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Nitin H. Vaidya

Researcher at Georgetown University

Publications -  424
Citations -  29364

Nitin H. Vaidya is an academic researcher from Georgetown University. The author has contributed to research in topics: Wireless network & Wireless ad hoc network. The author has an hindex of 72, co-authored 420 publications receiving 28645 citations. Previous affiliations of Nitin H. Vaidya include Intel & Urbana University.

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Interference aware channel allocation in a multichannel, multi-interface wireless network

TL;DR: An interference-aware channel allocation algorithm for a multichannel, multi-interface wireless network that minimizes the effect of adjacent and co-channel channel interference both within a node and across nodes and makes use of all the available channels for improving spatial reuse in the network.
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Asynchronous Convex Consensus in the Presence of Crash Faults

TL;DR: This paper explores the convex consensus problem under crash faults with incorrect inputs, and presents an asynchronous approximate conveX consensus algorithm with optimal fault tolerance that reaches consensus on an optimal output polytope.
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Multiparty Equality Function Computation in Networks with Point-to-Point Links

TL;DR: In this article, the authors considered a point-to-point communication model and showed that traditional techniques generalized from two-party communication complexity problem are not sufficient to obtain tight bounds under the point-topeach communication model, which significantly reduces the space of protocols to study.

Efficient network camouflaging in wireless networks

TL;DR: This three-part dissertation investigates techniques for providing network camouflaging services in wireless networks and designs an IEEE 802.11-compliant MAC protocol that provides receiver anonymity for unicast frames and offers better reliability than pure broadcast protocol.
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Asynchronous Distributed Optimization with Redundancy in Cost Functions.

TL;DR: An asynchronous DGD algorithm where in each iteration the server only waits for (any) n− r agents, instead of all the n agents, implying solvability of the original multi-agent optimization problem with accuracy, despite the removal of up to r agents from the system.