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The emerging technologies, such as smart sensors, 5G/6G wireless communication, artificial intelligence, etc., have being maturing the future Internet of Things (IoT) by connecting massive number of devices, which are expected to consistently collect and transmit real-time data to support business intelligence in an efficient and privacy-preserving way. The IoT can afford businesses predictive maintenance, improve field service, asset tracking, and further enhance customer satisfaction and facility management in industrial sectors. However, the privacy concern in IoT is a big challenge in IoT applications and services. This work proposed a lightweight privacy-preserving scheme based on homomorphic encryption in the context of the IoT, in which we investigated and analysed the privacy issues between the data owners, untrustworthy third-part cloud servers, and the data users. Meanwhile, computationally-efficient homomorphic algorithms are proposed to guarantee the privacy protection for the data users. Experimental results demonstrates that the proposed scheme can effectively prevent privacy breaches in IoT.

The problem of fault-tolerant adaptive fuzzy tracking control against actuator faults is investigated in this article for a type of uncertain nonaffine fractional-order nonlinear full-state-constrained multi-input-single-output (MISO) system. By means of the existence theorem of the implicit function and the intermediate value theorem, the design difficulty arising from nonaffine nonlinear terms is surmounted. Then, the unknown ideal control inputs are approximated by using some suitable fuzzy-logic systems. An adaptive fuzzy fault-tolerant control (FTC) approach is developed by employing the barrier Lyapunov functions and estimating the compounded disturbances. Moreover, under the drive of the reference signals, a sufficient condition ensuring semiglobal uniform ultimate boundedness is obtained for all the signals in the closed-loop system, and it is proved that all the states of nonaffine nonlinear fractional-order systems are guaranteed to remain inside the predetermined compact set. Finally, two numerical examples are provided to exhibit the validity of the designed adaptive fuzzy FTC approach.

This paper focuses on the design of on-line optimal input sequence for robust active fault diagnosis (AFD) of discrete-time linear parameter varying (LPV) systems using set-theoretic methods. Instead of the traditional set-separation constraint conditions leading to the design of off-line input sequence, the proposed approach focuses on on-line (re)shaping of the input sequence based on the real-time information of the output to discriminate system modes at each time instant such that the conservatism of robust AFD has the potential to be further reduced. The criterion on the design of optimal input is characterized based on a non-convex fractional programming problem at each time instant, which is shown to be efficiently solved within a convex optimization framework. Aside this main contribution, by exploiting Lagrange duality, the optimal input is explicitly obtained by solving a characteristic equation. At the end, a physical circuit model is provided to illustrate the effectiveness of the proposed method.

International audience; This paper presents a computational method to test the stabilizability of discrete-time switched linear systems. The existence of a conic cover of the space on whose elements a convex condition holds is proved to be necessary and sufficient for stabilizability. An algorithm for computing a conic partition that satisfies the new necessary and sufficient condition is given. The algorithm, that allows also to determine bounds on the exponential convergence rate, is proved to overcome the conservatism of conditions equivalent to periodic stabilizability and is applied to a four dimensional system.

Edge computing has emerged as a new paradigm to bring cloud applications closer to users for increased performance. Unlike back-end cloud systems which consolidate their resources in a centralized data center location with virtually unlimited capacity, edge-clouds comprise distributed resources at various computation spots, each with very limited capacity. In this paper, we consider Function-as-a-Service (FaaS) edge-clouds where application providers deploy their latency-critical functions that process user requests with strict response time deadlines. In this setting, we investigate the problem of resource provisioning and allocation. After formulating the optimal solution, we propose resource allocation and provisioning algorithms across the spectrum of fully-centralized to fully-decentralized. We evaluate the performance of these algorithms in terms of their ability to utilize CPU resources and meet request deadlines under various system parameters. Our results indicate that practical decentralized strategies, which require no coordination among computation spots, achieve performance that is close to the optimal fully-centralized strategy with coordination overheads.

International audience; This paper addresses the observability analysis and the optimal design of observation parameters in the presence of noisy measurements and parametric uncertainties. The notion of almost !-observability is introduced and a systematic procedure to assess its satisfaction for a given system with a priori known measurement noise statistics and parameter discrepancy is sketched. Moreover, the concept of observation-target quantities is introduced in order to analyze the precision with which specific chosen expressions of the state and the parameters can be reconstructed. The overall framework is exposed and validated through an illustrative example.

This paper focuses on the three-dimensional guidance strategy for a reusable launching vehicle (RLV) during terminal area energy management (TAEM) phase. Based on sliding mode and shrinking horizon techniques, the proposed scheme consists of trajectory generation and correction mechanisms, which can enhance the guidance precision and robustness against disturbances. The RLV guidance model, in the form of a set of highly non-linear differential equations in time-domain, is recast as an altitude-domain model. By this means, the main characteristics of TAEM gliding motion are extracted. The altitude-domain model is thereby used for trajectory generation. A sliding surface and guidance law are proposed. Hybrid TAEM constraints can be fully satisfied when the proposed guidance law drives the altitude-domain vehicle model to the designated altitude. Using the proposed guidance law as the input of altitude-domain model, a constrained TAEM trajectory is generated, leading to TAEM guidance commands simultaneously. The commands are utilized to drive the time-domain model to the terminal target. In an attempt to compensate for model uncertainties and initial deviations, the guidance commands are modified periodically by shrinking-horizon correction mechanism according to current states. Simulations on different scenarios are provided to demonstrate the performance of the proposed guidance strategy.

The fundamental limits of covert communication, where a message is sent from transmitter Alice to intended recipient Bob without detection by an attentive adversary warden Willie, has been considered extensively in recent years at the physical layer. In this paper, we consider the covert throughput when the adversary is only able to observe the medium access control (MAC) layer in a wireless communication system. In particular, given that the system has a rate of λ packets per slot transmitted over n channels by allowable system users, we study the allowable rate λ_a by covert users while maintaining covertness from an attentive warden observing the channel status in a slotted ALOHA system. We characterize performance for wardens with different abilities to discern the number of packets on a given channel, ranging from simple receivers that detect only whether there was a packet present to complicated receivers that can determine the number of packets involved in any collision, and also consider intended recipients Bob with varying abilities to perform multi-packet reception. In contrast to prior work in covert communications, the application considered motivates the consideration of results for finite (often small) observation vector lengths n at the adversary.

A robust algorithm to solve the low-thrust fuel-optimal trajectory optimization problem for interplanetary spacecraft is developed in this paper. The original nonlinear optimal control problem is convexified and transformed into a parameter optimization problem using an arbitrary-order Gauss-Lobatto discretization scheme with nonlinear control interpolation. A homotopic approach that considers the energy-to-fuel smoothing path is combined with an adaptive second-order trust-region mechanism to increase the convergence rate. The overall robustness is assessed in several fuel-optimal transfers with poor initial guesses. The results show a superior performance in terms of convergence compared to standard convex programming approaches in the literature.

The double-bridge voltage source inverter (DB-VSI) is a promising inverter topology for high performing motor drives. A DB-VSI comprises two VSIs, connected to the opposite sides of an open-end winding motor (no floating neutral point). Thanks to its inherent properties, a DB-VSI requires only half DC supply voltage, compared to a simple VSI, in order to generate the same motor voltage. Thereafter, by processing/switching only half of the DC supply voltage, the DB-VSI benefits from significantly lower semiconductor devices’ switching losses. The DB-VSI technology is the main focus of this paper. Namely, two different DB-VSI variants and/or modulation strategies are comparatively evaluated. After detailing the operating principle of each modulation strategy, the stresses on the inverter components are analytically derived. It is shown that the selection of the DB-VSI modulation strategy impacts the efficiency/power density of the inverter and the voltage quality of the motor. The theoretical considerations are subsequently verified within the context of a high-speed motor drive. In the investigated drive system, a fuel-cell supplies the inverter, which in return controls a 280krpm 1kW electric compressor. Two DB-VSI hardware prototypes are purposely assembled and compared against a third state-of-the-art hardware prototype of the same specifications. It is shown that, thanks to the DB-VSI technology it is possible to reduce simultaneously the volume and the losses by up to 50% compared to the state-of-the-art solution. The low DB-VSI volume enables a seamless integration of the inverter in the motor housing. Accordingly, the open-end winding motor is directly attached to the inverter, eliminating the need for cumbersome and costly interconnecting cables. A final, integrated (inverter/motor) hardware prototype is presented, that further highlights the advantages of the DB-VSI technology.

This paper develops a novel passive stochastic gradient algorithm. In passive stochastic approximation, the stochastic gradient algorithm does not have control over the location where noisy gradients of the cost function are evaluated. Classical passive stochastic gradient algorithms use a kernel that approximates a Dirac delta to weigh the gradients based on how far they are evaluated from the desired point. In this paper we construct a multi-kernel passive stochastic gradient algorithm. The algorithm performs substantially better in high dimensional problems and incorporates variance reduction. We analyze the weak convergence of the multi-kernel algorithm and its rate of convergence. In numerical examples, we study the multi-kernel version of the passive least mean squares (LMS) algorithm for transfer learning to compare the performance with the classical passive version.

We address the problem of determining optimal sensor precisions for estimating the states of linear time-varying discrete-time stochastic dynamical systems, with guaranteed bounds on the estimation errors. This is performed in the Kalman filtering framework, where the sensor precisions are treated as variables. They are determined by solving a constrained convex optimization problem, which guarantees the specified upper bound on the posterior error variance. Optimal sensor precisions are determined by minimizing the l1 norm, which promotes sparseness in the solution and indirectly addresses the sensor selection problem. The theory is applied to realistic flight mechanics and astrodynamics problems to highlight its engineering value. These examples demonstrate the application of the presented theory to a) determine redundant sensing architectures for linear time invariant systems, b) accurately estimate states with low-cost sensors, and c) optimally schedule sensors for linear time-varying systems.

Consensusability of multi-agent systems (MASs) certifies the existence of a distributed controller capable of driving the states of each subsystem to a consensus value. We study the consensusability of linear interconnected MASs (LIMASs) where, as in several real-world applications, subsystems are physically coupled. We show that consensusability is related to the simultaneous stabilizability of multiple LTI systems, and present a novel sufficient condition in form of a linear program for verifying this property. We also derive several necessary and sufficient consensusability conditions for LIMASs in terms of parameters of the subsystem matrices and the eigenvalues of the physical and communication graph Laplacians. The results show that weak physical couplings among subsystems and densely-connected physical and communication graphs are favorable for consensusability. Finally, we validate our results through simulations of networks of supercapacitors and DC microgrids.

This paper introduces a kilowatt-scale large air-gap capacitive wireless power transfer (WPT) system for electric vehicle (EV) charging that achieves high power transfer density and high efficiency. High power transfer density is achieved by operating at a multi-MHz frequency (13.56 MHz), and by utilizing innovatively designed matching networks that enable effective power transfer by providing gain and reactive compensation while absorbing the parasitics present in the EV charging environment. High efficiency is achieved through the use of new interleaved-foil air-core coupled inductors in the matching networks. Interleaved-foil inductors provide a better tradeoff between quality factor, size and self-resonant frequency compared to conventional solenoidal inductors, making them suitable for compactly and efficiently processing kilowatt-scale power at multi-MHz frequencies. Two variants of the interleaved-foil concept are presented: a semi-toroidal interleaved foil (STIF) inductor and a toroidal interleaved-foil (TIF) inductor. The superior performance of interleaved-foil inductors is demonstrated using analytical formulations, which are validated through finite-element analysis and measurements. Compared to the highest-quality-factor solenoidal inductor, the STIF inductor is shown to achieve 32% smaller box volume while having only 3% lower measured quality factor, while the TIF inductor is shown to achieve an even better tradeoff with 27% smaller box volume and 30% higher measured quality factor. A 13.56-MHz, 12-cm air-gap prototype capacitive WPT system utilizing TIF inductors with a quality factor of 2055 in its matching networks is designed, built and tested. This system achieves record-breaking performance for a capacitive EV charging system, with an efficiency of 94.7% while transferring 3.75 kW using 22-cm diameter coupling plates, corresponding to a power transfer density of 49.4 kW/m2. This TIF-inductor based prototype is also shown to outperform a second high-performance prototype utilizing STIF inductors.

Data collection in big data is an effective way to aggregate information that the collector is interested in. However, there is no assurance for the data that the users provide. Since collector does not have the ability to check the authenticity of every piece of information, the trustworthiness of users participated in the collection become important. In this paper, we design an efficient approach to calculate users' trustworthiness in data collection for big data context. We divide the trustworthiness into familiarity trustworthiness and similarity trustworthiness, and study the influences of user actions on trustworthiness. To prevent malicious users from raising their trustworthiness and providing false information that may mislead final results, we also design a security queue to record users' historical trust information, so that we can detect malicious users with high accuracy. Simulation results show that our model can sensitively resist the malicious actions of users.

This letter addresses the problem of steering a robotic vehicle along a geometric path specified with respect to a reference frame moving in three dimensions, termed the Moving Path Following (MPF) motion control problem. The MPF problem is solved for a large class of robotic vehicles that require a minimum positive forward speed to operate. This restriction poses additional challenges with respect to the control design, and is developed using geometric concepts, wherein the attitude control problem is formulated on the Special Orthogonal group SO(3). To this end, a MPF error model formulation is first derived that allows to exclude the conservative constraints on the initial position of the vehicle with respect to the reference path by enabling the explicit control of the progression of a virtual point moving along the reference path. The task of the MPF control law is then to steer the vehicle towards the moving path and to converge to the virtual point. Stability and convergence guarantees of the resulting closed-loop system are provided using the Input-to-State Stability concept. In particular, we show that the proposed controller is robust to imperfect tracking errors and external disturbances (e.g., autopilot errors and wind gusts). Simulation results are presented to illustrate the efficacy of the proposed MPF control law.

In this paper, the physical-layer security (PLS for an underlay cognitive radio network (CRN with multiple non colluding eavesdroppers is studied. We assume that a secondary user (SU transmitter communicates with an SU receiver over a cascaded -fading channel and under the threat of eavesdropping. A cooperative jammer exists to improve the SUs security by harvesting energy from the SU transmitter using power splitting technique. This harvested energy is utilized to generate jamming signals to deteriorate the eavesdroppers reception quality. Moreover, the eavesdroppers are assumed to be randomly distributed according to a homogeneous Poisson point process (HPPP, in which the kth closest eavesdropper to the SU transmitter will be selected in our analysis. Additionally, the legitimate receiver is assumed to harvest energy from the SU transmitter using power splitting technique. In this context, we propose an optimum power splitting factor at the legitimate receiver that achieves the best privacy while constraining the amount of the harvested energy. Furthermore, on enhancing system security, we investigate the effectiveness of the jamming technique. The impact of distances over security is also examined. The results reveal an improvement in privacy as the legitimate receiver utilizes the adaptable power splitting factor. PLS is evaluated in terms of the probability of non-zero secrecy capacity and the intercept probability.

Floods are the most common disaster in the world and cause deaths, damages to houses, buildings, and possessions, as well as disruption to communications. In such dynamic scenarios, it is needed to search and track mobile objects transported by the water flows, such as humans, animals, vehicles, and debris. The use of Unmanned Aerial Vehicles (UAVs) is essential in disaster scenarios to help first responders determine correct procedures in terms of searching, tracking, and rescuing the victims, as well as in defining the actions to minimize the risks in a sustainable and timely manner. However, the tracking of mobile objects and the delivery of real-time video sequences from UAVs with energy constraints toward first responders is still a hard task. Therefore, it is necessary to orchestrate a swarm of UAVs for searching and tracking objects while reacting fast to frequent changes in the topology and trajectory, as well as distributing real-time video flows with quality level support to the ground team. This article proposes an energy-aware swarm-based and mobility prediction scheme for UAVs, called SUAV. SUAV provides a unique UAV-based system for rescue scenarios with mobile objects and with support for route and path planning, searching and tracking procedures, mobility prediction and multi-hop communication management, video delivery with Quality of Service (QoS)/Quality of Experience (QoE) support, and energy-efficiency. Simulation results show the efficiency of SUAV in transmitting real-time video flows with QoS/QoE support while minimizing the energy consumption in flooding scenarios with mobile targets.

This article suggests a model-based controller for an all-wheel drive (AWD) vehicle using a novel vehicle dynamics model. Recently, an active type of AWD system that automatically controls the clutch engagement force of the transfer case has become popular. However, its performance has been limited by its rule-based controller, which necessitates the development of a model-based controller. Although the bicycle model has been most widely adopted for upper level controller of vehicle control systems, it is not appropriate for AWD controllers because the model does not have a direct relationship between the states and control inputs. In this sense, this study adopted a tire force-based full car model for the AWD controller. The clutch engagement force limit, which varies depending on the current engagement state, was calculated systematically by considering changeable dynamic behavior that is inherent in AWD vehicle and was used as a valid value for input limit in linear quadratic regulator (LQR) control algorithm. For the real-time application of the designed controller, only data obtained from the controller area network (CAN) of production vehicles were used. Then, the performance of the proposed controller was validated through vehicle experiments that included scenarios of both longitudinal and lateral movements.

The broad learning system (BLS) is designed based on the technology of compressed sensing and pseudo-inverse theory, and consists of feature nodes and enhancement nodes, has been proposed recently. Compared with the popular deep learning structures, such as deep neural networks, BLS has the ability of rapid incremental learning and can remodel the system without the usual tedious retraining process. However, given that BLS is still in its infancy, it still needs analysis, improvements, and verification. In this article, we first analyze the principle of fast incremental learning ability of BLS in depth. Second, in order to provide an in-depth analysis of the BLS structure, according to the novel structure design concept of deep neural networks, we present four brand-new BLS variant networks and their incremental realizations. Third, based on our analysis of the effect of feature nodes and enhancement nodes, a new BLS structure with a semantic feature extraction layer has been proposed, which is called SFEBLS. The experimental results show that SFEBLS and its variants can increase the accuracy rate on the NORB dataset 6.18%, Fashion-MNIST dataset by 3.15%, ORL data by 5.00%, street view house number dataset by 12.88%, and CIFAR-10 dataset by 18.42%, respectively, and the four brand-new BLS variant networks also obviously outperform the original BLS.

Aircraft electrification is currently the best alternative to address the rising demand for more air transportation and deal with anticipated economic and environmental impacts. Although the all-electric-aircraft (AEA) concept is not yet a feasible solution, the more-electric aircraft (MEA) is gaining significant attention. Electrical systems either partially or entirely replace the large and inefficient hydraulic, pneumatic, and mechanical conventional aircraft actuating systems. The upgrade could also encompass the propulsion system, as in hybrid- and turbo-electric aircraft. This upgrade reduces the aircraft weight, reduces the usage of pollutant fluids, increases fuel efficiency, reduces carbon emissions, and increases aircraft controllability and reliability. This article reviews various application areas of electric machines in electrified aircraft, such as actuation, taxiing, propulsion, and generation. Moreover, it reviews the main types of currently/to be utilized electric machines and the critically required specifications. Finally, a comparison between the different considered machines and potential future research is discussed.

To dynamically monitor areas of interest by a group of mobile robots, an effective coverage control strategy for heterogeneous driftless control affine systems is developed in this letter. The coverage strategy guides the robot to move in the direction of the effective coverage and escape the saddle point when the loss function stops descending. In addition, the connectivity of the underlying communication network of the robots is maintained during coverage control. Simulation validates the effectiveness of our proposed coverage strategy.

This letter explores the use of intelligent reflecting surfaces (IRSs) to address the line-of-sight (LoS) blockage issue in an indoor visible light communication (VLC) system. This is done while considering practical user behaviors such as random receiver orientation and the presence of obstructions in the direct link between the transmitter and the receiver. Specifically, a system model for an IRS-aided VLC system is proposed and a rate maximization problem is considered to determine the optimal orientation of the IRS mirror array to establish robust non-LoS links. A low-complexity iterative solution based on the sine-cosine algorithm is proposed for this non-convex optimization problem. Simulation results are used to verify the effectiveness of the proposed IRS-aided VLC system design and optimization algorithm in overcoming the LoS blockage issue.

Mobile edge computing has been an important supplement for traditional cloud computing architecture to offer low-delay computing services to mobile users. However, it is in general impossible for edge service providers to overdeploy so much edge resources to satisfy the rapidly increasing while diverse user demands. In this paper, we study a mobile edge computing system consisting of a service platform, cloudlets joining the system, and mobile users. In this study, we focus on a profit-driven perspective such that the service platform purchases computation resource from the resource-rich cloudlets and makes profit by processing tasks from user side. The design objective is to maximize the platform profit subject to budget constraint and stringent delay requirements for task processing. We formulate this problem as a mixed integer programming problem. Due to the NP-hardness of the problem, we design a logic based Benders decomposition algorithm as the offline solution. We further study the scenario where the task arrivals from user side and resource availability at the cloudlets are both stochastic and unknown in advance. We accordingly propose a Multi-Armed Bandit learning based resource purchasing and greedy task scheduling algorithm for the online scenario. Simulations results show the high performance of our proposed algorithms.

IEEE 802.11p standard defines wireless technology protocols that enable vehicular transportation and manage traffic efficiency. A major challenge in the development of this technology is ensuring communication reliability in highly dynamic vehicular environments, where the wireless communication channels are doubly selective, thus making channel estimation and tracking a relevant problem to investigate. In this paper, a novel deep learning (DL)-based weighted interpolation estimator is proposed to accurately estimate vehicular channels especially in high mobility scenarios. The proposed estimator is based on modifying the pilot allocation of the IEEE 802.11p standard so that more transmission data rates are achieved. Extensive numerical experiments demonstrate that the developed estimator significantly outperforms the recently proposed DL-based frame-by-frame estimators in different vehicular scenarios, while substantially reducing the overall computational complexity., 16 pages

The rapid charging of lithium ion battery cells while minimizing degradation is a key challenge in battery management. Offline optimal control frameworks employing physics-based models provide a first-principles approach to this problem that provides critical insights and benchmarks for real-time oriented online optimization algorithms. In this letter, we approach the optimal control problem of minimizing both charging time and degradation of a NMC battery using the single shooting optimization framework, employing a coupled electrochemical-thermal-aging model. The aging mechanism used captures growth of the SEI layer, solvent diffusion. The effectiveness of the single shooting method using a piecewise polynomial control and the low sensitivity of the method to chosen numerical parameters in the multi-objective problem is illustrated. The tradeoff between charging time and degradation is investigated numerically.

In this letter, the attitude reorientation problem for over-actuated rigid spacecraft subject to multiple attitude constrained zones is studied. Considering the pointing direction deviation of sensitive equipments and constraint operating set, a robust potential function for attitude constrained zones is proposed and is further leveraged to design a high-level attitude controller, which achieves asymptotic convergence of attitude reorientation error while avoiding attitude constrained zones. Moreover, to distribute the desired control torque from high-level controller to each actuator dynamically, a finite-time adaptive control allocation is developed, resulting in an improved allocation error convergence rate when compared with existing control allocation methods. Simulation examples involving a rest-to-rest attitude maneuver are given to demonstrate the effectiveness of the proposed overall control strategy.

This paper proposes a method for localization of avalanche victims by multiple UAVs. The method consists of three main parts. First, assuming that the UAVs and the victim are equipped with ARTVA receivers and a transmitter, respectively, we introduce a mathematical model of the receiver, which is used to estimate the position of the victim. Second, we derive a closed-form expression indicating the performance of this estimator. In particular, we show that the victim's observability index is captured by the persistency of excitation of a function of the UAVs trajectories. Third, we design and implement a motion planning algorithm that uses the estimation and the estima-tor's performance function for the (near) real-time generation of trajectories that guarantee feasible, safe, and time-efficient localization of avalanche victims.

This letter considers the rigid body attitude stabilization problem with actuator saturation. This problem has been considered in the literature before, but most approaches achieve stability at the expense of strict constraints on the control gains, thereby limiting small-signal performance. The approach proposed here removes the constraints on the gains of the nominal controller and instead deals with the control constraints through an anti-windup mechanism, effectively allowing for more freedom in small signal behavior whilst preserving stability in the large. Although the rigid body stabilization problem is inherently nonlinear, the spirit of the results developed here resemble constrained-control results for linear systems containing imaginary axis poles, despite some subtle differences which need careful consideration.

Intelligent offloading of computation-intensive tasks to a mobile cloud server provides an effective mean to expand the usability of wireless devices and prolong their battery life, especially for low-cost internet-of-things (IoT) devices. However, realization of this technology in multiple-input multiple-output (MIMO) systems requires sophisticated design of joint computation offloading and other functions such as channel state information (CSI) estimation, beamforming, and resource allocation. In this paper, we study the computation task offloading and resource allocation optimization in MIMO based mobile edge computing systems considering perfect/imperfect-CSI estimation. Our design aims to minimize the maximum weighted energy consumption subject to practical constraints on computing and radio resources and allowable latency. The optimal and low-complexity algorithms are proposed to solve the underlying mixed integer non-linear problems (MINLP). For the perfect-CSI, we employ bisection search to find the optimal solution. The low-complexity algorithms are developed by decomposing the original optimization problem into the offloading optimization (OP) and power allocation (PA) subproblems and solve them iteratively. Moreover, the difference of convex functions (DC) method is employed to deal with non-convex structure of (PA) in the imperfect-CSI scenario. Numerical results confirm the advantages of proposed designs over conventional local computation strategies in energy saving and fairness.

The problem of disseminating continuous data flow from a given source node to all other network nodes is known as the dynamic broadcast problem, which is also a fundamental problem in multi-hop wireless networks (e.g., wireless ad hoc and sensor networks). Due to link unreliability and node mobility, the topology of a network typically changes with time. Little work has been conducted to address the dynamic broadcast problem in time-varying wireless multi-hop networks, especially under the popular Signal-to-Interference-plus-Noise-Ratio (SINR) interference model. In this paper, we study the dynamic broadcast problem in SINR-based time-varying directed acyclic multi-hop wireless networks formed by point-to-point wireless links. We consider the SINR-based link transmission rate model involved in the slot-based link scheduling. We first prove a tight upper bound for the broadcast capacity of the multi-hop wireless networks under study. We then propose an online max-weight Throughput-Optimal Dynamic Broadcast algorithm (TODB) which performs link weight allocation, link scheduling, and data forwarding for each time slot according to the current network connectivity and data reception rates of nodes. We derive the throughput-optimality of the TODB algorithm by proving that the broadcast throughput of TODB can achieve the aforementioned upper bound of the broadcast capacity. We evaluate the performance of TODB in terms of throughput and latency via simulations and the results validate its effectiveness.

This study addresses the experimental validation of a linear time-invariant (LTI) energy-maximizing control strategy for wave energy converters (WECs), applied to a 1/20 scale Wavestar WEC. To fulfill this objective, system identification routines are utilized to compute a mathematical (parametric) model of the input-output dynamics of the device, suitable for control design and implementation. With this parametric model, the so-called LiTe-Con energy-maximizing strategy, recently published in the literature, is designed, synthesized, and tested under irregular wave excitation in the wave basin at Aalborg University. Given that the LiTe-Con requires instantaneous knowledge of the wave excitation effects, estimates are provided by means of an unknown-input Kalman filter, designed in close synergy with the so-called internal model principle. For the experimental assessment, both controller and estimator are directly implemented in a real-time architecture. The performance of the LiTe-Con is evaluated in terms of energy-absorption, showing consistent results with respect to those obtained in numerical simulation, hence validating the LiTe-Con controller in a realistic real-time scenario.

This letter discusses the eco-driving problem, considering both electric and conventional powertrains, and presents a pathway to solving it numerically using an indirect collocation method. Despite the low-order system dynamics, the piecewise fuel/efficiency map, gear shifting, and real-world traffic/road situations bring system discontinuities/switchings and pure state constraints into the problem formulation, which make the problem highly nonlinear and nontrivial to solve. This letter introduces smooth approximations to convert the original problem to an unconstrained (and penalized) smooth boundary-value problem. This approach eliminates the discussion of the switching structure and leads to a lightweight Newton-method-based solution procedure.

Having stepped into the era of information explosion, storing, processing and analyzing the vast data sometimes are quite intractable problems. However, it is impossible for personal computer or devices to tackle with such heavy workloads. Then, companies that provides cloud computational services come into business. Aiming at minimizing the expenditures, the most important part is how many cloud services the customers should reserve in advance because different amounts they consume will yield different expense. The emerging machine learning method provides a powerful tool to address such a prediction problem. In this paper, we propose a hierarchical Pythagorean fuzzy deep neural network to forecast the quantity of requisite cloud services. Besides the employment of fuzzy logic, the neural representation is also utilized as a complementary method. The knowledge acquired from fuzzy and neural perspectives are coalesced as the final transformed data to be put into the learning systems. Based on the anticipation of the deep neural network, the consumers are able to decide the amount of cloud services to purchase. Numerical results based on the real data set from Carnegie Mellon University demonstrate that the proposed model yields the economical predictions and outperforms the prediction by the traditional neural network.

Under ultra-reliable and low-latency communications (URLLC) for modern and future wireless networks, this work studies block-length-error probability (BLE) over an outdated-channel-state-information (oCSI) relay network deploying maximal ratio combining. Impact of finite-blocklength regimes on hybrid-automatic-repeat-request protocols with a finite pilot length is also studied. The network is operating under a generalised-Rician fading environment, which covers both line-of-sight (LoS) and non-LoS. The BLE is computed as a function of a bandwidth, payload, an oCSI coefficient, a diversity order and LoS power. Reliability-latency trade-off and effects of other parameters to URLLC performance are examined and discussed. Impact of system incoherence on URLLC performance will also be studied.

We are seeing a growth in the number of connected vehicles in Vehicular Ad-hoc Networks (VANETs) to achieve the goal of Intelligent Transportation System (ITS). This is leading to a connected vehicular network scenario with vehicles continuously broadcasting data to other vehicles on the road and the roadside network infrastructure. The presence of a large number of communicating vehicles greatly increases the number and types of possible anomalies in the network. Existing works provide solutions addressing specific anomalies in the network only. However, since there can be a multitude of anomalies possible in the network, there is a need for better anomaly detection frameworks that can address this unprecedented scenario. In this paper, we propose an anomaly detection framework for VANETs based on deep neural networks (DNNs) using a sequence reconstruction and thresholding algorithm. In this framework, the DNN architectures are deployed on the roadside units (RSUs) which receive the broadcast vehicular data and run anomaly detection tasks to classify a particular message sequence as anomalous or genuine. Multiple DNN architectures are implemented in this experiment and their performance is compared using key evaluation metrics. Performance comparison of the proposed framework is also drawn against the prior work in this area. Our best performing deep learning-based scheme detects anomalous sequences with an accuracy of 98%, a great improvement over the set benchmark.

Hybrid beamforming is a promising technique to reduce the complexity and cost of massive multiple-input multiple-output (MIMO) systems while providing high data rate. However, the hybrid precoder design is a challenging task requiring channel state information (CSI) feedback and solving a complex optimization problem. This paper proposes a novel RSSI-based unsupervised deep learning method to design the hybrid beamforming in massive MIMO systems. Furthermore, we propose i) a method to design the synchronization signal (SS) in initial access (IA); and ii) a method to design the codebook for the analog precoder. We also evaluate the system performance through a realistic channel model in various scenarios. We show that the proposed method not only greatly increases the spectral efficiency especially in frequency-division duplex (FDD) communication by using partial CSI feedback, but also has near-optimal sum-rate and outperforms other state-of-the-art full-CSI solutions., Submitted to IEEE Transactions on Wireless Communications

Heterogeneous cloud radio access network (H-CRAN) has been deemed as a promising architecture for the fifth generation wireless networks. In H-CRAN, small remote radio heads (SRRHs) are deployed underlaying the macro remote radio head (MRRH) to boost the spectral efficiency. H-CRANs with dense RRHs suffer from severe inter-cell interference due to the random deployment of dense SRRHs, which limits its performance. In this paper, we propose an Edge-Aware RRH-Cooperation (EARC) scheme to mitigate the impact of both the cross-tier and co-tier interference. We partition the users into two groups, Cell Edge Device (CED) and Non-cell Edge Device (NED). These two groups, CED and NED, are mapped into two association modes, dual-association and single-association mode, respectively. In single-association mode, the NED associates with the RRH that provides the maximum received signal. On the other hand, in the dual-association mode, the CED associates with the two strongest RRHs where the two RRHs may belong to the same or different tier(s). Using tools from stochastic geometry, we quantify the performance gains of the proposed EARC scheme in terms of outage probability and ergodic rate. To further show the efficacy of the proposed scheme, we compare it to four schemes developed in the literature.

Autonomous vehicles’ dynamics stability control is one key issue to ensure safety of self-driving. However, vehicle uncertainties and time-varying parameters could weaken the performance of autonomous vehicle stability control. Therefore, this article proposes a novel robust linear matrix inequality (LMI)-based $H$ -infinite feedback algorithm for vehicle dynamics stability control, and this algorithm controls vehicle steering system and brake system via direct yaw moment control (DYC) and active front steering control (AFS). The presented controller is robust against vehicle parametric uncertainties, including the vehicle mass and vehicle longitudinal velocity. A linear parameter varying lateral model is constructed utilizing polytopic uncertainty method considering time-varying vehicle longitudinal velocity and mass, where a polytope that contains finite vertices is established to contain all of the possible selections for uncertainty parameters. Then, the $H$ -infinite feedback controller integrating DYC and AFS is derived via LMI technique. Finally, experimental results based on hardware-in-the-loop (HIL) platform illustrate that the presented controller has better performance of ensuring autonomous vehicle dynamics stability than other controllers.

The sixth-generation (6G) communication requires supporting massive Internet of Things (IoT) devices and extremely differentiated IoT applications for the air–space–ground integrated network. Relying on the aerial superiority, unmanned aerial vehicle (UAV) is capable of acting as an aerial base station (BS) and supporting IoT deployment in remote and disaster areas. A UAV-supported clustered nonorthogonal multiple access (C-NOMA) system is put forward in this article. Specifically, the UAV provides services to IoT terminals as an aerial BS based on the wireless-powered communication (WPC) technique. According to this system, we propose a synergetic scheme for UAV trajectory planning and subslot allocation. Our goal is to maximize the uplink average achievable sum rate of IoT terminals by synergistically planning UAV trajectory and subslot duration, while guaranteeing the uplink achievable sum rate and the UAV mobility constraints. As the formulated problem suffers nonconvexity and complication, an efficient iterative algorithm is proposed to address it. First, for fixed UAV trajectory, all the terminals are clustered and a subslot allocation algorithm based on the Lagrange multiplier and bisection method is proposed. Then, for a fixed clustering state and subslot duration, we optimize the UAV trajectory. Finally, we solve these two subproblems alternatively until the objective function converges. The effectiveness of the proposed scheme in the UAV-supported C-NOMA system is verified by the numerical results.

In this paper, we investigate the detection problem for a massive machine-type communication (mMTC) system that has correlated user activities. Two deep learning assisted algorithms are proposed to exploit the user activity correlation to facilitate channel estimation and user identification. Due to the dependency among user activities, conventional element-wise minimum mean square error (MMSE) denoiser used in the orthogonal approximate message passing (OAMP) algorithm cannot achieve satisfying performance during the two-step iterative process. Therefore, we propose a deep learning modified OAMP (DL-mOAMP) algorithm, which iteratively modifies the user activity ratio via exploiting the user activity correlation in the MMSE denoiser based on the estimated sequence during each OAMP iteration. Moreover, given a specific false alarm probability, a constant threshold employed in the conventional user identification is not optimal in the presence of user activity correlation. Thus, we propose a neural network framework that is dedicated to the user identification (DL-mOAMP-UI algorithm), which minimizes the missed detection probability under a pre-determined false alarm probability. Numerical results show that the proposed DL-mOAMP algorithm provides a substantial mean squared error performance gain compared to the conventional OAMP algorithm and the DL-mOAMP-UI algorithm can further improve the user identification accuracy of an mMTC system.