Author name cluster

Jian Ma

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

19 papers

2 author rows

AAAI Conference 2026 Conference Paper

InterCoser: Interactive 3D Character Creation with Disentangled Fine-Grained Features

Yi Wang
Jian Ma
Zhuo Su
Guidong Wang
Jingyu Yang
Yu-Kun Lai
Kun Li

This paper aims to interactively generate and edit disentangled 3D characters based on precise user instructions. Existing methods generate and edit 3D characters via rough and simple editing guidance and entangled representations, making it difficult to achieve precise and comprehensive control over fine-grained local editing and free clothing transfer for characters. To enable accurate and intuitive control over the generation and editing of high-quality 3D characters with freely interchangeable clothing, we propose a novel user-interactive approach for disentangled 3D character creation. Specifically, to achieve precise control over 3D character generation and editing, we introduce two user-friendly interaction approaches: a sketch-based layered character generation/editing method, which supports clothing transfer; and a 3D-proxy-based part-level editing method, enabling fine-grained disentangled editing. To enhance 3D character quality, we propose a 3D Gaussian reconstruction strategy guided by geometric priors, ensuring that 3D characters exhibit detailed local geometry and smooth global surfaces. Extensive experiments on both public datasets and in-the-wild data demonstrate that our approach not only generates high-quality disentangled 3D characters but also supports precise and fine-grained editing through user interaction.

PDF Details DOI

JBHI Journal 2026 Journal Article

MVTT-GMamba: A Multimodal Graph Reasoning Framework for Anti-VEGF Efficacy Prediction in Diabetic Macular Edema

Shijing Wu
Yifan Zheng
Tianyu Chen
Gang Wu
Qinkun Zhi
Ailing Sui
Haixia Bai
Junhui Shen

Accurately predicting individual responses to Anti-Vascular Endothelial Growth Factor (Anti-VEGF) efficacy in diabetic macular edema (DME) remains a critical challenge in personalized ophthalmic care. Existing methods often rely on unimodal data or suffer from ineffective multimodal feature extraction and fusion, leading to modality redundancy and performance degradation. To address these limitations, we propose MVTT-GMamba, a novel multimodal learning framework that integrates optical coherence tomography (OCT) images and structured clinical indicators for early and accurate Anti-VEGF efficacy prediction. At the core of MVTT-GMamba is a feature-wise heterogeneous graph reasoning paradigm that explicitly models inter-patient and inter-feature relations, together with an adaptive, graph-guided prediction head that progressively anneals structural priors into the classifier. Building on this core, we adopt domain-tailored MambaVision and TabTransformer encoders and an early cross-attention fusion module to realize fine-grained multimodal representation learning. Extensive experiments on both a private clinical dataset (DMETHERA-ECSAHZU) and the public APTOS2021 benchmark demonstrate that MVTT-GMamba consistently outperforms state-of-the-art methods across all evaluation metrics. In addition, Grad-CAM visualizations reveal that the model attends to clinically relevant retinal regions, providing enhanced interpretability. Code is available at: https://github.com/DME666/DME.

Details DOI

AAAI Conference 2026 Conference Paper

X2Edit: Revisiting Arbitrary-Instruction Image Editing Through Self-Constructed Data and Task-Aware Representation Learning

Jian Ma
Xujie Zhu
Zihao Pan
Qirong Peng
Xu Guo
Chen Chen
Haonan Lu

Existing open-source datasets for arbitrary-instruction image editing remain suboptimal, while a plug-and-play editing module compatible with community-prevalent generative models is notably absent. In this paper, we first introduce the X2Edit Dataset, a comprehensive dataset covering 14 diverse editing tasks, including subject-driven generation. We utilize the industry-leading unified image generation models and expert models to construct the data. Meanwhile, we design reasonable editing instructions with the VLM and implement various scoring mechanisms to filter the data. As a result, we construct 3.7 million high-quality data with balanced categories. Second, to better integrate seamlessly with community image generation models, we design task-aware MoE-LoRA training based on FLUX.1, with only 8% of the parameters of the full model. To further improve the final performance, we utilize the internal representations of the diffusion model and define positive/negative samples based on image editing types to introduce contrastive learning. Extensive experiments demonstrate that the model's editing performance is competitive among many excellent models. Additionally, the constructed dataset exhibits substantial advantages over existing open-source datasets.

PDF Details DOI

EAAI Journal 2025 Journal Article

A deep reinforcement learning assisted adaptive genetic algorithm for flexible job shop scheduling

Jian Ma
Weinan Gao
Weitian Tong

The flexible job shop scheduling problem (FJSP) is a challenging combinatorial optimization problem in manufacturing systems. Existing intelligent optimization algorithms for FJSP are often hard to tune key parameters and operations efficiently, which losses the optimality of the obtained solution. To address the issues of genetic algorithms (GA) being prone to local optima and slow convergence, this paper proposes a deep reinforcement learning-assisted adaptive genetic algorithm (DRL-A-GA) for solving FJSP. In the proposed algorithm, continuous state vectors are used to represent the population state of the GA, and four mutation operations with respect to FJSP are designed as actions. Deep reinforcement learning is employed to adaptively tune the key parameters of the GA and dynamically select appropriate genetic operations. To validate the performance of DRL-A-GA, three sets of benchmark instances are selected for testing, and the results are compared with those of classical optimization algorithms and hybrid algorithms. The experimental results demonstrate that the proposed DRL-A-GA significantly outperforms both traditional optimization and intelligent hybrid optimization algorithms for solving FJSP, effectively improving solution quality and accelerating convergence.

Details DOI

AAAI Conference 2025 Conference Paper

GlyphDraw2: Automatic Generation of Complex Glyph Posters with Diffusion Models and Large Language Models

Jian Ma
Yonglin Deng
Chen Chen
Nanyang Du
Haonan Lu
Zhenyu Yang

Posters serve an essential function in marketing and advertising by improving visual communication and brand visibility, thus significantly contributing to industrial design. With the latest developments in controllable T2I diffusion models, research interest has surged in text rendering within synthesized images. Although text rendering accuracy has seen advancements, automatic poster generation remains a relatively untapped area. This paper presents an automatic poster generation framework featuring text rendering capabilities through the use of LLMs. Our framework employs a triple-cross attention mechanism based on alignment learning to achieve precise text placement within detailed contextual backgrounds. Moreover, it supports adjustable fonts, varying image resolutions, and poster rendering with textual prompts in both English and Chinese. Additionally, we present a comprehensive bilingual image-text dataset, GlyphDraw-3M, comprising 3 million image-text pairs, each with OCR annotations and resolutions exceeding 1024. Our method utilizes the SDXL architecture, and extensive experiments confirm its ability to generate posters with intricate and context-rich backgrounds.

PDF Details DOI

TMLR Journal 2025 Journal Article

L2G: Repurposing Language Models for Genomics Tasks

Wenduo Cheng
Junhong Shen
Mikhail Khodak
Jian Ma
Ameet Talwalkar

Pre-trained language models have transformed the field of natural language processing (NLP), and their success has inspired efforts in genomics to develop domain-specific foundation models (FMs). However, creating high-quality genomic FMs from scratch is resource-intensive, requiring significant computational power and high-quality pre-training data. The success of large language models (LLMs) in NLP has largely been driven by industrial-scale efforts leveraging vast, diverse corpora and massive computing infrastructure. In this work, we aim to bypass the data and computational bottlenecks of creating genomic FMs from scratch and instead propose repurposing existing LLMs for genomics tasks. Inspired by the recently observed 'cross-modal transfer' phenomenon -- where transformers pre-trained on natural language can generalize to other modalities -- we introduce L2G, which adapts a pre-trained LLM architecture for genomics using neural architecture search and a novel three-stage training procedure. Remarkably, without requiring extensive pre-training on DNA sequence data, L2G achieves superior performance to fine-tuned genomic FMs and task-specific models on more than half of tasks across multiple genomics benchmarks. In an enhancer activity prediction task, L2G further demonstrates its capacity to identify significant transcription factor motifs. Our work not only highlights the generalizability and efficacy of language models in out-of-domain tasks such as genomics, but also opens new avenues for more efficient and less resource-intensive methodologies in genomic research.

PDF Details

NeurIPS Conference 2025 Conference Paper

Repurposing AlphaFold3-like Protein Folding Models for Antibody Sequence and Structure Co-design

Nianzu Yang
Songlin Jiang
Jian Ma
Huaijin Wu
Shuangjia Zheng
Wengong Jin
Junchi Yan

Diffusion models hold great potential for accelerating antibody design, but their performance is so far limited by the number of antibody-antigen complexes used for model training. Meanwhile, AlphaFold3-like protein folding models, pre-trained on a large corpus of crystal structures, have acquired a broad understanding of biomolecular interaction. Based on this insight, we develop a new antigen-conditioned antibody design model by adapting the diffusion module of AlphaFold3-like models for sequence-structure co-diffusion. Specifically, we extend their structure diffusion module with a sequence diffusion head and fine-tune the entire protein folding model for antibody sequence-structure co-design. Our benchmark results show that sequence-structure co-diffusion models not only surpass state-of-the-art antibody design methods in performance but also maintain structure prediction accuracy comparable to the original folding model. Notably, in the antibody co-design task, our method achieves a CDR-H3 recovery rate of 65% for typical antibodies, outperforming the baselines by 87%, and attains a remarkable 63% recovery rate for nanobodies.

PDF Details

EAAI Journal 2025 Journal Article

Seismic denoising diffusion restoration model for seismic data processing

Kewen Li
Jian Ma
Yimin Dou
Yingzhi Zhao
Zhixuan Yang
Zhaohui Li

Interpolation and denoising are critical preprocessing steps for seismic data. Particularly for diverse tasks such as random discrete missing, continuous missing, and noise, while deep learning-based methods have been extensively applied to the fields of seismic data interpolation and denoising, current research lacks effective multitasking approaches. In this paper, We have extended Denoising Diffusion Restoration Model (DDRM) into the domain of seismic data processing, introducing the Seismic Denoising Diffusion Restoration Model (SeisDDRM). Built upon a pre-trained diffusion model, the SeisDDRM constructs a joint distribution encompassing data, measurements, and linear degradation operators tailored for seismic data interpolation and denoising. Posterior sampling is employed to facilitate both interpolation and denoising. The method accommodates various missing scenarios, including random discrete missing and continuous missing, and performs seismic data denoising without the need for retraining. Furthermore, we have refined the nonlinear activation-free network (NAFNet) as the noise prediction network, integrating a time embedding module to introduce time information for guiding the training of the diffusion model. This design not only achieves performance comparable to Unet but also reduces intra-block complexity. Numerous experiments on synthetic and field datasets validate the effectiveness of our model, and ablation studies confirm the necessity of key components.

Details DOI

EAAI Journal 2024 Journal Article

A rail defect detection framework under class-imbalanced conditions based on improved you only look once network

Yu Ding
Qin Zhao
Tianhao Li
Chen Lu
Laifa Tao
Jian Ma

In real rail operations, defects that can lead to serious accidents occur at very low frequencies, resulting in sample scarcity and class imbalances in rail defect datasets. Under imbalanced conditions, rail defect detection models tend to be biased toward majority classes and ignore minority classes, which further leads to inaccurate defect detection results. Therefore, a two-stage rail defect detection framework based on a latent diffusion model (LDM) and an improved You Only Look Once (YOLO) network operating under imbalanced conditions is proposed. This framework aims to enhance the detection performance achieved on imbalanced defect datasets through data augmentation and model improvements. First, the LDM is used to generate many defects with extremely small sample sizes and provide high-quality generated samples to expand the original imbalanced dataset. Furthermore, a coordinate attention module and a feature fusion module are integrated into the original YOLO version 8 (YOLOv8) model to improve its detection capabilities on imbalanced datasets. The coordinate attention mechanism enhances its focus on the positional information of various defects, whereas the feature fusion module enhances its ability to fuse the multiscale features of different defects. The results of the case study demonstrate that sample generation and filtration can provide high-quality samples for dataset augmentation purposes, alleviating the impact of minority defects on the overall detection accuracy. The results of the comparison and ablation experiments show that the improved YOLOv8 model has better detection performance than that of the comparison methods due to its introduction of coordinate attention and feature fusion modules.

Details DOI

AAAI Conference 2024 Conference Paper

Compositional Text-to-Image Synthesis with Attention Map Control of Diffusion Models

Ruichen Wang
Zekang Chen
Chen Chen
Jian Ma
Haonan Lu
Xiaodong Lin

Recent text-to-image (T2I) diffusion models show outstanding performance in generating high-quality images conditioned on textual prompts. However, they fail to semantically align the generated images with the prompts due to their limited compositional capabilities, leading to attribute leakage, entity leakage, and missing entities. In this paper, we propose a novel attention mask control strategy based on predicted object boxes to address these issues. In particular, we first train a BoxNet to predict a box for each entity that possesses the attribute specified in the prompt. Then, depending on the predicted boxes, a unique mask control is applied to the cross- and self-attention maps. Our approach produces a more semantically accurate synthesis by constraining the attention regions of each token in the prompt to the image. In addition, the proposed method is straightforward and effective and can be readily integrated into existing cross-attention-based T2I generators. We compare our approach to competing methods and demonstrate that it can faithfully convey the semantics of the original text to the generated content and achieve high availability as a ready-to-use plugin. Please refer to https://github.com/OPPO-Mente-Lab/attention-mask-control.

PDF Details DOI

NeurIPS Conference 2024 Conference Paper

End-to-end Learnable Clustering for Intent Learning in Recommendation

Yue Liu
Shihao Zhu
Jun Xia
Yingwei Ma
Jian Ma
Xinwang Liu
Shengju Yu
Kejun Zhang

Intent learning, which aims to learn users' intents for user understanding and item recommendation, has become a hot research spot in recent years. However, existing methods suffer from complex and cumbersome alternating optimization, limiting performance and scalability. To this end, we propose a novel intent learning method termed \underline{ELCRec}, by unifying behavior representation learning into an \underline{E}nd-to-end \underline{L}earnable \underline{C}lustering framework, for effective and efficient \underline{Rec}ommendation. Concretely, we encode user behavior sequences and initialize the cluster centers (latent intents) as learnable neurons. Then, we design a novel learnable clustering module to separate different cluster centers, thus decoupling users' complex intents. Meanwhile, it guides the network to learn intents from behaviors by forcing behavior embeddings close to cluster centers. This allows simultaneous optimization of recommendation and clustering via mini-batch data. Moreover, we propose intent-assisted contrastive learning by using cluster centers as self-supervision signals, further enhancing mutual promotion. Both experimental results and theoretical analyses demonstrate the superiority of ELCRec from six perspectives. Compared to the runner-up, ELCRec improves NDCG@5 by 8. 9\% and reduces computational costs by 22. 5\% on the Beauty dataset. Furthermore, due to the scalability and universal applicability, we deploy this method on the industrial recommendation system with 130 million page views and achieve promising results. The codes are available on GitHub\footnote{https: //github. com/yueliu1999/ELCRec}. A collection (papers, codes, datasets) of deep group recommendation/intent learning methods is available on GitHub\footnote{https: //github. com/yueliu1999/Awesome-Deep-Group-Recommendation}.

PDF Details DOI

NeurIPS Conference 2024 Conference Paper

Identify Then Recommend: Towards Unsupervised Group Recommendation

Yue Liu
Shihao Zhu
Tianyuan Yang
Jian Ma
Wenliang Zhong

Group Recommendation (GR), which aims to recommend items to groups of users, has become a promising and practical direction for recommendation systems. This paper points out two issues of the state-of-the-art GR models. (1) The pre-defined and fixed number of user groups is inadequate for real-time industrial recommendation systems, where the group distribution can shift dynamically. (2) The training schema of existing GR methods is supervised, necessitating expensive user-group and group-item labels, leading to significant annotation costs. To this end, we present a novel unsupervised group recommendation framework named $\underline{\text{I}}$dentify $\underline{\text{T}}$hen $\underline{\text{R}}$ecommend ($\underline{\text{ITR}}$), where it first identifies the user groups in an unsupervised manner even without the pre-defined number of groups, and then two pre-text tasks are designed to conduct self-supervised group recommendation. Concretely, at the group identification stage, we first estimate the adaptive density of each user point, where areas with higher densities are more likely to be recognized as group centers. Then, a heuristic merge-and-split strategy is designed to discover the user groups and decision boundaries. Subsequently, at the self-supervised learning stage, the pull-and-repulsion pre-text task is proposed to optimize the user-group distribution. Besides, the pseudo group recommendation pre-text task is designed to assist the recommendations. Extensive experiments demonstrate the superiority and effectiveness of ITR on both user recommendation (e. g. , 22. 22\% NDCG@5 $\uparrow$) and group recommendation (e. g. , 22. 95\% NDCG@5 $\uparrow$). Furthermore, we deploy ITR on the industrial recommender and achieve promising results.

PDF Details DOI

TMLR Journal 2024 Journal Article

Pre-trained Hypergraph Convolutional Neural Networks with Self-supervised Learning

Yihe Deng
Ruochi Zhang
Pan Xu
Jian Ma
Quanquan Gu

Hypergraphs are powerful tools for modeling complex interactions across various domains, including biomedicine. However, learning meaningful node representations from hypergraphs remains a challenge. Existing supervised methods often lack generalizability, thereby limiting their real-world applications. We propose a new method, Pre-trained Hypergraph Convolutional Neural Networks with Self-supervised Learning (PhyGCN), which leverages hypergraph structure for self-supervision to enhance node representations. PhyGCN introduces a unique training strategy that integrates variable hyperedge sizes with self-supervised learning, enabling improved generalization to unseen data. Applications on multi-way chromatin interactions and polypharmacy side-effects demonstrate the effectiveness of PhyGCN. As a generic framework for high-order interaction datasets with abundant unlabeled data, PhyGCN holds strong potential for enhancing hypergraph node representations across various domains.

PDF Details

IROS Conference 2024 Conference Paper

Rocket Landing Control with Random Annealing Jump Start Reinforcement Learning

Yuxuan Jiang 0011
Yujie Yang
Zhiqian Lan
Guojian Zhan
Shengbo Eben Li
Qi Sun 0004
Jian Ma
Tianwen Yu

Rocket recycling is a crucial pursuit in aerospace technology, aimed at reducing costs and environmental impact in space exploration. The primary focus centers on rocket landing control, involving the guidance of a nonlinear under-actuated rocket with limited fuel in real-time. This challenging task prompts the application of reinforcement learning (RL), yet goal-oriented nature of the problem poses difficulties for standard RL algorithms due to the absence of intermediate reward signals. This paper, for the first time, significantly elevates the success rate of rocket landing control from 8% with a baseline controller to 97% on a high-fidelity rocket model using RL. Our approach, called Random Annealing Jump Start (RAJS), is tailored for real-world goal-oriented problems by leveraging prior feedback controllers as guide policy to facilitate environmental exploration and policy learning in RL. In each episode, the guide policy navigates the environment for the guide horizon, followed by the exploration policy taking charge to complete remaining steps. This jump-start strategy prunes exploration space, rendering the problem more tractable to RL algorithms. The guide horizon is sampled from a uniform distribution, with its upper bound annealing to zero based on performance metrics, mitigating distribution shift and mismatch issues in existing methods. Additional enhancements, including cascading jump start, refined reward and terminal condition, and action smoothness regulation, further improve policy performance and practical applicability. The proposed method is validated through extensive evaluation and Hardware-in-the-Loop testing, affirming the effectiveness, real-time feasibility, and smoothness of the proposed controller.

Details

NeurIPS Conference 2022 Conference Paper

EPIC-KITCHENS VISOR Benchmark: VIdeo Segmentations and Object Relations

Ahmad Darkhalil
Dandan Shan
Bin Zhu
Jian Ma
Amlan Kar
Richard Higgins
Sanja Fidler
David Fouhey

We introduce VISOR, a new dataset of pixel annotations and a benchmark suite for segmenting hands and active objects in egocentric video. VISOR annotates videos from EPIC-KITCHENS, which comes with a new set of challenges not encountered in current video segmentation datasets. Specifically, we need to ensure both short- and long-term consistency of pixel-level annotations as objects undergo transformative interactions, e. g. an onion is peeled, diced and cooked - where we aim to obtain accurate pixel-level annotations of the peel, onion pieces, chopping board, knife, pan, as well as the acting hands. VISOR introduces an annotation pipeline, AI-powered in parts, for scalability and quality. In total, we publicly release 272K manual semantic masks of 257 object classes, 9. 9M interpolated dense masks, 67K hand-object relations, covering 36 hours of 179 untrimmed videos. Along with the annotations, we introduce three challenges in video object segmentation, interaction understanding and long-term reasoning. For data, code and leaderboards: http: //epic-kitchens. github. io/VISOR

PDF Details

JBHI Journal 2022 Journal Article

MCG-Net: End-to-End Fine-Grained Delineation and Diagnostic Classification of Cardiac Events From Magnetocardiographs

Rong Tao
Shulin Zhang
Yuexia Wang
Xianqiang Mi
Jian Ma
Chengxing Shen
Guoyan Zheng

In this paper, we propose an end-to-end deep learning architecture, referred as MCG-Net, integrating convolutional neural network (CNN) with transformer-based global context block for fine-grained delineation and diagnostic classification of four cardiac events from magnetocardiogram (MCG) data, namely Q-, R-, S- and T-waves. MCG-Net takes advantage of a multi-resolution CNN backbone as well as the state-of-the-art (SOTA) transformer encoders that facilitate global temporal feature aggregation. Besides the novel network architecture, we introduce a multi-task learning scheme to achieve simultaneous delineation and classification. Specifically, the problem of MCG delineation is formulated as multi-class heatmap regression. Meanwhile, a binary diagnostic classification label as well as a duration are jointly estimated for each cardiac event using features that are temporally aligned by event heatmaps. The framework is evaluated on a clinical MCG dataset, containing data collected from 270 subjects with cardiac anomalies and 108 control subjects. We designed and conducted a two-fold cross-validation study to validate the proposed method and to compare its performance with the SOTA methods. Experimental results demonstrated that our method outperformed counterparts on both event delineation and diagnostic classification tasks, achieving respectively an average ECG-F1 of 0. 987 and an average Event-F1 of 0. 975 for MCG delineation, and an average accuracy of 0. 870, an average sensitivity of 0. 732, an average specificity of 0. 914 and an average AUC of 0. 903 for diagnostic classification. Comprehensive ablation experiments are additionally performed to investigate effectiveness of different network components.

Details DOI

NeurIPS Conference 2021 Conference Paper

MQBench: Towards Reproducible and Deployable Model Quantization Benchmark

Yuhang Li
Mingzhu Shen
Jian Ma
Yan Ren
Mingxin Zhao
Qi Zhang
Ruihao Gong
Fengwei Yu

Model quantization has emerged as an indispensable technique to accelerate deep learning inference. Although researchers continue to push the frontier of quantization algorithms, existing quantization work is often unreproducible and undeployable. This is because researchers do not choose consistent training pipelines and ignore the requirements for hardware deployments. In this work, we propose Model Quantization Benchmark (MQBench), a first attempt to evaluate, analyze, and benchmark the reproducibility and deployability for model quantization algorithms. We choose multiple different platforms for real-world deployments, including CPU, GPU, ASIC, DSP, and evaluate extensive state-of-the-art quantization algorithms under a unified training pipeline. MQBench acts like a bridge to connect the algorithm and the hardware. We conduct a comprehensive analysis and find considerable intuitive or counter-intuitive insights. By aligning up the training settings, we find existing algorithms have about-the-same performance on the conventional academic track. While for the hardware-deployable quantization, there is a huge accuracy gap and still a long way to go. Surprisingly, no existing algorithm wins every challenge in MQBench, and we hope this work could inspire future research directions.

PDF Details

NeurIPS Conference 2017 Conference Paper

Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization

Pan Xu
Jian Ma
Quanquan Gu

We study the estimation of the latent variable Gaussian graphical model (LVGGM), where the precision matrix is the superposition of a sparse matrix and a low-rank matrix. In order to speed up the estimation of the sparse plus low-rank components, we propose a sparsity constrained maximum likelihood estimator based on matrix factorization and an efficient alternating gradient descent algorithm with hard thresholding to solve it. Our algorithm is orders of magnitude faster than the convex relaxation based methods for LVGGM. In addition, we prove that our algorithm is guaranteed to linearly converge to the unknown sparse and low-rank components up to the optimal statistical precision. Experiments on both synthetic and genomic data demonstrate the superiority of our algorithm over the state-of-the-art algorithms and corroborate our theory.

PDF Details

TIST Journal 2015 Journal Article

An Event-Driven QoI-Aware Participatory Sensing Framework with Energy and Budget Constraints

Bo Zhang
Zheng Song
Chi Harold Liu
Jian Ma
Wendong Wang

Participatory sensing systems can be used for concurrent event monitoring applications, like noise levels, fire, and pollutant concentrations. However, they are facing new challenges as to how to accurately detect the exact boundaries of these events, and further, to select the most appropriate participants to collect the sensing data. On the one hand, participants’ handheld smart devices are constrained with different energy conditions and sensing capabilities, and they move around with uncontrollable mobility patterns in their daily life. On the other hand, these sensing tasks are within time-varying quality-of-information (QoI) requirements and budget to afford the users’ incentive expectations. Toward this end, this article proposes an event-driven QoI-aware participatory sensing framework with energy and budget constraints. The main method of this framework is event boundary detection. For the former, a two-step heuristic solution is proposed where the coarse-grained detection step finds its approximation and the fine-grained detection step identifies the exact location. Participants are selected by explicitly considering their mobility pattern, required QoI of multiple tasks, and users’ incentive requirements, under the constraint of an aggregated task budget. Extensive experimental results, based on a real trace in Beijing, show the effectiveness and robustness of our approach, while comparing with existing schemes.

Details DOI