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This is a collection of Edge computing frameworks. I shall test each framework and make a comparison in the future work.
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会议回放: http://rongnuo-bj.com/forum/2022CEC/html/live.html
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The term wireless refers to the communication or transmission of information over a distance without requiring wires, cables, or any other electrical conductors. Wireless communication is one of the important mediums of transmission of data or information to other devices. The Communication is set and the information is transmitted through the air, without requiring any cables, by using electromagnetic waves like radio frequencies, infrared, satellite, etc., in a wireless communication technology network.
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Comparison of three Edge Computing (EC) Implementations: Fog Computing, Cloudlet and Mobile Edge Computing
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Industry investment and research interest in edge computing, in which computing and storage nodes are placed at the Internet’s edge in close proximity to mobile devices or sensors, have grown dramatically in recent years. This emerging technology promises to deliver highly responsive cloud services for mobile computing, scalability and privacy-policy enforcement for the Internet of Things, and the ability to mask transient cloud outages [1].
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Many people may confused with these concepts, here I would like to make a easy-understanding interpretation of each.
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In my project, I need to build a server for the mobile users. After a research, I found that the python’s Flask library probably is a great framework. In addition, the server is built as a docker container, so it’s easy for us to transfer. For the mobile Android users, they need to upload the audio data to the server, so we implement the OKHTTP for the communication and data transmission between client and server.
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Short description of portfolio item number 1
Short description of portfolio item number 2
Published in Journal of Shanghai Jiaotong University (Science), 2018
Recommended citation: Zhang, Ying, Peisong Li, and Lin Mao. "Research on improved low-energy adaptive clustering hierarchy protocol in wireless sensor networks." Journal of Shanghai Jiaotong University (Science) 23 (2018): 613-619. https://peisong0109.github.io/files/s12204-018-1991-0.pdf
Published in 2018 International Symposium in Sensing and Instrumentation in IoT Era (ISSI), 2018
Recommended citation: Li, Peisong, Ying Zhang, and Qiman Zhang. "Monte Carlo Location Algorithm Based on Model Prediction." In 2018 International Symposium in Sensing and Instrumentation in IoT Era (ISSI), pp. 1-5. IEEE, 2018. https://peisong0109.github.io/files/Monte_Carlo_Location_Algorithm_Based_on_Model_Prediction.pdf
Published in 2018 Chinese Control and Decision Conference (CCDC), 2018
Recommended citation: Zhang, Ying, and Peisong Li. "Key management scheme based on nodes capture probability for wireless sensor networks." In 2018 Chinese Control and Decision Conference (CCDC), pp. 5470-5475. IEEE, 2018. https://peisong0109.github.io/files/Key_management_scheme_based_on_nodes_capture_probability_for_wireless_sensor_networks.pdf
Published in IEEE Access, 2019
The citation number of the paper on Google Scholar is over 290.
Recommended citation: Zhang, Ying, Peisong Li, and Xinheng Wang. "Intrusion detection for IoT based on improved genetic algorithm and deep belief network." IEEE Access 7 (2019): 31711-31722. https://ieeexplore.ieee.org/abstract/document/8662673
Published in 2019 Chinese control and decision conference (CCDC), 2019
Recommended citation: Li, Peisong, and Ying Zhang. "A novel intrusion detection method for internet of things." In 2019 Chinese control and decision conference (CCDC), pp. 4761-4765. IEEE, 2019. https://peisong0109.github.io/files/A_Novel_Intrusion_Detection_Method_for_Internet_of_Things.pdf
Published in IEEE transactions on intelligent transportation systems, 2021
JCR Q1 TOP, IF=8.5.
Recommended citation: Li, Peisong, Xinheng Wang, Honghao Gao, Xiaolong Xu, Muddesar Iqbal, and Keshav Dahal. "A dynamic and scalable user-centric route planning algorithm based on polychromatic sets theory." IEEE Transactions on Intelligent Transportation Systems 23, no. 3 (2021): 2762-2772. https://ieeexplore.ieee.org/abstract/document/9447817
Published in Sensors, 2022
JCR Q2, IF=3.9.
Recommended citation: Li, Peisong, Xinheng Wang, Kaizhu Huang, Yi Huang, Shancang Li, and Muddesar Iqbal. "Multi-model running latency optimization in an edge computing paradigm." Sensors 22, no. 16 (2022): 6097. https://www.mdpi.com/1424-8220/22/16/6097
Published in Journal of Physics: Conference Series, 2023
Recommended citation: Li, Peisong, Rui Qiu, Minzhen Wang, Xinheng Wang, Shan Jaffry, Ming Xu, Kaizhu Huang, and Yi Huang. "Online Monitoring of Overhead Power Lines Against Tree Intrusion via a Low-cost Camera and Mobile Edge Computing Approach." In Journal of Physics: Conference Series, vol. 2422, no. 1, p. 012018. IOP Publishing, 2023. https://iopscience.iop.org/article/10.1088/1742-6596/2422/1/012018/meta
Published in IEEE Transactions on Intelligent Vehicles, 2023
JCR Q1, IF=8.2.
Recommended citation: Li, Peisong, Ziren Xiao, Xinheng Wang, Kaizhu Huang, Yi Huang, and Honghao Gao. "EPtask: Deep Reinforcement Learning based Energy-efficient and Priority-aware Task Scheduling for Dynamic Vehicular Edge Computing." IEEE Transactions on Intelligent Vehicles (2023). https://ieeexplore.ieee.org/abstract/document/10269672
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Through multiple iterations of the GA, the optimal number of hidden layers and the number of neurons in each layer are generated.
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In this project, we focus on designing an energy-efficient task scheduling strategy based on the Deep Reinforcement Learning (DRL) algorithm, with the target of reducing task completion time and energy consumption in vehicular edge computing scenarios.
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An Android application (App) is designed, which is mainly used for sharing real-time positions with others. In addition, some interesting functions are also provided.
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The existing practice of creating structure plans in urban design seems to be arbitrary, and few practitioners consider employing algorithms(DL) to meet functional demands and minimize budgets.
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Multi-agent collaborative navigation is prevalent in modern transportation systems, including delivery logistics, warehouse automation, and personalised tourism, where multiple agents must converge at a common destination from different starting points. However, the challenges lie in optimising routes for multiple agents while dynamically adjusting the common destination in response to changing traffic conditions. Therefore, we would like to propose a generic Multi-Agent Collaborative Navigation System (MACNS) to address the challenges.
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Simulated navigation app provided by the FengMap.
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.