Pavarangkoon, Praphan

Preferred name

Main Affiliation

School of Information Technology

praphan.pa@kmitl.ac.th

Now showing 1 - 9 of 9

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Energy-Efficient and Fair Computation Offloading for Multi-user MEC with EH Devices
(2025-01-01)
Mustika, I. Wayan
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Triyanto, Dedi
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Halimah, Noor Siti
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Pavarangkoon, Praphan
The increasing demand for low-latency and energy-efficient mobile applications has propelled the development of Mobile Edge Computing (MEC), enabling the offloading of computational activities from resource-constrained Mobile Devices (MDs) to nearby edge servers. This study examines a joint problem of computation offloading and resource allocation issue in a wireless multi-user, multi-server MEC system with Energy Harvesting (EH) capabilities. Our objective is to reduce long-term energy consumption while adhering to limitations related to latency, energy causality, server capacity, and Signal-To-Interference-Plus-Noise Ratio (SINR). To address the complexities of system dynamics and uncertainty in energy arrivals, we propose a low-complexity online approach utilizing Lyapunov optimization. The proposed method dynamically modifies offloading ratios, transmission power, CPU frequencies, and server allocations without requiring future data. The simulation results show that our method achieves significant energy savings, has low delays, and ensures fairness among users, even in highly congested scenarios. A comparative analysis with benchmark algorithms validates the efficacy and resilience of the proposed framework in real MEC situations.
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Prediction of Medication-Related Osteonecrosis of the Jaw in Patients Receiving Antiresorptive Therapy Using Machine Learning Models
(2025-03-01)
Warin, Kritsasith
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Lochanachit, Sirasit
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Pavarangkoon, Praphan
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Techapanurak, Engkarat
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Somyanonthanakul, Rachasak
Background: Medication-related osteonecrosis of the jaw (MRONJ) is a serious complication associated with the use of antiresorptive agents, impacting patient quality of life and treatment outcomes. Predictive modeling may aid in a better understanding of MRONJ development. Purpose: The study aimed to evaluate machine learning (ML)–based models for predicting MRONJ in patients receiving antiresorptive therapy. Study Design, Setting, Sample: This retrospective in silico study analyzed electronic medical records from Thammasat University Hospital, covering the period from January 2012 to December 2022. The sample included subjects receiving antiresorptive therapy, excluding those with a history of radiation therapy or metastatic jaw disease. Predictor Variables: The primary predictor variable was the predicted probability of MRONJ development from the ML models. Outcome Variables: The outcome variable was MRONJ status coded as present or absent based on chart review. Covariates: Covariates included demographic data, MRONJ occurrence, location and staging of MRONJ, comorbidities, diseases related to antiresorptive agents, types of antiresorptive agents, therapy duration, concurrent medications, blood calcium levels, and dental factors. Analyses: Model performance was assessed via accuracy, sensitivity, specificity, positive and negative predictive values, and the area under the receiver operating characteristic curve. Additionally, univariate and multivariate Cox regression analyses were conducted to identify factors significantly associated with MRONJ development. P ≤ .05 was statistically significant. Results: The study analyzed data from 5,305 subjects with a mean age of 75 ± 11.1 years, predominantly female. MRONJ was observed in 81 cases (1.5%), with a median time to development of 33 months (interquartile range = 3). Among the 6 models tested, the best-performing model had an accuracy of 0.95 and an area under the receiver operating characteristic curve of 0.89-0.90. Significant predictors identified through Cox regression included metabolic syndrome (hazard ratio = 14.064, 95% confidence interval = 1.111-178.067, P = .041) and patients receiving intravenous pamidronate (hazard ratio = 5.932, 95% confidence interval = 1.755-20.051, P = .004), indicating their association with MRONJ development. Conclusions and Relevance: ML-based predictive and time-to-event models effectively predict MRONJ risk, aiding in the strategic prevention and management for patients undergoing antiresorptive therapy.
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Spectrum allocation scheme considering spectrum slicing in elastic optical networks
(2021-07-01)
Kitsuwan, Nattapong
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Akaki, Kaito
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Pavarangkoon, Praphan
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Nag, Avishek
Recent advances in physical layer optics have made slicing of optical bands into multiple subbands with different bandwidths possible. Due to this development, spectrum allocation has become easier in elastic optical networks (EONs). More specifically, owing to the slicing technology, the defragmentation problem in EONs can be addressed easily, i.e., more demands can be fit into empty spectrum slots by breaking the demands as needed using the slicing technology. This paper proposes a spectrum allocation scheme considering the slicing process at any nodes, e.g., source node and intermediate nodes, in EONs. Slicing-and-stitching technology is applied to break the contiguous-spectrum constraint in an EON so that spectrum fragmentation is reduced. While slicing a demand from one node to another, the following questions must be answered: (1) Which parts of the spectrum band should be sliced? (2) In which node(s) along the path of a demand should the slicings be done to reduce the bandwidth blocking rate? To answer the above questions, we formulate a mixed-integer linear programming (MILP) model that jointly addresses the above questions as well as minimizes the total number of slicers in a network. To measure the performance of the MILP, we used the bandwidth blocking ratio (BBR) of the network as a performance metric. Our results from the MILP show that introducing slicing at every node in the network improves the BBR by as much as 68% compared to a conventional case where slicing a demand is allowed only at the source node.
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Fairness-Aware Computation Offloading for Mobile Edge Computing with Energy Harvesting
(2025-01-01)
Triyanto, Dedi
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Wayan Mustika, I.
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Widyawan
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Pavarangkoon, Praphan
Mobile edge computing (MEC) improves network performance by minimizing latency and assigning computing tasks to edge servers. Nonetheless, delegating computations in environments with high device density poses considerable difficulties. Ensuring fairness in resource distribution among users is essential for preserving network stability and user satisfaction in these contexts. This research formulates the Fairness-aware Computation Offloading Optimization (FACOO) algorithm. The Lyapunov approach and sequential least squares quadratic programming (SLSQP) are used to ascertain the best offloading ratio, transmission power, and CPU frequency while complying with signal-to-interference-plus-noise ratio (SINR) limitations. Energy harvesting (EH) is built into FACOO to prolong device battery life and to ensure that MEC systems, which have limited resources, are more sustainable. The results show that FACOO greatly improves throughput and fairness while using significantly less energy, especially in settings with numerous nodes dispersed across large areas. Comprehensive simulations demonstrate that the method effectively balances fairness, throughput, and energy use, making it a workable way to improve resource allocation in MEC systems.
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Reducing Bandwidth Blocking Rate in Elastic Optical Networks Through Scale-Based Slicer Placement Strategy
(2023-01-01)
Kitsuwan, Nattapong
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Pavarangkoon, Praphan
In this paper, we present a scale-based slicer placement strategy aimed at decreasing the bandwidth blocking rate (BBR) in elastic optical networks (EONs). EONs adopt slicing-and-stitching technology to bypass the requirement of consecutive spectrum slots on the same link for a single request. This is achieved through the use of slicers to split the requested spectrum band into multiple sub-spectrum components. However, a uniform distribution of slicers across all nodes may not guarantee low BBR due to varying traffic volumes on each node. Our proposed scale-based policy determines the necessary number of slicers for each node based on our investigation. Simulation results demonstrate that our strategy leads to a 91% reduction in BBR compared to conventional methods.
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YOLO-augment strategy with diffusion-based inpainting for enhanced traffic sign detection
(2026-01-01)
Sub-r-pa, Chayanon
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Pavarangkoon, Praphan
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Huang, Su Wen
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Fan, Ming Zhong
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Chen, Rung Ching
Traffic sign datasets often suffer from data scarcity and class imbalance, which challenge the development of robust autonomous driving systems. This article proposes a novel dataset augmentation method that leverages Stable Diffusion inpainting to generate realistic synthetic traffic signs. The method fine-tunes a Stable Diffusion model and introduces an object-size-based crop (OSB-crop) technique with mask adjustments to ensure high-quality augmentations that maintain contextual consistency. Evaluations using the Fréchet Inception Distance (FID) show average scores of 195.85 for the DFG-T10 subset and 247.077 for the DFG-B10 subset, demonstrating the ability to produce realistic inpainted signs, particularly for more represented minority classes. Qualitative analyses further highlight seamless integration into real-world scenes, although challenges remain for extremely underrepresented classes and ensuring perfect visual fidelity. The benefits of this approach include its potential to enhance traffic sign datasets, address class imbalances, and improve the potential for training more reliable autonomous driving systems by providing more diverse and realistic training data. This study focuses on evaluating the quality of the generated data itself as a foundational step toward enhancing downstream detection models. However, limitations include the computational cost of fine-tuning and the difficulty in achieving high-quality inpainting for all underrepresented classes, especially those with poor initial data quality. This work lays a strong foundation for advancing dataset augmentation techniques for real-world applications.
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Heuristic Approaches for Cache Node Placement in Content-Centric Networking Under Maximum Link Traffic Minimization
(2026-01-01)
Pavarangkoon, Praphan
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Nakajima, Shohei
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Kitsuwan, Nattapong
This paper proposes heuristic approaches for cache node placement in Content-Centric Networking (CCN) with the objective of minimizing maximum link traffic. The work builds on an exact Integer Linear Programming (ILP) formulation from our earlier study, which jointly models routing and caching decisions but becomes computationally expensive for large-scale networks. To address this limitation, we develop scalable heuristic algorithms that approximate the ILP solution while requiring much lower computation time. We introduce four heuristic approaches, comprising two Linear Programming (LP)-based local search algorithms, 2Swap and GreedySwap, which exploit the fractional solution of the LP relaxation, and two proxy-based heuristics, Population-Weighted Closeness (PWC) and Population-Weighted Betweenness (PWB), which estimate cache utility using population-weighted centrality measures without solving any optimization model. Experimental evaluations on four network topologies show that the LP-based heuristics remain within 0-6% of the ILP optimum while reducing computation time by factors ranging from about 40× to more than 160×. Compared with the proxy-based heuristics, the LP-based heuristics consistently yield lower maximum link traffic across all settings. These results indicate that the proposed heuristic approaches provide effective and scalable solutions for cache node placement in practical CCN deployments.
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Elastic optical network with spectrum slicing for fragmented bandwidth allocation
(2020-09-01)
Kitsuwan, Nattapong
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Pavarangkoon, Praphan
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Nag, Avishek
Elastic Optical Networks (EONs) allow the channel spacing and the spectral width of an optical signal to be dynamically adjusted and hence have become an important paradigm in managing the heterogeneous bandwidth demands of optical backbone networks. The entire available optical spectrum is divided into some spectrum slots which define the smallest granularity of bandwidth and optical signals with variable bandwidths can occupy different number of such slots. The constraints imposed by the physical layer of an EON require that the slots occupied by an optical signal from source to destination have to be consecutive and contiguous in terms of their relative position in the optical spectrum. Furthermore, the same spectrum slots need to be reserved throughout the entire optical signal's path from its source to destination. The above constraints make the routing and spectrum allocation (RSA) in EONs very challenging because unavailability of enough spectrum slots that together equals the spectral width of the optical signal associated with an end-to-end request, will result in blocking of the request. Recent developments in the physical layer technologies have made all-optical ‘slicing’ of a request possible and make the request to be ‘fit’ into multiple non-consecutive spectral slots in an EON. But these all-optical ‘slicers’ employ complex technologies and can be very costly to employ. In this paper, we propose a spectrum allocation scheme for an EON node architecture with these ‘slicers’ and we also formulate a modified RSA scheme for EONs employing slicers, both as a mixed-integer linear programming (MILP) model and a heuristic algorithm. Our main aim is to analyze the tradeoff between the number of slicers that can be used per node versus the spectrum utilization and bandwidth blocking rate. The numerical results show that the proposed scheme with slicers can significantly improve bandwidth blocking rate, compared to the conventional scheme without slicer.
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Spectrum Slicer Placement for Elastic Optical Network With Sparse Slicers
(2023-01-01)
Pavarangkoon, Praphan
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Akaki, Kaito
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Kitsuwan, Nattapong
This paper investigates spectrum slicer placement problem under a spectrum allocation scheme that considers the splitting process to minimize the blocking probability for elastic optical network with sparse slicers. Unlike the conventional elastic optical network without slicers, the elastic optical network with slicers is able to split a spectrum band into several spectrum components by replicating the original spectrum band and filtering out an unwanted signal on each spectrum band. The largest L-shape fit allocation algorithm, which allocates a request to available slot areas with L-shape, is utilized to reduce the request blocking probability. We evaluate the performance of our scheme with various centrality policies on elastic optical network with sparse slicers, where the number of slicers is given. Simulation results show that our scheme with betweenness centrality significantly outperforms that with random centrality, degree centrality, and closeness centrality in terms of bandwidth blocking rate.

Pavarangkoon, Praphan

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