4 Search Results for "Wang, Chenglong"

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.ECOOP.2025.10
Bottom-Up Synthesis of Memory Mutations with Separation Logic

Authors: Kasra Ferdowsi and Hila Peleg

Published in: LIPIcs, Volume 333, 39th European Conference on Object-Oriented Programming (ECOOP 2025)

Abstract
Programming-by-Example (PBE) is the paradigm of program synthesis specified via input-output pairs. It is commonly used because examples are easy to provide and collect from the environment. A popular optimization for enumerative synthesis with examples is Observational Equivalence (OE), which groups programs into equivalence classes according to their evaluation on example inputs. Current formulations of OE, however, are severely limited by the assumption that the synthesizer’s target language contains only pure components with no side-effects, either enforcing this in their target language, or ignoring it, leading to an incorrect enumeration. This limits their ability to use realistic component sets. We address this limitation by borrowing from Separation Logic, which can compositionally reason about heap mutations. We reformulate PBE using a restricted Separation Logic: Concrete Heap Separation Logic (CHSL), transforming the search for programs into a proof search in CHSL. This lets us perform bottom-up enumerative synthesis without the need for expert-provided annotations or domain-specific inferences, but with three key advantages: we (i) preserve correctness in the presence of memory-mutating operations, (ii) compact the search space by representing many concrete programs as one under CHSL, and (iii) perform a provably correct OE-reduction. We present SObEq (Side-effects in OBservational EQuivalence), a bottom-up enumerative algorithm that, given a PBE task, searches for its CHSL derivation. The SObEq algorithm is proved correct with no purity assumptions: we show it is guaranteed to lose no solutions. We also evaluate our implementation of SObEq on benchmarks from the literature and online sources, and show that it produces high-quality results quickly.
Cite as

Kasra Ferdowsi and Hila Peleg. Bottom-Up Synthesis of Memory Mutations with Separation Logic. In 39th European Conference on Object-Oriented Programming (ECOOP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 333, pp. 10:1-10:32, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{ferdowsi_et_al:LIPIcs.ECOOP.2025.10,
  author =	{Ferdowsi, Kasra and Peleg, Hila},
  title =	{{Bottom-Up Synthesis of Memory Mutations with Separation Logic}},
  booktitle =	{39th European Conference on Object-Oriented Programming (ECOOP 2025)},
  pages =	{10:1--10:32},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-373-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{333},
  editor =	{Aldrich, Jonathan and Silva, Alexandra},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2025.10},
  URN =		{urn:nbn:de:0030-drops-233036},
  doi =		{10.4230/LIPIcs.ECOOP.2025.10},
  annote =	{Keywords: Program synthesis, observational equivalence}
}
Document
DOI: 10.4230/LIPIcs.ICDT.2025.4
Learning Aggregate Queries Defined by First-Order Logic with Counting

Authors: Steffen van Bergerem and Nicole Schweikardt

Published in: LIPIcs, Volume 328, 28th International Conference on Database Theory (ICDT 2025)

Abstract
In the logical framework introduced by Grohe and Turán (TOCS 2004) for Boolean classification problems, the instances to classify are tuples from a logical structure, and Boolean classifiers are described by parametric models based on logical formulas. This is a specific scenario for supervised passive learning, where classifiers should be learned based on labelled examples. Existing results in this scenario focus on Boolean classification. This paper presents learnability results beyond Boolean classification. We focus on multiclass classification problems where the task is to assign input tuples to arbitrary integers. To represent such integer-valued classifiers, we use aggregate queries specified by an extension of first-order logic with counting terms called FOC₁. Our main result shows the following: given a database of polylogarithmic degree, within quasi-linear time, we can build an index structure that makes it possible to learn FOC₁-definable integer-valued classifiers in time polylogarithmic in the size of the database and polynomial in the number of training examples.
Cite as

Steffen van Bergerem and Nicole Schweikardt. Learning Aggregate Queries Defined by First-Order Logic with Counting. In 28th International Conference on Database Theory (ICDT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 328, pp. 4:1-4:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{vanbergerem_et_al:LIPIcs.ICDT.2025.4,
  author =	{van Bergerem, Steffen and Schweikardt, Nicole},
  title =	{{Learning Aggregate Queries Defined by First-Order Logic with Counting}},
  booktitle =	{28th International Conference on Database Theory (ICDT 2025)},
  pages =	{4:1--4:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-364-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{328},
  editor =	{Roy, Sudeepa and Kara, Ahmet},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICDT.2025.4},
  URN =		{urn:nbn:de:0030-drops-229457},
  doi =		{10.4230/LIPIcs.ICDT.2025.4},
  annote =	{Keywords: Supervised learning, multiclass classification problems, counting logic}
}
Document
DOI: 10.4230/LIPIcs.ECOOP.2016.25
Transforming Programs between APIs with Many-to-Many Mappings

Authors: Chenglong Wang, Jiajun Jiang, Jun Li, Yingfei Xiong, Xiangyu Luo, Lu Zhang, and Zhenjiang Hu

Published in: LIPIcs, Volume 56, 30th European Conference on Object-Oriented Programming (ECOOP 2016)

Abstract
Transforming programs between two APIs or different versions of the same API is a common software engineering task. However, existing languages supporting for such transformation cannot satisfactorily handle the cases when the relations between elements in the old API and the new API are many-to-many mappings: multiple invocations to the old API are supposed to be replaced by multiple invocations to the new API. Since the multiple invocations of the original APIs may not appear consecutively and the variables in these calls may have different names, writing a tool correctly to cover all such invocation cases is not an easy task. In this paper we propose a novel guided-normalization approach to address this problem. Our core insight is that programs in different forms can be semantics-equivalently normalized into a basic form guided by transformation goals, and developers only need to write rules for the basic form to address the transformation. Based on this approach, we design a declarative program transformation language, PATL, for adapting Java programs between different APIs. PATL has simple syntax and basic semantics to handle transformations only considering consecutive statements inside basic blocks, while with guided-normalization, it can be extended to handle complex forms of invocations. Furthermore, PATL ensures that the user-written rules would not accidentally break def-use relations in the program. We formalize the semantics of PATL on Middleweight Java and prove the semantics-preserving property of guided-normalization. We also evaluated our language with three non-trivial case studies: i.e. updating Google Calendar API, switching from JDom to Dom4j, and switching from Swing to SWT. The result is encouraging; it shows that our language allows successful transformations of real world programs with a small number of rules and little manual resolution.
Cite as

Chenglong Wang, Jiajun Jiang, Jun Li, Yingfei Xiong, Xiangyu Luo, Lu Zhang, and Zhenjiang Hu. Transforming Programs between APIs with Many-to-Many Mappings. In 30th European Conference on Object-Oriented Programming (ECOOP 2016). Leibniz International Proceedings in Informatics (LIPIcs), Volume 56, pp. 25:1-25:26, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

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@InProceedings{wang_et_al:LIPIcs.ECOOP.2016.25,
  author =	{Wang, Chenglong and Jiang, Jiajun and Li, Jun and Xiong, Yingfei and Luo, Xiangyu and Zhang, Lu and Hu, Zhenjiang},
  title =	{{Transforming Programs between APIs with Many-to-Many Mappings}},
  booktitle =	{30th European Conference on Object-Oriented Programming (ECOOP 2016)},
  pages =	{25:1--25:26},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-014-9},
  ISSN =	{1868-8969},
  year =	{2016},
  volume =	{56},
  editor =	{Krishnamurthi, Shriram and Lerner, Benjamin S.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2016.25},
  URN =		{urn:nbn:de:0030-drops-61195},
  doi =		{10.4230/LIPIcs.ECOOP.2016.25},
  annote =	{Keywords: Program transformation, API migration}
}
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