2020
DOI: 10.1109/jiot.2019.2949352
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Complexity and Algorithms for Superposed Data Uploading Problem in Networks With Smart Devices

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Cited by 78 publications
(35 citation statements)
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“…Obtaining abnormal road information in advance can effectively prevent traffic accidents, but due to road geometry, weather, lighting conditions, etc., the driver may not be able to notice the abnormal road ahead. The information-sharing technology is used to construct an abnormal road sharing system, especially encouraged by the development of the vehicle network and intelligent transportation system [17][18][19][20][21]. The road information sharing system is designed to promptly warn the driver when the driver approaches an abnormal road at a dangerous speed.…”
Section: Road Information Sharing Systemmentioning
confidence: 99%
“…Obtaining abnormal road information in advance can effectively prevent traffic accidents, but due to road geometry, weather, lighting conditions, etc., the driver may not be able to notice the abnormal road ahead. The information-sharing technology is used to construct an abnormal road sharing system, especially encouraged by the development of the vehicle network and intelligent transportation system [17][18][19][20][21]. The road information sharing system is designed to promptly warn the driver when the driver approaches an abnormal road at a dangerous speed.…”
Section: Road Information Sharing Systemmentioning
confidence: 99%
“…We will combine these attributes with latencies for measuring the system performance in our future work. Although the main motivation scenario of the continuous data flow problem in this paper is E-health monitoring system, the continuous data flow problem can also be extended to more application scenarios, such as mobile social network [40], intelligent industrial monitoring system [41], etc. We will further consider the location of fog nodes and MEC nodes in continuous data flow problem [42].…”
Section: Discussionmentioning
confidence: 99%
“…These feature maps will be fused with the feature maps extracted by Resnet for classification. From these works [2,13,30], we find that the additional modules used at the inference stage bring about a performance improvement, but also bring about more parameters and complexity [33][34][35], which may be unfavorable for deployment on devices with limited memory and computational resources [36]. Therefore, it is necessary to explore how to improve the classification performance of the model without increasing the parameters.…”
Section: Motivationmentioning
confidence: 99%