Recent years have witnessed the flourish of new promising technologies to prolong the lifetime of wireless sensor networks (WSNs). Employing mobile vehicles for wireless charging and data collection is able to balance the load of WSNs, and meanwhile, provide a reliable energy replenishment for sustainable network operations. Different from traditional charging scheduling policies where sensor nodes passively wait for the arrival of mobile vehicles, a novel dynamic clustering based mobile-to-cluster (M2C) scheme is proposed to optimize the service process for both sensor nodes and the vehicle in an active way. Requiring only local residual energy information, M2C refreshes the cluster-based network topology to ensure no sensor nodes will run out of energy before getting charged. By estimating the energy consumption of being a cluster head, the sensor nodes with residual energy close to the estimation are actively elected as head nodes. More head nodes with low residual energy will be visited, reducing the travel distance along with a higher energy efficiency of charging. Furthermore, the convergence and worst-case performance of M2C are theoretically analyzed. Performance evaluation results show that compared with the state-of-the-art schemes, our design reduces the travel distance and charging latency by about 10% and 50%, respectively.

This article studies the group coordinated control problem for distributed nonlinear multiagent systems (MASs) with unknown dynamics. Cloud computing systems are employed to divide agents into groups and establish networked distributed multigroup-agent systems (ND-MGASs). To achieve the coordination of all agents and actively compensate for communication network delays, a novel networked model-free adaptive predictive control (NMFAPC) strategy combining networked predictive control theory with model-free adaptive control method is proposed. In the NMFAPC strategy, each nonlinear agent is described as a time-varying data model, which only relies on the system measurement data for adaptive learning. To analyze the system performance, a simultaneous analysis method for stability and consensus of ND-MGASs is presented. Finally, the effectiveness and practicability of the proposed NMFAPC strategy are verified by numerical simulations and experimental examples. The achievement also provides a solution for the coordination of large-scale nonlinear MASs.

As the two classical power allocation methods in battery-supercapacitor hybrid energy storage systems, split-frequency methods and power-level methods have been developed separately for many years. In this article, we made an attempt to integrate the advantages of the two methods and proposed an adaptive frequency-split-based quantitative power allocation strategy. First, an adaptive power preallocation is quantitatively determined according to the state of charge (SoC) of batteries and supercapacitors. Then, a windowed fast Fourier transform (FFT)-based power spectrum calculation algorithm is designed to derive the power level corresponding to each sampling frequency. By mapping the preallocated power to the power spectrum, the split frequency is adaptively computed. Finally, the power allocation is implemented through a low-pass filter (LPF) with the derived split frequency. Extensive experiments verify that the proposed method provides an improved performance in suppressing the dc bus voltage fluctuations and protecting batteries when compared with existing methods.

The remaining useful life estimation has been widely studied for engineering systems. A system commonly works under varying operating conditions, which may affect the system degradation trajectory differently and consequently reduce the accuracy of remaining useful life estimation. In this paper, we propose CNN-XGB with extended time window to tackle this issue. Firstly, the extended time window is created by feature extension and time window processing in data preprocessing. In feature extension, multiple degradation features are extracted by an improved differential method, and these features are appended to the raw data as additional features. To make the time window cover more information for better prognostic accuracy, a time window padding method is used considering the problem of missing data in some samples. Secondly, a convolutional neural network architecture with multichannel 1*1 filter kernel is proposed considering the effect of varying operating conditions. Furthermore, to improve the prognostic robustness and avoid the sensitivity to the abnormal data, convolutional neural network and extreme gradient boosting are fused by model averaging (CNN-XGB). The validity of the proposed method is verified using aero-engine datasets from NASA.

This paper aims to solve the problem of the coordination of cloud-based model-free multiagent systems (CBMF-MASs) with communication constraints. To improve the data processing ability of multiagent systems for massive real-time data and decrease the communication burden of individual agents, cloud computing systems are utilized to establish the multiagent system. To actively compensate for network delays and data losses in all communication channels and coordinate the output of the CBMF-MAS, a novel data-driven networked distributed predictive control method (DDNDPC) is presented, which is independent of system's structure model and only relies on system's input and output data. Furthermore, the stability and consensus criterion of the CBMF-MAS are established by proposing a simultaneous analysis approach for the stability and consensus. Finally, the effectiveness and practicality of the DDNDPC method are verified through numerical simulations and cloud-based experimental tests. The achievements promote the application of large-scale multiagent systems in practice.

For periodical beacon broadcasting in cellular vehicle-to-everything (C-V2X) networks, a distributed reservation media access control (MAC) protocol, the sensing-based semi-persistent scheduling (SPS), is adopted. However, how to quantify the communication reliability and latency is an open issue, which is critical for low-latency and high-reliability services. In this paper, an analytical model for SPS is presented, based on which the impacts of beacon rate, range settings and system configuration on access collision probability and delay outage probability are quantified. The analytical model provides important insights and guideline to adapt and optimize protocol parameters including the sensing range, transmit power and resource reservation. The enhanced MAC protocol can maintain high-reliability and low-latency services with a wide range of vehicle density. Simulations are conducted to validate the analysis and the results demonstrate that the proposed MAC enhancement solution can reduce collision probability while ensuring the delay outage probability based on service requirements.