Speech Emotion Recognition Enhanced Traffic Efficiency Solution for Autonomous Vehicles in a 5G-Enabled Space–Air–Ground Integrated Intelligent Transportation System

Tan, Liang; Yu, Keping; Lin, Long; Cheng, Xiaofan; Srivastava, Gautam; Lin, Jerry Chun‐Wei; Wei, Wei

doi:10.1109/tits.2021.3119921

Cited by 118 publications

(35 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Clustering, which is a hierarchical representation of the network, is one of the most successful strategies to address those difficulties. In clustering, each cluster consists of the head of the cluster and each cluster node is grouped into a fitting cluster [ 28 , 29 ]. An important role of cluster head is to provide a communication between sensor nodes and the base station efficiently [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Cluster Formation in IoT-Enabled Wireless Body Area Network

Zeb

Wakeel

Rahman

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Wireless sensor network is widely used in different IoT-enabled applications such as health care, underwater sensor networks, body area networks, and various offices. A sensor node may face operational difficulties due to low computing capacity. Moreover, mobility has become an open challenge in the healthcare wireless body area network that is highly affected by message loss due to topological manipulation. In this article, an enhanced version of the well-known algorithm MT-MAC is proposed, namely DT-MAC, to ensure successful message delivery. It considers node handover mechanism among virtual clusters to ensure network integrity and also uses the concept of minimum connected dominating set for network formation to achieve efficient energy utilization. It is then compared with well-known algorithms such as MT-MAC. The simulation results show that an increase in little latency of roughly 3 percent in using the proposed protocol improves the MT-MAC's packet delivery by 13–17 percent and the response time by around 15 percent. Therefore, the algorithm is best fitted for real-time applications where the high packet delivery and response time are required.

show abstract

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Cluster Formation in IoT-Enabled Wireless Body Area Network

Zeb

Wakeel

Rahman

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…The data processing of the EIoT has high requirements on energy efficiency and delay. On one hand, batterypowered EIoT devices have limited energy [6][7][8]. Improving energy efficiency can increase the utilization of energy resources and extend the network life cycle.…”

Section: Introductionmentioning

confidence: 99%

Delay and Energy-Efficiency-Balanced Task Offloading for Electric Internet of Things

et al. 2022

View full text Add to dashboard Cite

With the development of the smart grid, massive electric Internet of Things (EIoT) devices are deployed to collect data and offload them to edge servers for processing. However, the task of offloading optimization still faces several challenges, such as the differentiated quality of service (QoS) requirements, decision coupling among multiple devices, and the impact of electromagnetic interference. In this paper, a low-complexity delay and energy-efficiency-balanced task offloading algorithm based on many-to-one two-sided matching is proposed for an EIoT. The proposed algorithm shows its novelty in the dynamic tradeoff between energy efficiency and delay as well as in low-complexity and stable task offloading. Specifically, we firstly formulate the minimization problem of weighted difference between delay and energy efficiency to realize the joint optimization of differentiated QoS requirements. Then, the joint optimization problem is transformed into a many-to-one two-sided matching problem. Through continuous iteration, a stable matching between devices and servers is established to cope with decision coupling among multiple devices. Finally, the effectiveness of the proposed algorithm is validated through simulations. Compared with two advanced algorithms, the weighted difference between the energy efficiency and delay of the proposed algorithm is increased by 29.01% and 45.65% when the number of devices is 120, and is increased by 11.57% and 22.25% when the number of gateways is 16.

show abstract

“…The reason it has not been generally accepted by humans until recent years is that computers can store more data and many datasets have been specifically developed for the study of neural networks. As these datasets became higher and higher quality, there was a desire to have an algorithm that could discover the nature of things from massive amounts of data [ 5 ]. The second is the emergence of large-scale models.…”

Section: Introductionmentioning

confidence: 99%

An Improved Convolutional Neural Network-Based Scene Image Recognition Method

Wang

Qiao

Liu

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

To solve the problems existing in the research of scene recognition, this paper studies a new convolutional neural network target detection model to achieve a better balance between the accuracy and speed of high-speed scene image recognition. First, aiming at the problem that the image is easy to be disturbed by impurities and poor quality in fine-grained image recognition, a preprocessing method based on the Canny edge detection is designed and the Canny operator is introduced to process the gray image. Second, the L2 regularization algorithm is used to optimize the basic network framework of the convolutional neural network, enhance the stability of the model in a complex environment, improve the generalization ability of the model, and improve the recognition accuracy of the algorithm to a certain extent. Finally, by collecting the campus environment datasets under different environmental conditions, the location recognition experiment and heat map visualization experiment are carried out. Experiments show that compared with the basic convolution neural network algorithm, the algorithm has better recognition performance and good generalization ability. The research of this study realizes the effective combination of multiframe convolution neural network and batch normalization algorithm and has a good practical effect on scene image recognition.

show abstract

Speech Emotion Recognition Enhanced Traffic Efficiency Solution for Autonomous Vehicles in a 5G-Enabled Space–Air–Ground Integrated Intelligent Transportation System

Cited by 118 publications

References 34 publications

Energy-Efficient Cluster Formation in IoT-Enabled Wireless Body Area Network

Energy-Efficient Cluster Formation in IoT-Enabled Wireless Body Area Network

Delay and Energy-Efficiency-Balanced Task Offloading for Electric Internet of Things

An Improved Convolutional Neural Network-Based Scene Image Recognition Method

Contact Info

Product

Resources

About