The national weather sensor grid

Lim, Hyuk; Ling, Keck Voon; Wang, Wenqiang; Yuxia, Yao; Iqbal, Mudasser; Li, Boyang; Yin, Xiaonan; Sharma, Tarun

doi:10.1145/1322263.1322299

Cited by 8 publications

(2 citation statements)

References 2 publications

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“…The precision of the estimated calculus for each grid is proportional to the number of weather stations distributed over the city. In Lim et al [ 4 ], the National Weather Sensor Grid (NWSG) system is designed to monitor weather information in real time over distributed areas in a city, where the weather stations are set in schools. In Sutar [ 5 ], a system is developed to enable the monitoring of weather parameters like temperature, humidity and light intensity.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Weather Monitoring and Prediction Using City Buses and Machine Learning

Huang

Chen²,

Wen

2020

Sensors

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Accurate weather data are important for planning our day-to-day activities. In order to monitor and predict weather information, a two-phase weather management system is proposed, which combines information processing, bus mobility, sensors, and deep learning technologies to provide real-time weather monitoring in buses and stations and achieve weather forecasts through predictive models. Based on the sensing measurements from buses, this work incorporates the strengths of local information processing and moving buses for increasing the measurement coverage and supplying new sensing data. In Phase I, given the weather sensing data, the long short-term memory (LSTM) model and the multilayer perceptron (MLP) model are trained and verified using the data of temperature, humidity, and air pressure of the test environment. In Phase II, the trained learning model is applied to predict the time series of weather information. In order to assess the system performance, we compare the predicted weather data with the actual sensing measurements from the Environment Protection Administration (EPA) and Central Weather Bureau (CWB) of Taichung observation station to evaluate the prediction accuracy. The results show that the proposed system has reliable performance at weather monitoring and a good forecast for one-day weather prediction via the trained models.

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Section: Introductionmentioning

confidence: 99%

Real-Time Weather Monitoring and Prediction Using City Buses and Machine Learning

Huang

Chen²,

Wen

2020

Sensors

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“…It is highly profitable for applications from different domains such as weather monitoring and forecasting [2], air pollution monitoring [3], healthcare monitoring of patients [4] and many other uses that we do not yet imagine. However, most of the existing works on Sensor-Grid focus on how to integrate WSN with Grid effectively to collect and process sensor data [1,[5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A dynamic and fast event matching algorithm for a content-based publish/subscribe information dissemination system in Sensor-Grid

2009

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In this paper, we discuss one of the most important issues in Sensor-Grid, i.e., to develop a fast and flexible content-based publish/subscribe information dissemination (CBPSID) system for automatic fusion, interpretation, sharing and delivery of huge sensor data to consumers as the entire Sensor-Grid environment is very dynamic. Existing works to develop the CBPSID system in Sensor-Grid mostly focus on reducing the effort to define and maintain subscriptions and to handle the difficulty of dynamic changes of publishers and consumers data. However, the performance of a CBPSID system in Sensor-Grid is bounded by the expensive matching/evaluation cost of events. Existing event-matching algorithms are not very efficient, especially for interval range predicates or overlapping predicates in subscriptions which are practical in Sensor-Grid as well as other application areas. So in this paper we discuss the above challenge and propose a dynamic and fast event-matching algorithm called CGIM for the CBPSID system in Sensor-Grid. The algorithm supports range predicates or overlapping predicates very well and provides single and composite event matching. It uses two approaches, called SGIM and DGIM, to group the subscriptions by the predicates and dynamically identifies appropriate number of groups considering different statistical distributions of subscriptions at run time. Also, we present an experimental evaluation of the proposed algorithm in a Sensor-Grid based u-Healthcare scenario using synthetic workloads and compare its performance with existing algorithms. The experimental results show that our algorithm significantly reduces the evaluation cost (on average using SGIM by 79% and DGIM by 88%) CBPSID system in Sensor-Grid 331 comparing with others and guarantees the scalability with respect to the number of subscriptions as well as the number of predicates and events. In addition, further experiments were conducted by applying the CGIM algorithm in other application areas, e.g. in the publish/subscribe system for online job sites, to show its diverse utilization and scalability.

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