VLDB 2026 Research / reviewers in the wild / expert
Nandakishore Kushalnagar
dblp:05/4189
· DBLP profile ↗
5ranked-venue papers
0as first author
0since 2021 · last 2005
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 4
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer networks
4 papers |
Internet of things and sensor networks · 75% Software-defined and programmable networks · 11% Wireless networking · 11% | |
| Computer architecture, parallel and distributed computing, and storage systems
3 papers |
Embedded and real-time systems · 100% |
Topics — the 11 heaviest of 13, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Internet of things and sensor networks › wireless sensor network
heterogeneous sensor networks |
0.1 | 1 | 2005 | Exploiting heterogeneity in sensor networks · INFOCOM 2005 |
Internet of things and sensor networks › industrial iot
industrial sensor networks |
0.1 | 1 | 2005 | Design and deployment of industrial sensor networks: experiences from a semiconductor plant and the north sea · SenSys 2005 |
Wireless networking
medium access control |
0.1 | 1 | 2005 | Exploiting heterogeneity in sensor networks · INFOCOM 2005 |
Software-defined and programmable networks
resource-aware deployment |
0.1 | 1 | 2005 | Exploiting heterogeneity in sensor networks · INFOCOM 2005 |
Internet of things and sensor networks › wireless sensor network
sensor deployment |
0.1 | 1 | 2005 | Design and deployment of industrial sensor networks: experiences from a semiconductor plant and the north sea · SenSys 2005 |
Internet of things and sensor networks
wireless sensor network |
0.1 | 1 | 2005 | Exploiting heterogeneity in sensor networks · INFOCOM 2005 |
Internet of things and sensor networks › wireless sensor network
wireless sensor network platform |
0.1 | 1 | 2005 | Intel mote 2: an advanced platform for demanding sensor network applications · SenSys 2005 |
Internet of things and sensor networks › reliability
sensor network reliability |
0.0 | 1 | 2004 | Sensor networks in intel fabrication plants · SenSys 2004 |
Routing and switching
routing protocol |
0.0 | 1 | 2005 | Exploiting heterogeneity in sensor networks · INFOCOM 2005 |
Embedded and real-time systems
cyber-physical system platforms |
0.0 | 1 | 2005 | Design and deployment of industrial sensor networks: experiences from a semiconductor plant and the north sea · SenSys 2005 |
Embedded and real-time systems › cyber-physical system platforms › industrial automation
industrial monitoring |
0.0 | 1 | 2005 | Design and deployment of industrial sensor networks: experiences from a semiconductor plant and the north sea · SenSys 2005 |
Methods — techniques the papers use, named apart from their topics
vibration signature sensing · 0.1state preservation · 0.1oversampling · 0.1heterogeneous wireless networking · 0.1testbed measurement · 0.1simulation · 0.1analysis · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2005 | Heavy Industry Applications of Sensornets
Philip Buonadonna, Jasmeet Chhabra, Lakshman Krishnamurthy, Nandakishore Kushalnagar |
DCOSS | 4 |
| 2005 | Exploiting heterogeneity in sensor networksabstractThe presence of heterogeneous nodes (i.e., nodes with an enhanced energy capacity or communication capability) in a sensor network is known to increase network reliability and lifetime. However, questions of where how many, and what types of heterogeneous resources to deploy remain largely unexplored. We focus on energy and link heterogeneity in ad hoc sensor networks and consider resource-aware MAC and routing protocols to utilize those resources. Using analysis, simulation, and real testbed measurements, we evaluate the impact of number and placement of heterogeneous resources on performance in networks of different sizes and densities. While we prove that optimal deployment is very hard in general, we also show that only a modest number of reliable, long-range backhaul links and line-powered nodes are required to have a significant impact. Properly deployed, heterogeneity can triple the average delivery rate and provide a 5-fold increase in the lifetime (respectively) of a large batten-powered network of simple sensors. Mark D. Yarvis, Nandakishore Kushalnagar, Harkirat Singh, Anand Rangarajan 0002, York Liu, Suresh Singh 0001 |
INFOCOM | 2 |
| 2005 | Intel mote 2: an advanced platform for demanding sensor network applicationsabstractNo abstract available. Robert Adler, Mick Flanigan, Jonathan Huang, Ralph Kling, Nandakishore Kushalnagar, Lama Nachman, Chieh-Yih Wan, Mark D. Yarvis |
SenSys | 5 |
| 2005 | Design and deployment of industrial sensor networks: experiences from a semiconductor plant and the north seaabstractSensing technology is a cornerstone for many industrial applications. Manufacturing plants and engineering facilities, such as shipboard engine rooms, require sensors to ensure product quality and efficient and safe operation. We focus on one representative application, preventative equipment maintenance, in which vibration signatures are gathered to predict equipment failure. Based on application requirements and site surveys, we develop a general architecture for this class of industrial applications. This architecture meets the application's data fidelity needs through careful state preservation and over-sampling. We describe the impact of implementing the architecture on two sensing platforms with differing processor and communication capabilities. We present a systematic performance comparison between these platforms in the context of the application. We also describe our experience and lessons learned in two settings: in a semiconductor fabrication plant and onboard an oil tanker in the North Sea. Finally, we establish design guidelines for an ideal platform and architecture for industrial applications. This paper includes several unique contributions: a study of the impact of platform on architecture, a comparison of two deployments in the same application class, and a demonstration of application return on investment. Lakshman Krishnamurthy, Robert Adler, Philip Buonadonna, Jasmeet Chhabra, Mick Flanigan, Nandakishore Kushalnagar, Lama Nachman, Mark D. Yarvis |
SenSys | 6 |
| 2004 | Sensor networks in intel fabrication plantsabstractThe deployment of large-scale sensor networks in industrial environments presents technical challenges in achieving ease of deployment, flexibility in operation, and overall commercial viability. Sensor deployments are characterized by non-uniform placement of nodes, intermittent node connectivity, and the aggregation and reliable transfer of a large amount of data as networks scale to larger and larger sizes. Operation challenges include efficiently utilizing battery powered nodes, dynamically selecting sample periods and equipment clusters of interest, integrating with existing sensing and analysis infrastructure and easily correlating faults identified by the sensor network back to key factory operations and equipment. Commercial aspects involve returning value to the organization with hardened network nodes and reliable network operation that easily justifies the sensor network deployment and operating costs. We will demonstrate a sensor network in the ultra-pure water facility of an Intel fabrication plant, including details on the fab vibration application, the hardware nodes, and the heterogeneous wireless network. Jasmeet Chhabra, Nandakishore Kushalnagar, Benjamin Metzler, Allen Sampson |
SenSys | 2 |