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Sanjeev Setia

Researcher at George Mason University

Publications -  66
Citations -  5702

Sanjeev Setia is an academic researcher from George Mason University. The author has contributed to research in topics: Wireless sensor network & Key distribution in wireless sensor networks. The author has an hindex of 33, co-authored 66 publications receiving 5600 citations. Previous affiliations of Sanjeev Setia include University of Maryland, College Park.

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Localized Multicast: Efficient and Distributed Replica Detection in Large-Scale Sensor Networks

TL;DR: This paper presents a novel distributed approach called Localized Multicast for detecting node replication attacks and shows that it is more efficient in terms of communication and memory costs in large-scale sensor networks, and at the same time achieves a higher probability of detecting node replicas.
Proceedings ArticleDOI

GKMPAN: an efficient group rekeying scheme for secure multicast in ad-hoc networks

TL;DR: The GKMPAN protocol exploits the property of ad hoc networks that each member of a group is both a host and a router, and distributes the group key to member nodes via a secure hop-by-hop propagation scheme.
Proceedings ArticleDOI

Poster abstract: LEAP—efficient security mechanisms for large-scale distributed sensor networks

TL;DR: LEAP (Localized Encryption and Authentication Protocol), a key management protocol for sensor networks that is designed to support in-network processing techniques such as passive participation, is described.
Proceedings ArticleDOI

CORD: Energy-Efficient Reliable Bulk Data Dissemination in Sensor Networks

TL;DR: The results show that in comparison to Deluge (the de facto network reprogramming protocol for TinyOS) CORD significantly reduces the energy consumption for reliable data dissemination while achieving a comparable latency.
Proceedings ArticleDOI

Performance analysis of job scheduling policies in parallel supercomputing environments

TL;DR: The authors analyze three general classes of scheduling policies under a workload typical of large-scale scientific computing and indicate that existing static schemes to not perform well under varying workloads.