Nisheeth Shrivastava

Computer Networks and Communications · 2004–2008

A researcher in the Science corpus: 2 papers; trusts 7 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

Whom Nisheeth Shrivastava trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Samuel Madden
  2. Yonggang Zhao
  3. Nicolas Bruno
  4. Jeffrey Considine
  5. Ramesh Govindan
  6. Jason L. Hill
  7. Deborah Estrin
  8. Sanjeev Khanna
  9. Yong Yao
  10. M. Franklin

Who trusts Nisheeth Shrivastava overall

  1. Ted Dunning
  2. German Castignani
  3. Tom Diethe
  4. Otmar Ertl
  5. S. Muthu Muthukrishnan
  6. Jaydip Sen
  7. Wei Wang
  8. Palash Goyal
  9. Thierry Derrmann
  10. Tom Borchert

Papers from Nisheeth Shrivastava's perspective

Ordered by how strongly the researchers Nisheeth Shrivastava trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Proceedings of the 5th Symposium on Operating Systems Design and Implementation Tag: a Tiny Aggregation Service for Ad-hoc Sensor Networks
  2. Directed diffusion: a scalable and robust communication paradigm for sensor networks (ACM/IEEE International Conference on Mobile Computing and Networking)
  3. System architecture directions for networked sensors (SIGP)
  4. Computing aggregates for monitoring wireless sensor networks (Proceedings of the First IEEE International Workshop on Sensor Network Protocols and Applications, 2003.)
  5. Next century challenges: scalable coordination in sensor networks
  6. The Anatomy of a Large-Scale Hypertextual Web Search Engine
  7. Energy-efficient communication protocol for wireless microsensor networks (Proceedings of the Annual Hawaii International Conference on System Sciences)
  8. Approximate query processing using wavelets (The VLDB journal)
  9. The cougar approach to in-network query processing in sensor networks (SGMD)
  10. Approximate aggregation techniques for sensor databases (Proceedings / International Conference on Data Engineering)

Their papers