Path Loss Models for Low-Power, Low-Data Rate Sensor Nodes for Smart Car Parking Systems

Olasupo, Tajudeen O.; Otero, Carlos E.; Otero, Luis Daniel; Olasupo, Kehinde O.; Kostanic, Ivica

doi:10.1109/tits.2017.2741467

Cited by 46 publications

(12 citation statements)

References 36 publications

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“…To overcome the problem described above, there currently exist various systems, such as those proposed in [2,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23]. In some studies, the parking management system problem was addressed by using smart devices [24,25,26,27].…”

Section: Related Workmentioning

confidence: 99%

Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network

Nurullayev

Lee

2019

Sensors

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The importance of vacant parking space detection systems is increasing dramatically as the avoidance of traffic congestion and the time-consuming process of searching an empty parking space is a crucial problem for drivers in urban centers. However, the existing parking space occupancy detection systems are either hardware expensive or not well-generalized for varying images captured from different camera views. As a solution, we take advantage of an affordable visual detection method that is made possible by the fact that camera monitoring is already available in the majority of parking areas. However, the current problem is a challenging vision task because of outdoor lighting variation, perspective distortion, occlusions, different camera viewpoints, and the changes due to the various seasons of the year. To overcome these obstacles, we propose an approach based on Dilated Convolutional Neural Network specifically designed for detecting parking space occupancy in a parking lot, given only an image of a single parking spot as input. To evaluate our method and allow its comparison with previous strategies, we trained and tested it on well-known publicly available datasets, PKLot and CNRPark + EXT. In these datasets, the parking lot images are already labeled, and therefore, we did not need to label them manually. The proposed method shows more reliability than prior works especially when we test it on a completely different subset of images. Considering that in previous studies the performance of the methods was compared with well-known architecture—AlexNet, which shows a highly promising achievement, we also assessed our model in comparison with AlexNet. Our investigations showed that, in comparison with previous approaches, for the task of classifying given parking spaces as vacant or occupied, the proposed approach is more robust, stable, and well-generalized for unseen images captured from completely different camera viewpoints, which has strong indications that it would generalize effectively to other parking lots.

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Section: Related Workmentioning

confidence: 99%

Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network

Nurullayev

Lee

2019

Sensors

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“…It also includes path loss at a reference distance (d o ) of 1 m which can be obtained from measurement data. Thus, measurement-based path loss models (as found in [26,27]) can be adopted to improve the localization accuracy of AP/BS RSS radio ranging.…”

Section: Rss-basedmentioning

confidence: 99%

Infrastructure Wi-Fi for connected autonomous vehicle positioning: A review of the state-of-the-art

Adegoke

Zidane

Kampert

et al. 2019

Vehicular Communications

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“…The path loss is modeled as a log-distance path model, which follows a normal distribution in

as described in ref [ 50 , 61 , 63 ]. Defining the path loss with a distance between two nodes valuing

as the reference distance, then, the path loss with a distance of

can be calculated by the following equation:

…”

Section: The Design Of the Ectra Schemementioning

confidence: 99%

An Energy Conserving and Transmission Radius Adaptive Scheme to Optimize Performance of Energy Harvesting Sensor Networks

Liu

Zhang

et al. 2018

Sensors

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In energy harvesting wireless sensor networks (EHWSNs), the energy tension of the network can be relieved by obtaining the energy from the surrounding environment, but the cost on hardware cannot be ignored. Therefore, how to minimize the cost of energy harvesting hardware to reduce the network deployment cost, and further optimize the network performance, is still a challenging issue in EHWSNs. In this paper, an energy conserving and transmission radius adaptive (ECTRA) scheme is proposed to reduce the cost and optimize the performance of solar-based EHWSNs. There are two main innovations of the ECTRA scheme. Firstly, an energy conserving approach is proposed to conserve energy and avoid outage for the nodes in hotspots, which are the bottleneck of the whole network. The novelty of this scheme is adaptively rotating the transmission radius. In this way, the nodes with maximum energy consumption are rotated, balancing energy consumption between nodes and reducing the maximum energy consumption in the network. Therefore, the battery storage capacity of nodes and the cost on hardware. Secondly, the ECTRA scheme selects a larger transmission radius for rotation when the node can absorb enough energy from the surroundings. The advantages of using this method are: (a) reducing the energy consumption of nodes in near-sink areas, thereby reducing the maximum energy consumption and allowing the node of the hotspot area to conserve energy, in order to prevent the node from outage. Hence, the network deployment costs can be further reduced; (b) reducing the network delay. When a larger transmission radius is used to transmit data in the network, fewer hops are needed by data packet to the sink. After the theoretical analyses, the results show the following advantages compared with traditional method. Firstly, the ECTRA scheme can effectively reduce deployment costs by 29.58% without effecting the network performance as shown in experiment analysis; Secondly, the ECTRA scheme can effectively reduce network data transmission delay by 44–71%; Thirdly, the ECTRA scheme shows a better balance in energy consumption and the maximum energy consumption is reduced by 27.89%; And lastly, the energy utilization rate is effectively improved by 30.09–55.48%.

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Path Loss Models for Low-Power, Low-Data Rate Sensor Nodes for Smart Car Parking Systems

Cited by 46 publications

References 36 publications

Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network

Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network

Infrastructure Wi-Fi for connected autonomous vehicle positioning: A review of the state-of-the-art

An Energy Conserving and Transmission Radius Adaptive Scheme to Optimize Performance of Energy Harvesting Sensor Networks

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