Rachel Cardell‐Oliver

1.9k total citations
84 papers, 1.2k citations indexed

About

Rachel Cardell‐Oliver is a scholar working on Computer Networks and Communications, Artificial Intelligence and Water Science and Technology. According to data from OpenAlex, Rachel Cardell‐Oliver has authored 84 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Computer Networks and Communications, 17 papers in Artificial Intelligence and 16 papers in Water Science and Technology. Recurrent topics in Rachel Cardell‐Oliver's work include Energy Efficient Wireless Sensor Networks (22 papers), Water Quality Monitoring Technologies (13 papers) and Formal Methods in Verification (11 papers). Rachel Cardell‐Oliver is often cited by papers focused on Energy Efficient Wireless Sensor Networks (22 papers), Water Quality Monitoring Technologies (13 papers) and Formal Methods in Verification (11 papers). Rachel Cardell‐Oliver collaborates with scholars based in Australia, Germany and United Kingdom. Rachel Cardell‐Oliver's co-authors include Keith Smettem, Amitava Datta, Adrian Keating, Wei Liu, Tim French, Andrew Ward, Daniela Ciancio, Jin Wang, Chris Beckett and Damien J. Batstone and has published in prestigious journals such as Water Research, Water Resources Research and IEEE Access.

In The Last Decade

Rachel Cardell‐Oliver

81 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Rachel Cardell‐Oliver Australia 18 359 300 289 195 194 84 1.2k
Δημήτριος Γ. Ηλιάδης Cyprus 18 96 0.3× 345 1.1× 228 0.8× 531 2.7× 157 0.8× 64 1.1k
Yuting Bai China 24 102 0.3× 347 1.2× 103 0.4× 90 0.5× 257 1.3× 73 1.6k
Yunsi Fei United States 26 789 2.2× 1.1k 3.6× 161 0.6× 64 0.3× 53 0.3× 148 2.6k
Wen‐Tsai Sung Taiwan 21 362 1.0× 336 1.1× 223 0.8× 23 0.1× 94 0.5× 121 1.2k
A. Vasan India 16 263 0.7× 166 0.6× 166 0.6× 351 1.8× 100 0.5× 41 932
Wu United States 12 230 0.6× 141 0.5× 37 0.1× 162 0.8× 60 0.3× 223 822
Suresh Sankaranarayanan India 16 306 0.9× 292 1.0× 136 0.5× 28 0.1× 89 0.5× 101 1.1k
Guillermo Barrenetxea Switzerland 13 548 1.5× 356 1.2× 176 0.6× 14 0.1× 157 0.8× 28 1.1k
Pedro Sánchez Spain 15 229 0.6× 200 0.7× 137 0.5× 15 0.1× 41 0.2× 58 908
Jose M. Jiménez Spain 16 620 1.7× 571 1.9× 278 1.0× 33 0.2× 95 0.5× 111 1.6k

Countries citing papers authored by Rachel Cardell‐Oliver

Since Specialization
Citations

This map shows the geographic impact of Rachel Cardell‐Oliver'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 Rachel Cardell‐Oliver with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rachel Cardell‐Oliver more than expected).

Fields of papers citing papers by Rachel Cardell‐Oliver

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rachel Cardell‐Oliver. 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 Rachel Cardell‐Oliver. The network helps show where Rachel Cardell‐Oliver may publish in the future.

Co-authorship network of co-authors of Rachel Cardell‐Oliver

This figure shows the co-authorship network connecting the top 25 collaborators of Rachel Cardell‐Oliver. A scholar is included among the top collaborators of Rachel Cardell‐Oliver 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 Rachel Cardell‐Oliver. Rachel Cardell‐Oliver 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.
French, Tim, et al.. (2023). Enhanced Deep Predictive Modeling of Wastewater Plants With Limited Data. IEEE Transactions on Industrial Informatics. 20(2). 1920–1930. 5 indexed citations
2.
Keating, Adrian, et al.. (2023). Apis-Prime: A deep learning model to optimize beehive monitoring system for the task of daily weight estimation. Applied Soft Computing. 144. 110546–110546. 7 indexed citations
3.
Cardell‐Oliver, Rachel, et al.. (2023). Towards an Activity-aware Pufferfish Framework for Local Privacy of Household Smart Water Meter Data. UWA Profiles and Research Repository (UWA). 328–332. 1 indexed citations
4.
Batstone, Damien J., Christian Kazadi Mbamba, Tim French, et al.. (2023). Deep learning in wastewater treatment: a critical review. Water Research. 245. 120518–120518. 96 indexed citations
5.
Cardell‐Oliver, Rachel, et al.. (2020). An Advanced Sensor Placement Strategy for Small Leaks Quantification Using Lean Graphs. Water. 12(12). 3439–3439. 5 indexed citations
6.
Yuan, Zhiguo, Gustaf Olsson, Rachel Cardell‐Oliver, et al.. (2019). Sweating the assets – The role of instrumentation, control and automation in urban water systems. Water Research. 155. 381–402. 88 indexed citations
7.
Beckett, Chris, et al.. (2017). Measured and simulated thermal behaviour in rammed earth houses in a hot-arid climate. Journal of Building Engineering. 8 indexed citations
8.
Cardell‐Oliver, Rachel, et al.. (2017). Sensor placement strategy for locating leaks using lean graphs. UWA Profiles and Research Repository (University of Western Australia). 11–14. 5 indexed citations
9.
Cardell‐Oliver, Rachel & Chayan Sarkar. (2017). Buildsense. UWA Profiles and Research Repository (UWA). 1–10. 4 indexed citations
10.
Cardell‐Oliver, Rachel & Chayan Sarkar. (2016). Robust sensor data collection over a long period using virtual sensing. UWA Profiles and Research Repository (University of Western Australia). 2–7. 7 indexed citations
11.
Cardell‐Oliver, Rachel, et al.. (2015). Learning Time Delay Mealy Machines From Programmable Logic Controllers. IEEE Transactions on Automation Science and Engineering. 13(2). 1155–1164. 6 indexed citations
12.
Cardell‐Oliver, Rachel, et al.. (2013). Making sense of smart metering data: a data mining approach for discovering water use patterns. Water. 40(2). 124–128. 6 indexed citations
13.
Liu, Wei, et al.. (2013). An investigation on window size selection for human activity recognition. UWA Profiles and Research Repository (University of Western Australia). 181–188. 1 indexed citations
14.
Cardell‐Oliver, Rachel. (2013). Water use signature patterns for analyzing household consumption using medium resolution meter data. Water Resources Research. 49(12). 8589–8599. 45 indexed citations
15.
Cardell‐Oliver, Rachel. (2011). How can software metrics help novice programmers. UWA Profiles and Research Repository (University of Western Australia). 55–62. 25 indexed citations
16.
Cardell‐Oliver, Rachel, et al.. (2011). Long‐range wireless sensor networks with transmit‐only nodes and software‐defined receivers. Wireless Communications and Mobile Computing. 13(17). 1499–1510. 15 indexed citations
17.
Yang, Chi, Rachel Cardell‐Oliver, & Chris McDonald. (2011). Combining temporal and spatial data suppression for accuracy and efficiency. 91. 347–352. 5 indexed citations
18.
Hübner, Christof, et al.. (2010). Wireless soil moisture sensor networks for environmental monitoring and irrigation. EGU General Assembly Conference Abstracts. 2539.
19.
Datta, Amitava, et al.. (2010). A Review of Redundancy Elimination Protocols for Wireless Sensor Networks. Lecture notes in computer science. 336–351. 2 indexed citations
20.
Hübner, Christof, et al.. (2009). Wireless soil moisture sensor networks for environmental monitoring and vineyard irrigation. UWA Profiles and Research Repository (University of Western Australia). 408–415. 15 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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