Clustering sensor nodes is an effective method in designing routing algorithms for Wireless Sensor Networks (WSNs), which improves network lifetime and energy efficiency. In clustered WSNs, cluster heads are the key nodes, they need to perform more tasks, so they consume more energy. Therefore, it is an important problem to select the optimal cluster heads. In this paper, we propose a clustering algorithm that selects cluster heads using an improved artificial bee colony (ABC) algorithm. Based on the standard ABC algorithm, an efficient improved ABC algorithm is proposed, and then the network cluster head energy, cluster head density, cluster head location and other similar factors are introduced into the improved ABC algorithm theory to solve the clustering problem in WSNs. In the network initialization period, all nodes have the same energy level, the improved ABC algorithm is used to optimize fuzzy C-means clustering to find the optimal clustering method. We also propose an energy-efficient routing algorithm based on an improved ant colony optimization for routing between the cluster heads and the base station. In order to improve energy efficiency and further improve network throughput, in the stable transmission phase, we introduce a polling control mechanism based on busy/idle nodes into intra-cluster communication. The performance of the proposed protocol is evaluated in several different scenarios. The simulation results show that the proposed protocol has a better performance compared to a number of recent similar protocols. INDEX TERMS WSN, clustering, energy efficiency, network lifetime, high throughput, polling, routing algorithm, artificial bee colony
Salp swarm algorithm (SSA) is a relatively new and straightforward swarm-based meta-heuristic optimization algorithm, which is inspired by the flocking behavior of salps when foraging and navigating in oceans. Although SSA is very competitive, it suffers from some limitations including unbalanced exploration and exploitation operation, slow convergence. Therefore, this study presents an improved version of SSA, called OOSSA, to enhance the comprehensive performance of the basic method. In preference, a new opposition-based learning strategy based on optical lens imaging principle is proposed, and combined with the orthogonal experimental design, an orthogonal lens opposition-based learning technique is designed to help the population jump out of a local optimum. Next, the scheme of adaptively adjusting the number of leaders is embraced to boost the global exploration capability and improve the convergence speed. Also, a dynamic learning strategy is applied to the canonical methodology to improve the exploitation capability. To confirm the efficacy of the proposed OOSSA, this paper uses 26 standard mathematical optimization functions with various features to test the method. Alongside, the performance of the proposed methodology is validated by Wilcoxon signed-rank and Friedman statistical tests. Additionally, three well-known engineering optimization problems and unknown parameters extraction issue of photovoltaic model are applied to check the ability of the OOSA algorithm to obtain solutions to intractable real-world problems. The experimental results reveal that the developed OOSSA is significantly superior to the standard SSA, currently popular SSA-based algorithms, and other state-of-the-artmetaheuristic algorithms for solving numerical optimization, real-world engineering optimization, and photovoltaic model parameter extraction problems. Finally, an OOSSA-based path planning approach is developed for creating the shortest obstacle-free route for autonomous mobile robots. Our introduced method is compared with several successful swarm-based metaheuristic techniques in five maps, and the comparative results indicate that the suggested approach can generate the shortest collision-free trajectory as compared to other peers.
Object detection is one of the main tasks of computer vision. Object detection algorithms usually rely on deep convolutional neural networks, which require the host device to have high computing capabilities, greatly limiting the application of object detection methods for mobile devices with limited computing capabilities, such as embedded devices. Among the current object detection algorithms, the you only look once (YOLO) series takes both speed and accuracy into consideration and is one of the most commonly used methods for object detection. In this article, TRC-YOLO is proposed, which improves the mean average precision (mAP) and real-time detection speed of the model while reducing the size of the model. In TRC-YOLO, the convolution kernel of YOLO v4-tiny is pruned and an expansive convolution layer is introduced into the residual module of the network to produce an hourglass Cross Stage Partial ResNet (CSPResNet) structure. A receptive field block (RFB) that simulates human vision is also added, increasing the receptive field of the model and strengthening the feature extraction ability of the network. In addition, the convolutional block attention module is applied, which combines spatial attention and channel attention, to enhance the effective features of the model and reduce the negative impact of noise on the model. The size of the TRC-YOLO model is 17.8 MB, which is 5.9 MB smaller than YOLO v4-tiny, and the model parameter is 2.983 billion floating point operations per second (BFLOP/s) (3.834 BFLOP/s less than YOLO v4-tiny). In addition, TRC-YOLO achieves a real-time performance of 36.9 frames per second on a Jetson Xavier NX, and its mAP on the PASCAL VOC dataset is 66.4% (3.83% higher than YOLO v4-tiny). In addition, the mAP of TRC-YOLO on the MS COCO dataset is 37.7%, which is 1.9% higher than that of the baseline model. K E Y W O R D SCBAM, dilated convolution, object detection, receptive field block (RFB), TridentNet, YOLO | INTRODUCTIONObject detection is a challenging task in the field of computer vision. Traditional object detection algorithms, such as histograms of oriented gradient (HOG) [1] and the deformable part-based model (DPM) [2], are mainly based on region selection using sliding windows but have high time complexity and cannot meet real-time requirements. In recent years, with the development of deep neural networks and the improvement of hardware computing power [3,4], a series of major breakthroughs with excellent performance have been made in the field of object detection.Compared with two-stage object detection algorithms, one-stage object detection algorithms, such as the single shot multibox detector (SSD) [5] and you only look once (YOLO) series [6][7][8][9][10], achieve a balance between speed and accuracy and have been widely used in practice. The YOLO series includes YOLO v1, YOLO v2, YOLO v3, and YOLO v4, the This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any...
The development of wireless communication technology has become increasingly important in the communications industry. How to allocate limited channel resources reasonably and reliably to each competing user is a problem that the access protocol of the MAC (Multiple Access Control) layer needs to solve. As an important way of random access, NP-CSMA (Non Persistent Carrier Sense Multiple Access) has a higher network throughput rate when the arrival rate is higher. This paper analyzes and improves the implemented NP-CSMA model, and obtains a three-slot NP-CSMA model. The mathematical tool MATLAB is used to analyze the network throughput, delay and energy efficiency of the model, and compare several random multiple access protocols adheres to the network throughput, they are NP-CSMA, Three-slot NP-CSMA, 1-persistent CSMA, P-persistent CSMA, and concludes that the three-slot NP-CSMA is significantly letter than several other multiple access protocols. Finally, using Quartus to design the three-slot NP-CSMA circuit, the statistical value of the circuit system is compared with the theoretical value of the model, and the error is less than 0.01, which proves that the system is reasonable in design and can be applied to wireless communication networks.
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