Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
A wireless sensor network For structural monitoring
2004797 citationsNing Xu, Krishna Chintalapudi et al.profile →
Highly-resilient, energy-efficient multipath routing in wireless sensor networks
2001786 citationsDeepak Ganesan, Ramesh Govindan et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Deepak Ganesan
Since
Specialization
Citations
This map shows the geographic impact of Deepak Ganesan'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 Deepak Ganesan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deepak Ganesan more than expected).
This network shows the impact of papers produced by Deepak Ganesan. 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 Deepak Ganesan. The network helps show where Deepak Ganesan may publish in the future.
Co-authorship network of co-authors of Deepak Ganesan
This figure shows the co-authorship network connecting the top 25 collaborators of Deepak Ganesan.
A scholar is included among the top collaborators of Deepak Ganesan 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 Deepak Ganesan. Deepak Ganesan is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Xie, Binbin, et al.. (2023). Wall Matters. Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies. 7(4). 1–22.2 indexed citations
Zhang, Pengyu, et al.. (2013). QuarkOS: pushing the operating limits of micro-powered sensors. 7–7.19 indexed citations
10.
Ganesan, Deepak, et al.. (2012). The Role of Super Agents in Mobile Crowdsourcing.. National Conference on Artificial Intelligence.2 indexed citations
11.
Li, Ming, et al.. (2009). Block-switched networks: a new paradigm for wireless transport. Networked Systems Design and Implementation. 423–436.55 indexed citations
Li, Ming, Deepak Ganesan, & Prashant Shenoy. (2006). PRESTO: feedback-driven data management in sensor networks. Scholarworks (University of Massachusetts Amherst). 23–23.34 indexed citations
16.
Desnoyers, Peter, Deepak Ganesan, Huan Li, Ming Li, & Prashant Shenoy. (2005). PRESTO: a predictive storage architecture for sensor networks. ScholarWorks@UMassAmherst (University of Massachusetts Amherst). 23–23.57 indexed citations
17.
Ganeriwal, Saurabh, et al.. (2005). SYS4: Estimating Clock Uncertainty for Efficient Duty-Cycling in Sensor Networks. eScholarship (California Digital Library).1 indexed citations
18.
Xu, Ning, Krishna Chintalapudi, Deepak Ganesan, et al.. (2004). A Wireless Sensor Network for Structural Monitoring. eScholarship (California Digital Library).1 indexed citations
19.
Ganesan, Deepak, et al.. (2003). An evaluation of multi-resolution search and storage in resource-constrained sensor networks. eScholarship (California Digital Library).23 indexed citations
20.
Elson, Jeremy, Vladimir Bychkovskiy, Alberto Cerpa, et al.. (2003). EmStar: An Environment for Developing Wireless Embedded Systems Software. eScholarship (California Digital Library).51 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.