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Daniel Jay Koch

dblp:339/6818 · DBLP profile ↗
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3ranked-venue papers
1as first author
3since 2021 · last 2024
—ORCID · none

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

Computer networks · 3 · 1 first-author · 3 since 2021

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
3 papers
Internet of things and sensor networks · 42% Wireless networking · 22% Cellular and mobile networks · 19%

Topics — the 8 heaviest of 10, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Cellular and mobile networks › radio access networks
cloud radio access network
0.812024
Cloud-LoRa: Enabling Cloud Radio Access LoRa Networks Using Reinforcement Learning Based Bandwidth-Adaptive Compression · NSDI 2024
Internet of things and sensor networks
lora networks
0.812024
Cloud-LoRa: Enabling Cloud Radio Access LoRa Networks Using Reinforcement Learning Based Bandwidth-Adaptive Compression · NSDI 2024
Internet of things and sensor networks › LPWAN
lora
0.712023
OpenLoRa: Validating LoRa Implementations through an Extensible and Open-sourced Framework · NSDI 2023
Network management and operations › protocol verification
protocol validation
0.712023
OpenLoRa: Validating LoRa Implementations through an Extensible and Open-sourced Framework · NSDI 2023
Internet of things and sensor networks › LPWAN
LoRaWAN
0.612022
Spreading Factor Detection for Low-Cost Adaptive Data Rate in LoRaWAN Gateways · SenSys 2022
Wireless networking › link adaptation
rate adaptation
0.612022
Spreading Factor Detection for Low-Cost Adaptive Data Rate in LoRaWAN Gateways · SenSys 2022
Internet of things and sensor networks
LPWAN
0.212023
OpenLoRa: Validating LoRa Implementations through an Extensible and Open-sourced Framework · NSDI 2023
Internet architecture and protocols › network interconnection
gateway
0.212022
Spreading Factor Detection for Low-Cost Adaptive Data Rate in LoRaWAN Gateways · SenSys 2022

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

reinforcement learning · 0.8software-defined radio · 0.6discrete wavelet transform · 0.6
YearPublicationVenuePosition
2024 Cloud-LoRa: Enabling Cloud Radio Access LoRa Networks Using Reinforcement Learning Based Bandwidth-Adaptive Compression
Muhammad Osama Shahid, Daniel Jay Koch, Jayaram Raghuram, Bhuvana Krishnaswamy, Krishna Chintalapudi, Suman Banerjee 0001
NSDI2
2023 OpenLoRa: Validating LoRa Implementations through an Extensible and Open-sourced Framework
Manan Mishra, Daniel Jay Koch, Muhammad Osama Shahid, Bhuvana Krishnaswamy, Krishna Chintalapudi, Suman Banerjee 0001
NSDI2
2022 Spreading Factor Detection for Low-Cost Adaptive Data Rate in LoRaWAN Gateways
abstract
In order to meet the capacity needs of LoRa networks, Adaptive Data Rate (ADR) has been proposed and implemented in LoRaWANs. The network server running ADR determines the optimum data-rate and hence spreading factor setting for each LoRa device in a network. This in turn requires the gateway to be capable of receiving all possible spreading factors. Existing gateways achieve this by using multiple RF front ends, increasing their overall cost and complexity. In this work, we propose a Discrete Wavelet Transform based spreading factor detection algorithm that is agnostic to transmitter settings. This computationally light-weight algorithm can be implemented on any off-the-shelf SDR, bringing down the cost and ease of LoRaWAN gateway implementations. Using experimental, real-world datasets, we show that the proposed algorithm can detect the spreading factor of over 99.5% of the received packets at SNRs down to -10dB.
Daniel Jay Koch, Muhammad Osama Shahid, Bhuvana Krishnaswamy
SenSys1