Schedule-aware power management for energy-efficiency improvement in IEEE 802.11u WLAN by Di ZHANG

“…A mobile terminal supporting the IEEE 802.11u standard can automatically access a wireless local area network within the coverage of a network service provided by a wireless local area network.Aiming at the characteristics of IEEE 802.11u WLAN,a time series sensing based PSM (S-PSM) scheme was proposed.The transmission sequence information was broadcasted using a generic advertisement service (GAS) defined in the IEEE 802.11u standard,and all terminals controlled their own operating states according to the sequence information.In order to reduce the collision probability of the contention channel,a response contention window was introduced.When there was no data packet in the access point (AP),the AP broadcasted the GAS frame and activated the idle timer.All terminals went to sleep until the idle timer expires.The simulation results show that compared with the proposed traditional PSM,the scheme can significantly reduce power consumption and improve terminal energy-efficiency.…”
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RAW online regrouping method in IEEE 802.11ah protocol for industrial Internet of things by Yanjing SUN, Junqiu YANG, Yan CHEN, Yanfen WANG, Xiaojing CHEN, Fengzhen LIU, Zhi SUN

“…Aiming at the problem that the standard RAW mechanism cannot adjust to changes in the network and the grouping depends on continuous AIDs,a RAW regrouping method based on a pseudo-random sequence was proposed.The AP assigned a pseudo-random sequence to the node as the address code during the association phase,and used compound code to carry groups information during the grouping phase.It implemented online regrouping of discontinuous AID nodes and reduced the overhead of regrouping.In order to rationally allocate resources in RAW and solve the problem of load imbalance between groups,a RAW regrouping method based on expected channel time was proposed.Based on channel utilization,the RAW groups,RAW duration,and numbers of RAW were optimized.NS3 simulation results show that the proposed method significantly improves the performance of IEEE 802.11ah protocol networks.…”
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Analysis of the throughput in full-duplex WLAN by Chen ZHAO, Yunzhou LI, Xibin XU, ANGJing W

“…WLAN is one of the important ways of accessing mobile internet.However，the performance of WLAN declines rapidly as the number of users increases.Improving the performance of WLAN with high density deployment is an important research direction of WLAN technology.Distributed coordination function（DCF）which was the basic media access method of IEEE 802.11 in infrastructure-based networks was modeled and the bottleneck of downlink throughput was pointed out.Taking advantage of full-duplex technology where there was a breakthrough in recent years，a new MAC protocol was designed and analyzed.By providing the AP full-duplex transmitting ability and simple modifications to traditional DCF protocol，the new protocol made the downlink throughput breaking through the bottleneck in infrastructure-based WLAN and increased the total throughput by more than 60%.…”
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RTRS algorithm in low-power Internet of things by Yuchen CHEN, Yuan CAO, Laipeng ZHANG, Lianghui DING, Feng YANG

“…Considering the feature of periodical uplink data transmission in IEEE 802.11ah low-power wide area network (LWPAN),a real-time RAW setting (RTRS) algorithm was proposed.Multiple node send data to an access point (AP),and the uplink channel resources were divided into Beacon periods in time.During a Beacon period,AP firstly predicted the next data uploading time and the total amount of devices that will upload data in the next Beacon period.The AP calculated the optimal RAW parameters for minimum energy cost and broadcasted the information to all node.Then all devices upload data according to the RAW scheduling.The simulation results show that the current network state can be predicted accurately according to the upload time of the terminal in the last period.According to the predicted state,raw configuration parameters can be dynamically adjusted and the energy efficiency can be significantly improved.…”
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Enhancing Sentiment Analysis and Rating Prediction Using the Review Text Granularity (RTG) Model by Rajesh Garapati, Manomita Chakraborty

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VRU-YOLO: A Small Object Detection Algorithm for Vulnerable Road Users in Complex Scenes by Yunxiang Liu, Yuqing Shi

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Automated Morphology Detection of Nail-Fold Capillaries Through Enhanced Object Detection Network by Hang Thi Phuong Nguyen, Hieyong Jeong

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FECI-RTDETR a Lightweight Unmanned Aerial Vehicle Infrared Small Target Detector Algorithm Based on RT-DETR by Renzheng Xue, Shijie Hua, Haiqiang Xu

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Advanced Air Quality Forecasting Using an Enhanced Temporal Attention-Driven Graph Convolutional Long Short-Term Memory Model With Seasonal-Trend Decomposition by Yuvaraja Boddu, A. Manimaran, B. Arunkumar, M. Sucharitha, J. Suresh Babu

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Link Prediction in Complex Hyper-Networks Leveraging HyperCentrality by Y. V. Nandini, T. Jaya Lakshmi, Murali Krishna Enduri, Mohd Zairul Mazwan Jilani

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Design of an Iterative Model for Incremental Enhancements in Quantum Image Processing Using Reinforcement Learning-Based Optimizations by Lalitha Kumari Pappala, Sai Babu Veesam, Kannaiah Chattu, Janapati Venkata Krishna, Jyostna Devi Bodapati, B. Tarakeswara Rao

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An Optimized Hybrid PSO-ELM for Parkinson’s Disease Diagnosis by Gunakala Archana, Afzal Hussain Shahid

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Corrections to “Multi Objective Prioritized Workflow Scheduling Using Deep Reinforcement Based Learning in Cloud Computing” by Sudheer Mangalampalli, Syed Shakeel Hashmi, Amit Gupta, Ganesh Reddy Karri, K. Varada Rajkumar, Tulika Chakrabarti, Prasun Chakrabarti, Martin Margala

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DFTD-YOLO: Lightweight Multi-Target Detection From Unmanned Aerial Vehicle Viewpoints by Yuteng Chen, Zhaoguang Liu

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Steel Surface Defect Detection Based on Improved GCHS-YOLO Algorithm by Ruiqiang Guo, Peiyong Ji, Yapin Zhang, Jingqi Hu, Wenlong Liu, Xuejian Li, Min Li

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Leveraging Quantum LSTM for High-Accuracy Prediction of Viral Mutations by Prashanth Choppara, Bommareddy Lokesh

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LE-YOLOv5: A Lightweight and Efficient Neural Network for Steel Surface Defect Detection by Chengshun Zhu, Yong Sun, Hongji Zhang, Shilong Yuan, Hui Zhang

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An Efficient Detector for Automatic Tomato Classification Systems by Duy-Linh Nguyen, Xuan-Thuy Vo, Adri Priadana, Jehwan Choi, Kang-Hyun Jo

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A Multi-Branch Anchor-Free Detection Algorithm for Hospital Pedestrian by Keqiang Li, Yifan Li, Yiyi Wang, Haining Yu, Huan Zhang

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Transformer-Based Multi-Player Tracking and Skill Recognition Framework for Volleyball Analytics by Lei Jiang, Zhihong Yang, Lei Gang

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