Tie Luo

2.6k total citations · 1 hit paper
65 papers, 1.6k citations indexed

About

Tie Luo is a scholar working on Computer Networks and Communications, Computer Science Applications and Artificial Intelligence. According to data from OpenAlex, Tie Luo has authored 65 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Computer Networks and Communications, 23 papers in Computer Science Applications and 22 papers in Artificial Intelligence. Recurrent topics in Tie Luo's work include Mobile Crowdsensing and Crowdsourcing (23 papers), Auction Theory and Applications (10 papers) and Privacy-Preserving Technologies in Data (8 papers). Tie Luo is often cited by papers focused on Mobile Crowdsensing and Crowdsourcing (23 papers), Auction Theory and Applications (10 papers) and Privacy-Preserving Technologies in Data (8 papers). Tie Luo collaborates with scholars based in Singapore, United States and China. Tie Luo's co-authors include Hwee-Pink Tan, Tony Q. S. Quek, Chen‐Khong Tham, Lirong Xia, Mehul Motani, Sajal K. Das, Salil S. Kanhere, Fan Wu, Jianwei Huang and V. Srinivasan and has published in prestigious journals such as IEEE Journal on Selected Areas in Communications, IEEE Communications Magazine and Pattern Recognition.

In The Last Decade

Tie Luo

63 papers receiving 1.6k citations

Hit Papers

Sensor OpenFlow: Enabling Software-Defined Wireless Senso... 2012 2026 2016 2021 2012 100 200 300

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Tie Luo Singapore 20 881 550 480 378 250 65 1.6k
Xiaoying Gan China 24 1.1k 1.2× 408 0.7× 812 1.7× 377 1.0× 202 0.8× 152 2.0k
Jia Xu China 20 375 0.4× 521 0.9× 313 0.7× 417 1.1× 193 0.8× 98 1.3k
Xiumin Wang China 19 658 0.7× 271 0.5× 645 1.3× 443 1.2× 77 0.3× 81 1.6k
Xiang-Yang Li China 20 722 0.8× 713 1.3× 538 1.1× 432 1.1× 384 1.5× 40 1.8k
Demetrios Zeinalipour-Yazti Cyprus 21 1.1k 1.3× 403 0.7× 564 1.2× 352 0.9× 65 0.3× 129 2.0k
Yufeng Zhan China 23 827 0.9× 613 1.1× 397 0.8× 1.1k 3.0× 131 0.5× 47 2.0k
Claudio Fiandrino Spain 19 505 0.6× 654 1.2× 486 1.0× 328 0.9× 63 0.3× 70 1.4k
Fen Hou Macao 26 1.5k 1.7× 178 0.3× 1.4k 2.9× 227 0.6× 98 0.4× 128 2.3k
Baik Hoh United States 9 301 0.3× 702 1.3× 515 1.1× 972 2.6× 208 0.8× 14 1.7k
Eyuphan Bulut United States 27 1.3k 1.4× 217 0.4× 1.1k 2.2× 169 0.4× 37 0.1× 109 2.0k

Countries citing papers authored by Tie Luo

Since Specialization
Citations

This map shows the geographic impact of Tie Luo's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Tie Luo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tie Luo more than expected).

Fields of papers citing papers by Tie Luo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tie Luo. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Tie Luo. The network helps show where Tie Luo may publish in the future.

Co-authorship network of co-authors of Tie Luo

This figure shows the co-authorship network connecting the top 25 collaborators of Tie Luo. A scholar is included among the top collaborators of Tie Luo based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Tie Luo. Tie Luo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Luo, Tie, et al.. (2024). LRS: Enhancing Adversarial Transferability through Lipschitz Regularized Surrogate. Proceedings of the AAAI Conference on Artificial Intelligence. 38(6). 6135–6143. 2 indexed citations
2.
Luo, Tie, et al.. (2023). YOGA: Deep object detection in the wild with lightweight feature learning and multiscale attention. Pattern Recognition. 139. 109451–109451. 12 indexed citations
4.
Luo, Tie, et al.. (2022). AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platforms. 2022 IEEE International Conference on Big Data (Big Data). 5478–5487. 26 indexed citations
5.
Luo, Tie, et al.. (2021). A Blockchain-Enabled Quantitative Approach to Trust and Reputation Management with Sparse Evidence. Autonomous Agents and Multi-Agent Systems. 1707–1708. 3 indexed citations
6.
Zheng, Zhenzhe, et al.. (2021). Data-Free Evaluation of User Contributions in Federated Learning. 1–8. 19 indexed citations
7.
Luo, Tie, et al.. (2020). COBRA: Context-Aware Bernoulli Neural Networks for Reputation Assessment. Proceedings of the AAAI Conference on Artificial Intelligence. 34(5). 7317–7324. 3 indexed citations
8.
Zhang, Chaoli, et al.. (2020). Mechanism Design with Predicted Task Revenue for Bike Sharing Systems. Proceedings of the AAAI Conference on Artificial Intelligence. 34(2). 2144–2151. 4 indexed citations
9.
Luo, Tie, Jianwei Huang, Salil S. Kanhere, Jie Zhang, & Sajal K. Das. (2019). Improving IoT Data Quality in Mobile Crowd Sensing: A Cross Validation Approach. IEEE Internet of Things Journal. 6(3). 5651–5664. 66 indexed citations
10.
Yang, Shuo, Dan Peng, Tong Meng, et al.. (2018). On Designing Distributed Auction Mechanisms for Wireless Spectrum Allocation. IEEE Transactions on Mobile Computing. 18(9). 2129–2146. 8 indexed citations
11.
Zhang, Jie, et al.. (2018). MASA: Multi-Agent Subjectivity Alignment for Trustworthy Internet of Things. 2013–2020. 3 indexed citations
12.
Luo, Tie, Salil S. Kanhere, Jianwei Huang, Sajal K. Das, & Fan Wu. (2017). Sustainable Incentives for Mobile Crowdsensing: Auctions, Lotteries, and Trust and Reputation Systems. IEEE Communications Magazine. 55(3). 68–74. 92 indexed citations
13.
Tham, Chen‐Khong & Tie Luo. (2014). Quality of Contributed Service and Market Equilibrium for Participatory Sensing. IEEE Transactions on Mobile Computing. 14(4). 829–842. 44 indexed citations
14.
Wang, Yu, Mehul Motani, Hari Krishna Garg, Qian Chen, & Tie Luo. (2014). Multi-channel Directional Medium Access Control for ad hoc networks: A cooperative approach. 9. 53–58. 6 indexed citations
15.
Law, Yee Wei, Zheng Gong, Tie Luo, Slaven Marusic, & Marimuthu Palaniswami. (2013). Comparative study of multicast authentication schemes with application to wide-area measurement system. 287–298. 9 indexed citations
16.
Tham, Chen‐Khong & Tie Luo. (2013). Quality of Contributed Service and Market Equilibrium for Participatory Sensing. National University of Singapore. 133–140. 21 indexed citations
17.
Luo, Tie, et al.. (2012). Enhancing responsiveness and scalability for OpenFlow networks via control-message quenching. Swinburne Research Bank (Swinburne University of Technology). 348–353. 20 indexed citations
18.
Tham, Chen‐Khong, et al.. (2011). Participatory Cyber Physical System in Public Transport Application. National University of Singapore. 355–360. 24 indexed citations
19.
Luo, Tie, Mehul Motani, & V. Srinivasan. (2008). Cooperative Asynchronous Multichannel MAC: Design, Analysis, and Implementation. IEEE Transactions on Mobile Computing. 8(3). 338–352. 85 indexed citations
20.
Luo, Tie, Mehul Motani, & Vikram Srinivasan. (2008). Analyzing DISH for multi-channel MAC protocols in wireless networks. National University of Singapore. 43–52. 8 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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