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Smart city article details

Title	Inference Techniques For Ultrasonic Parking Lot Occupancy Sensing Based On Smart City Infrastructure
ID_Doc	31309
Authors	Lücken V.; Baag T.; Schreier J.; Lanius C.; Wolff R.; Ascheid G.
Year	2019
Published	Big Data Analytics for Cyber-Physical Systems: Machine Learning for the Internet of Things
DOI	http://dx.doi.org/10.1016/B978-0-12-816637-6.00005-1
Abstract	The search for parking lots represents a significant problem for drivers and cities. Especially in urban areas, this search is often time consuming and creates convenience issues, while also resulting in increased traffic volumes. Possible solutions, such as fixed installations of parking guidance systems or smartphone applications, can help to alleviate these problems. However, accurate real-time information on the parking lot occupancy status is required as a basis for these types of systems. Several types of sensors are available on the market; however, they either require high installation efforts, exhibit privacy issues, or do not integrate well into the cityscape. Smart City applications target a tight city integration of such solutions. In our chapter, we show how the joint application of sensor signal processing algorithms, machine learning approaches, and distributed embedded systems can enable these types of applications. Based on an intelligent smart street lamp platform, an ultrasonic sensing system is presented, which realizes a city-integrated solution for parking lot occupancy sensing. © 2019 Elsevier Inc. All rights reserved.
Author Keywords	Edge computing; Embedded systems; Machine learning; Parking lot sensing; Principal component analysis; Smart city infrastructure; Support vector machines; Ultrasonic sensing

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