. An uplink rate-splitting multiple access scheme is employed to mitigate the interdrone interference at the drone pairs, and an RRB pricing method is exploited to control the interference between the aerial and cellular communication links. Our goal is to maximize the uplink capacity of the IoD network while reducing interference over the shared RRBs between the IoD and cellular networks. Toward this goal, a joint optimization of the drones’ transmit power allocation, drone pairing, and RRB scheduling among the drone pairs is presented. In order to obtain an efficient suboptimal solution, an iterative optimization is devised. Particularly, the presented joint optimization problem is decomposed into three subproblems for transmit power allocation, drone pairing and RRB scheduling, and RRB price update. By solving theses subproblems iteratively, a convergent rate-splitting-empowered resource allocation and clustering for interference management (REACT) algorithm is proposed. Extensive simulations are conducted to verify the effectiveness of the proposed REACT algorithm over several benchmark schemes.

This paper investigates the problem of communication relay establishment for multi-ground units using an unmanned aircraft. It is required to drive the aircraft to the optimal position for communication relay without knowledge of ground units' positions. As an alternative to positions information, two measurements are employed for each ground unit, the signal strength and its angle of arrival. Two navigation laws are proposed, the first employs all measured signals whereas the second only employs the two smallest signals. Simulations are carried out to show the effectiveness of the proposed approaches.

In this work, we address the stability of compression systems and the active control of performance limiting phenomena: surge and rotating stall. Despite considerable efforts to stabilize axial compressors at efficient operating points, preventing and suppressing rotating stall and surge are still challenging problems. Due to certain passivity properties of the widely used Moore and Greitzer model for axial compressors, a robust passivity-based control approach is applied here to tackle the problem. The main advantage of this approach is that robust stabilization and high performance control can be achieved by simple control laws and limited control efforts. Analytical developments and time-domain simulations demonstrate that the developed control laws can effectively damp out rotating stall and surge limit cycles by throttle and close-coupled valve actuations. The robust performance of the controller is validated in the presence of bounded mass flow and pressure disturbances, as well as model uncertainties.

Microbial fuel cell (MFC) is a novel bio-renewable energy source, whose maximum produced power is, like most other renewable energy sources (photovoltaic panels, wind turbines, etc.) highly dependent on external disturbances. Hence, an appropriate real-time optimization method should be used so that the MFC always operates at its optimum. Extremum-seeking control (ESC) can be applied to optimize this type of system. However, when the optimal operating point of the MFC varies very fast due to external disturbances, the slow convergence of the ESC induces a lack of precision in the tracking of the optimal power point. In this paper, it is proposed to use adaptive excitation signal amplitude in the ESC scheme to improve the precision of the tracking. The amplitude adaptation is performed using a neural-network (NN) model which estimates in real-time the error between the optimal and the actual point of operation of the MFC based on the external disturbance measurements. The improved ESC performance in terms of convergence speed and precision will be demonstrated at the level of simulation in the case of a comparative study between classic ESC and ESC with adaptive excitation applied to an MFC model subject to variations of a measurable external disturbance, namely, the inlet substrate concentration.

This paper proposes a nonlinear control strategy for a hybrid PV-battery system insuring frequency and voltage support of the power system. The hybrid system includes a PV panel and battery connected to three-phase DC-AC inverter via DC-DC boost converter and bidirectional DC-DC boost converter. A synchronous generator represents the power grid. The voltage regulators control DC-DC boost converter and DC-AC inverter while the frequency regulator controls the bidirectional DC-DC boost converter. A conventional MPPT is used to adjust the reference for nonlinear PV voltage regulator. The voltage regulator is designed based on multi-input multi-output exact feedback linearization technique. It consists of a module that uses the terminal voltage deviation to generate q-axis voltage component. A module that maintains the DC-link voltage is also added to generate d-axis voltage component as well. The proposed frequency regulator includes a module that changes the reference signal of a battery current control module when the frequency deviation is significant. The battery current regulator is designed based on partial input-output feedback linearization strategy. The proposed control system is evaluated in simulation. The results reveal that with the proposed control scheme, the PV-battery generator reacts like a conventional synchronous generator when the grid frequency changes considerably.

Microbial fuel cell (MFC) is a recent technology of producing bioelectricity from wastewater by using bacteria as catalyst in the oxydo-reduction reaction. Because of the biological nature of MFC, the level of delivered electrical power is greatly affected by the operating conditions. In order to extract the maximum produced power, extremum-seeking control (ESC) methods are used to keep MFC working at its optimal operating point. As the ESC scheme is based on a feedback control loop, its performance can be limited in presence of high frequency disturbances. In this paper a method is proposed to improve the performance of the well-known perturbation method in presence of frequent variations of the inlet substrate concentration which is considered here as a disturbance for the MFC.