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Wireless networks have been widely utilized in industries, where wireless links are challenged by the severe nonstationary Rician fading channel, which requires online link quality estimation to support high-quality wireless services. However, most traditional Rician estimation approaches are designed for channel measurements and work only with nonmodulated symbols. Then, the online Rician estimation usually requires a priori aiding pilots or known modulation order to cancel the modulation interference. This article proposes a nondata-Aided method with redundant Gaussian mixture model (GMM). The convergence paradigm of GMM with redundant subcomponents has been analyzed, guided by which the redundant subcomponents can be iteratively discriminated to approach the global optimization. By further adopting the constellation constraint, the probability to identify the redundant subcomponent is significantly increased. As a result, accurate estimation of the Rician parameters can be achieved without additional overhead. Experiments illustrate not only the feasibility but also the near-optimal accuracy.

Long Range Wide Area Network (LoRaWAN) is one of the most promising IoT technologies that are widely adopted in low-power wide-area networks (LPWAN). LoRaWAN faces scalability issues due to a large number of nodes connected to the same gateway and sharing the same channel. Therefore, LoRa networks seek to achieve two main objectives: successful delivery rate and efficient energy consumption. This paper proposes a novel game theoretic framework for LoRaWAN named Best Equal LoRa (BE-LoRa), to jointly optimize the packet delivery ratio and the energy efficiency (bit/Joule). The utility function of LoRa node is defined as the ratio of the throughput to the transmit power. LoRa nodes act as rational users (players) which seek to maximize their utility. The aim of the BE-LoRa algorithm is to maximize the utility of LoRa nodes while maintaining the same signal-to-interference-and-noise-ratio (SINR) for each SF. The power allocation algorithm is implemented at the network server, which leads to an optimum SINR, spreading factors (SFs) and transmission power settings of all nodes. Numerical and simulation results show that the proposed BE-LoRa power allocation algorithm has a significant improvement in packet delivery ratio and energy efficiency as compared to the Adaptive Data Rate (ADR) algorithm of legacy LoRaWAN. For instance, in very dense networks (624 nodes), BE-LoRa can improve the delivery ratio by 17.44% and reduce power consumed by 46% compared with LoRaWAN ADR.

Gait abnormalities are typically derived from neurological conditions or orthopaedic problems and can cause severe consequences such as limited mobility and falls. Gait analysis plays a crucial role in monitoring gait abnormalities and discovering underlying deficits can help develop rehabilitation programs. Contemporary gait analysis requires a multi-modal gait analysis approach where spatio-temporal, kinematic and muscle activation gait characteristics are investigated. Additionally, protocols for gait analysis are going beyond labs/clinics to provide more habitual insights, uncovering underlying reasons for limited mobility and falls during daily activities. Wearables are the most prominent technology that are reliable and allow multi-modal gait analysis beyond the labs/clinics for extended periods. There are established wearable-based algorithms for extracting informative gait characteristics and interpretation. This paper proposes a multi-layer fusion framework with sensor, data and gait characteristics. The wearable sensors consist of four units (inertial and electromyography, EMG) attached to both legs (shanks and thighs) and surface electrodes placed on four muscle groups. Inertial and EMG data are interpreted by numerous validated algorithms to extract gait characteristics in different environments. This paper also includes a pilot study to test the proposed fusion approach in a small cohort of stroke survivors. Experimental results in various terrains show healthy participants experienced the highest pace and variability along with slightly increased knee flexion angles (≈1°) and decreased overall muscle activation level during outdoor walking compared to indoor, incline walking activities. Stroke survivors experienced slightly increased pace, asymmetry, and knee flexion angles (≈4°) during outdoor walking compared to indoor. A multi-modal approach through a sensor, data and gait characteristic fusion presents a more holistic gait assessment process to identify changes in different testing environments. The utilisation of the fusion approach presented here warrants further investigation in those with neurological conditions, which could significantly contribute to the current understanding of impaired gait.

International audience; Use of unmanned aerial vehicles (UAVs) to provide on-demand communications has been receiving growing interest, especially for use in remote and hard-to-reach areas. Also, the use of light-emitting diode-based lighting in UAVs has opened opportunities for data transmission through visible-light communications. To manage multiuser interference while avoiding complex handover procedures, we consider the use of zero forcing (ZF) pre-coding. Since the performance of ZF pre-coding depends on the correlation between channel gains of users, we propose in this paper to reduce it by means of location optimization of UAVs. More specifically, we use particle swarm optimization with the objective of maximizing the overall achievable network throughput. Furthermore, to relax the optimization requirements at UAVs, we investigate the case when the optimization is performed at a specific rate under different mobility conditions.

A novel model reference adaptive system for the rotor position/speed estimation and sensorless operation of a brushless doubly fed reluctance generator with maximum power point tracking is presented. Its main advantage over the existing designs are the inherent robustness and stability afforded by the complete parameter independence of the reference model using only the measured currents of the converter-fed (secondary) winding. In addition, the respective stationary frame current components are estimated by the adaptive model using the voltage and current measurements of the grid-connected (primary) winding at line frequency, offering higher accuracy. The simulation and experimental results have demonstrated the excellent real and reactive power controller performance for typical operating characteristics of wind turbines.

Precise timing among wireless sensor nodes is a key enabling technology for time-sensitive industrial wireless sensor networks (WSNs). However, the accuracy of timing is degraded by manufacturing tolerance, aging of crystal oscillators, and communication delays. This article develops a framework of packet-coupled oscillators (PkCOs) to characterize the dynamics of communication and time synchronization of clocks in WSNs. The nonidentical clock is derived to describe the embedded clock's behavior accurately. A proportional–integral (PI) packet coupling scheme is proposed for synchronizing networked embedded clocks, while, scheduling wireless Sync packets to different slots for transmission. It also possesses the feature of automatically eliminating the effects of unknown processing delay, which further improves the synchronization performance. The rigorous theoretical analysis of PI-based PkCOs is presented via studying a closed-loop time synchronization system. The performance of PI-based PkCOs is evaluated on a hardware testbed of IEEE 802.15.4 WSN. The experimental results show that the precision of the proportional-integral PkCOs protocol is as high as 60 $\mu s$ (i.e., 2 ticks) for 32.768 kHz crystal oscillator-based clocks.

The future e-mobility ecosystem will be a complex structure with different stakeholders seeking to optimize their operation and benefits. In this paper, a day-ahead grid-to-vehicle (G2V) and vehicle-to-grid (V2G) scheduling framework is proposed including electric vehicles (EVs), charging stations (CSs), and retailers. To facilitate V2G services and to avoid congestion at CSs, two types of trips, i.e., mandatory and optional trips, are defined and formulated. Also, EV drivers’ preferences are added to the model as cost/revenue threshold and extra driving distance to enhance the practical aspects of the scheduling framework. An iterative process is proposed to solve the non-cooperative Stackelberg game by determining the optimal routes and CS for each EV, optimal operation of each CS and retailers, and optimal V2G and G2V prices. Extensive simulation studies are carried out for two different e-mobility ecosystems of multiple retailers and CSs as well as numerous EVs based on real data from San Francisco, the USA. The simulation results show that the optional trips not only reduces the cost of EVs and PV curtailment by 8.8-24.2% and 26.4-28.5% on average, respectively, in different scenarios, but also mitigates congestion during specific hours while respecting EV drivers’ preferences. Moreover, the simulation results revealed the significant impact of EV drivers' preferences on the optimal solutions and cost/revenue of the stakeholders.

This article proposes a fuzzy logic-based energy-management system (FEMS) for a grid-connected microgrid with renewable energy sources (RESs) and energy storage system (ESS). The objectives of the FEMS are reducing the average peak load (APL) and operating cost through arbitrage operation of the ESS. These objectives are achieved by controlling the charge and discharge rate of the ESS based on the state of charge of ESS, the power difference between load and RES, and electricity market price. The effectiveness of the fuzzy logic greatly depends on the membership functions (MFs). The fuzzy MFs of the FEMS are optimized offline using a Pareto-based multiobjective evolutionary algorithm, nondominated sorting genetic algorithm (NSGA-II). The best compromise solution is selected as the final solution and implemented in the fuzzy-logic controller. A comparison with other control strategies with similar objectives is carried out at a simulation level. The proposed FEMS is experimentally validated on a real microgrid in the energy storage test bed at Newcastle University, U.K.

International audience; Visible light communications (VLC) (VLC) has been introduced as a complementary wireless technology that can be widely used in industrial indoor environments where automated guided vehicles aim to ease and accelerate logistics. Despite its advantages, there is one significant drawback of using an indoor vehicular VLC (V-VLC) network that is there is a high handover outage duration. In line-of-sight VLC links, such handovers are frequently due to mobility, shadowing, and obstacles. In this paper, we propose a frequency diversity and link aggregation solution, which is a novel technique in Data link layer to tackle handover challenge in indoor V-VLC networks. We have developed a smallscale prototype and experimentally evaluated its performance for a variety of scenarios and compared the results with other handover techniques. We also assessed the configuration options in more detail, in particular focusing on different network traffic types and various address resolution protocol intervals. The measurement results demonstrate the advantages of our approach for lowoutage duration handovers in V-VLC. The proposed idea is able to decrease the handover outage duration in a two-dimensional network to about 0.2 s, which is considerably lower compared to previous solutions.

Device-free context awareness is important to many applications. There are two broadly used approaches for device-free context awareness, i.e., video-based and radio-based. Video-based approaches can deliver good performance, but privacy is a serious concern. Radio-based context awareness applications have drawn researchers' attention instead, because it does not violate privacy and radio signal can penetrate obstacles. The existing works design explicit methods for each radio-based application. Furthermore, they use one additional step to extract features before conducting classification and exploit deep learning as a classification tool. Although this feature extraction step helps explore patterns of raw signals, it generates unnecessary noise and information loss. The use of raw CSI signal without initial data processing was, however, considered as no usable patterns. In this article, we are the first to propose an innovative deep learning–based general framework for both signal processing and classification. The key novelty of this article is that the framework can be generalised for all the radio-based context awareness applications with the use of raw CSI. We also eliminate the extra work to extract features from raw radio signals. We conduct extensive evaluations to show the superior performance of our proposed method and its generalisation.

Layered or hybrid optical orthogonal frequency division multiplexing (OFDM) has been proposed for use in optical communications due to its excellent spectral and power efficiencies, especially in visible light communications (VLC). However, most of the current works concentrate on transmitter and receiver design as well as the quality of service in communication networks. In this paper, we propose a spectrum-efficient dimmable triple-layer hybrid optical OFDM (DTH-OFDM) scheme to tackle the illumination requirements, considering different practical indoor VLC scenarios from low illumination to high illumination intensities. In the proposed DTH-OFDM scheme, the required dimming level is achieved by jointly adjusting the dimming factors and direct current bias. We investigate the comprehensive performance analysis of the proposed DTH-OFDM in detail, including probability density function, bit error rate (BER), spectral and energy efficiencies. In addition, a joint dimming control and optimal power allocation problem for DTH-OFDM is formulated and solved using convex optimization under the constraints of light emitting diode (LED) nonlinearity, dimming target and communications reliability. Numerical results show that, the proposed DTH-OFDM can offer continuous and arbitrary dimming target with higher spectral efficiency and lower BER compared with its counterparts, as well as an enhanced tolerance to the LED nonlinearity.

In industrial internet of things (IIoT), ultra-reliable and low-latency communication (URLLC) is proposed to guarantee the requirement of real-time wireless control systems in worst case, so as to maintain the system working in all cases. However, it is extremely challenging to maintain URLLC throughout the whole control process due to the scarcity of wireless resource. This paper develops an autonomous device-to-device (D2D) communication scheme by jointly considering reliability in URLLC and control requirement. In the proposed scheme, we consider the actual control requirement, i.e., control convergence rate, into communication design, where we find that it can be converted into a constraint on communication reliability. Then, the communication reliability constraint comes from control aspect, instead of URLLC, which leads to that the system does not need to guarantee worst case in URLLC. Second, the sensors autonomously decide whether to be activated with optimal probabilities to participate in the control process, which can maintain the communication reliability requirement with significantly less resource consumption. Simulation results show remarkable performance gain of our method. For instance, compared with fixed activation probability 40% only considering URLLC, the average power consumption of the proposed method can be reduced by at most about 100%.

In unmanned aerial vehicle (UAV)-assisted data collection system, UAVs can be deployed to charge ground terminals (GTs) via wireless power transfer (WPT) and collect data from them via wireless information transmission (WIT). In this letter, we aim to minimize the time required by a UAV via jointly optimizing the trajectory of the UAV and the transmission scheduling for all the GTs. This problem is formulated as a mixed integer nonlinear programming (MINLP) which are difficult to address in general. To this end, we develop an iterative algorithm based on binary search and successive convex optimization (SCO) to solve it. The simulation shows that our proposed solution outperforms the benchmark algorithms.

In this paper, we investigate the design of robust and secure transmission in intelligent reflecting surface (IRS) aided wireless communication systems. In particular, a multi-antenna access point (AP) communicates with a single-antenna legitimate receiver in the presence of multiple single-antenna eavesdroppers, where the artificial noise (AN) is transmitted to enhance the security performance. Besides, we assume that the cascaded AP-IRS-user channels are imperfect due to the channel estimation error. To minimize the transmit power, the beamforming vector at the transmitter, the AN covariance matrix, and the IRS phase shifts are jointly optimized subject to the outage rate probability constraints under the statistical cascaded channel state information (CSI) error model. To handle the resulting non-convex optimization problem, we first approximate the outage rate probability constraints by using the Bernstein-type inequality. Then, we develop a suboptimal algorithm based on alternating optimization, the penalty-based and semidefinite relaxation methods. Simulation results reveal that the proposed scheme significantly reduces the transmit power compared to other benchmark schemes.

Multicarrier energy systems create new challenges as well as opportunities in future energy systems. One of these challenges is the interaction among multiple energy systems and energy hubs in different energy markets. By the advent of the local thermal energy market in many countries, energy hubs' scheduling becomes more prominent. In this article, a new approach to energy hubs' scheduling is offered, called virtual energy hub (VEH). The proposed concept of the energy hub, which is named as the VEH in this article, is referred to as an architecture based on the energy hub concept beside the proposed self-scheduling approach. The VEH is operated based on the different energy carriers and facilities as well as maximizes its revenue by participating in the various local energy markets. The proposed VEH optimizes its revenue from participating in the electrical and thermal energy markets and by examining both local markets. Participation of a player in the energy markets by using the integrated point of view can be reached to a higher benefit and optimal operation of the facilities in comparison with independent energy systems. In a competitive energy market, a VEH optimizes its self-scheduling problem in order to maximize its benefit considering uncertainties related to renewable resources. To handle the problem under uncertainty, a nonprobabilistic information gap method is implemented in this study. The proposed model enables the VEH to pursue two different strategies concerning uncertainties, namely risk-averse strategy and risk-seeker strategy. For effective participation of the renewable-based VEH plant in the local energy market, a compressed air energy storage unit is used as a solution for the volatility of the wind power generation. Finally, the proposed model is applied to a test case, and the numerical results validate the proposed approach.

The inappropriate mechanism designs for demand response (DR) in the community of microgirds (CoMGs) may cause massive problems, such as increase of consumers’ costs, rebound peaks, and thereby lack of optimality in the network. In this article, a bilevel energy management system (EMS) is proposed to tackle the challenges associated with DR programs for CoMGs. The current structure successfully models users’ behavior and dissatisfaction in the first level of optimization to develop best DR program for each of them. Moreover, in the second level, power system constraints are taken into account to prevent voltage and current deviation from their statutory limits. Each user is assumed to be part of a microgrid (MG) whose operation is controlled and optimized through its local EMS in the first level. On the other hand, the overall operation of all MGs is delegated to the whole system operator, which acts as the central EMS (CEMS) in the second level. An iterative transactive energy management method is proposed by CEMS to fairly limit the excess power of the MGs one day ahead for voltage and current regulation. The obtained results indicate the effectiveness of the proposed structure in preventing discomfort issues, voltage deviation and creation of the rebound peaks in the system.

This work presents a nonlinear backstepping control scheme for rapid earth fault current limiters (REFCLs) in resonant grounded power distribution systems to mitigate the severity of powerline bushfires. The main feature of the proposed control scheme is that it quickly eliminates both active and reactive components of the fault current to make it zero for reducing the chance of igniting bushfires. The nonlinear backstepping control scheme is employed on the dynamical model of a REFCL equipped with a T-type inverter. The desired tracking of the fault current is ensured with the proposed scheme by appropriately injecting the current to the neutral point. The performance of the controller is evaluated in terms of the fault current and faulty phase-to-ground voltage under different fault conditions while following the standard criteria for the practical operation. The fault current compensation capability of the proposed scheme is evaluated for both low and high impedance faults. Simulation results in software and processor-in-loop platforms clearly demonstrate the fault current is limited to a value much lower than its desired value of 0.5~A in less than 1~s which means that the chance of igniting bushfires will be reduced with the proposed controller.

Recent research has devoted significant efforts on the utilization of WiFi signals to recognize various human activities. An individual’s limb motions in the WiFi coverage area could interfere with wireless signal propagation, that manifested as unique patterns for activity recognition. Existing approaches though yielding reasonable performance in certain cases, are ignorant of two major challenges. The performed activities of the individual normally have inconsistent speed in different situations and time. Besides that the wireless signal reflected by human bodies normally carries substantial information that is specific to that subject. The activity recognition model trained on a certain individual may not work well when being applied to predict another individual’s activities. Since only recording activities of limited subjects in a certain speed and scale, recent works commonly have a moderate amount of activity data for training the recognition model. The small-size data could often incur the overfitting issue that negative affect the traditional classification model. To address these challenges, we propose a WiFi-based human activity recognition system that synthesizes variant activities data through eight channel state information (CSI) transformation methods to mitigate the impact of activity inconsistency and subject-specific issues, and also design a novel deep-learning model that caters to the small-size WiFi activity data. We conduct extensive experiments and show synthetic data improve performance by up to 34.6% and our system achieves around 90% of accuracy with well robustness in adapting to small-size CSI data.

In this letter, a low-complexity receiver (Rx) is proposed for hybrid asymmetrically clipped optical orthogonal frequency division multiplexing (HACO-OFDM). Owing to the special time-domain property of HACO-OFDM, overlaid asymmetrically clipped OFDM (ACO-OFDM) and pulse-amplitude-modulated discrete multitoned (PAM-DMT) signals can be distinguished in the time domain to reduce its computational complexity. Besides, the near-optimal optical power allocation is further applied to optimize the proposed system performance. Theoretical analysis and simulation results demonstrate that, the proposed Rx can achieve nearly the same bit error rate (BER) performance as the BER-optimal iterative Rx but with an effective complexity reduction, thus demonstrating its application potential in high-speed optical communication systems.

This paper presents an approach to design a non‐singular fast terminal sliding model controller for residual current compensation inverters in compensated distribution networks to compensate the fault current due to most commonly occurred single line‐to‐ground faults. The main control objective is to completely eliminate the fault current in order to mitigate the impacts of powerline bushfires. A non‐singular fast terminal sliding surface is used to design the controller so that the residual current compensation inverter can quickly ensure the desired control performance without experiencing singularity problems. In this scheme, the chattering effects are minimised by replacing the discontinuous function appearing in the control law with a continuous function and the Lyapunov stability theory is utilised to demonstrate the theoretical stability of the control law. This paper also includes an overview of the non‐singular terminal sliding model controller as the performance of the non‐singular fast terminal sliding model controller is compared with this controller through rigorous simulation results over a range of fault currents. Simulation results clearly demonstrate the faster convergence speed of the non‐singular fast terminal sliding model controller over the non‐singular terminal sliding model controller for compensating the fault current and hence, mitigating powerline bushfires.

In the above article [1] , the references in Tables V – IX were incorrectly assigned. The corrected tables are provided here.

Continuously changing electricity demand and intermittent renewable energy sources pose challenges to the operation of power systems. An alternative to reinforcing the grid infrastructure is to deploy and manage distributed energy storage systems. In this work, a micro-energy market is proposed for smart domestic energy trading in the low-voltage distribution systems in the context of high penetration of photovoltaic systems and battery energy storage systems. In addition, a micro-balancing market is proposed to address the congestions due to unforeseen energy imbalance. Centralised and decentralised management strategies are simulated in real time, based on generation and demand forecasts. In addition, electric vehicles are also simulated as potential storage solutions to improve grid operation. A techno-economic evaluation informs key stakeholders, in particular grid operators on strategies for a sustainable implementation of the proposed strategies. The results show that the micro-energy market reduces the energy cost for all grid users by 4.1–20.2%, depending on their configuration. In addition, voltage deviation, peak electricity demand and reverse power flow have been reduced by 12.8%, 7.7% and 85.6% respectively, with the proposed management strategies. The micro-balancing market has been demonstrated to keep the voltage profile and thermal characteristic within the set limit in case of contingency.

Effective wind power prediction will facilitate the world’s long-term goal in sustainable development. However, a drawback of wind as an energy source lies in its high variability, resulting in a challenging study in wind power forecasting. To solve this issue, a novel data-driven approach is proposed for wind power forecasting by integrating data pre-processing & re-sampling, anomalies detection & treatment, feature engineering, and hyperparameter tuning based on gated recurrent deep learning models, which is systematically presented for the first time. Besides, a novel deep learning neural network of Gated Recurrent Unit (GRU) is successfully developed and critically compared with the algorithm of Long Short-term Memory (LSTM). Initially, twelve features were engineered into the predictive model, which are wind speeds at four different heights, generator temperature, and gearbox temperature. The simulation results showed that, in terms of wind power forecasting, the proposed approach can capture a high degree of accuracy at lower computational costs. It can also be concluded that GRU outperformed LSTM in predictive accuracy under all observed tests, which provided faster training process and less sensitivity to noise in the used Supervisory Control and Data Acquisition (SCADA) datasets.

The sharply growing demand for increased transmission capacity and bandwidth in last meter and last mile access networks together with the commercialization of fifth generation (5G) wireless systems has been opening up new opportunities for non-radio frequency (RF)-based wireless technologies. Visible light communications (VLC) technology is a potential candidate for access networking in 5G, which offers a higher spectral efficiency than RF-based Femtocell networks by three orders of magnitude. This paper proposes an all wireless optical bidirectional VLC multiple access scheme for pure VLC network access points in terminals. Centralized coordination is adopted to reduce the system complexity. And reservation of resource is introduced to guarantee collision avoidance during data frame transmission. The proposed multiple access scheme introduces special system parameters to achieve the balance of system throughput and access latency. The feasibility of the proposed scheme is verified by both theoretical analysis and experimental investigation. We show that the proposed scheme is suitable for a bidirectional pure VLC access network and can be used as a supplement in the IEEE 802.11 bb for 5G+bidirectional VLC application scenarios.

The indoor connected environment has witnessed significant research and development attention from industries and academia due to the growing number of smaller smart indoor devices around us. Developing an effective and efficient wireless access standard is one of the challenging tasks to enable the next generation indoor connected environment. The technical characteristics of existing wireless access standards, including IEEE 802.11a, 802.11n, and 802.11ac, are considerably limited for realizing indoor connected environments, particularly with a growing number of smaller intelligent devices. Moreover, their backward compatibility and migration strategies are significant for developing the next-generation wireless access standard for the indoor Internet of Things environment. In this context, this paper presents an indoor environmental experimental study focusing on the backward compatibility and migration-centric performance analysis of existing wireless access standards. Three wireless access standards that operate in the 5 GHz frequency spectrum are evaluated considering the metrics, including throughput, range, efficiency, and backward compatibility in an indoor environment. The experimental results are also compared with the analytical path loss model to observe the attributes for next-generation wireless access between the observed and analytical models. The evaluation can attest to the suitable migration strategy for stable next-generation wireless access development and deployment for an indoor smart Internet of Things environment.

With the increase of the number of electric vehicles (EVs), it is of vital importance to develop the efficient and effective charging scheduling schemes for all the EVs. In this article, we aim to maximize the social welfare of all the EVs, charging stations (CSs) and power plant (PP), by taking into account the changing demand of each EV, the changing price, the capacity and the congestion balance between different CSs. To this end, two efficient scheduling algorithms, i.e., Centralized Charging Strategy (CCS) and Distributed Charging Strategy (DCS) are proposed. CCS has a slightly better performance than the DCS, as it takes all the information and make the decision in the central control unit. On the other hand, DCS dose not require the private information from EVs and can make decentralized decision. Extensive simulation are conducted to verify the effectiveness of the proposed algorithms, in terms of the performance, congestion balance, and computing complexity.

In this paper, an unmanned aerial vehicle (UAV)-aided wireless emergence communication system is studied, where an UAV is deployed to support ground user equipments (UEs) for emergence communications. We aim to maximize the number of the UEs served, the fairness, and the overall uplink data rate via optimizing the trajectory of UAV and the transmission power of UEs. We propose a Deep Q-Network (DQN) based algorithm, which involves the well-known Deep Neural Network (DNN) and Q-Learning, to solve the UAV trajectory problem. Then, based on the optimized UAV trajectory, we further propose a successive convex approximation (SCA) based algorithm to tackle the power control problem for each UE. Numerical simulations demonstrate that the proposed DQN based algorithm can achieve considerable performance gain over the existing benchmark algorithms in terms of fairness, the number of UEs served and overall uplink data rate via optimizing UAV’s trajectory and power optimization.

Losses in light-emitting-diode (LED) driver cause increasing temperature and shorten their lifespan. Therefore, improving the efficiency of LED drivers not only saves energy but also is indispensable to increase their lifespan. In this article, a new LED driver topology is proposed to improve the performance of valley switching by decreasing the mosfet switching losses. The proposed topology is designed in a way that the mosfet works at the significantly lower switching and conduction losses in compared with conventional LED drivers. It elaborates how the proposed topology also improves the overall efficiency by decreasing power losses in other main elements of the driver, including inductance and diode. In addition, a new valley switching implementation is introduced for the new converter, which decreases the cost and dimension of the LED drivers. The experimental results confirm the high efficient operation of the proposed LED driver by reaching the efficiency up to 97% at a wide range of operating voltage.

International audience; This paper proposes inter-cell interference mitiga-tion in a dimming-aware way for multi-cell visible-light communication networks, through efficient time-scheduling, scaling, and coordination of non-orthogonal multiple access (NOMA) transmissions at the access points. By this method, users are grouped and served in different time slots depending on whether they are at the center or at the edge of the cell. This reduces the number of NOMA users per time slot, which decreases the network computational complexity, by reducing the average number of successive interference cancellation steps. Comparison of the proposed scheme with the classical NOMA over different dimming constraints shows an improvement of up to 39% and 37% in the average sum-rate and fairness, respectively, for a 30% duty cycle transmission in a 4-cell scenario with 8 users.

Evolution and accessibility of smart-phones have led to a huge demand in network bandwidth. The ubiquitous use of smart-phones in high-speed trains poses a unique challenge in delivering high-speed internet service on board. This challenge can be overcome by employing free-space optics, an alternative to radio-frequency technology. Previous coverage models for ground-train communications employed single laser systems with a larger divergence angle to cover a larger distance. Larger divergence angles lead to larger geometric losses, which may result in a non-reliable communication link. This study proposes a sectorised multi-beam coverage model with a smaller divergence angle to reduce the impact of geometric losses in the system. This study also proposes two receiver (Rx) architectures for Rxs deployed on the train. Along with geometric losses, the atmospheric attenuation is taken into consideration for the FSO link. The performance of the ground-train communications system in terms of bit-error-rate is evaluated under weak turbulence conditions via numerical simulation.

In this letter, we analyse and optimize an intelligent reflecting surface (IRS)-assisted ultra-reliable and low-latency communications (uRLLC) system supported by wireless energy transfer (WET) technology, in which short packets are used in both the WET and wireless information transfer (WIT) phases. We first present the statistical features of the signal-to-noise ratio (SNR) of the system. Then, we derive an approximate closed-form expression of the average packet error probability (APEP). Additionally, we optimize the channel uses in the WET and WIT phases to maximize the effective throughput (ET) of the system. Finally, the effectiveness of the proposed solution is verified by Monte-Carlo simulation.

Offshore wind energy is drawing increased attention for the decarbonization of electricity generation. Due to the unpredictable and complex nature of offshore aero-hydro dynamics, the Wind Turbine Power Curve (WTPC) model is an important tool for power forecasting and, hence, providing a reliable, predictable, and stable power supply. With the development of data-driven approaches, the Artificial Neural Network (ANN) has become a popular method for estimating WTPCs. This paper integrates the Isolation Forest (iForest), Nonsymmetric Fuzzy Means (NSFM) Radial Basis Neural Network (RBFNN), and metaheuristic algorithm to form a novel WTPC model. iForest performed anomaly detection and removal, NSFM RBFNN approximated the WTPC, and the metaheuristic solved NSFM optimization without training RBFNN. Four real-world datasets were used to assess the performance of NSFM RBFNN. According to multiple evaluation metrics and the Diebold-Mariano test, the accuracy of NSFM RBFNN was significantly better than the other competitive neural network-based methods. Additionally, NSFM RBFNN was shown to be more robust to anomalies than competitors, which is highly beneficial for practical applications.

Surface acoustic wave (SAW) devices are increasingly applied in life sciences, biology, and point-of-care applications due to their combined acoustofluidic sensing and actuating properties. Despite the advances in this field, there remain significant gaps in interfacing hardware and control strategies to facilitate system integration with high performance and low cost. In this work, we present a versatile and digitally controlled acoustofluidic platform by demonstrating key functions for biological assays such as droplet transportation and mixing using a closed-loop feedback control with image recognition. Moreover, we integrate optical detection by demonstrating in situ fluorescence sensing capabilities with a standard camera and digital filters, bypassing the need for expensive and complex optical setups. The Acousto-Pi setup is based on open-source Raspberry Pi hardware and 3-D printed housing, and the SAW devices are fabricated with piezoelectric thin films on a metallic substrate. The platform enables the control of droplet position and speed for sample processing (mixing and dilution of samples), as well as the control of temperature based on acousto-heating, offering embedded processing capability. It can be operated remotely while recording the measurements in cloud databases toward integrated in-field diagnostic applications such as disease outbreak control, mass healthcare screening, and food safety.

By constructing digital twins (DT) of an integrated energy system (IES), one can benefit from DT's predictive capabilities to improve coordinations among various energy converters, hence enhancing energy efficiency, cost savings and carbon emission reduction. This paper is motivated by the fact that practical IESs suffer from multiple uncertainty sources, and complicated surrounding environment. To address this problem, a novel DT-based day-ahead scheduling method is proposed. The physical IES is modelled as a multi-vector energy system in its virtual space that interacts with the physical IES to manipulate its operations. A deep neural network is trained to make statistical cost-saving scheduling by learning from both historical forecasting errors and day-ahead forecasts. Case studies of IESs show that the proposed DT-based method is able to reduce the operating cost of IES by 63.5%, comparing to the existing forecast-based scheduling methods. It is also found that both electric vehicles and thermal energy storages play proactive roles in the proposed method, highlighting their importance in future energy system integration and decarbonisation., 28 pages, 8 figures, journal paper accepted by Applied Energy

In a power system with high penetration of renewable power sources, gas-fired units can be considered as a back-up option to improve the balance between generation and consumption in short-term scheduling. Therefore, closer coordination between power and natural gas systems is anticipated. This article presents a novel hybrid information gap decision theory (IGDT)-stochastic cooptimization problem for integrating electricity and natural gas networks to minimize total operation cost with the penetration of wind energy. The proposed model considers not only the uncertainties regarding electrical load demand and wind power output, but also the uncertainties of gas load demands for the residential consumers. The uncertainties of electric load and wind power are handled through a scenario-based approach, and residential gas load uncertainty is handled via IGDT approach with no need for the probability density function. The introduced hybrid model enables the system operator to consider the advantages of both approaches simultaneously. The impact of gas load uncertainty associated with the residential consumers is more significant on the power dispatch of gas-fired plants and power system operation cost since residential gas load demands are prior than gas load demands of gas-fired units. The proposed framework is a bilevel problem that can be reduced to a one-level problem. Also, it can be solved by the implementation of a simple concept without the need for Karush–Kuhn–Tucker conditions. Moreover, emerging flexible energy sources such as the power to gas technology and demand response program are considered in the proposed model for increasing the wind power dispatch, decreasing the total operation cost of the integrated network as well as reducing the effect of system uncertainties on the total operating cost. Numerical results indicate the applicability and effectiveness of the proposed model under different working conditions.

Integrated energy carriers in the framework of energy hub system (EHS) have an undeniable role in reducing operating costs and increasing energy efficiency as well as the system's reliability. Nowadays, power-to-gas (P2G), as a novel technology, is a great choice to intensify the interdependency between electricity and natural gas networks. The proposed strategy of this study is divided into two parts: (i) a conditional value-at-risk-based stochastic model is presented to determine the optimal day-ahead scheduling of the EHS with the coordinated operating of P2G storage and tri-state compressed air energy storage (CAES) system. The main objective of the proposed strategy is to indicate the positive impact of P2G storage and tri-state CAES on lessening the system uncertainties including electricity market price, power generation of the wind turbine, and even electrical, gas, and thermal demands. (ii) A demand response program focusing on day-ahead load shifting is applied to the multiple electrical loads according to the load's activity schedule. The proposed strategy is successfully applied to an illustrative example and is solved by general algebraic modeling system software. The obtained results validate the proposed strategy by demonstrating the considerable diminution in the operating cost of the EHS by almost 4.5%.

In this paper, we study the impact of correlation on the bit error rate (BER) and the channel capacity of a free-space optical (FSO) multiple-input-multiple-output (MIMO) system employing optical space shift keying (OSSK) over a fading channel. In order to study a practical correlated channel, we consider the effect of channel correlation due to both small-and large-scale eddies and show that the use of OSSK over correlated FSO channel can lead to an improved system performance with increasing correlation level of upto 0.9. In this work, we first develop an analytical framework for different performance metrics of the OSSK multiple-input single-output system with correlation and then extend our investigation by proposing an asymptotically accurate mathematical framework for MIMO. We also validate all the analytical results using MATLAB simulations. Finally, we develop an experimental setup of FSO with two correlated links to study the throughput and latency of the links at different turbulence levels.

Reliable information about the active loads in the energy system allows for effective and optimized energy management. An important aspect of intelligent energy monitoring system is load disaggregation. The proliferation of direct current (dc) loads has spurred the increasing research interest in extra low voltage (ELV) dc grids. Artificial intelligence, such as deep learning algorithms of stacked recurrent neural network (RNN), improved results on a variety of regression and classification tasks. This paper proposes a 1-D convolutional stacked long short-term memory RNN technique for the bottom-up approach in load disaggregation using single sensor multiple loads ELV dc picogrids. This eliminates the requirement for communication and intelligence on every load in the grid. The proposed technique was applied on two different dc picogrids to test the algorithm's robustness. The proposed technique produced excellent result of over 98% accuracy for smart loads and over 99% accuracy for dumb loads in ELV dc picogrid.

This paper proposed a grid connected solar Photovoltaic (PV) Systems with a new voltage balancing converter suitable for Neutral-Point-Clamped (NPC) Multilevel Inverter (MLI). The switched-capacitors used in the proposed converter is able to balance the DC link capacitor voltage effectively by using proper switching states. The proposed balancing converter can be extended to any higher levels and it can boost the DC input voltage to a higher voltage levels without using any magnetic components. This feature allows the converter to operate with the boosting capability of the input voltage to the desired output voltage while ensuring the self-balancing. In this paper the proposed converter is used for a grid connected solar PV system with NPC multilevel inverter, which is controlled using vector control scheme. The proposed grid connected solar PV system with associated controllers and maximum power point tracking (MPPT) is implemented in Matlab/SimPowerSystem and experimentally validated using dSPACE system and designed converters. The simulation and experimental results show that the proposed topology can effectively balance the DC link voltage, extract maximum power from PV module and inject power to the grid under varying solar irradiances with very good steady state and dynamic performances.

Intelligent Transportation System (ITS) is one of the main systems which have been developed to achieve safe traffic and efficient transportation. It enables the vehicles to establish connections with other road entities and infrastructure\ud units using Vehicle-to-Everything (V2X) communications. As a consequence, all road entities become exposed to either internal or external attacks. Internal attacks cannot be detected by traditional security schemes. In this paper, a recommendation based trust model for V2X communications is proposed to defend against internal attacks. Four types of malicious attacks are analysed. In addition, we conduct various experiments with different percentage of malicious nodes to measure the performance of the proposed model. In comparison with the existing model, the proposed model shows an improvement in the network throughput and the detection rate for all types of considered malicious behaviors. Our model improves the Packet Dropping Rate (PDR) with 36% when the percentage of malicious nodes is around 87.5%.

With the emergence of the fifth generation and beyond mobile networks, both visible light communications (VLC) and radio frequency (RF) or millimetre wave (mmW) systems are expected to maintain the connectivity in various environments. In outdoor environments the link (VLC or RF) availability is paramount, which is affected by channel conditions. In particular, in vehicular communications other vehicles, harsh environment, and road geometry and structure will have the impact on the link connectivity and availability. In such cases, a front-end antenna solution, which benefits both optical and RF communication links, can be seen as an attractive option that can be fitted in future vehicles. In this paper, we present the design and practical implementation of a planar hybrid VLC/mmW antenna operating at 20.8 GHz and show measured results for characterization of RF and VLC links as well as communications performance. We have used the widely adopted on-off keying and quadrature amplitude modulation schemes with different orders to demonstrate data rates of 5 Mb/s and up to 100 Mb/s for the VLC and mmW links, respectively. By measuring the bit error rate and the error vector magnitude for VLC and Rf links, respectively for each modulation we have shown that the proposed hybrid planar antenna is suitable for example in a typical vehicle -to-vehicle communications.

This paper proposes a two-stage robust-stochastic framework to evaluate the effect of the battery-based energy storage transport (BEST) system in a day-ahead market-clearing model. The model integrates the energy market-clearing process with a train routing problem, where a time-space network is used to describe the limitations of the rail transport network (RTN). Likewise, a price-sensitive shiftable (PSS) demand bidding approach is applied to increase the flexibility of the power grid operation and reduce carbon emissions in the system. The main objective of the proposed model is to determine the optimal hourly location, charge/discharge scheduling of the BEST system, power dispatch of thermal units, flexible loads scheduling as well as finding the locational marginal price (LMP) considering the daily carbon emission limit of thermal units. The proposed two-stage framework allows the market operator to differentiate between the risk level of all existing uncertainties and achieve a more flexible decision-making model. The operator can modify the conservatism degree of the market-clearing using a non-probabilistic method based on info-gap decision theory (IGDT), to reduce the effect of wind power fluctuations in real-time. In contrast, a risk-neutral-based stochastic technique is used to meet power demand uncertainty. The results of the proposed mixed-integer linear programming (MILP) problem, confirm the potential of BEST and PSS demand in decreasing the LMP, line congestion, carbon emission, and daily operation cost.

Here, the authors propose and apply two novel schemes of multi-band carrierless amplitude and phase ( m -CAP) modulation for spatial modulation (SM)-based visible light communications (VLC) systems. In SM, both spatial and signal constellation are utilised to improve the performance of the m -CAP system. Here, they have adopted the non-DC m -CAP which is a power efficient technique with no DC-bias. The bit error rate (BER) performance and the spectral efficiency of the proposed systems are compared with single-input single-output (SISO) and multiplexing multiple-input multiple-output (MIMO) m -CAP systems. Results demonstrate that non-DC m -CAP, with the same spectral efficiency as that of SISO m -CAP, outperforms other schemes in terms of BER due to its power efficiency; however, a higher spectral efficiency can be achieved by the SM-MIMO m -CAP scheme.

In this paper, a robust fault estimation approach is proposed for multi-input and multi-output nonlinear dynamic systems on the basis of back propagation neural networks. The augmented system approach, input-to-state stability theory, linear matrix inequality optimization, and neural network training/learning are integrated so that a robust simultaneous estimate of system states and actuator faults are achieved. The proposed approaches are finally applied to a 4.8 MW wind turbine benchmark system, and the effectiveness is well demonstrated.

Nowadays, the enormous rising demand for hydrogen fuel cell vehicles (HFCVs) and electric vehicles (EVs) in the transportation sector has a significant contribution in growing of multi-energy microgrids (MEMGs) accompanied by hydrogen refueling stations (HRSs), EV parking lots (EVPLs) and power-to-hydrogen (P2H2) technologies. The competency to enhance the efficiency and the reliability in MEMG systems leads to form a networked structure called multi-microgrids (MMG). In this paper, a robust decentralized energy management framework is proposed for the optimal day-ahead scheduling of a set of interconnected hydrogen, heat, and power-based microgrids (MGs) in the presence of HRSs and EVPLs. The proposed MMG is a collaborative structure of hydrogen provider company (HPC) and electricity markets with novel technologies such as power-to-heat (P2H), power-to-hydrogen (P2H2), combined heat and power (CHP) units, multiple energy storages and demand response to improve the system flexibility in meeting multi-energy demands. The necessity of data privacy preservation methods for MGs has emerged when the interconnected MGs are operated as an MMG to satisfy different energy demands with minimum cost. Therefore, an iterative-based algorithm called the alternating direction method of multipliers (ADMM) is utilized to decompose the structure of the scheduling problem to minimize the total daily cost of the MMG system while protecting the data privacy of MEMGs. In the proposed structure, the robust optimization model is able to manage the uncertainty by considering the worst-case scenario for electricity price in different conservativeness levels as MEMGs are sensitive to electricity price fluctuations. Finally, the simulation results represent the effectiveness of the proposed decentralized model under the worst case of electricity market price to meet the demand for electricity, heat, and hydrogen.

Electric vehicles (EVs) require an onboard battery charger unit and a battery management system (BMS) unit that balances the voltage levels for each battery cell. So far, both units are two completely autarkic power electronics systems. The circuit presented here operates as a battery charger when the EV is connected to the grid and as a voltage balancer when the EV is driving. Thus, the proposed circuit utilises two functions in one and therefore eliminates the need of having two autarkic units reducing complexity and reduction in component count. The proposed circuit operates as a flyback converter and achieves power factor correction during battery charging. The constant‐current constant‐voltage (CC–CV) charging method is employed to charge the batteries. However, to limit the number of sensors that will be employed as a result of varying cells during charging, the battery current is estimated using a single current transducer and embedding a converter model in the controller. The operation of the circuit is presented in detail and is supported by simulation results. A laboratory prototype is built to verify the effectiveness of the proposed topology. Experiment results show that the proposed method provides an integrated solution of on‐board charging and voltage equalisation.

Upright balance control is the most fundamental, yet essential, function of a humanoid robot to enable the performance of various tasks that are traditionally performed by human being in various unstructured environments. Such control schemes were conventionally implemented by developing accurate physical and kinematic models based on fixed torque-ankle states, which often lack robustness to external disturbing forces. This paper presents a variable impedance control method that generates the desired torques for stable humanoid robot upright balance control, to address this limitation. The robustness of the proposed method was brought by a variable parameter approach with the support of the impedance model. The variable parameter of the ankle angle is able to describe the balance state of a humanoid robot, and the proper adjustment of such parameter ensures the effectiveness of the control model. The proposed approach was applied to a humanoid robot on a moving vehicle, and the experimental results demonstrated its efficacy and robustness.

This study investigates the strategic scheduling of a multi-energy system (MES) in the day-ahead wholesale market (DWM) as a price-maker that can submit offers/bids to purchase/sell energy. In this regard, the proposed model presents a bi-level optimization problem, wherein the upper-level is the cost minimization objective of the MES, while the lower-level is considered as the wholesale market operator (WMO) that clears the market according to the received offers/bids from producers/consumers intending to maximize public satisfaction. The Karush-Kuhn-Tucker (KKT) conditions are utilized to convert the bi-level nonlinear problem into a single level mixed-integer linear problem (MILP). A combined heat and power (CHP) unit and wind turbines (WT) are integrated into MES as the production units, while various storage technologies, such as hydrogen energy storage (HES), natural gas storage (GS) and thermal energy storage (TES), as well as demand response program (DRP), are integrated to increase the flexibility of the system. A hybrid robust optimization (RO) and stochastic programming (SP) method is deployed to deal with uncertainties of MES. The results illustrate the efficacy of this model in manipulating market clearing price in favor of the MES, while different case studies show the privileges of utilizing a hybrid RO-SP method.

In recent years, multi-energy microgrid (MEM) has gained increasing interest, which could use clean and efficient electro-thermal resources, multi-energy storages (MESs) and demand response potential to improve the flexibility of MEM. However, maximizing the flexibility potential of MEM and alongside managing the electrical parameters (EPs) is a challenging modeling problem. In this paper, a probabilistic nonlinear model is presented to maximize the flexibility with all the power grid constraints taking into account EPs constraints using power flow. To this end, voltage profile and congestion improvement, robust thermal comfort provision during reserve call and MESs utilization are the key properties of the proposed model. The outcome of suggested model ensures sustainability in the MEM performance, which is an essential feature in modern smart cities. The presented model is applied to a distribution network in the UK and results illustrate how equipment scheduling and demand response leads to observe the EPs limitation and maximizes MEM flexibility. The achieved results show a decrease in MEM revenue (decrease of 34% and 24% without and with reserve commitment, respectively) and in contrast, a significant increase in flexibility compared to non-compliance with EPs constraints.

Monitoring water turbidity accurately at a large scale provides vital information to alert abnormal water pollution event. However, scientists and engineers have to make the tradeoff among accuracy, range, and cost of turbidity measurement solutions. Consequently, the state of the art solutions utilize high-end hardware configurations to maintain high accuracy at wide dynamic range, which are either too expensive or complex to be adopted in large-scale monitoring. A low cost turbidity sensor without compromising on accuracy and dynamic range raises a big challenge. This paper approaches this challenge with Chirp modulation and signal convolution in the statistical domain, which can provide more than 40 dB gain with traditional low-cost photodiode. The proposed solution can significantly increase the system performance scaled with hardware configurations. As a result, the implemented proof of concept system can provide as high as 2% measurement accuracy over a wide range of 0–1000 NTU with low-cost hardware configurations.