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The smart parking industry continues to evolve as an increasing number of cities struggle with traffic congestion and inadequate parking availability. For urban dwellers, few things are more irritating than anxiously searching for a parking space. Research results show that as much as 30% of traffic is caused by drivers driving around looking for parking spaces in congested city areas. There has been considerable activity among researchers to develop smart technologies that can help drivers find a parking spot with greater ease, not only reducing traffic congestion but also the subsequent air pollution. Many existing solutions deploy sensors in every parking spot to address the automatic parking spot detection problems. However, the device and deployment costs are very high, especially for some large and old parking structures. A wide variety of other technological innovations are beginning to enable more adaptable systems-including license plate number detection, smart parking meter, and vision-based parking spot detection. In this paper, we propose to design a more adaptable and affordable smart parking system via distributed cameras, edge computing, data analytics, and advanced deep learning algorithms. Specifically, we deploy cameras with zoom-lens and motorized head to capture license plate numbers by tracking the vehicles when they enter or leave the parking lot; cameras with wide angle fish-eye lens will monitor the large parking lot via our custom designed deep neural network. We further optimize the algorithm and enable the real-time deep learning inference in an edge device. Through the intelligent algorithm, we can significantly reduce the cost of existing systems, while achieving a more adaptable solution. For example, our system can automatically detect when a car enters the parking space, the location of the parking spot, and precisely charge the parking fee and associate this with the license plate number.
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Local and Low-Cost White Space Detection
White spaces are portions of the TV spectrum that are allocated but not used locally. If accurately detected, white spaces offer a valuable new opportunity for highspeed wireless communications. We propose a new method for white space detection that allows a node to act locally, based on a centrally constructed model, and at low cost, while detecting more spectrum opportunities than best known approaches. We leverage two ideas. First, we demonstrate that low-cost spectrum monitoring hardware can offer "good enough" detection capabilities. Second, we develop a model that combines locally-measured signal features and location to more efficiently detect white space availability. We incorporate these ideas into the design, implementation, and evaluation of a complete system we call Waldo. We deploy Waldo on a laptop in the Atlanta metropolitan area in the US covering 700 km2. Our results show that using signal features, in addition to location, can improve detection accuracy by up to10x for some channels. We also deploy Waldo on an Android smartphone, demonstrating the feasibility of real-time white space detection with efficientuse of smartphone resources.
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- Award ID(s):
- 1637280
- PAR ID:
- 10063501
- Date Published:
- Journal Name:
- IEEE 37th International Conference on Distributed Computing Systems (ICDCS)
- Page Range / eLocation ID:
- 503 to 516
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICDCS.2017.292
