Mitigating Hotspot Issues in Heterogeneous Wireless Sensor Networks

Khalaf, Osamah Ibrahim; Romero, Carlos Andrés Tavera; Hassan, Shahzad; Iqbal, Muhammad Taimoor

doi:10.1155/2022/7909472

Cited by 62 publications

(37 citation statements)

References 70 publications

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“…The evaluation score S t is the evaluation score of frame t in the user video, its value range is [0,1], the degree of similarity between the input user video, and the template video is proportional to the evaluation score S t , that is, when the evaluation score is 0, the golf swing in the input user video and the action in the template video are not similar at all, and when the evaluation score is 1, the golf swing in the input user video and the action in the template video are identical. The formula for calculating S t is as follows:

where n is the number of human limb angles and its value is 13, M is the maximum human limb angle, and in the golf swing, M is 180 because the range of human limb angles in the Cartesian coordinate system is [0,180], Δ α i t is the difference between the i th human limb angle in the t th frame with the video and the template video, W i is the weight of the i th human limb angle, and its calculation formula is as follows:

where W i T is the cumulative time human limb angle weight of the template video action and W i U is the cumulative time human limb angle weight of the user video action [ 21 , 22 ].…”

Section: Golf Swing Evaluation Algorithmmentioning

confidence: 99%

Research on Intelligent Sports Training System for Golf Based on Body Sense Recognition

Wang

Zang

2022

Computational Intelligence and Neuroscience

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In recent years, significant advances in the development of computer vision technology have produced many platforms and systems that combine computer technology and sports-assisted training, including intelligent systems that are integrated with golf training and instruction. However, the existing intelligent systems for golf-assisted teaching usually use three-dimensional depth information, which will significantly increase the cost of intelligent systems. In this paper, the extraction of golf club slope is carried out on the basis of golf sport video capture using a common monocular camera in order to match the club slope information with the professional coach swing video information. At the same time, in order to facilitate the interframe matching, the joint point information is complemented using the projection approximation point algorithm, and the segmentation of the swing video is performed using the complemented human hand joints and the fixed characteristics of the golf swing. Then, in order to solve the problem that human joints will have the same joint angle under different movements, the human limb joint angles are defined and then the swing movements in the user video frames are evaluated.

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where W i T is the cumulative time human limb angle weight of the template video action and W i U is the cumulative time human limb angle weight of the user video action [ 21 , 22 ].…”

Section: Golf Swing Evaluation Algorithmmentioning

confidence: 99%

Research on Intelligent Sports Training System for Golf Based on Body Sense Recognition

Wang

Zang

2022

Computational Intelligence and Neuroscience

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“…Inertial sensors are used as acceleration sensors and rotators, and the corresponding inertial values are speed increase and edge velocity; electromagnet is a resistive sensor, and the output value is magnetic field strength; the image sensor is a K sensor, and the output value is color image, depth image, and bone information [27][28][29].…”

Section: Design Of Action Information Collection Systemmentioning

confidence: 99%

Walking Position Data in Football Training Based on Embedded Action Recognition System

Li¹

2022

Mobile Information Systems

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With the development of image recognition and pattern recognition, action recognition has become a hot research direction in the field of computer actions. Because the embedded platform has the advantages of small size and low power consumption, it is a good choice to use the embedded platform for action recognition. These sensor data are used to overcome the drift error of the gyroscope when the positioning data are collected, and the accuracy and reliability of the data are improved. The system uses two data forwarding nodes to aggregate the data of each sensor and then transmit it to the host computer. The data aggregation node can communicate and upload data by means of Bluetooth or wire. In order to deeply study the relevance of the embedded action recognition system in collecting football training positioning data, this paper uses simulation model establishment method, data collection method, and theory and practice combination method to collect samples, analyzes the embedded action recognition system, and streamlines the algorithm. Using computer technology as a support when building a simulation model, the first thing you need to have is a certain computer technology because when building a simulation model, not all objects can be modeled. At this time, only computer technology can be used as a support to build a simulation model of some objects and create an action recognition system that can record the position data in football training. After establishing the human body simulation recognition model, use MATLAB to extract the K bone information. When collecting, the human body is facing the K device, the number of acquisition frames is 25, 55, 75, 95, 105, and 205, and each frame number is collected 10 times. Take the average. The delay time is 4s. The result shows that the 20 key bone point outputs by K come from the RGB camera on the same side. Further study the actual utility of the compensated model in the presence of occlusion. They are worn on the middle of the thigh and calf, respectively. In a sensor, the measured value of thigh length L1 is 0.6 m, and the value of calf length L2 is 0.4 m. Take the right knee as an example. When the leg is raised, the b-axis coordinate increases by 2%, and the c-axis coordinate decreases by 1.8%. When the leg is lowered, the opposite is true. It can be seen that the compensated coordinate is consistent with the action. It is basically realized that starting from the embedded action recognition system, a model that can support the analysis of football positioning data is designed.

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“…[60] presented research to map out the current state of knowledge on energy efficiency's impact on cloud computing and, in turn, help to build a better picture of the future of this area of study. No emphasis was given to techniques to reduce energy consumption [132][133][134][135][136][137][138][139][140][141][142][143][144][145].…”

Section: Related Surveysmentioning

confidence: 99%

“…These solutions, however, depend on both hardware and software. Now, we discuss the new techniques invented by various researcher using power management technology [139][140][141].…”

Section: Software-level Solutionsmentioning

confidence: 99%

A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing

et al. 2022

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Global warming is one of the most compelling environmental threats today, as the rise in energy consumption and CO2 emission caused a dreadful impact on our environment. The data centers, computing devices, network equipment, etc., consume vast amounts of energy that the thermal power plants mainly generate. Primarily fossil fuels like coal and oils are used for energy generation in these power plants that induce various environmental problems such as global warming ozone layer depletion, which can even become the cause of premature deaths of living beings. The recent research trend has shifted towards optimizing energy consumption and green fields since the world recognized the importance of these concepts. This paper aims to conduct a complete systematic mapping analysis on the impact of high energy consumption in cloud data centers and its effect on the environment. To answer the research questions identified in this paper, one hundred nineteen primary studies published until February 2022 were considered and further categorized. Some new developments in green cloud computing and the taxonomy of various energy efficiency techniques used in data centers have also been discussed. It includes techniques like VM Virtualization and Consolidation, Power-aware, Bio-inspired methods, Thermal-management techniques, and an effort to evaluate the cloud data center’s role in reducing energy consumption and CO2 footprints. Most of the researchers proposed software level techniques as with these techniques, massive infrastructures are not required as compared with hardware techniques, and it is less prone to failure and faults. Also, we disclose some dominant problems and provide suggestions for future enhancements in green computing.

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Mitigating Hotspot Issues in Heterogeneous Wireless Sensor Networks

Cited by 62 publications

References 70 publications

Research on Intelligent Sports Training System for Golf Based on Body Sense Recognition

Research on Intelligent Sports Training System for Golf Based on Body Sense Recognition

Walking Position Data in Football Training Based on Embedded Action Recognition System

A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing

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