Extensive Simulation Results Research Articles

PMU-based Real-time Distribution System State Estimation Considering Anomaly Detection, Discrimination and Identification

In this paper, a real-time state estimation platform for distribution grids monitored by Phasor Measurement Units (PMUs) is developed, tested, and validated using Real Time Digital Simulator (RTDS). The developed platform serves as a proof-of-concept for potential implementation in an existing 50 kV ring network of the Dutch distribution utility Stedin medium voltage distribution grid located in the southwest (Zeeland area) of the Netherlands. To catch up with the fast sampling rates of PMUs, the platform incorporates computationally efficient techniques for state estimation and detection, discrimination and identification of anomalies like bad data and sudden load changes. Forecasting Aided State Estimation has been utilized to enable measurement innovations needed for fast anomaly detection, discrimination, and identification, whilst the Extended Kalman Filter (EKF) algorithm is selected to provide fast state forecasting and filtering. The platform has been tested under various normal and abnormal operating conditions considering different statistical properties of measurement noise as well as different bad data and sudden load change scenarios. To demonstrate advantages and disadvantages for embedding EKF into the platform, EKF is compared with Unscented Kalman Filter (UKF) in terms of estimation accuracy, computational efficiency, and compatibility with the module for anomaly detection, discrimination, and identification. The results of extensive simulations provide good hints about the feasibility of PMU-based real-time state estimation for the Stedin distribution grid.

Maximizing the energy efficiency in intelligent reflecting surface‐aided unmanned aerial vehicle data collection system in presence of malicious jammers

AbstractIn this article, intelligent reflecting surface (IRS) aided an unmanned aerial vehicle (UAV) data collection system is investigated, where an IRS is deployed to assist in the communication from a cluster of Internet‐of‐Things (IoT) devices to a UAV in the presence of a jammer. We aim to improve the energy efficiency (EE) via the joint design of UAV trajectory, IRS passive beamforming, device power allocation and communication scheduling. However, the formulated nonlinear fractional programming problem is challenging to solve due to its nonconvexity and coupled variables. To overcome the difficulty, we propose an alternating optimization based algorithm to solve it sub‐optimally by leveraging the Dinkelbach's algorithm, successive convex approximation (SCA) technique, and block coordinate descent (BCD) method. Extensive simulation results show that the proposed design can significantly improve the anti‐jamming performance. In particular, for the remote jammer case, the proposed design can largely shorten the flight path and thus decrease the energy consumption via the signal enhancement; while for the local jammer case, which is deemed highly challenging in conventional systems without IRS since the retreating away strategy becomes ineffective, our proposed design even achieves a higher performance gain owing to the efficient jamming signal mitigation.

Analysis of predicted mean vote-based model predictive control for residential HVAC systems

Model Predictive Control (MPC) is an advanced process control method that has attracted much attention in building heating, ventilation, and air conditioning (HVAC) systems. This paper analyzes the optimal precooling performance in residential buildings using MPC with two different comfort indices, namely, temperature and predicted mean vote (PMV). It first formulates, for each comfort index, an optimization problem that accounts for different factors, such as weather, home thermal condition, prediction horizon, time-of use (TOU) utility rate, and rated cooling capacity. The problem is then solved, resulting in an MPC strategy that determines the HVAC on/off control signal and minimizes energy cost over a receding time horizon while maintaining thermal comfort. The energy performance difference between temperature-based and PMV-based MPC strategies is subsequently investigated, especially in light of the interior wall surface temperature and under different combinations of the factors. Extensive simulation results demonstrated that the proposed MPC strategies are adaptive and their performances depend primarily on weather, home thermal condition, and prediction horizon, while the impact of TOU utility rate and rated cooling capacity is relatively small. Because the PMV-based MPC strategy can take advantage of the lower interior wall surface temperature due to precooling, it resulted in 8–45% cost savings for the scenarios investigated and an average increase of 0.042–0.113 in the absolute value of the PMV index compared to the temperature-based MPC strategy.

Robust Trajectory and Resource Optimization in UAV-Enabled IoT Networks under Probabilistic LoS Channel in Presence of Jammers

This paper studies the anti-jamming problem of unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) communication networks in the presence of a jammer under the accurate probabilistic line-of-sight (LoS) model. Our goal is to maximize the information collection throughput of the system under the assumption that only the jammer's approximate location is known. To this end, we formulate a throughput maximization problem by optimizing the UAV trajectory, the IoT devices' transmit power, and communication scheduling under the accurate real-time probabilistic LoS channel. However, the proposed optimization problem is non-convex and coupled, and hence intractable to be solved. In order to tackle the problem, a robust iterative algorithm is proposed by leveraging the block coordinate descent (BCD) method, the successive convex approximation (SCA) technology, the difference of convex (D.C) programming approach, and the S-procedure. Extensive simulation results show that our proposed algorithm significantly improves the system throughput while achieving a practical anti-jamming effect compared with the benchmark algorithms.

A linearity enhancement technique for low-noise transconductance amplifiers in SAW-less receivers

A new linearization technique for the transconductance of MOS transistors has been proposed. In the presented method a new composite transistor has been proposed. Moreover, composite transistors with different operating points are connected in parallel to boost the transconductance and cancel the distortion of the overall structure. The superiority of the proposed technique over the previously reported methods is that it also improves the linearity for high-power input signals. A detailed analysis has been given to prove the effectiveness of the newly proposed technique. In order to verify the analysis, a Low-Noise Transconductance Amplifier has been designed exploiting the presented approach and the extensive post-layout simulation results demonstrate at least 26 dB and 7 dB improvements in IIP3 and 1-dB compression point, respectively.

Network Lifetime Optimization in Multi-hop Industrial Cognitive Radio Sensor Networks

Industrial cognitive radio sensor networks (ICRSNs) extend channel resources by occupying the vacant licensed channels in the absence of licensed users. In ICRSNs, industrial devices should switch to a common available channel to set up a communication link. However, channel switching leads to severe energy consumption. As the energy resources of battery-powered industrial devices are limited, it is crucial to carefully allocate channels to prolong the network lifetime of multi-hop ICRSNs. This paper is the first work that studies the channel allocation problem to optimize the network lifetime by considering the channel-switching (CS) energy consumption and the time-critical requirements of industrial applications. The problem is formulated to maximize the minimum residual energy at each round of data transmission, which is linearized as integer linear programming. As the channel allocation results will affect the residual energy at subsequent rounds, we propose a switching distance-optimized channel allocation (SDOCA) scheme that shortens the CS distances to improve the residual energy of each device. Moreover, we analyze the characteristics of SDOCA, i.e., convergent CS distance and guaranteed end-to-end delay. Extensive simulation results show that SDOCA can adaptively allocate channels according to the end-to-end delay requirement and significantly prolong the network lifetime.

On the Performance of Channel Occupancy Rate Estimation With Iterative Threshold Setting in Cognitive Radios

With the knowledge of channel occupancy rate (COR), cognitive radios can significantly improve their performance in exploring and exploiting spectrum holes. However, most existing COR estimators suffer from overestimation or underestimation even at high signal-to-noise ratios (SNRs). The iterative threshold-setting algorithm (ITA) is promising to address this issue. In this work, we revisit ITA and provide a thorough theoretical analysis of ITA. First, we prove that ITA converges to the true COR with a sufficiently large number of traffic samples. Then, we investigate its convergence when the number of traffic samples is small, and show that ITA deviates from the true COR especially at low SNRs. To address this issue, we analyze the upper bound of the number of traffic samples required to achieve a certain estimation error, and further propose an improved ITA (iITA). The proposed iITA enables us to achieve a prespecified estimation accuracy by adaptively adjusting the number of traffic samples. Extensive simulation results are provided, which validate our analyses and demonstrate the superior performance of ITA and iITA compared to state-of-the-art COR estimators.

Trajectory Design and Communication Resources Allocation for Wireless Powered Secure UAV Communication Systems

Benefiting from the intrinsic convenience, flexibility, low cost, and unmanned aerial vehicles (UAVs) are envisioned to play an important role in future wireless communication systems. However, the limited on-board battery capacities and the broadcast characteristic of air-to-ground wireless channels bring some nonnegligible challenges on achieving secure UAV communications. To tackle these challenges, we propose to apply both the wireless power transfer and physical layer security techniques in a UAV communication system. We investigate the secrecy rate maximization problem by jointly optimizing the wireless charging duration, the trajectory, and transmit power of the UAV, subject to limited battery capacity, maximum flying speed, and energy-harvesting causal constraints. To solve this nonconvex problem, we first transform this problem into an equivalent problem with a smooth objective function, and then propose a low-complexity trajectory design and communication resources allocation algorithm for settling the reformulated problem via employing alternating optimization and SCP. Specifically, the reformulated problem is decomposed into three independent subproblems: the wireless charging duration optimization, the transmit power control, and the trajectory design of the UAV. Subsequently, we iteratively solve one of these three subproblems, while the others being fixed until the proposed algorithm converges. Extensive simulation results are provided to demonstrate that the proposed algorithm can significantly enhance the secrecy rate relative to the benchmark algorithms in absence of rechargeable battery or with fixed trajectory and transmit power.

A Computational Developmental Model of Perceptual Learning for Mobile Robot

During the perceptual learning (PL) of a mobile robot, how to improve the efficiency of its behavior decision is a great challenge. The existing methods suffer from the less flexibility and low efficiency. This article proposes a novel methodology to address these problems. It mimics the principle that the human’s brain can still think, even when he/she neither receives any sensory stimulus nor performs any action, during the off-task process, a gated self-organization mechanism is used to trigger the “brain” of the mobile robot to work, analyze the scenarios that it encountered in the former tasks to decide the best moving direction, then store the sensed scenario information by means of the lateral excitation of the internal neurons in a developmental network (DN), and set up the weight connections from the internal area to high-level decision-making area of the DN. In the following PL, if the robot encounters a similar scenario, it can make a better and faster behavior decision. Therefore, this methodology can greatly enhance the efficiency of behavior decision with the limited training samples, without retraining in facing a new scenario. Most importantly, this methodology makes the robot continuously improve its intelligence through the autonomous learning, even in offline state. Extensive simulation and experiment results of the mobile robot PL demonstrate its potential. Since it is task nonspecific, the same learning principles are potentially suitable for other fields. To the best of our knowledge, it is the first trial to apply the transfer learning through the lateral excitation mechanism of internal neurons to the robot field with the emergent representation.

Continuous Nonsingular Terminal Sliding-Mode Control With Integral-Type Sliding Surface for Disturbed Systems: Application to Attitude Control for Quadrotor UAVs Under External Disturbances

This article proposes a continuous nonsingular terminal sliding-mode control with integral-type sliding surface (CNTSMC-ISS) framework for disturbed systems, in which we consider two types of finite-time controllers: the state feedback CNTSMC-ISS and the disturbance-observer-based CNTSMC-ISS. Compared with the existing sliding-mode controllers, the noteworthy contributions of two finite-time controllers in the proposed CNTSMC-ISS framework are the alleviation of the chattering phenomenon, the fast finite-time stability, and the singularity-free and ease-of-implementation characteristics. In the proposed CNTSMC-ISS framework, we first introduce a nonsingular integral terminal sliding-mode surface (NITSMS) such that the finite-time convergence of the system states to zero in the sliding phase is ensured and the singularity problem is avoided. Besides, a finite-time observer is developed to recover the external disturbance. Then, based on the constructed NITSMS with the supertwisting-like algorithm, the state feedback CNTSMC-ISS and the disturbance-observer-based CNTSMC-ISS are proposed, which generate the continuous control signals and guarantee the fast finite-time convergence of the system states to the designed sliding surface. Rigorous finite-time stability of the closed-loop system under two proposed controllers is provided. Finally, we apply two finite-time controllers in the proposed CNTSMC-ISS framework to the attitude control for quadrotor unmanned aerial vehicles under external disturbances. Extensive simulation and experimental results are illustrated to prove the effectiveness of the proposed attitude controllers.

DL-Based Anomaly Detection at the Physical-Layer of Cognitive Radio by Deep Support Vector Data Description

Aiming at achieving anomaly detection at the physical-layer of cognitive radio, a Deep Support Vector Data Description (Deep SVDD) based anomaly detection scheme is proposed in this paper. Specifically, two novel techniques with different sensitivities related to Signal-to-Noise Ratio (SNR)-walls are proposed for two typical application scenarios. On one hand, for studying the optimal boundary of the desired signal in the feature space, an Improved Deep SVDD (IDSVDD) is conceived for extracting low-dimensional features of the samples represented in the time-frequency domain. On the other hand, a joint algorithm of Deep SVDD and Modulation Classification (JDSMC) is developed, which is capable of extracting low-dimensional features utilizing deep Convolutional Neural Networks (CNN). Extensive simulation and experimental results demonstrate that: 1) the detection performance and the real-time performance of the proposed techniques outperform conventional control algorithms. 2) The proposed techniques can effectively detect abnormal signals in complex electromagnetic environments and effectively improve the self-aware ability of Cognitive Radio Network (CRN). 3) Over-the-Air (OTA) measurements using Software Defined Radio (SDR) in the lab validate the proposed techniques’ anomaly detection performance.

Tenant-Grained Request Scheduling in Software-Defined Cloud Computing

Cloud providers host various services for tenants’ requests (e.g., software-as-a-service) and seek to serve as many requests as possible for revenue maximization. Considering a large number of requests, the previous works on fine-grained request scheduling may lead to poor system scalability (or high schedule overhead) and break tenant isolation. In this article, we design a tenant-grained request scheduling framework to conquer the above two disadvantages. We formulate the tenant-grained request scheduling problem as an integer linear programming and prove its NP-hardness. We consider two complementary cases: the offline case (where we know all request demands in advance), and the online case (where we have to make immediate scheduling decisions for requests arriving online). A normalization-based algorithm with an approximation factor of <inline-formula><tex-math notation="LaTeX">$ {O}(1)$</tex-math></inline-formula> is proposed to solve the offline problem and a primal-dual-based algorithm with a competitive ratio of <inline-formula><tex-math notation="LaTeX">$[(1-\epsilon), {O}(\log 3\cdot n+\log (1/\epsilon))]$</tex-math></inline-formula> is designed for the online scenario, where <inline-formula><tex-math notation="LaTeX">$\epsilon \in (0,1)$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the number of racks in the cloud. We also discuss how to integrate our proposed algorithms with the previous (fine-grained) request scheduling mechanism. Extensive simulation and experiment results show that our algorithms can obtain significant performance gains, e.g., the online algorithm reduces the scheduler's overhead more than <inline-formula><tex-math notation="LaTeX">$90\%$</tex-math></inline-formula> and achieves tenant isolation, while obtaining similar network performance (e.g., throughput) compared with the fine-grained request scheduling methods.

Sustainable Maintenance of Connected Dominating Set by Solar Energy Harvesting for IoT Networks

A new scheme, Sustainability in Dominating Set (SID), to construct and maintain a sustainable and adaptive node disjoint weighted connected dominating set (CDS) is presented for solar energy harvesting-enabled Internet of Things (IoT) networks. The primary contribution of this work is the design of an approximation algorithm for CDS construction and maintenance in energy harvesting-enabled IoT networks, which is triggered when energy of any CDS node falls below a threshold. This is a new CDS construction approach of approximation ratio <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\frac {\delta w(G) + 1}{2} + (1 + \ln 5) |O_{p}|$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta w(G)$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$|O_{p}|$ </tex-math></inline-formula> are weighted average degree of the network and optimal number of Steiner nodes, respectively. The extensive simulation results show that per-slot-rotation-based SID (SIDS) improves residual energy (saves by 14.3%, 25.6%, 8.25% and 8%), message complexity (reduces by 88.5%, 34% and 72.75% and 83.25%), and average CDS size (reduces by 9.01%, 9.09%, 41.75%, and 54.5%) for two scenarios and cloud cover 0.25, 0.5 and 0.8, compared to LMW, Improved LMW (ILMW), DataCDS and UCDS, respectively. Merely based on threshold switching, SID enhances residual energy, message complexity, and CDS size by 3.08%, 51.88%, and 4% for all scenarios and cloud covers compared to SIDS, respectively.

Knowledge Transfer-Based Multiagent Q-Learning for Medium Access in Dense Cellular Networks

Inter-cell interference in dense cellular networks causes severe network performance degradation because the base stations (BSs) and devices cannot fully utilize all the network status information in practice. Reinforcement learning enables the wireless network to find an effective transmission strategy with limited information and the network entities to operate in a distributed manner. We herein propose a medium access algorithm based on multi-agent reinforcement learning by exchanging knowledge of transmissions from other cells. Specifically, a state mapping function between the device and the BS and a transfer value iteration are proposed. The BS transfers reward processes derived from inter-cell interference so that the partially observed medium of devices is expanded to monitor entire networks. Extensive simulation results show that simple knowledge transfer can help performance improvement in terms of network stability and throughput.

Joint optimization of task caching, computation offloading and resource allocation for mobile edge computing

Applications with sensitive delay and sizeable data volumes, such as interactive gaming and augmented reality, have become popular in recent years. These applications pose a huge challenge for mobile users with limited resources. Computation offloading is a mainstream technique to reduce execution delay and save energy for mobile users. However, computation offloading requires communication between mobile users and mobile edge computing (MEC) servers. Such a mechanism would difficultly meet users' demand in some data-hungry and computation-intensive applications because the energy consumption and delay caused by transmissions are considerable expenses for users. Caching task data can effectively reduce the data transmissions when users offload their tasks to the MEC server. The limited caching space at the MEC server calls for judiciously decide which tasks should be cached. Motivated by this, we consider the joint optimization of computation offloading and task caching in a cellular network. In particular, it allows users to proactively cache or offload their tasks at the MEC server. The objective of this paper is to minimize the system cost, which is defined as the weighted sum of task execution delay and energy consumption for all users. Aiming at establishing optimal performance bound for the system design, we formulate an optimization problem by jointly optimizing the task caching, computation offloading, and resource allocation. The problem is a challenging mixed-integer non-linear programming problem and is NP-hard in general. To solve it efficiently, by using convex optimization, Karmarkar's algorithm and the proposed fast search algorithm, we obtain an optimal solution of the formulated problem with manageable computational complexity. Extensive simulation results show that in comparison to some representative benchmark methods, the proposed solution can effectively reduce the system cost.

BETA: Beacon-Based Traffic-Aware Routing in Vehicular Ad Hoc Networks

Data transmission in Vehicular Ad Hoc Networks (VANETs) often suffers from routing interruptions due to the unstable communication links between vehicles. Over the past decades, many traffic-aware routing protocols have been proposed to alleviate routing interruptions by sensing traffic conditions. However, in most traffic-aware routing protocols, vehicles must transmit a large number of control packets to accumulate traffic information, which may degrade network performance due to the resulting intense competition over the wireless medium. Instead of using control packets, we propose to leverage the beacon mechanism that has been widely used in VANETs to realize traffic awareness. Vehicles broadcast beacons to exchange necessary information with their neighbors periodically. We can leverage this information exchange process among vehicles to replace control packets. To realize this idea, first, a mathematical analysis is provided to demonstrate its feasibility. Then, we propose a concrete protocol to address the technical challenges of using beacons. Extensive simulation results show that our protocol performs better than the state-of-the-art counterparts regarding packet delivery ratio, average delivery time, and network overhead.

Networked predictive control method of multi‐vehicle cooperative control at communication‐constrained unsignalized multi‐intersection

AbstractTo reduce the degradation of multi‐vehicle cooperative control performance for connected and automated vehicles caused by time‐varying communication delays when the edge‐cloud is used for centralized control through the vehicle to infrastructure communication, and further improve the traffic efficiency based on driving safety at unsignalized multi‐intersection. A networked predictive control method based on an improved model‐free adaptive predictive control method and multi‐intersection distributed cooperative control scheme is proposed to realize the multi‐vehicle cooperative control under the conditions of time‐varying communication delays at an unsignalized multi‐intersection system, including multi‐intersection edge‐cloud networked predictive control layer and multi‐vehicle car‐following control layer. The control objective can be calculated by the moving horizon prediction control scheme based on compact form dynamic linearization technology through the edge computing controller, and then assigns the anticipated speed to each target vehicle entering the intersection subsystem based on decoupling the unsignalized multi‐intersection system into the multiple networked control intersection subsystems. The extensive numerical simulation results confirmed the benefits of the proposed scheme compared to the benchmark methods.

Vibration suppression of a linear oscillator by a chain of nonlinear vibration absorbers with geometrically nonlinear damping

Nonlinear energy sink (NES) is a lightweight vibration absorber that is coupled to linear primary structure for passive vibration mitigation. In this work, we aim to investigate the effects of geometrically nonlinear damping on the capacity of multiple-degree-of-freedom (MDOF) NES to mitigate forced responses of the impulsively and harmonically loaded linear oscillator (LO). For the system under applied shock excitation, extensive simulation results show that the internal use of nonlinear damping enables the MDOF NES to rapidly and effectively absorb, transfer and dissipate the shock-induced energy for a broad range of initially imparted energies, particularly for the high or very high energy levels. The effectiveness of the optimized MDOF NES is further improved since the large-amplitude oscillations of the LO are reduced to a sufficiently low level during initial regime of motion. When the LO is subjected to harmonic excitations, the analytical study is performed to obtain the slow invariant manifold (SIM) by means of complexification-averaging (CX-A) method. The complex geometry of SIM reveals the energy transfer paths when the system reaches steady state. The global system behavior (strongly modulated response or periodic oscillation regimes) is analytically predicted by the singular points and ordinary equilibrium points, and a close match is achieved between theoretical analysis and numerical results. It is found that the proposed MDOF NES with nonlinear damping can eliminate the upper closed high-amplitude isolated branch, and the frequency bandwidth of multiple steady-state responses also decreases, the robustness and capacity of vibration attenuation by the proposed NES is dramatically enhanced. The findings in this work facilitate the design and broad applications in diverse engineering fields for the protection and safe operation of mechanical structures.

Simulation of an integrated spiral inductor and inter-digital capacitor in a buck micro converter

In this paper, a novel radiofrequency circuit is designed and developed for an integrated on-chip spiral inductor and inter-digital capacitor of the substrate. Results of extensive simulations concerning the influence of both spiral inductor and inter-digital capacitor geometrical parameters on quality factor value are presented. Two scenarios are considered in the simulation; the first operation of buck converter including an ideal capacitor and inductor and then its operation including an inter-digital capacitor and integrated spiral inductor. For both scenarios, we observed that their respective output voltages as well as their respective output currents present the same time responses.

Reaction loads analysis of floating offshore wind turbines: Methods and applications in the modal-based modeling framework

This paper investigates methods for reaction loads extraction in floating offshore wind turbines (FOWTs) within the modal-based aeroelastic modeling framework. Three methods belonging to two different principles (internal and external loads) are presented, where the two external load principle-based methods (‘AuxDOFs’ and ‘d’Alembert’) are rigorously developed for extracting 6-dimensional reactions loads at different cross sections. Verification and evaluation of the developed methods are performed using three different structural systems, as well as comparison with FAST. It is shown that ‘AuxDOFs’ and ‘d’Alembert’ methods are preferred over the internal load method due to the significantly improved accuracy and the ability to extract reactions in rigid substructures. Finally, the developed methods are applied to evaluate the influence of tower torsional degree of freedom (DOF) on FOWT loads for four different types of foundations, considering different load cases. Extensive simulation results indicate that neglecting the tower torsional DOF in the modal-based simulation tools can lead to underestimation of FOWT loads.