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Ido Nevat
Researcher at Institute for Infocomm Research Singapore
Publications - 115
Citations - 1169
Ido Nevat is an academic researcher from Institute for Infocomm Research Singapore. The author has contributed to research in topics: Wireless sensor network & Gaussian process. The author has an hindex of 18, co-authored 111 publications receiving 936 citations. Previous affiliations of Ido Nevat include Commonwealth Scientific and Industrial Research Organisation & Heriot-Watt University.
Papers
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Journal ArticleDOI
Anomaly Detection and Attribution in Networks With Temporally Correlated Traffic
Ido Nevat,Dinil Mon Divakaran,Sai Ganesh Nagarajan,Pengfei Zhang,Le Su,Li Ling Ko,Vrizlynn L. L. Thing +6 more
TL;DR: A new statistical decision theoretic framework for temporally correlated traffic in networks via Markov chain modeling is developed and two low-complexity anomaly detection algorithms are developed based on the cross entropy method, which detects anomalies as well as attributes anomalies to flows.
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Distributed Detection in Sensor Networks Over Fading Channels With Multiple Antennas at the Fusion Centre
TL;DR: In this article, the authors developed new and optimal algorithms for distributed detection in sensor networks over fading channels with multiple receive antennas at the Fusion Center (FC) where the sensors observe a hidden physical phenomenon over fading channel and transmit their observations using the amplify-and-forward scheme over the fading channels to the FC.
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Random Field Reconstruction With Quantization in Wireless Sensor Networks
TL;DR: This work considers spatial physical phenomena which are partially observed by a wireless sensor network and derives the Posterior Cramér Rao Lower Bound (PCRLB) and quantifies the achievable MSE in the estimation.
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Optimal Information-Theoretic Wireless Location Verification
TL;DR: This work develops a new location verification system (LVS) focused on network-based intelligent transport systems (ITSs) and vehicular ad hoc networks (VANETs) based on an information-theoretic framework in which the mutual information between the system's input and output data is maximized.
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Geo-Spatial Location Estimation for Internet of Things (IoT) Networks With One-Way Time-of-Arrival via Stochastic Censoring
TL;DR: New algorithms for geo-spatial location estimation for Internet of Things (IoT) networks by utilizing a one way time of arrival (OW-TOA) approach are developed, and the Cramér–Rao bounds of the source location estimate for both algorithms are developed.