2 Search Results for "Modekurthy, Venkata Prashant"

Document
DOI: 10.4230/LIPIcs.ECRTS.2025.10
LoRaHART: Hardware-Aware Real-Time Scheduling for LoRa

Authors: Soumya Ranjan Sahoo, Amalinda Gamage, Niraj Kumar, and Arvind Easwaran

Published in: LIPIcs, Volume 335, 37th Euromicro Conference on Real-Time Systems (ECRTS 2025)

Abstract
Time-sensitive data acquisition is critical for many Low-Power Wide-Area Network (LPWAN) applications, such as healthcare monitoring and industrial Internet of Things. Among the available LPWAN technologies, LoRa (Long Range) has emerged as a leading choice, offering kilometer-scale communication with minimal power consumption and enabling high-density deployments across large areas. However, the conventional ALOHA-based Medium Access Control (MAC) in LoRa is not designed to support real-time communication over large-scale networks. This paper introduces LoRaHART, a novel approach that overcomes two critical, under-explored limitations in Commercial Off The Shelf (COTS) LoRa gateways that impact real-time performance. LoRa gateways have limited capacity for demodulation of parallel transmissions and their antenna can either transmit or receive at any time instant. LoRaHART incorporates a hardware-aware super-frame structure, comprising both Time Division Multiple Access (TDMA) slots as well as opportunistic retransmissions using Carrier Sense Multiple Access (CSMA), designed to mitigate the above constraints. We use a partial packing and makespan minimization algorithm to schedule periodic real-time transmissions efficiently within the TDMA slots, and also develop a probabilistic node contention model for CSMA retransmissions, providing analytical guarantees for deadline satisfaction under ideal channel conditions. Our evaluation of LoRaHART on a 40-node LoRa testbed demonstrates significant improvements over existing solutions in practice, achieving an average Packet Reception Ratio of 98% and a 45% higher airtime utilization than the best performing baseline.
Cite as

Soumya Ranjan Sahoo, Amalinda Gamage, Niraj Kumar, and Arvind Easwaran. LoRaHART: Hardware-Aware Real-Time Scheduling for LoRa. In 37th Euromicro Conference on Real-Time Systems (ECRTS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 335, pp. 10:1-10:28, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{sahoo_et_al:LIPIcs.ECRTS.2025.10,
  author =	{Sahoo, Soumya Ranjan and Gamage, Amalinda and Kumar, Niraj and Easwaran, Arvind},
  title =	{{LoRaHART: Hardware-Aware Real-Time Scheduling for LoRa}},
  booktitle =	{37th Euromicro Conference on Real-Time Systems (ECRTS 2025)},
  pages =	{10:1--10:28},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-377-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{335},
  editor =	{Mancuso, Renato},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2025.10},
  URN =		{urn:nbn:de:0030-drops-235880},
  doi =		{10.4230/LIPIcs.ECRTS.2025.10},
  annote =	{Keywords: LoRa, LPWAN, Real-time Scheduling, Hardware Constraints}
}
Document
DOI: 10.4230/LIPIcs.ECRTS.2023.8
Precise Scheduling of DAG Tasks with Dynamic Power Management

Authors: Ashikahmed Bhuiyan, Mohammad Pivezhandi, Zhishan Guo, Jing Li, Venkata Prashant Modekurthy, and Abusayeed Saifullah

Published in: LIPIcs, Volume 262, 35th Euromicro Conference on Real-Time Systems (ECRTS 2023)

Abstract
The rigid timing requirement of real-time applications biases the analysis to focus on the worst-case performances. Such a focus cannot provide enough information to optimize the system’s typical resource and energy consumption. In this work, we study the real-time scheduling of parallel tasks on a multi-speed heterogeneous platform while minimizing their typical-case CPU energy consumption. Dynamic power management (DPM) policy is integrated to determine the minimum number of cores required for each task while guaranteeing worst-case execution requirements (under all circumstances). A Hungarian Algorithm-based task partitioning technique is proposed for clustered multi-core platforms, where all cores within the same cluster run at the same speed at any time, while different clusters may run at different speeds. To our knowledge, this is the first work aiming to minimize typical-case CPU energy consumption (while ensuring the worst-case timing correctness for all tasks under any execution condition) through DPM for parallel tasks in a clustered platform. We demonstrate the effectiveness of the proposed approach with existing power management techniques using experimental results and simulations. The experimental results conducted on the Intel Xeon 2680 v3 12-core platform show around 7%-30% additional energy savings.
Cite as

Ashikahmed Bhuiyan, Mohammad Pivezhandi, Zhishan Guo, Jing Li, Venkata Prashant Modekurthy, and Abusayeed Saifullah. Precise Scheduling of DAG Tasks with Dynamic Power Management. In 35th Euromicro Conference on Real-Time Systems (ECRTS 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 262, pp. 8:1-8:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{bhuiyan_et_al:LIPIcs.ECRTS.2023.8,
  author =	{Bhuiyan, Ashikahmed and Pivezhandi, Mohammad and Guo, Zhishan and Li, Jing and Modekurthy, Venkata Prashant and Saifullah, Abusayeed},
  title =	{{Precise Scheduling of DAG Tasks with Dynamic Power Management}},
  booktitle =	{35th Euromicro Conference on Real-Time Systems (ECRTS 2023)},
  pages =	{8:1--8:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-280-8},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{262},
  editor =	{Papadopoulos, Alessandro V.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2023.8},
  URN =		{urn:nbn:de:0030-drops-180372},
  doi =		{10.4230/LIPIcs.ECRTS.2023.8},
  annote =	{Keywords: Parallel task, mixed-criticality scheduling, energy minimization, dynamic power management, cluster-based platform}
}
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