James V. Krogmeier

Manish Kumar Krishne Gowda, Andrew Balmos, Shin Boonam et al.

Effective strategies for sensor data management are essential for advancing transportation research, especially in the current data-driven era, due to the advent of novel applications in artificial intelligence. This paper presents comprehensive guidelines for managing transportation sensor data, encompassing both archived static data and real-time data streams. The real-time system architecture integrates various applications with data acquisition systems (DAQ). By deploying the in-house designed, open-source Avena software platform alongside the NATS messaging system as a secure communication broker, reliable data exchange is ensured. While robust databases like TimescaleDB facilitate organized storage, visualization platforms like Grafana provide real-time monitoring capabilities. In contrast, static data standards address the challenges in handling unstructured, voluminous datasets. The standards advocate for a combination of cost-effective bulk cloud storage for unprocessed sensor data and relational databases for recording summarized analyses. They highlight the role of cloud data transfer tools like FME for efficient migration of sensor data from local storages onto the cloud. Further, integration of robust visualization tools into the framework helps in deriving patterns and trends from these complex datasets. The proposals were applied to INDOT's real-world case studies involving the I-65 and I-69 Greenfield districts. For real-time data collection, Campbell Scientific DAQ systems were used, enabling continuous generation and monitoring of sensor metrics. In the case of the archived I-69 database, summary data was compiled in Oracle, while the unprocessed data was stored in SharePoint. The results underline the effectiveness of the proposed guidelines and motivate their adoption in research projects.

Ahmed P. Mohamed, Byunghyun Lee, Yaguang Zhang et al.

Machine learning (ML) offers a promising solution to pathloss prediction. However, its effectiveness can be degraded by the limited availability of data. To alleviate these challenges, this paper introduces a novel simulation-enhanced data augmentation method for ML pathloss prediction. Our method integrates synthetic data generated from a cellular coverage simulator and independently collected real-world datasets. These datasets were collected through an extensive measurement campaign in different environments, including farms, hilly terrains, and residential areas. This comprehensive data collection provides vital ground truth for model training. A set of channel features was engineered, including geographical attributes derived from LiDAR datasets. These features were then used to train our prediction model, incorporating the highly efficient and robust gradient boosting ML algorithm, CatBoost. The integration of synthetic data, as demonstrated in our study, significantly improves the generalizability of the model in different environments, achieving a remarkable improvement of approximately 12dB in terms of mean absolute error for the best-case scenario. Moreover, our analysis reveals that even a small fraction of measurements added to the simulation training set, with proper data balance, can significantly enhance the model's performance.

Manish Kumar, Tzu-Hsuan Chou, Byunghyun Lee et al.

Low-latency localization is critical in cellular networks to support real-time applications requiring precise positioning. In this paper, we propose a distributed machine learning (ML) framework for fingerprint-based localization tailored to cell-free massive multiple-input multiple-output (MIMO) systems, an emerging architecture for 6G networks. The proposed framework enables each access point (AP) to independently train a Gaussian process regression model using local angle-of-arrival and received signal strength fingerprints. These models provide probabilistic position estimates for the user equipment (UE), which are then fused by the UE with minimal computational overhead to derive a final location estimate. This decentralized approach eliminates the need for fronthaul communication between the APs and the central processing unit (CPU), thereby reducing latency. Additionally, distributing computational tasks across the APs alleviates the processing burden on the CPU compared to traditional centralized localization schemes. Simulation results demonstrate that the proposed distributed framework achieves localization accuracy comparable to centralized methods, despite lacking the benefits of centralized data aggregation. Moreover, it effectively reduces uncertainty of the location estimates, as evidenced by the 95\% covariance ellipse. The results highlight the potential of distributed ML for enabling low-latency, high-accuracy localization in future 6G networks.

Bharath Keshavamurthy, Yaguang Zhang, Christopher R. Anderson et al.

In this paper, we discuss the design of a sliding-correlator channel sounder for 28 GHz propagation modeling on the NSF POWDER testbed in Salt Lake City, UT. Beam-alignment is mechanically achieved via a fully autonomous robotic antenna tracking platform, designed using commercial off-the-shelf components. Equipped with an Apache Zookeeper/Kafka managed fault-tolerant publish-subscribe framework, we demonstrate tracking response times of 27.8 ms, in addition to superior scalability over state-of-the-art mechanical beam-steering systems. Enhanced with real-time kinematic correction streams, our geo-positioning subsystem achieves a 3D accuracy of 17 cm, while our principal axes positioning subsystem achieves an average accuracy of 1.1 degrees across yaw and pitch movements. Finally, by facilitating remote orchestration (via managed containers), uninhibited rotation (via encapsulation), and real-time positioning visualization (via Dash/MapBox), we exhibit a proven prototype well-suited for V2X measurements.