Moving object map analytics: A framework enabling contextual spatial-temporal analytics of Internet of Things applications

Sun, Wei; Zhu, Jun; Duan, Ning; Gao, Peng; Hu, Guo Qiang; Dong, Wei Shan; Wang, Zhi Hu; Zhang, Xin; Ji, Peng; Yang, Chunhua; Huang, Jing

doi:10.1109/soli.2016.7551669

Cited by 8 publications

(3 citation statements)

References 6 publications

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“…Such the impact factors are difficult to acquire or model in advance [10], [11]. Second, the air quality exhibits high uncertainty in the time dimension [12], [13]. As shown in Figure 1, the PM2.5 pollutant concentration values of the given air quality monitoring station at the same moment along the two days is hugely different.…”

Section: Introductionmentioning

confidence: 99%

A Predictive Data Feature Exploration-Based Air Quality Prediction Approach

et al. 2019

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In recent years, people have been paying more and more attention to air quality because it directly affects people's health and daily life. Effective air quality prediction has become one of the hot research issues. However, this paper is suffering many challenges, such as the instability of data sources and the variation of pollutant concentration along time series. Aiming at this problem, we propose an improved air quality prediction method based on the LightGBM model to predict the PM2.5 concentration at the 35 air quality monitoring stations in Beijing over the next 24 h. In this paper, we resolve the issue of processing the high-dimensional large-scale data by employing the LightGBM model and innovatively take the forecasting data as one of the data sources for predicting the air quality. With exploring the forecasting data feature, we could improve the prediction accuracy with making full use of the available spatial data. Given the lack of data, we employ the sliding window mechanism to deeply mine the high-dimensional temporal features for increasing the training dimensions to millions. We compare the predicted data with the actual data collected at the 35 air quality monitoring stations in Beijing. The experimental results show that the proposed method is superior to other schemes and prove the advantage of integrating the forecasting data and building up the high-dimensional statistical analysis. INDEX TERMS Predictive data fusion, high dimensional statistical features, air quality prediction, machine learning.

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Section: Introductionmentioning

confidence: 99%

A Predictive Data Feature Exploration-Based Air Quality Prediction Approach

et al. 2019

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“…In addition, the authors illustrated the spatial BD handling in different smart city scenarios. Sun et al [28] introduced an analytical framework of geospatial-temporal data with componentized service architecture. Mainly, the authors focused on the geospatialtemporal data that produced from IoT moving objects such as vehicles.…”

Section: Iot With Spatial Contextmentioning

confidence: 99%

Internet of Spatial Things: A New Reference Model With Insight Analysis

2019

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The Internet of Things (IoT) is the concept of everyday objects that make us live in the era of the IoT. The spatial characteristics of things around us can be considered the reins of the IoT operability. In other words, the spatial variation of a thing can be the core of the IoT reaction. For example, the spatial variation in crop indicates the requirement and characteristic of agriculture production. Also, the spatial variation of a human movement can alarm the security and monitoring systems. This issue agitates the contemplating of the ''Internet of Spatial Things (IoST)'' concept. For the first time, this paper draws an inspiration towards the perspective vision of the IoST, which is concerned with revise IoT with the spatial perspective. The IoST concept is argued by the presentation of its definition and architectural components. Besides, the IoST layers are discussed in details. Furthermore, a new proposed reference model of the IoST is proposed. Finally, the new trends and open issues regarding the IoST are aroused. INDEX TERMS Internet of Things (IoT), Internet of Spatial Things (IoST), spatial, framework.

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“…In the follow up work of Dey et al [14], the authors also study the impact of alternative IoT processing topologies for real-time processing pipelines, leading to improved latency and accuracy. Furthermore, in the area of trajectory mining, Sun et al [15] provide a framework, which is capable of tackling several demands in this area (including map matching) in contrast to several publications that only tackle a specific trajectory mining task.…”

Section: Related Workmentioning

confidence: 99%

A Scalable Architecture for Real-Time Stream Processing of Spatiotemporal IoT Stream Data—Performance Analysis on the Example of Map Matching

Laska

Herlé

Klamma

et al. 2018

IJGI

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Scalable real-time processing of large amounts of data has become a research topic of particular importance due to the continuously rising amount of data that is generated by devices equipped with sensing components. While existing approaches allow for fault-tolerant and scalable stream processing, we present a pipeline architecture that consists of well-known open source tools to specifically integrate spatiotemporal internet of things (IoT) data streams. In a case study, we utilize the architecture to tackle the online map matching problem, a pre-processing step for trajectory mining algorithms. Given the rising amount of vehicle location data that is generated on a daily basis, existing map matching algorithms have to be implemented in a distributed manner to be executable in a stream processing framework that provides scalability. We demonstrate how to implement state-of-the-art map matching algorithms in our distributed stream processing pipeline and analyze measured latencies.

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Moving object map analytics: A framework enabling contextual spatial-temporal analytics of Internet of Things applications

Cited by 8 publications

References 6 publications

A Predictive Data Feature Exploration-Based Air Quality Prediction Approach

A Predictive Data Feature Exploration-Based Air Quality Prediction Approach

Internet of Spatial Things: A New Reference Model With Insight Analysis

A Scalable Architecture for Real-Time Stream Processing of Spatiotemporal IoT Stream Data—Performance Analysis on the Example of Map Matching

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