Real-time Spatiotemporal Data-management Technology (Axispot™)

Isomura, Atsushi; Shigematsu, Naoko; Ueno, Isoo; Oki, Nobuhiro; Arakawa, Yutaka

doi:10.53829/ntr202207fa8

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…Figure 1 shows the proposed framework of the traffic jam information service considering lanes. In Step 1, a real-time spatiotemporal data utilization technique [13] aggregates the number of cars in each mesh (a 150 m × 150 m area in the current implementation) by collecting CAN data. CAN data includes the position of the car (GPS), and this technique sums the cars in a specified time and mesh.…”

Section: Frameworkmentioning

confidence: 99%

Prioritization of Lane-Specific Traffic Jam Detection for Automotive Navigation Framework Utilizing Suddenness Index and Automatic Threshold Determination

HAYASHI¹,

YOKOHATA²,

HATA³

et al. 2023

IEICE Trans. Inf. & Syst.

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Car navigation systems provide traffic jam information. In this study, we attempt to provide more detailed traffic jam information that considers the lane in which a traffic jam is in. This makes it possible for users to avoid long waits in queued traffic going toward an unintended destination. Lane-specific traffic jam detection utilizes image processing, which incurs long processing time and high cost. To reduce these, we propose a "suddenness index (SI)" to categorize candidate areas as sudden or periodic. Sudden traffic jams are prioritized as they may lead to accidents. This technology aggregates the number of connected cars for each mesh on a map and quantifies the degree of deviation from the ordinary state. In this paper, we evaluate the proposed method using actual global positioning system (GPS) data and found that the proposed index can cover 100% of sudden lane-specific traffic jams while excluding 82.2% of traffic jam candidates. We also demonstrate the effectiveness of time savings by integrating the proposed method into a demonstration framework. In addition, we improved the proposed method's ability to automatically determine the SI threshold to select the appropriate traffic jam candidates to avoid manual parameter settings.

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Section: Frameworkmentioning

confidence: 99%

Prioritization of Lane-Specific Traffic Jam Detection for Automotive Navigation Framework Utilizing Suddenness Index and Automatic Threshold Determination

HAYASHI¹,

YOKOHATA²,

HATA³

et al. 2023

IEICE Trans. Inf. & Syst.

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Big Data and precision agriculture: a novel spatio-temporal semantic IoT data management framework for improved interoperability

et al. 2023

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Precision agriculture in the realm of the Internet of Things is characterized by the collection of data from multiple sensors deployed on the farm. These data present a spatial, temporal, and semantic characterization, which further complicates the performance in the management and implementation of models and repositories. In turn, the lack of standards is reflected in insufficient interoperability between management solutions and other non-native services in the framework. In this paper, an innovative system for spatio-temporal semantic data management is proposed. It includes a data query system that allows farmers and users to solve queries daily, as well as feed decision-making, monitoring, and task automation solutions. In the proposal, a solution is provided to ensure service interoperability and is validated against two European smart farming platforms, namely AFarCloud and DEMETER. For the evaluation and validation of the proposed framework, a neural network is implemented, fed through STSDaMaS for training and validation, to provide accurate forecasts for the harvest and baling of forage legume crops for livestock feeding. As a result of the evaluation for the training and execution of neural networks, high performance on complex spatio-temporal semantic queries is exposed. The paper concludes with a distributed framework for managing complex spatio-temporal semantic data by offering service interoperability through data integration to external agricultural data models. Graphical Abstract

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Spatio-Temporal Semantic Data Model for Precision Agriculture IoT Networks

et al. 2023

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In crop and livestock management within the framework of precision agriculture, scenarios full of sensors and devices are deployed, involving the generation of a large volume of data. Some solutions require rapid data exchange for action or anomaly detection. However, the administration of this large amount of data, which in turn evolves over time, is highly complicated. Management systems add long-time delays to the spatio-temporal data injection and gathering. This paper proposes a novel spatio-temporal semantic data model for agriculture. To validate the model, data from real livestock and crop scenarios, retrieved from the AFarCloud smart farming platform, are modeled according to the proposal. Time-series Database (TSDB) engine InfluxDB is used to evaluate the model against data management. In addition, an architecture for the management of spatio-temporal semantic agricultural data in real-time is proposed. This architecture results in the DAM&DQ system responsible for data management as semantic middleware on the AFarCloud platform. The approach of this proposal is in line with the EU data-driven strategy.

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Real-time Spatiotemporal Data-management Technology (Axispot™)

Cited by 4 publications

References 6 publications

Prioritization of Lane-Specific Traffic Jam Detection for Automotive Navigation Framework Utilizing Suddenness Index and Automatic Threshold Determination

Prioritization of Lane-Specific Traffic Jam Detection for Automotive Navigation Framework Utilizing Suddenness Index and Automatic Threshold Determination

Big Data and precision agriculture: a novel spatio-temporal semantic IoT data management framework for improved interoperability

Spatio-Temporal Semantic Data Model for Precision Agriculture IoT Networks

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