Fan Dang

Tsinghua University

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A Reference Architecture for Integrating Time-Sensitive Networking in Industrial Internet of Things

Zhui Zhu Xu Wang Fan Dang

Industrial Internet of Things (IIoT) play an important role in the new round of industrial revolution. However, the isolation of Information Technology (IT) networks and Operational Technology (OT) networks impedes the innovations, developments, and disruptive business models applied in IIoT. With the emergence of time-sensitive networking (TSN), it is feasible to interoperate between IT/OT networks with deterministic data transmission. By integrating TSN in IIoT architecture, the article proposes a reference architecture with multi-modal interconnection, endogenous safety and security, and integrated scheduling as the core elements. We believe the design could give a guidance for IIoT system design and development.

Industrial Internet

10.1145/3603165.3607382

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SOScheduler: Toward Proactive and Adaptive Wildfire Suppression via Multi-UAV Collaborative Scheduling

IEEE Internet of Things Journal (2024)

Xuecheng Chen Zijian Xiao Yuhan Cheng Chen-Chun Hsia Haoyang Wang

Multi-UAV systems have shown immense potential in handling complex tasks in large-scale, dynamic, and cold-start (i.e., limited prior knowledge) scenarios, such as wildfire suppression. Due to the dynamic and stochastic environmental conditions, the scheduling for sensing tasks (i.e., fire monitoring) and operation tasks (i.e., fire suppression) should be executed concurrently to enable real-time information collection and timely intervention of the environment. However, the planning inclinations of sensing and operation tasks are typically inconsistent and evolve over time, complicating the task of identifying the optimal strategy for each UAV. To solve this problem, this paper proposes SOScheduler, a collaborative multi-UAV scheduling framework for integrated sensing and operation in large-scale and dynamic wildfire environments. We introduce a spatio-temporal confidence-aware assessment model to dynamically and directly pinpoint locations that can optimally enhance the understanding of environmental dynamics and operational effectiveness, as well as a priority graph-instructed scalable scheduler to coordinate multi-UAV in an efficient manner. Experiments on real multi-UAV testbeds and large-scale physical feature-based simulations show that our SOScheduler reduces the fire expansion ratio by 59% and enhances the fire coverage ratio by 190% compared to state-of-the-art (SOTA) solutions.

10.1109/jiot.2024.3389771

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Citations (7)

A Simple and Robust Attentional Encoder-Decoder Model for License Plate Recognition

Lecture notes in computer science (2019)

Linjiang Zhang Peng Wang Fan Dang Shaojie Zhang

Robustness

10.1007/978-3-030-31654-9_26

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Citations (1)

ST-ICM

Proceedings of the 28th Annual International Conference on Mobile Computing And Networking (2022)

Chengzhao Yu Ji Luo Rongye Shi Xinyu Liu Fan Dang

In order to reduce the measurement error of low cost sensor in the real-time mobile sensing network, rendezvous calibration mechanism is widely used. To tackle the sparsity of reference data and the lack of calibration opportunities, we propose ST-ICM: a Spatial-Temporal Inference Calibration Model based on Gaussian Process Regression, assisting the calibration task by creating more calibration grids in both spatial and temporal dimensions. By using the GPR, the inferred grids generated by ST-ICM are associated with various confidence levels. Based on this property, we propose to make use of a hyperparameter, i.e., variance threshold, to balance the tradeoff between the quantity and quality of the inferred grids. Specifically, only the grids with variances below the threshold will be employed. We conducted experiments using a real-world dataset collected in Nanjing, China, to evaluate the performance of the proposed ST-ICM. The experimenal results show that our model achieves 24% improvement on error calibration compared to the baseline.

Hyperparameter

Baseline (sea)

10.1145/3495243.3558280

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Citations (6)

Enabling Network Diagnostics in Time-Sensitive Networking: Protocol, Algorithm, and Hardware

Zeyu Wang Xiaowu He Xiangwen Zhuge Xu Shen Fan Dang

10.1109/iwqos61813.2024.10682902

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A real independent centimeter-grade 3D indoor localization system on smartphone

Fan Dang X. X. Ding Pengfei Zhou

Fine grained indoor localization is attractive for its wide usage in indoor navigation system, infrastructure management, and blooming augmented reality applications. In this paper, we propose a smartphone based indoor localization system called Plotter, providing a centimeter-grade localization service without any prior knowledge or additional devices. Leveraging the simultaneous localization and mapping (SLAM) technology, Plotter not only learns its relative position among surroundings, but also simultaneously constructs and updates the map of unknown area. We take advantage of a modified Kalman Filter algorithm in the system in order to eliminate unacceptable errors produced by motion sensors on smartphones. Evaluation result shows that Plotter achieves centimeter-grade accuracy, which is competitive comparing with prior works assisted by additional devices.

Plotter

Centimeter

10.1109/iwcmc.2016.7577164

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MobiAir: Unleashing Sensor Mobility for City-scale and Fine-grained Air-Quality Monitoring with AirBERT

Yuxuan Liu Haoyang Wang Fanhang Man Jingao Xu Fan Dang

Mobile air pollution sensing methods are developed to collect air quality data with higher spatial-temporal resolutions. However, existing methods cannot process the spatially mixed gas samples effectively due to the highly dynamic temporal and spatial fluctuations experienced by the sensor, leading to significant measurement deviations. We find an opportunity to tackle the problem by exploring the potential patterns from sensor measurements. In light of this, we propose MobiAir, a novel city-scale fine-grained air quality estimation system to deliver accurate mobile air quality data. First, we design AirBERT, a representation learning model to discern mixed gas concentrations. Second, we design a knowledge-informed training strategy leveraging massive unlabeled city-scale data to enhance the AirBERT performance. To ensure the practicality of MobiAir, we have invested significant efforts in implementing the software stack on our meticulously crafted Sensing Front-end, which has successfully gathered air quality data at a city-scale for more than 1200 hours. Experiments conducted on collected data show that MobiAir reduces sensing errors by 96.7% with only 44.9ms latency, outperforming the SOTA baseline by 39.5%.

10.1145/3643832.3661872

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Citations (11)

FireHunter: Toward Proactive and Adaptive Wildfire Suppression via Multi-UAV Collaborative Scheduling

IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (2024)

Xuecheng Chen Zijian Xiao Yuhan Cheng Chen-Chun Hsia Haoyang Wang

10.1109/infocomwkshps61880.2024.10620862

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Citations (2)

H-SwarmLoc

Haoyang Wang Xuecheng Chen Yuhan Cheng Chenye Wu Fan Dang

Emergency rescue scenarios are considered to be high-risk scenarios. Using a micro air vehicle (MAV) swarm to explore the environment can provide valuable environmental information. However, due to the absence of localization infrastructure and the limited on-board capabilities, it's challenging for the low-cost MAV swarm to maintain precise localization. In this paper, a collaborative localization system for the low-cost heterogeneous MAV swarm is proposed. This system takes full advantage of advanced MAV to effectively achieve accurate localization of the heterogeneous MAV swarm through collaboration. Subsequently, H-SwarmLoc, a reinforcement learning-based planning method is proposed to plan the advanced MAV with a non-myopic objective in real-time. The experimental results show that the localization performance of our method improves 40% on average compared with baselines.

10.1145/3560905.3568432

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Citations (20)

A Comprehensive Evaluation of Bluetooth Low Energy Mesh

Yao Zhao Lin Wang Zijuan Liu Yifan Xu Fan Dang

Bluetooth Low Energy

Low energy

10.1109/icpads63350.2024.00077

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