An Edge Based Smart Parking Solution Using Camera Networks and Deep Learning

2018 ◽  
Author(s):  
Harshitha Bura ◽  
Nathan Lin ◽  
Naveen Kumar ◽  
Sangram Malekar ◽  
Sushma Nagaraj ◽  
...  
Keyword(s):  
Deep Learning ◽  
Camera Networks ◽  
Smart Parking ◽  
Edge Based

Predictive Analytics for Smart Parking: A Deep Learning Approach in Forecasting of IoT Data

2021 ◽  
Vol 21 (3) ◽  
pp. 1-21
Author(s):  
Francesco Piccialli ◽  
Fabio Giampaolo ◽  
Edoardo Prezioso ◽  
Danilo Crisci ◽  
Salvatore Cuomo
Keyword(s):  
Deep Learning ◽  
Smart City ◽  
Predictive Analytics ◽  
Positive Impact ◽  
Search Time ◽  
Building Blocks ◽  
Ensemble Technique ◽  
Smart Parking ◽  
Polluting Emissions ◽  
The City

Nowadays, a sustainable and smart city focuses on energy efficiency and the reduction of polluting emissions through smart mobility projects and initiatives to “sensitize” infrastructure. Smart parking is one of the building blocks of intelligent mobility, innovative mobility that aims to be flexible, integrated, and sustainable and consequently integrated into a Smart City. By using the Internet of Things (IoT) sensors located in the parking areas or the underground car parks in combination with a mobile application, which indicates to citizens the free places in the different areas of the city and guides them toward the chosen parking, it is possible to reduce air pollution and fluidifying noise traffic. In this article, we present and discuss an innovative Deep Learning-based ensemble technique in forecasting the parking space occupancy to reduce the search time for parking and to optimize the flow of cars in particularly congested areas, with an overall positive impact on traffic in urban centres. A genetic algorithm has also been used to optimize predictors parameters. The main goal is to design an intelligent IoT-based service that can predict, in the next few hours, the parking spaces occupancy of a street. The proposed approach has been assessed on a real IoT dataset composed by over than 15M of collected sensor records. Obtained results demonstrate that our method outperforms both single predictors and the widely used strategy of the mean providing inherently robust predictions.

Smart Street Litter Detection and Classification Based on Faster R-CNN and Edge Computing

2020 ◽  
Vol 30 (04) ◽  
pp. 537-553
Author(s):  
Ping Ping ◽  
Guoyan Xu ◽  
Effendy Kumala ◽  
Jerry Gao
Keyword(s):  
Public Health ◽  
Deep Learning ◽  
Mobile Devices ◽  
Service System ◽  
Environment And Public Health ◽  
City Environment ◽  
Edge Based ◽  
Cleaning Methods ◽  
City Systems ◽  
Deep Learning Model

Cleanliness of city streets has an important impact on city environment and public health. Conventional street cleaning methods involve street sweepers going to many spots and manually confirming if the street needs to be clean. However, this method takes a substantial amount of manual operations for detection and assessment of street’s cleanliness which leads to a high cost for cities. Using pervasive mobile devices and AI technology, it is now possible to develop smart edge-based service system for monitoring and detecting the cleanliness of streets at scale. This paper explores an important aspect of cities — how to automatically analyze street imagery to understand the level of street litter. A vehicle (i.e. trash truck) equipped with smart edge station and cameras is used to collect and process street images in real time. A deep learning model is developed to detect, classify and analyze the diverse types of street litters such as tree branches, leaves, bottles and so on. In addition, two case studies are reported to show its strong potential and effectiveness in smart city systems.

Leveraging CNN Deep Learning Model for Smart Parking

2021 ◽  
pp. 151-174
Author(s):  
Guruvareddiyur Rangaraju Karpagam ◽  
Abishek Ganapathy ◽  
Aadhavan Chellamuthu Kavin Raj ◽  
Saravanan Manigandan ◽  
J. R. Neeraj Julian ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Smart Parking ◽  
Deep Learning Model

Deep Learning based Smart Parking for a Metropolitan Area

2021 ◽  
Author(s):  
Md Ifraham Iqbal ◽  
Mazharul Islam Leon ◽  
Nilambar Haldar Tonmoy ◽  
Jahidul Islam ◽  
Amit Ghosh
Keyword(s):  
Deep Learning ◽  
Metropolitan Area ◽  
Smart Parking

scholarly journals Gait-Based Implicit Authentication Using Edge Computing and Deep Learning for Mobile Devices

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4592
Author(s):  
Xin Zeng ◽  
Xiaomei Zhang ◽  
Shuqun Yang ◽  
Zhicai Shi ◽  
Chihung Chi
Keyword(s):  
Deep Learning ◽  
Mobile Devices ◽  
User Authentication ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Edge Computing ◽  
Behavior Modeling ◽  
Security And Privacy ◽  
Positive Rate ◽  
Edge Based

Implicit authentication mechanisms are expected to prevent security and privacy threats for mobile devices using behavior modeling. However, recently, researchers have demonstrated that the performance of behavioral biometrics is insufficiently accurate. Furthermore, the unique characteristics of mobile devices, such as limited storage and energy, make it subject to constrained capacity of data collection and processing. In this paper, we propose an implicit authentication architecture based on edge computing, coined Edge computing-based mobile Device Implicit Authentication (EDIA), which exploits edge-based gait biometric identification using a deep learning model to authenticate users. The gait data captured by a device’s accelerometer and gyroscope sensors is utilized as the input of our optimized model, which consists of a CNN and a LSTM in tandem. Especially, we deal with extracting the features of gait signal in a two-dimensional domain through converting the original signal into an image, and then input it into our network. In addition, to reduce computation overhead of mobile devices, the model for implicit authentication is generated on the cloud server, and the user authentication process also takes place on the edge devices. We evaluate the performance of EDIA under different scenarios where the results show that i) we achieve a true positive rate of 97.77% and also a 2% false positive rate; and ii) EDIA still reaches high accuracy with limited dataset size.

scholarly journals Edge Loss for Remote Sensing Image Super-Resolution

2021 ◽  
Author(s):  
Jiaoyue Li ◽  
Weifeng Liu ◽  
Kai Zhang ◽  
Baodi Liu
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Super Resolution ◽  
Ground Truth ◽  
Remote Sensing Image ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Sensing Applications ◽  
Image Super Resolution ◽  
Edge Based

Remote sensing image super-resolution (SR) plays an essential role in many remote sensing applications. Recently, remote sensing image super-resolution methods based on deep learning have shown remarkable performance. However, directly utilizing the deep learning methods becomes helpless to recover the remote sensing images with a large number of complex objectives or scene. So we propose an edge-based dense connection generative adversarial network (SREDGAN), which minimizes the edge differences between the generated image and its corresponding ground truth. Experimental results on NWPU-VHR-10 and UCAS-AOD datasets demonstrate that our method improves 1.92 and 0.045 in PSNR and SSIM compared with SRGAN, respectively.

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