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Animesh Pathak
Researcher at French Institute for Research in Computer Science and Automation
Publications - 44
Citations - 1105
Animesh Pathak is an academic researcher from French Institute for Research in Computer Science and Automation. The author has contributed to research in topics: Wireless sensor network & Middleware. The author has an hindex of 18, co-authored 44 publications receiving 1009 citations. Previous affiliations of Animesh Pathak include University of Southern California & Indraprastha Institute of Information Technology.
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Proceedings ArticleDOI
CCS-TA: quality-guaranteed online task allocation in compressive crowdsensing
TL;DR: A novel framework called CCS-TA is proposed, combining the state-of-the-art compressive sensing, Bayesian inference, and active learning techniques, to dynamically select a minimum number of sub-areas for sensing task allocation in each sensing cycle, while deducing the missing data of unallocated sub-wereas under a probabilistic data accuracy guarantee.
Journal ArticleDOI
Software diversity: state of the art and perspectives
Ina Schaefer,Rick Rabiser,Dave Clarke,Lorenzo Bettini,David Benavides,Goetz Botterweck,Animesh Pathak,Salvador Trujillo,Karina Villela +8 more
TL;DR: This introductory article to the special section “Software Diversity—Modeling, Analysis and Evolution” provides an overview of the current state of the art in diverse systems development and discusses challenges and potential solutions.
Proceedings ArticleDOI
Probabilistic registration for large-scale mobile participatory sensing
TL;DR: A probabilistic registration approach is presented, based on a realistic human mobility model, that allows devices to decide whether or not to register their sensing services depending on the probability of other, equivalent devices being present at the locations of their expected path.
Journal ArticleDOI
SPACE-TA: Cost-Effective Task Allocation Exploiting Intradata and Interdata Correlations in Sparse Crowdsensing
TL;DR: This article proposes a novel crowdsensing task allocation framework called SPACE-TA (SPArse Cost-Effective Task Allocation), combining compressive sensing, statistical analysis, active learning, and transfer learning, to dynamically select a small set of subareas for sensing in each timeslot (cycle), while inferring the data of unsensed subarea under a probabilistic data quality guarantee.
Proceedings ArticleDOI
Towards application development for the internet of things
TL;DR: This paper presents a domain model for applications in the Internet of Things, based on a survey of recently proposed IoT applications from the real world that represent a wide class of behaviors found in IoT use cases.