Search

Barrier coverage is an important research issue in wireless sensor networks (WSNs). Barrier gaps may exist due to deployment randomness or battery depletion. It is critical to address the problem with an energy-efficient method due to the high cost of mobile sensors. Besides, it is a challenge to introduce the realistic probabilistic sensing model of sensors while mending barrier gaps. In this paper, we study how to efficiently use mobile sensors with a probabilistic sensing model to improve barrier coverage. Firstly, we define safe cell and design algorithms to find safe cells and barrier gaps. Secondly, we study how to improve the barrier coverage with minimum number of mobile sensors . Thirdly, we divide minimum movement of mobile sensors problem into the simple case and general case, and the general case is resorted to Kuhn-Munkres (KM) algorithm. Simulation results are conducted to demonstrate the effectiveness and advantage of our proposed algorithms.

Optimization of energy management strategy (EMS) for fuel cell/battery/ultracapacitor hybrid electrical vehicle (FCHEV) is primarily aimed on reducing fuel consumption. However, serious power fluctuation has effect on the durability of fuel cell, which still remains one challenging barrier for FCHEVs. In this paper, we propose an optimized frequency decoupling EMS using fuzzy control method to extend fuel cell lifespan and improve fuel economy for FCHEV. In the proposed EMS, fuel cell, battery and ultracapacitor are employed to supply low, middle and high-frequency components of required power, respectively. For accurately adjusting membership functions of proposed fuzzy controllers, genetic algorithm (GA) is adopted to optimize them considering multiple constraints on fuel cell power fluctuation and hydrogen consumption. The proposed EMS is verified by Advisor-Simulink and experiment bench. Simulation and experimental results confirm that the proposed EMS can effectively reduce hydrogen consumption in three typical drive cycles, limit fuel cell power fluctuation within 300 W/s and thus extend fuel cell lifespan.

In order to improve transient response and robust tracking performance, an adaptive nonsingular terminal sliding mode control (ANTSMC) strategy is developed for bidirectional dc–dc converter (BDC) of hybrid energy storage systems (HESSs). By using the adaptive rules, the estimated values of load variations, external input voltage, input current, and output voltage can be obtained. Then, the voltage tracking error can be calculated based on the estimated value and the actual value. The fast terminal SMC strategy is proposed such that the tracking error converges in a finite time. Simulation results, compared with conventional PID, second-order sliding mode, show that output voltage can robustly track the reference voltage with fast response.

In order to extend fuel cell lifespan and improve fuel economy of electrical hybrid vehicle with fuel cell/battery/ultracapacitor (FCHEV), a frequency decoupling-based energy management strategy (EMS) for FCHEV using fuzzy control method is proposed. In detail, firstly, according to different characteristics of energy sources, required power of FCHEV is decomposed into three frequency ranges based on Harr wavelet transform and an adaptive-fuzzy filter. Secondly, based on the proposed frequency decoupling, the obtained three frequency required power is supplied by fuel cell-battery and ultracapacitor, respectively, which can guarantee power performance of vehicle and reduce pressure and power fluctuation on fuel cell and battery. Thirdly, for improving fuel economy, one fuzzy controller is proposed to split the power between fuel cell and battery. Finally, the proposed strategy in this paper is verified by advisor-simulink and experimental bench. Simulation and experimental results show that the proposed EMS can effectively reduce impact of power fluctuations on fuel cell, extend its lifespan and reduce fuel consumption on 7.94% compared to equivalent consumption minimization strategy.

In this paper, a hierarchical energy management strategy (EMS) based on low-pass filter and equivalent consumption minimization strategy (ECMS) is proposed in order to lift energy sources lifespan, power performance and fuel economy for hybrid electrical vehicles equipped with fuel cell, battery and supercapacitor. As for the considered powertrain configuration, fuel cell serves as main energy source, and battery and supercapacitor are regarded as energy support and storage system. Supercapacitor with high power density and dynamic response acts during great power fluctuations, which relives stress on fuel cell and battery. Meanwhile, battery is used to lift the economy of hydrogen fuel. In higher layer strategy of the proposed EMS, supercapacitor is employed to supply peak power and recycle braking energy by using the adaptive low-pass filter method. Meantime, an ECMS is designed to allocate power of fuel cell and battery such that fuel cell can work in a high efficient range to minimize hydrogen consumption in lower layer. The proposed EMS for hybrid electrical vehicles is modeled and verified by advisor-simulink and experiment bench. Simulation and experiment results are given to confirm effectiveness of the proposed EMS of this paper.

A reinforcement-learning-based energy management strategy is proposed in this paper for managing energy system of Fuel Cell Hybrid Electric Vehicles (FCHEV) equipped with three power sources. A hierarchical power splitting structure is employed to shrink large state-action space based on an adaptive fuzzy filter. Then, the reinforcement-learning-based algorithm using Equivalent Consumption Minimization Strategy (ECMS) is proposed for tackling high-dimensional state-action space, and finding a trade-off between global learning and real-time implementation. The power splitting policy based on experimental data is obtained by using reinforcement learning algorithm, which allows for many different driving cycles and traffic conditions. The proposed energy management strategy can achieve low computation cost, optimal fuel cell efficiency and energy consumption economy. Simulation results confirm that, compared with existing learning algorithms and optimization methods, the proposed reinforcement-learning-based energy management strategy using ECMS can achieve high computation efficiency, lower power fluctuation of fuel cell and optimal fuel economy of FCHEV.

Barrier coverage, an important research area with respect to camera sensor networks, consists of a number of camera sensors to detect intruders that pass through the barrier area. Existing works on barrier coverage such as local face-view barrier coverage and full-view barrier coverage typically assume that each intruder is considered as a point. However, the crucial feature (e.g., size) of the intruder should be taken into account in the real-world applications. In this paper, we propose a realistic resolution criterion based on a three-dimensional (3D) sensing model of a camera sensor for capturing the intruder’s face. Based on the new resolution criterion, we study the barrier coverage of a feasible deployment strategy in camera sensor networks. Performance results demonstrate that our barrier coverage with more practical considerations is capable of providing a desirable surveillance level. Moreover, compared with local face-view barrier coverage and full-view barrier coverage, our barrier coverage is more reasonable and closer to reality. To the best of our knowledge, our work is the first to propose barrier coverage for 3D camera sensor networks.

Correct and realistic sensing model is of great significance for camera sensor networks to perform target coverage task. In most previous works, each target is considered as a point. However, some crucial features (e.g., size) of the target should be considered in the real world. In this paper, we propose an effective resolution sensing model based on region of interest (ROI), in which rigorous theoretical analysis was used to get the factors influencing the sensing model. Performance results demonstrate that our sensing model with more practical considerations is capable of providing desirable surveillance level. To the best of our knowledge, our work is the first to propose the 3D sensing model based on ROI.