While differential privacy (DP) contributes to pre serving data privacy during federated learning (FL), DP-FL suffers from either premature convergence or underutilized privacy budgets and subsequently degraded accuracy. Some recent studies heuristically adjusted the variance of the DP noises but offered no guarantee of optimality, little insight, and limited scalability. This paper presents a new control framework for (ε, δ)-DP FL to address the prevalent issues of DP-FL, i.e., premature convergence or underutilized privacy budgets. The key idea is to interpret the DP perturbation of DP-FL as a control process, where the DP noise variance and communication rounds are interdependent and jointly and adaptively determined. An optimal control framework is proposed to adjust the communication rounds and DP noise variance, adapting to the training accuracy of DP-FL. The optimality gap of (ε, δ)-DP FL is derived under the optimal control framework. The importance of joint orchestration of the DP noise and communication rounds is delineated. Experiments on MLP, CNN, and ResNet-9 models show that, given a privacy level, our control framework allows DP-FL to converge much faster with better accuracy than existing techniques, including those with persistent or heuristically reconfigurable DP noise variances.

This paper investigates an integrated sensing and communication (ISAC) network operating over millimeter-wave and sub-Terahertz bands, where a base station serves downlink communication users (CUs), while simultaneously sensing targets. First, we propose a novel hybrid beamforming structure that reduces power consumption in high-frequency bands by using a low number of phase shifters for analog beamforming and enhances spatial diversity in baseband beamforming through improper Gaussian signaling (IGS), addressing the limitations of having only a few radio frequency chains by boosting the number of supported data streams. Together, these techniques establish a new phased-MIMO radar structure and an energy-efficient signaling strategy designed for joint sensing and communication. Second, we formulate a new beampattern-optimization objective that enables computationally efficient algorithms, which iteratively update the hybrid beamformers through closed-form expressions. This design ensures tight mainlobe concentration for sensing while simultaneously serving multiple CUs. A new soft-min function, paired with a closed-form algorithm, secures both strong worst-rate and sum-rate performance. By unifying sensing and communication objectives, the proposed framework offers a well-balanced trade-off between high CU rates and high-quality sensing beampatterns, while maintaining computational complexity scalable. Simulation results validate the practicality of the proposed approach.

Intelligent Automation Systems, such as automated storage and retrieval systems, self-driving vehicles, various types of autonomous robots, and autonomous workshop plants, act independently of direct human supervision. Their impact on society and human life will be significant. These automation systems are safety-critical, complex, and powerful with a higher-level functionality. Safety and reliability to perform their tasks safely and minimize failures is one of the key challenges and becomes costly and difficult to achieve. The development of novel safety and reliability technologies dealing with theoretical aspects and for intelligent automation systems has become a hot spot in recent years. With the novel safety and reliability technologies, the intelligent automation systems can detect system failures, identify operation risks, predict unknown safety hazards and vulnerabilities, and avoid situations that pose risks to humans, property, or the automation systems themselves. Recent developments confirm that there are still areas of research to be explored within the safety-critical approaches. This Special Issue on Emerging Trends in Safety-critical Issues for Intelligent Automation Systems of IEEE Transactions on Automation Science and Engineering (TASE) aims to present recent advances in theories, methods, and applications that address safety-critical challenges in autonomous intelligent systems. The objective is to compile state-of-the-art research that contributes to the development of resilient and trustworthy automation solutions in safety-sensitive scenarios. After a thorough and rigorous peer-review process, 27 high-quality articles were selected from numerous submissions worldwide. These articles closely align with the Special Issue’s scope and can be categorized into seven key topics. - Risk assessment and model-based safety and cybersecurity analysis [A1], [A2], [A3], [A4], [A5]. - Intelligent fault detection and fault-tolerant control [A6], [A7], [A8], [A9], [A10]. - Reliability and traceability of decision-making for intelligent automation systems [A11], [A12], [A13]. - Conflict detection and resolution in intelligent automation systems [A14], [A15]. - Safety- and security-related issues including functional safety and system security [A16], [A17], [A18]. - Design, development, validation, and applications of intelligent automation systems such as UAVs, UGVs, and UUVs [A19], [A20], [A21], [A22], [A23]. - Human-robot collaboration, risk assessment of intelligent automation [A24], [A25], [A26], [A27]. We believe this collection will provide a valuable reference for academia and industry alike, and inspire further research in this rapidly evolving field.

The paper studies an Internet of Vehicles system consisting of several UAVs sending downlink information to ground vehicles to move an uneven terrain. In such environments, the transmission from the UAV to the ground vehicles may be blocked by tall buildings or mountains. Our goal is to construct multi-UAV navigation laws, transmission schedules, and transmit power levels of the UAVs that maximize the total downlink throughput of the system while minimizing the total transmit power and the propulsion energy of the multi-UAV team subject to a communication interference constraint for the UAVs. An important feature of the investigation is that a safety constraint that guarantees to avoid collisions between UAVs with a required safety margin should always be satisfied. A real-time law for joint communication and multi-UAV navigation is proposed. A mathematically rigorous analysis of the developed algorithm and proof of its optimality are given. Illustrative examples and simulations demonstrate the effectiveness of the approach. The main purpose of the proposed framework is to achieve joint communication and safe 3D path optimization, which means that the objective is to jointly construct 3D paths of the UAVs, transmission schedules, and transmit power levels of the UAVs. An advantage of this approach is that it allows to simultaneously maximize the total downlink throughput of the system and minimizes the total transmit power and the propulsion energy of all the UAVs subject to safety constraints that guarantee the UAVs avoid collisions between the UAVs as well as the UAVs and the uneven terrain.Note to Practitioners—Unmanned aerial vehicles (UAVs) have been regarded as a promising platform to serve as aerial access points, especially in complex environments with tall buildings and uneven terrains where conventional ground access points might usually provide unqualified service to users. In this paper, we have investigated an Internet of Vehicles (IoV) system that involves multiple unmanned aerial vehicles (UAVs) transmitting downlink information to ground vehicles navigating through challenging terrains. Our primary objective is to develop effective multi-UAV navigation laws, transmission schedules, and transmission power allocations that optimize the total downlink throughput of the system. At the same time, we aim to minimize the overall transmit power and propulsion energy of the UAV team, while adhering to communication interference constraints. It is worth noting that ensuring safety is of utmost importance in our investigation. We have incorporated a safety constraint that guarantees the prevention of collisions between UAVs, maintaining a required safety margin at all times. To tackle these challenges, we propose a real-time law that combines dynamic programming and model predictive control techniques for joint transmission and multi-UAV trajectory planning. The developed algorithm has undergone a rigorous mathematical analysis, and we have provided proof of its optimality.

In this paper, we propose the innovative use of Intelligent Vehicle Carriers (IVCs) as a key solution to address the energy constraints of small-scale unmanned Intelligent Vehicles (IVs). IVCs function as both transporters and charging stations, significantly boosting the operational range and efficiency of IVs. Our research delves into the IV-IVC collaborative framework, highlighting the existing challenges, exploring potential solutions, and examining a range of applications. This study offers a visionary approach to revolutionizing intelligent transportation systems by leveraging the synergistic relationship between IVs and IVCs.

The transient stability of the grid-feeding voltage source converter (GFD-VSC) has been studied in the context of weak-grid connections. This article investigates the vulnerability of the fault ride-through (FRT) transient of the GFD-VSC in inverter-dominated autonomous microgrids, where the GFD-VSC observes different impedance characteristics. The transient impedance model of the GFD-VSC is developed considering the current controller/saturation block and studying the impact of the phase-locked loop (PLL) synchronizing unit. The saturation of the current controller imposes a significant phase shift and the PLL's consequent action drives the GFD-VSC to a floating reference frame. The boundedness of the trajectories is evaluated through the nonlinear phase system analysis. It is shown that the system is susceptible to instability depending on its operating conditions such as power factor and the X/R ratio of feeder impendence. A state feedback control is proposed to bound the FRT trajectories of the GFD-VSC. The robust performance of the proposed method is reinforced by utilizing the intelligent deep reinforcement learning (DRL) method to adjust the feedback gain. A convolutional neural network based architecture is proposed for the DRL agent to solve the computational issue related to training and operating the DRL agent in a dynamic time scale of power converters. Numerical simulations validate the proposed method.

Unmanned aerial vehicles (UAVs) are utilized to improve the performance of wireless communication networks (WCNs), notably, in the context of Internet of Things (IoT). However, the application of UAVs, as active aerial base stations (BSs)/relays, is questionable in the fifth-generation (5G) WCNs with quasi-optic millimeter wave (mmWave) and beyond in 6G (visible light) WCNs. Because path loss is high in 5G/6G networks that attenuate, even, the Line-of-Sight (LoS) communicating signals propagated by UAVs. Besides, the limited energy/size/weight of UAVs makes it cost-deficient to design aerial multi-input/output BSs for active beamforming to strengthen the signals. Equipping UAVs with the reconfigurable intelligent surface (RIS), a passive component, can help to address the problems with UAV-assisted communication in 5G and optical 6G networks. We propose adopting the RIS-equipped UAV (RISeUAV) to provide aerial LoS service and facilitate communication for mobile Internet-of-Vehicles (IoVs) in an obstructed dense urban area covered by 5G/6G. RISeUAV-aided wireless communication facilitates vehicle-to-vehicle/everything communication for IoVs for updating IoT information required for sensor fusion and autonomous driving. However, autonomous navigation of RISeUAV for this purpose is a multilateral problem and is computationally challenging for being optimally implemented in real time. We intelligently automated RISeUAV navigation using deep reinforcement learning to address the optimality and time complexity issues. Simulation results show the effectiveness of the method.

This article addresses a joint path planning and communication scheduling problem for a team of unmanned aerial vehicles (UAVs) equipped with central processing units and performing computing tasks for Internet of Things (IoT) devices located on uneven terrain. On such terrains, the Line of Sight (LoS) between a UAV and a ground IoT device may be blocked by buildings or mountains. In this article, reconfigurable intelligent surfaces (RISs) are deployed on the ground to improve wireless communication between UAVs and IoT devices. Computing tasks and their results can be sent to and from UAVs either directly or via RIS-reflected paths. The objective is to develop a joint multi-UAV path planning/transmission scheduling algorithm that maximizes the number of computing tasks successfully and timely performed by the UAVs and transmitted back to the IoT ground devices and minimizes the total energy consumption of the UAVs. An effective path planning algorithm for this optimization problem is proposed. A mathematically rigorous proof of its asymptotic optimality is given. The developed algorithm is computationally effective and easily implementable in real time. Computer simulations and comparisons with other effective methods prove the effectiveness of the developed approach.

The reconfigurable intelligent surface (RIS) technology has gained increased attention for improving the performance and efficiency of the fifth-generation (5G) millimeter-wave (mmWave) wireless communication by obviating the propagation and blockage issues. On the other hand, the great flexibility of unmanned aerial vehicles (UAVs) has made them effective gadgets to enhance the coverage of wireless communication networks. Combining these two emerging technologies, the RIS-outfitted UAV (RISoUAV) is a promising solution for providing line-of-sight (LoS) wireless links for mobile targets (MTs) in obstructed high-dense urban areas. In this light, high-speed real-time communication is essential for some vital municipal services like ambulances, fire engines, security guards, police, etc. This important goal is achievable thanks to the RISoUAV-assisted 5G/quasi-optic wireless communication. This paper develops a framework for optimal navigation of a team of RISoUAVs for maintaining LoS links with a team of ground vehicles in a dense urban area. The trajectories of the RISoUAVs are optimized considering the energy efficiency, communication channel gains, and constraints associated with RISoUAVs motion and LoS service. A consensus-based coordinating approach is adopted to coordinate the RISoUAVs navigation to cover all MTs under a good quality of service. Simulation results show the effectiveness of the method. Note to Practitioners—In this paper, we consider a scenario where vehicles need to have high-speed, uninterrupted data links in obstructed, highly dense urban environments. Due to spectrum crunch, the data links are increasingly likely to rely on a high-frequency spectrum, including mmWave with quasi-optic nature, visible light communications, or even laser. However, the obstructed LoS and propagation are critical issues with the 5G and beyond as they rely on the availability of an unobstructed path (e.g., the LoS or a quality reflective path). On the other hand, the RIS performs as a passive reflective element that provides an indirect LoS link, a one-bounce channel, to improve the performance and efficiency of mmWave, and beyond, wireless communication networks. UAVs equipped with RISs are suggested in this paper to be adopted as aerial transponders to reflect signals and facilitate communication in high-density environments. Therefore, the problem of UAV navigation and 3D trajectory planning should be addressed regarding the application, particularly, for providing LoS service for mobile vehicles with arbitrary directions. Autonomous navigation of RISoUAVs for LoS wireless communication is a natural multi-dimensional extension of autonomous navigation with obstacle avoidance where regions in which LoS communication is lost are viewed as obstacles to avoid. However, the environment and valid LoS links can change dynamically due to moving vehicles in the obstructed environment. This makes the navigation design an NP-hard problem that is tackled in this paper by developing an effective navigation program.

This paper considers using unmanned aerial vehicles (UAVs) to survey important sites across a city. When the sites are relatively far from the UAVs’ depot, the UAVs may not be able to reach many of the sites. Suppose that a UAV can take public transportation vehicles (PTVs) like a passenger. Then, it may reach a site that is unreachable by flying only. Based on this UAV-PTV scheme, we investigate a task-UAV assignment problem, which assigns a set of surveillance tasks to UAVs. We formulate a mixed-integer linear programming (MILP) problem that minimizes the overall energy consumption of UAVs, subject to that every site is surveyed by a certain number of UAVs during a given time window, and all UAVs successfully return to the depot. Considering that this problem is NP-hard, we present two sub-optimal solutions. The first solution orders the surveillance tasks according to the starting times of their time windows. Then, starting from the earliest one, it assigns the tasks one by one to UAVs. The second solution breaks the tasks into small non-overlapping groups. It then assigns tasks to UAVs group by group. The former solution quickly addresses the assignment problem, but it lacks the overall management of UAV resources. The latter improves this by assigning a group of tasks simultaneously, and it can control the computation complexity by limiting the group size. The comparison with the brute force method shows that the proposed solutions can achieve competitive performance in a reasonable time. Note to Practitioners—Unmanned aerial vehicles (UAVs) have been widely used in surveillance missions. However, one challenge practitioners often meet is the limited flight duration. Commercial UAVs are in general powered by the onboard battery. Due to the restriction of payload, the battery capacity is constrained, which limits the UAVs’ operation time. In this paper, we present the approach exploiting public transportation vehicles (PTVs). In our design, a UAV can take a public transportation vehicles such as buses, trams and trains on the roof and transfer between vehicles when necessary. With this UAV-PTV collaboration scheme, we consider how to efficiently assign surveillance tasks to UAVs. Due to the NP-hardness of the considered problem, two suboptimal algorithms are presented.

A grid-feeding voltage source converter (GFD-VSC) requires a phase-locked loop (PLL) synchronization unit to be connected to the grid. The PLL critically affects the dynamic performance and stability of the GFD-VSC. In particular, a PLL with pre/in-loop filtering, for working under distorted/polluted conditions, possesses a narrow stability margin and deficient performance in weak grid connections and fault ride-through (FRT) transients, also poor performance in frequency estimation. To address these problems, a robust PLL with several enhanced characteristics is proposed in this article. The robust PLL with a dynamic state feedback controller is designed using an${{\bm{H}}}_\infty $robust control. The feedback controller is designed to improve the dynamic stability/response of the PLL, exposed to control uncertainties and exogenous disturbances, weak-grid connection, FRT transients and to improve its performance in frequency estimation. Numerical simulations validate the effectiveness of the proposed PLL.

This paper addresses a trajectory planning problem for a team of UAVs following several ground vehicles on uneven terrain for video surveillance. A model predictive control-based multi-UAV path planning algorithm is designed. A theoretical justification of the path planning algorithm is provided. Extensive simulation studies demonstrate the performance of the proposed method.

This article studies the quality of coverage of deploying flying base stations mounted on unmanned aerial vehicles (UAV-BSs) after disasters or during some occasional events. In particular, we focus on the problem of minimizing the average UAV-user distance, while maintaining connectivity between the UAV-BSs and some nearby stationary base stations (SBSs). The UAV-BSs can be deployed at different altitudes, and their transmission powers may also be different. We first propose a decentralized deployment algorithm for a Line-of-Sight (LoS) scenario. This algorithm allows UAV-BSs to determine their movements based on only local information. So, it is applicable in a large scale. The local optimality and the convergence of the algorithm are proved. Moreover, we discuss how to use the algorithm in Non-LoS (NLoS) scenarios. Specifically, during its movement, each UAV-BS needs to verify the connectivity requirement as well as if a future movement will lose any already covered users. This extension guarantees that the average UAV-user distance keeps reducing during the movements of UAV-BSs. Computer simulations and comparisons with a benchmark method confirm the effectiveness of the proposed algorithms in terms of the quality of coverage.

This article considers the use of an unmanned aerial vehicle (UAV) for covert video surveillance of a mobile target on the ground and presents a new online UAV trajectory planning technique with a balanced consideration of the energy efficiency, covertness, and aeronautic maneuverability of the UAV. Specifically, a new metric is designed to quantify the covertness of the UAV, based on which a multiobjective UAV trajectory planning problem is formulated to maximize the disguising performance and minimize the trajectory length of the UAV. A forward dynamic programming method is put forth to solve the problem online and plan the trajectory for the foreseeable future. In addition, the kinematic model of the UAV is considered in the planning process so that it can be tracked without any later adjustment. Extensive computer simulations are conducted to demonstrate the effectiveness of the proposed technique.Note to Practitioners—The “Follow Me” flight mode is available in many unmanned aerial vehicle (UAV) products, and this technique enables a UAV to automatically follow a target. However, this flight mode may make the UAV noticeable to the target and compromise the video surveillance missions of the UAV. Inspired by some security surveillance applications where UAV surveillance is conducted so that a target would not take actions to avoid being monitored, we propose an efficient method to construct the trajectory for the UAV. The proposed method considers the visual covertness and the battery capacity limitation of the UAV, and it can produce a trajectory online for the UAV. The proposed method and scenario can potentially extend the “Follow Me” flight mode and generate new applications and market for UAVs.

Unmanned aerial vehicles (UAVs) are increasingly applied to surveillance tasks, thanks to their excellent mobility and flexibility. Different from existing works using UAVs for video surveillance, this paper employs a UAV team to carry out collaborative radio surveillance on ground moving nodes and disguise the purpose of surveillance. We consider two aspects of disguise. The first is that the UAVs do not communicate with each other (or the ground nodes can notice), and each UAV plans its trajectory in a decentralized way. The other aspect of disguise is that the UAVs avoid being noticed by the nodes for which a metric quantifying the disguising performance is adopted. We present a new decentralized method for the online trajectory planning of the UAVs, which maximizes the disguising metric while maintaining uninterrupted surveillance and avoiding UAV collisions. Based on the model predictive control (MPC) technique, our method allows each UAV to separately estimate the locations of the UAVs and the ground nodes, and decide its trajectory accordingly. The impact of potential estimation errors is mitigated by incorporating the error bounds into the online trajectory planning, hence achieving a robust control of the trajectories. Computer-based simulation results demonstrate that the developed strategy ensures the surveillance requirement without losing disguising performance, and outperforms existing alternatives. Note to Practitioners—The paper is motivated by the covertness requirement in the radio surveillance (also called eavesdropping) by UAVs. In some situations, the UAV user (such as the police department) wishes to disguise the surveillance intention from the targets, and the trajectories of UAVs play a significant role in the disguising. However, the typical UAV trajectories such as standoff tracking and orbiting can easily be noticed by the targets. Considering this gap, we focus on how to plan the UAVs’ trajectories so that they are less noticeable while conducting effective eavesdropping. We formulate a path planning problem aiming at maximizing a disguising metric, which measures the magnitude of the relative position change between a UAV and a target. A decentralized method is proposed for the online trajectory planning of the UAVs based on MPC, and its robust version is also presented to account for the uncertainty in the estimation and prediction of the nodes’ states.

It is explored in this article that theinteraction of droop controllers through the power network(IDCPN) is the dominant factor affecting the dynamic stability of an autonomous networked microgrid (ANMG) and new models are developed to support the IDCPN. The models are developed to analyze the impacts of three parts on the IDCPN: 1) the X/R ratio of the power network impedance; 2) the droop controllers including low-pass filters, and 3) the impedance characteristics of the grid-forming voltage source inverters (VSIs). The low-frequency oscillations (LFO), excited by IDCPN, is identified and quantified by modeling the first and second parts. The critical impact of the low X/R ratio on amplifying the LFO is clarified. Then, the output impedance of the grid-forming VSI, including the virtual inductance loop (VIL), is developed and rigorously observed that reveals inefficient performance. It is shown that the VIL improves dynamic performance by providing sufficient damping to suppress LFOs and not by effectively boosting the X/R of the VSI output impedance. This, however, is problematic in the current limiting that puts the ANMG at instability risk because of the resistive–capacitive impedance characteristics of the VSIs. A${{\boldsymbol{H}}_\infty }$robust controller is proposed to replace VIL for suppressing LFO and stabilizing ANMG. Numerical/simulation results are provided to prove the accuracy of the models.

To enable the drone delivery service in a remote area, this paper considers the approach of deploying charging stations and collaborating with public transportation vehicles. From the warehouse which is far from a customer, a drone takes some public transportation vehicles to reach some position close to the remote area. When the customer is unreachable from the position where the drone leaves the public transportation vehicle, the drone swaps the battery at a charging station. The focus of this paper is the deployment of charging stations. We propose a new model to characterize the delivery time for customers. We formulate the optimal deployment problem to minimize the average delivery time for the customers, which is a reflection of customer satisfaction. We then propose a sub-optimal algorithm that relocates the charging stations in sequence, which ensures that any movement of a charging station leads to a decrease in the average flight distance. The comparison with a baseline method confirms that the proposed model can more accurately estimate the flight distance of a customer than the commonly used model, and the proposed algorithm can relocate the charging stations achieving lower flight distance.

With the rapid increase of vehicles in recent years, traffic surveillance becomes a crucial issue of traffic management. Since the traditional static sensor-based surveillance system can only passively monitor traffic, this paper considers the usage of unmanned aerial vehicles (UAVs), which can proactively conduct traffic surveillance thanks to the excellent mobility of UAVs. Specifically, we consider the navigation problem of a network of UAVs to effectively monitor a group of ground targets which move along a curvy road. A surveillance optimization problem is stated, and a distributed navigation algorithm for the UAV network is developed. It is proved that the proposed algorithm is locally optimal. Simulations confirm the effectiveness of the proposed navigation algorithm.

The paper is the first work that considers a constrained feedback control strategy to navigate an unmanned aerial vehicle (UAV) from a given starting point to a given terminal point while harvesting solar energy and providing a wireless communication service for ground users. Wireless communication channels are stochastic and cannot be known off-line, making the problem of off-line UAV path planning for wireless communication as considered in most existing works less meaningful. We consider the problem of navigating a solar-powered UAV from a starting point to a terminal point to harvest solar energy while serving the two-way communication between multiple pairs of ground users in a complex terrain. The objective is to jointly optimize the UAV’s flight time and its flight path by trading-off between the harvested energy and power consumption subject to the ground users’ minimum throughput requirement. We develop a new model predictive control (MPC) technique to address this problem. Namely, based on the well-known statistics of the air-to-ground (A2G) and ground-to-air (G2A) wireless channels, a predictive control model is proposed at each time-instant, which leads to an optimization problem over a receding horizon for the control design. This problem is non-convex due to the involvement of various optimization variables, which is then solved via novel convex iterations. Simulation results show the merits of the proposed algorithm. The results obtained by the proposed algorithm match with the benchmark non-MPC and offline-MPC approaches.

Drones are a promising tool for parcel delivery, since they are cost-efficient and environmentally friendly. However, owing to the limited capacity of the on-board battery, their flight range is constrained. Thus, they cannot deliver some parcels if the customers are too far from the depot. To address this issue, this article proposes a novel method, in which a parcel delivery drone can “take” a public transportation vehicle and travel on its roof. The problem under consideration is how to make use of the public transportation network to route the drone between the depot and the customer. Compared to the currently available methods that use drones, the most important merit of this approach is a significant expansion of the delivery area. We construct a multimodal network consisting of public transportation vehicles' trips and drone flights. Because of the complexity of this multimodal network, we convert it to a simple network with a set of simple procedures. In the extended network, we formulate the shortest drone path problem that minimizes the return instant to the depot, subject to that the drone energy consumption on this path is no greater than the initial energy. We present a Dijkstra-based method to find the shortest drone path. Moreover, we extend the proposed method to the case with uncertainty, because the public transportation vehicles cannot exactly follow their timetables in practice. Simulation results are presented to demonstrate how the method works.

The increasing number of electric vehicles (EVs) demands management solutions to deal with the impacts of EV charging on the efficiency of distribution grids. Many suggested methods are derived from analysis on laboratory-scale systems with declared data, which cannot be implemented for real networks. In this article, a two-step scheduling model is developed that effectively guides a large-scale EV fleet in microgrids without demanding a dynamic monetary scheme. The first step corresponds to prediction-based day-ahead optimal scheduling for large scale EVs, which minimizes the costs of electricity supply and EVs' battery degradation. To avoid dimensional problems in calculations, an improved K-means clustering algorithm is presented to divide vehicles into different clusters. In the second step, online coordination is deployed based on an effective scoring system to encourage drivers to follow the first-step provided model. The proposed model is analyzed on a grid-connected microgrid with photovoltaic system integration. The problem (real) data are derived based on an estimate of the development process on the Ontario energy network over the next ten years. Results show that the introduced model can guarantee the accurate deployment of optimal charging/discharging schedules in large-scale systems.

Drones have been regarded as a promising means for future delivery industry by many logistics companies. Several drone-based delivery systems have been proposed but they generally have a drawback in delivering customers locating far from warehouses. This paper proposes an alternative system based on a public transportation network. This system has the merit of enlarging the delivery range. As the public transportation network is actually a stochastic time-dependent network, we focus on the reliable drone path planning problem (RDPP). We present a stochastic model to characterize the path traversal time and develop a label setting algorithm to construct the reliable drone path. Furthermore, we consider the limited battery lifetime of the drone to determine whether a path is feasible, and we account this as a constraint in the optimization model. To accommodate the feasibility, the developed label setting algorithm is extended by adding a simple operation. The complexity of the developed algorithm is analyzed and how it works is demonstrated via a case study.

This article studies deploying autonomous drones to provide Internet service to affected users in disaster areas. Specifically, the problem of minimizing the average drone-user distance is considered. Unlike some existing location-based approaches, a range-based reactive drone deployment algorithm is proposed. Instead of assuming that the users' locations are known, the proposed algorithm only requires the drones to measure the received signal strength and to share such information with other nearby drones. It is decentralized and easily implementable in real time. The algorithm's convergence is proved and its performance is validated by simulations and comparisons with a benchmark scheme.

A current limiting scheme is employed for Inverter-Interfaced Distributed Generation (IIDG) units, mostly in the control loops, to limit the current within the tolerable range and to facilitate the IIDG units being connected to the grid during the transient. However, current-limiting/saturation of the droop-controlled grid-forming inverters affects their transient stability and may make them unstable, which yet has not been explored in the literature in the context of autonomous microgrids (MGs). It is disclosed in this paper that the sharp phase angle variation and the arbitrary resistive output impedance of grid-forming inverters in the current limiting mode make the autonomous MGs unstable. Time-domain simulations prove the idea.

The interaction of frequency and voltage droop controllers through power network is the dominant factor affecting dynamic stability of autonomous networked microgrids (ANMGs). The X/R ratio of power lines is low in the low-voltage microgrids (MGs) which makes the interaction/cross-coupling of droop controllers high and raises low/subsynchronous frequency oscillations and thus stability concerns. This issue is investigated in this work by developing new models to support the idea of interaction of droop controllers. To this end, the impacts of power network (mainly the X/R ratio) and the droop rules including the low-pass filter (LPF) are evaluated. The developed models can be used to design sufficient damping to the system by considering the interaction of droop controllers and power network requirements to improve stability margins of ANMGs.

This paper considers using UAVs to carry out an active and collaborative radio surveillance on a group of ground mobile nodes while disguising the surveillance intention. The UAVs try to prevent being visually noticed by the nodes, and a new measure to quantify the disguising performance is proposed. We formulate a new trajectory optimization problem to maximize the new disguising metric subject to an uninterrupted surveillance requirement and the collision avoidance and the aeronautic maneuverability of the UAVs. A model predictive control (MPC) based scheme is developed. Computer simulations and comparisons with a random trajectory construction method show that the proposed method guarantees the surveillance performance without the loss of disguising performance.

This article considers a wireless networked control system (WNCS) consisting of a dynamic system to be controlled (i.e., a plant), a sensor, an actuator, and a remote controller for mission-critical Industrial Internet of Things (IIoT) applications. A WNCS has two types of wireless transmissions, i.e., the sensor's measurement transmission to the controller and the controller's command transmission to the actuator. In the literature of WNCSs, the controllers are commonly assumed to work in a full-duplex (FD) mode by default, i.e., being able to simultaneously receive the sensor's information and transmit its own command to the actuator. In this article, we consider a practical half-duplex (HD) controller, which introduces a novel transmission-scheduling problem for WNCSs. A frequent scheduling of sensor's transmission results in a better estimation of plant states at the controller and thus a higher quality of control command, but it leads to a less frequent/timely control of the plant. Therefore, considering the overall control performance of the plant in terms of its average cost function, there exists a fundamental tradeoff between the sensor's and the controller's transmissions. We formulate a new problem to optimize the transmission-scheduling policy for minimizing the long-term average cost function. We derive the necessary and sufficient condition of the existence of a stationary and deterministic optimal policy that results in a bounded average cost in terms of the transmission reliabilities of the sensor-to-controller and controller-to-actuator channels. Also, we derive an easy-to-compute suboptimal policy, which notably reduces the average cost of the plant compared to a naive alternative-scheduling policy.

Shark attacks can make beach tourists anxious about sharing the ocean with apex predators. Although the raw number of shark attacks is deficient, the absolute terror caused by sharks is genuine. This article introduces a novel method named as the “drone shark shield system,” which uses communicating autonomous drones to intervene and prevent shark attacks for protecting swimmers and surfers. We detail the design of the drone shark shield system and the strategy for repelling sharks through multiple intersections. A shark interception algorithm is developed to guide drones to predicted intersection points for deterring sharks. Computer simulations are conducted to illustrate our method.

This paper investigates phasor measurement unit (PMU) placement for informative state estimation in smart grid by incorporating various constraints for observability. Observability constitutes an important property for PMU placement to characterize the depth of the buses' reachability by the placed PMUs, but addressing it solely by binary linear programming as in many works still does not guarantee a good estimate for the grid state. Some existing works have considered optimization of some estimation indices by ignoring the observability requirements for computational ease and thus potentially lead to trivial results such as acceptance of the estimate for an unobserved state component as its unconditional mean. In this work, the PMU placement optimization problem is considered by minimizing the mean squared error or maximizing the mutual information between the measurement output and grid state subject to observability constraints, which incorporate operating conditions such as presence of zero injection buses, contingency of measurement loss, and limitation of communication channels per PMU. The proposed design is thus free from the fundamental shortcomings in the existing PMU placement designs. The problems are posed as large scale binary nonlinear optimization problems involving thousands of binary variables, for which this paper develops efficient algorithms for computational solutions. Their performance is analyzed in detail through numerical examples on large scale IEEE power networks. The solution method is also shown to be extendable to AC power flow models, which are formulated by nonlinear equations.

This paper focuses on the application of surveillance and monitoring using unmanned aerial vehicles (UAVs). A novel coverage model is proposed to characterize the quality of coverage (QoC) of a target by a UAV. On the basis of this model, a reactive collision free three-dimensional deployment algorithm is proposed, with the goal of maximizing the overall QoC of targets by a network of UAVs. The algorithm consists of two navigation laws for the horizontal movement and the vertical movement, both of which are easily implementable in real time. The convergence of the algorithm is proved, and the computational complexity is analyzed. Computer simulations are conducted to demonstrate the performance of the proposed method.

A parcel delivery system with a unmanned aerial vehicle (UAV) and a public train is presented, where the train moves naturally as it is and the UAV can depart the train to deliver parcels. The UAV can travel with the train and replace its battery on the roof. An optimization problem is formulated to minimize the total delivery time and two algorithms are proposed. The exact algorithm gives the optimal schedule, but it is not scalable. The developed suboptimal algorithm is computationally efficient and achieves close performance to that of the exact algorithm. Realistic simulations are conducted to evaluate the proposed algorithms and they are compared with existing schemes.

Though drones have become a promising tool for parcel delivery, due to the limited capacity of the on-board battery, their flight range is constrained. To enlarge the coverage range, the paper proposes a novel method, in which a parcel delivery drone can "take" a public transportation vehicle and travel on its roof. We investigate how to make use of the public transportation network to route the drone between the depot and a customer. Considering the complexity of the public transportation network, its randomness and time-dependency, instead of off-line planning a global optimal path, an adaptive algorithm is developed. Simulation results are presented to demonstrate how the proposed approach works.

A navigation algorithm for source seeking in a sensor network environment is presented in this paper. The solution consists of a gradient-free approach and maximum likelihood topology maps of sensor networks. The robot is navigated using an angular velocity limited by maximum and minimum constants, and by sensor measurements gathered by sensors that are close to robot's current location. The location of the robot is calculated using sensor topology coordinates, which is an alternative to the physical coordinate system and does not depend on physical distance measurement techniques such as received signal strength. However, actual physical distances are hidden in topology maps due to nonlinear distortions compared to physical distance between nodes. Thus, the proposed control law does not depend on any distance-based information. The performance of the algorithm is evaluated using computer simulations and experiments with a real mobile robot.

We focus on the problem of deploying unmanned aerial vehicles to service mobile users in a cellular network, with the aim of maximizing coverage and reducing interference effects. An optimization model that is distributed in nature is proposed and a maximizing algorithm is developed to find a locally optimal solution. The performance of this distributed algorithm is shown to be superior in quality and solution time to a standard greedy algorithm. Testing on a simulation of a practical scenario is performed to demonstrate the application of the method to real scenarios as well as to illustrate the tradeoff between maximizing coverage and minimizing interference.

In this paper, we propose a wireless sensor network based safe navigation algorithm for micro flying robots in the industrial Internet of things (IIoT). A micro flying robot cannot be equipped with heavy obstacle detection sensors for local navigation. Therefore, in our method, a wireless sensor network consisting of three-dimensional range finder is used to detect the static and dynamic obstacles in an indoor industrial environment and navigate the micro flying robots to avoid any collisions with the obstacles. Only a path tracking controller is required for the micro flying robot and there is not any complex computation on the micro flying robot. It is an economical and efficient solution for multiple micro flying robots' navigation and management in the IIoT. The computer simulations confirm the expected performance of the proposed algorithm in static and dynamic environment with multiple micro flying robots.

Millimeter wave communication shows promise in realizing next generation wireless sensor networks for bandwidth demanding applications. Despite its support of multi Gbps data rates, MmWaves requires unobstructed line-of-sight and suffers from heavy path losses. Overcoming these in complex 3D environments requires sectored antenna arrays with narrow beam widths and adaptive beamforming. Therefore, network topology maps would be significant than ever in millimeter wave sensor networks. Traditional topology mapping algorithms rely on omnidirectional transmission and reception and are thus not tailored to such networks. A novel topology mapping algorithm, Millimeter Wave Topology Map (MmTM) is proposed for 3D deployments, which take advantage of the directional information available from beamforming antennas as well as their beam steering capability. An autonomous robot traverses the network recording the packet reception from different nodes along with the receiving antenna sector ID that delivers the packet with highest signal quality. The techniques used in standard IEEE 802.11ad protocol are used for the optimum sector selection and collision avoidance. MmTM is evaluated using two realistic sensor network environments and compared with prominent localization approaches based on received signal strength and hop count. The results show that proposed algorithm has a less than 0.7m distance error and more than 50% of nodes are located in the correct direction, which is 7m and 35% improvement in distance error and sector displacement matrices compared to other algorithms.

A distributed target search and predictive approach that uses wireless sensor network topology map is presented in this paper. The decision-making and information gathering is distributed, and the target prediction is based on the weighted least square method. The knowledge of target location and moving direction is obtained by time sets recorded by each sensor node. Time set is a collection of time values that describes when the target was detected by a particular node. All the locations are calculated in a topology map, which is an economical alternative to physical map. The results show that the proposed method has been able to search the target, even though the target changes the moving pattern from time to time. In addition, the robot avoids the obstacles and searches for the target in an effective way.

This paper studies the problem of using path constrained mobile sinks (MSs) to collect data in Wireless Sensor Networks (WSNs). Accounting the feature of nonuniform node distribution, the proposed protocol aims at balancing the energy consumption, including energy expenditure to transmit data packet and network overhead, to make the network operate as long as possible with all nodes alive. We propose an energy-aware unequal clustering algorithm and an energy-aware routing algorithm. Through simulations, we confirm that the proposed approach achieves longer network lifetime against alternatives.

This paper considers the data delivery delay problem in wireless sensor networks. The delivery delay is a significant measure when the data freshness is the first concern of users. The goal of this paper is to route delay-sensitive data to mobile nodes (M nodes) within an allowed latency. The data collection system is composed of a set of M nodes amounted on buses, a set of sensor nodes to detect the interested phenomenon and a set of special nodes deployed at bus stops to assist data routing. An optimization-based approach called improved-unusual message delivery path construction (I-UMDPC) is proposed. Considering the actual features of bus operation, two aspects of uncertainties are accounted in our approach: 1) the bus arrival time and 2) the stop duration. Extensive simulations as well as practical experiments on our testbed demonstrate that I-UMDPC is able to route delay-sensitive data reliably and efficiently and performs better than existing work.

Topology maps represent the layout arrangement of nodes while maintaining the connectivity. As it is extracted using connectivity information only, it does not accurately represent the physical layout such as physical voids, shape, and relative distances among physical positions of sensor nodes. A novel concept Maximum Likelihood-Topology Maps for Wireless Sensor Networks is presented. As it is based on a packet reception probability function, which is sensitive to the distance, it represents the physical layout more accurately. In this paper, we use a binary matrix recorded by a mobile robot representing the reception of packets from sensor nodes by the mobile robot at different locations along the robots trajectory. Maximum likelihood topology coordinates are then extracted from the binary matrix by using a packet receiving probability function. Also, the robot trajectory is automated to avoid the obstacles and cover the entire network within least possible amount of time. The result shows that our algorithm generates topology maps for various network shapes under different environmental conditions accurately, and that it outperforms the existing algorithms by representing the physical layout of the network more accurately.

A set-valued descriptor state estimation algorithm is given for determining all those descriptor state values consistent with a given uncertain descriptor system model, sensor model and a general class of uncertainty model. An approach for detecting faults or attacks signals that are corrupting the uncertain descriptor system and/or the set of sensor readings is also provided.

We study a problem of blanket coverage by employing a network of self-deployed, autonomous mobile sensors or agents. The coverage problem is to drive the mobile sensor network to form a sensor lattice that completely covers a two-dimensional (2-D) region between two boundaries. In particular, the sensors form into the so-called triangular lattice pattern, and it is optimal in terms of the minimum number of sensors required for complete coverage of a bounded 2-D set. A distributed motion coordination algorithm is proposed for the mobile sensors to address the coverage problem. The algorithm is developed based on some simple consensus algorithms that only rely on local information. To illustrate the proposed algorithm, numerical simulations have been carried out for a number of scenarios.

We consider the problem of reactively navigating an unmanned vehicle along an obstacle in the case where data about its boundary are limited to the distance along and the reflection angle of the ray perpendicular to the vehicle centerline. Such situation holds if e.g., the measurements are supplied by several range sensors rigidly mounted at nearly right angles. A sliding mode control law is proposed that drives the vehicle at a pre-specified distance from the boundary and maintains this distance afterwards. This is achieved without estimation of the boundary curvature and holds for obstacles with both convexities and concavities. Mathematically rigorous analysis of the proposed control law is provided, including explicit account for the global geometry of the obstacle. Computer simulations and experiments with real wheeled robots confirm the applicability and performance of the proposed guidance approach.

We propose a biologically inspired navigation algorithm and implement it on an intelligent wheelchair. The intelligent wheelchair demonstrates an excellent performance in detecting and avoiding static and moving obstacles under the guidance of the proposed algorithm, and it is able to safely and efficiently reach the target in a cluttered dynamic environment.

We study a problem of K-barrier coverage by employing a network of self-deployed, autonomous mobile robotic sensors. A distributed motion coordination algorithm is proposed for the mobile robotic sensors to address the coverage problem. The algorithm is theoretically developed based on some simple consensus algorithms that only rely on local information. By applying the algorithm to the sensors, K layers of sensor barriers are formed between two given points. To illustrate the proposed algorithm, numerical simulations are carried out for a number of scenarios.

When employing autonomous vehicles, it is desirable to use controllers which can be rigorously shown to always ensure safety is maintained. In this manuscript we compare two approaches for the problem of navigation through environments containing obstacles. The first uses boundary following to maintain an avoidance distance to obstacles, and the second uses a MPC-type algorithm to plan short range trajectories around detected obstacles, while ensuring the vehicle can be brought to a halt within the sensor radius. The controllers are subjected to analysis for robustness, and simulations are carried out with both a simple second order linear model and a realistic helicopter model for verification. The controller that planned ahead was found to give significantly better trajectories.

This paper presents a method for an efficient operation of a battery energy storage system (BESS) associated with frequency control problem. A control system model is proposed to simulate the BESS for frequency control application. A controller based on model predictive control (MPC) is designed for the reliable operation of the BESS for primary frequency regulation. A frequency prediction model based on Grey theory is also designed to optimize the performance of our predictive controller. The method is tested using real measurements from a real power grid in the presence of multiple and realistic physical system constraints. The simulation results depict the effectiveness of the proposed frequency regulation scheme.

The paper considers the problem of automatic path tracking by autonomous farming vehicles subject to wheel slips, which are characteristic for agricultural applications. Two guidance laws are proposed to solve this problem, and both explicitly take into account the constraints on the steering angle and ensure tracking an arbitrarily curved path. The first law is implemented by the pure sliding-mode controller, whereas the second one combines the sliding mode approach with a smooth nonlinear control law, using control chattering at the reduced amplitude as compared with the first law. Mathematically rigorous proofs of global convergence and robust stability of the proposed guidance laws are presented. In doing so, the slipping effects are treated as bounded uncertainties. Simulation results confirm the applicability and performance of the proposed guidance approach.

We consider a single Dubins-like mobile robot traveling with a constant longitudinal speed in a planar region supporting an unknown field distribution. A single sensor provides the distribution value at the current vehicle location. We present a new sliding mode control method for tracking environmental level sets: the controller drives the vehicle to the set where the distribution assumes a pre-specified value and ensures that the vehicle circulates along this set afterwards. The proposed control algorithm does not employ gradient estimation and is non-demanding with respect to both computation and motion. Its mathematically rigorous analysis and justification are provided. The effectiveness of the proposed guidance law is confirmed by illustrative examples and computer simulations.

Tracking a target from a video stream (or a sequence of image frames) involves nonlinear measurements in Cartesian coordinates. However, the target dynamics, modeled in Cartesian coordinates, result in a linear system. We present a robust linear filter based on an analytical nonlinear to linear measurement conversion algorithm. Using ideas from robust control theory, a rigorous theoretical analysis is given which guarantees that the state estimation error for the filter is bounded, i.e., a measure against filter divergence is obtained. In fact, an ellipsoidal set-valued estimate is obtained which is guaranteed to contain the true target location with an arbitrarily high probability. The algorithm is particularly suited to visual surveillance and tracking applications involving targets moving on a plane.

In this paper, we consider a group of wheeled mobile robots, where each robot has very limited information on other robots in the group. We propose a simple bio-inspired decentralized navigation law, which guarantees that all robots will eventually move in the same direction and with the same speed.

This paper investigates the problem of estimating the location and velocity of a mobile agent using the received signal strength (RSS) measurements. Typical power measurements are inherently nonlinear and in this approach we derive a linear measurement scheme using an analytical measurement conversion technique which can readily be used with RSS measuring sensors. Power measurements are hence used in our robust version of a linear Kalman filter to estimate the dynamic parameters of the moving transmitter.

In this letter, we provide a robust version of a linear Kalman filter for target tracking based on a measurement conversion technique on the nonlinear radar measurements. We prove that the state estimation error is bounded in a probabilistic sense. We compare our approach with the current state of the art in converted radar measurement-based linear filtering.

We present a novel scheme for node localization in a delay-tolerant sensor network (DTN). In a DTN, sensor devices are often organized in network clusters that may be mutually disconnected. Some mobile robots may be used to collect data from the network clusters. The key idea in our scheme is to use this robot to perform location estimation for the sensor nodes it passes based on the signal strength of the radio messages received from them. Thus, we eliminate the processing constraints of static sensor nodes and the need for static reference beacons. Our mathematical contribution is the use of a robust extended Kalman filter (REKF)-based state estimator to solve the localization. Compared to the standard extended Kalman filter, REKF is computationally efficient and also more robust. Finally, we have implemented our localization scheme on a hybrid sensor network test bed and show that it can achieve node localization accuracy within 1 m in a large indoor setting.

This paper provides mobility estimation and prediction for a variant of GSM network which resembles an adhoc wireless mobile network where base stations and users are both mobile. We propose using Robust Extended Kalman Filter (REKF)as a location heading altitude estimator of mobile user for next node (mobile-base station)in order to improve the connection reliability and bandwidth efficiency of the underlying system. Through analysis we demonstrate that our algorithm can successfully track the mobile users with less system complexity as it requires either one or two closest mobile-basestation measurements. Further, the technique is robust against system uncertainties due to inherent deterministic nature in the mobility model. Through simulation, we show the accuracy and simplicity in implementation of our prediction algorithm.