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Ajay K. Kanthila

dblp:283/6082 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1

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
1 paper
Wireless sensing and localization · 87% Internet of things and sensor networks · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Wireless sensing and localization › device-free sensing
device-free localization
0.412020
LOCI: Privacy-aware, Device-free, Low-power Localization of Multiple Persons using IR Sensors · IPSN 2020
Wireless sensing and localization › secure localization
privacy-preserving localization
0.412020
LOCI: Privacy-aware, Device-free, Low-power Localization of Multiple Persons using IR Sensors · IPSN 2020
Internet of things and sensor networks › iot applications
smart environments
0.112020
LOCI: Privacy-aware, Device-free, Low-power Localization of Multiple Persons using IR Sensors · IPSN 2020

Methods — techniques the papers use, named apart from their topics

sensor fusion · 0.4
YearPublicationVenuePosition
2020 LOCI: Privacy-aware, Device-free, Low-power Localization of Multiple Persons using IR Sensors
abstract
High accuracy and device-free indoor localization is still a holy grail to enable smart environments. With the growing privacy concerns and regulations, it is necessary to develop methods and systems that can be low-power, device-free as well as privacy-aware. While IR-based solutions fit the bill, they require many modules to be installed in the area of interest for higher accuracy, or proper planning during installation, or they may not work if the background has multiple heat-emitting objects, etc. In this paper, we propose a custom-built miniature device called LOCI that uses IR sensing. One unit of LOCI can provide three-dimensional localization at best. LOCI uses only a thermopile and a PIR sensor built within a 5x5x2 cm3module. Since IR-based sensing is used, LOCI consumes around 80 mW. LOCI uses analog waveform from the PIR sensor with the gain of the PIR sensor dynamically controlled through software in real-time to simulate spatial diversity. LOCI proposes low-complexity techniques with sensor fusion to eliminate the noise in the background, which has not been handled in previous works even with sophisticated signal processing techniques. Since LOCI uses raw data from the thermopile, the computations are power-efficient. We present the complete design of LOCI and the proposed methodology to estimate height and location. LOCI achieves accuracies of sub-22 cm with a confidence of 0.5 and sub-35 cm with a confidence of 0.8. The best-case location accuracy is 12.5 cm. The accuracy of height estimation is within 8 cm in majority cases. LOCI can easily be extended to recognize activities.
Sujay Narayana, Vijay S. Rao, R. Venkatesha Prasad, Ajay K. Kanthila, Kavya Managundi, Luca Mottola, Prabhakar Venkata Tamma
IPSN4