Nandakishore Kushalnagar

dblp:05/4189 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Internet of things and sensor networks › wireless sensor network
heterogeneous sensor networks
0.112005
Exploiting heterogeneity in sensor networks · INFOCOM 2005
Internet of things and sensor networks › industrial iot
industrial sensor networks
0.112005
Design and deployment of industrial sensor networks: experiences from a semiconductor plant and the north sea · SenSys 2005
Wireless networking
medium access control
0.112005
Exploiting heterogeneity in sensor networks · INFOCOM 2005
Software-defined and programmable networks
resource-aware deployment
0.112005
Exploiting heterogeneity in sensor networks · INFOCOM 2005
Internet of things and sensor networks › wireless sensor network
sensor deployment
0.112005
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
0.112005
Exploiting heterogeneity in sensor networks · INFOCOM 2005
Internet of things and sensor networks › wireless sensor network
wireless sensor network platform
0.112005
Intel mote 2: an advanced platform for demanding sensor network applications · SenSys 2005
Internet of things and sensor networks › reliability
sensor network reliability
0.012004
Sensor networks in intel fabrication plants · SenSys 2004
Routing and switching
routing protocol
0.012005
Exploiting heterogeneity in sensor networks · INFOCOM 2005
Embedded and real-time systems
cyber-physical system platforms
0.012005
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
0.012005
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
YearPublicationVenuePosition
2005 Heavy Industry Applications of Sensornets
Philip Buonadonna, Jasmeet Chhabra, Lakshman Krishnamurthy, Nandakishore Kushalnagar
DCOSS4
2005 Exploiting heterogeneity in sensor networks
abstract
The 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
INFOCOM2
2005 Intel mote 2: an advanced platform for demanding sensor network applications
abstract
No abstract available.
Robert Adler, Mick Flanigan, Jonathan Huang, Ralph Kling, Nandakishore Kushalnagar, Lama Nachman, Chieh-Yih Wan, Mark D. Yarvis
SenSys5
2005 Design and deployment of industrial sensor networks: experiences from a semiconductor plant and the north sea
abstract
Sensing 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
SenSys6
2004 Sensor networks in intel fabrication plants
abstract
The 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
SenSys2