Shiqiang Wang
- Computer Networks and Communications top 0.2%
- Artificial Intelligence top 0.2%
- Electrical and Electronic Engineering top 5%
- Information Systems top 0.5%
- Computer Science Applications top 0.5%
- Co-authors
- Kin K. LeungTing HeKevin ChanTiffany TuorTheodoros SalonidisChristian MakayaMurtaza ZaferLeandros Tassiulas
- Topics
- Privacy-Preserving Technologies in Data (32 papers)IoT and Edge/Fog Computing (20 papers)Stochastic Gradient Optimization Techniques (18 papers)
- Journals
- SHILAP Revista de lepidopterologíaIEEE AccessIEEE Journal on Selected Areas in Communications
- Partner nations
- United StatesUnited KingdomChina
In The Last Decade
Shiqiang Wang
87 papers receiving 5.0k citations
Hit Papers
Peers
Comparison fields: 5 of 98
- Computer Networks and Communications 2.9k
- Artificial Intelligence 2.8k
- Electrical and Electronic Engineering 1.3k
- Information Systems 1.1k
- Computer Science Applications 585
Countries citing papers authored by Shiqiang Wang
This map shows the geographic impact of Shiqiang Wang'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 Shiqiang Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shiqiang Wang more than expected).
Fields of papers citing papers by Shiqiang Wang
This network shows the impact of papers produced by Shiqiang Wang. 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 Shiqiang Wang. The network helps show where Shiqiang Wang may publish in the future.
Co-authorship network of co-authors of Shiqiang Wang
This figure shows the co-authorship network connecting the top 25 collaborators of Shiqiang Wang. A scholar is included among the top collaborators of Shiqiang Wang 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 Shiqiang Wang. Shiqiang Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 4 | |
| 4 | 4 | |
| 5 | 0 | |
| 6 | 0 | |
| 7 | 1 | |
| 8 | 3 | |
| 9 | 19 | |
| 10 | 0 | |
| 11 | 14 | |
| 12 | 45 | |
| 13 | 1 | |
| 14 | 11 | |
| 15 | A Survey on Federated Learning for Resource-Constrained IoT Devicesbreakdown → | 473 |
| 16 | 12 | |
| 17 | 177 | |
| 18 | 9 | |
| 19 | A New Method of Sorting Radar Emitter Signals | 2 |
| 20 | Physical-Layer Network Coding with M-QAM Modulation | 1 |
About Shiqiang Wang
Shiqiang Wang is a scholar working on Computer Networks and Communications, Artificial Intelligence and Computer Science Applications, having authored 94 papers that have together received 5.1k indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (32 papers), IoT and Edge/Fog Computing (20 papers) and Stochastic Gradient Optimization Techniques (18 papers). The work is most often cited by research in Computer Networks and Communications (2.9k citations), Computer Science Applications (585 citations) and Artificial Intelligence (2.8k citations). Shiqiang Wang has collaborated with scholars based in United States, United Kingdom and China. Frequent co-authors include Kin K. Leung, Ting He, Kevin Chan, Tiffany Tuor, Theodoros Salonidis, Christian Makaya, Murtaza Zafer, Leandros Tassiulas, Rahul Urgaonkar and Urmish Thakker. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Access and IEEE Journal on Selected Areas in Communications.
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.