A novel algorithm which combined the merits of the Clustering strategy and the Compressive Sensing-based (CS-based) scheme was proposed in this paper. The lemmas for the relationship between any two adjacent layers, the optimal size of clusters, the optimal distribution of the Cluster Head (CH) and the corresponding proofs were presented firstly. In addition, to alleviate the “Hot Spot Problem” and reduce the energy consumption resulted from the rotation of the role of CHs, a third role of Backup Cluster Head (BCH) as well as the corresponding mechanism to rotate the roles between the CH and BCH were proposed. Subsequently, the Energy-Efficient Compressive Sensing-based clustering Routing (EECSR) protocol was presented in detail. Finally, extensive simulation experiments were conducted to evaluate its energy performance. Comparisons with the existing clustering algorithms and the CS-based algorithm verified the effect of EECSR on improving the energy efficiency and extending the lifespan of WSNs.

A comprehensive analysis on the energy-efficient strategy in static Wireless Sensor Networks (WSNs) that are not equipped with any energy harvesting modules is conducted in this article. First, a novel generic mathematical definition of Energy Efficiency (EE) is proposed, which takes the acquisition rate of valid data, the total energy consumption, and the network lifetime of WSNs into consideration simultaneously. To the best of our knowledge, this is the first time that the EE of WSNs is mathematically defined. The energy consumption characteristics of each individual sensor node and the whole network are expounded at length. Accordingly, the concepts concerning EE, namely the Energy-Efficient Means, the Energy-Efficient Tier, and the Energy-Efficient Perspective, are proposed. Subsequently, the relevant energy-efficient strategies proposed from 2002 to 2019 are tracked and reviewed. Specifically, they respectively are classified into five categories: the Energy-Efficient Media Access Control protocol, the Mobile Node Assistance Scheme, the Energy-Efficient Clustering Scheme, the Energy-Efficient Routing Scheme, and the Compressive Sensing--based Scheme. A detailed elaboration on both of the basic principle and the evolution of them is made. Finally, further analysis on the categories is made and the related conclusion is drawn. To be specific, the interdependence among them, the relationships between each of them, and the Energy-Efficient Means, the Energy-Efficient Tier, and the Energy-Efficient Perspective are analyzed in detail. In addition, the specific applicable scenarios for each of them and the relevant statistical analysis are detailed. The proportion and the number of citations for each category are illustrated by the statistical chart. In addition, the existing opportunities and challenges facing WSNs in the context of the new computing paradigm and the feasible direction concerning EE in the future are pointed out.

Meta-surfaces, also known as reconfigurable intelligent surfaces (RISs), have emerged as a cost-effective, low-power consumption, and flexible solution for enabling multiple applications in Internet of Things (IoT). However, in the context of meta-surface-assisted multipair IoT communications, significant interference issues often arise among multiple channels. This issue is particularly pronounced in scenarios characterized by Line-of-Sight (LoS) conditions, where the channels exhibit low rank due to the significant correlation in propagation paths. These challenges pose a considerable threat to the quality of communication when multiplexing data streams. In this article, we introduce a meta-surface-aided communication scheme for multipair interactions in IoT environments. Inspired by holographic technology, a novel compensation method on the whole meta-surface has been proposed, which allows for independent multipair direct data streams transmission with low interference. To further reduce correlations under LoS channel conditions, we propose a vortex beam-based solution that leverages the low-correlation property between distinct topological modes. We use different vortex beams to carry distinct data streams, thereby enabling distinct receivers to capture their intended signal with low interference, aided by holographic meta-surfaces. Moreover, a prototype has been performed successfully to demonstrate a two-pair multinode communication scenario operating at 10 GHz with quadrature phase shift keying (QPSK)/16-QAM modulation. The experiment results demonstrate that, even under LoS conditions, the isolation between the two-pair channels exceeds 21 dB. This allows receiving users to undertake simultaneous, same-frequency multiplexed data transmission under extremely low-interference conditions, with a real-time demodulation bit error rate (BER) remaining below $3.8 \times 10^{-3}$ at achievable signal-to-noise ratio (SNR) conditions. Through the convergence of holographic meta-surfaces and vortex beams, we present a fresh perspective on achieving efficient, low-interference multipair IoT communications.

A novel energy-efficient clustering algorithm was proposed which aimed at improving the energy efficiency of WSNs via reducing and balancing energy consumption in this paper. The lemma concerning the dual-cluster-head mechanism which was designed to reduce the energy overhead during the process of rotation of Cluster Heads (CHs) was proposed and proven at first. In addition, a non-cooperative game model was presented with the purpose of balancing the energy consumption among the Cluster Heads. Besides, the Nash Equilibrium Point (NEP) of the game model was presented and the corresponding proof was provided. Subsequently, the Energy-efficient Clustering algorithm combined Game theory and Dual-cluster-head (ECGD) mechanism was detailed, which took the energy efficiency in both of the intra-cluster and inter-cluster communication into consideration. Finally, extensive experiments were conducted via simulation and the simulation results were compared with the existing Clustering strategies in terms of energy efficiency and network performance. The analyses of results have shown that the ECGD can improve energy efficiency and extend the network lifespan effectively.

Extracting vehicle information is of great significance to the construction of the Internet of Vehicles (IoV). Vehicle logo detection (VLD) technology can effectively extract vehicle information. Due to the complex traffic environment, existing methods have difficulty to accurately detect the vehicle logo, especially when the vehicle logo has motion blur. To alleviate these problems, a new approach is proposed to detect vehicle logo under motion blur with the combination of Filter-DeblurGAN and VL-YOLO. Filter-DeblurGAN possesses a judgment mechanism, which can determine whether the image needs to be deblurred based on the degree of blur of each image. It can also deblur images with arbitrary resolution. Filter-DeblurGAN solves the defect that DeblurGAN lacks the judgment mechanism and is too harsh on resolution. It can be directly applied to the deblurring process of VLD. We also propose a method using VL-YOLO and Eliminate Outliers Cluster (EOC) algorithm to further improve the detection accuracy. VL-YOLO achieves accurate detection of the vehicle logo by constructing a deeper multi-scale detection network and using the initial candidate boxes provided by EOC algorithm. The EOC algorithm is not affected by outliers. In order to further promote the research of VLD, we have labeled a new vehicle logo dataset named LOGO-17, which contains 17 different categories of vehicle logos. The experimental results demonstrate that the proposed method achieves good detection accuracy in the environment of motion blur, and outperforms existing methods.