SSL: Smart Street Lamp Based on Fog Computing for Smarter Cities

Jia, Gangyong; Han, Guangjie; Li, Aohan; Du, Jian Hua

doi:10.1109/tii.2018.2857918

Cited by 69 publications

(38 citation statements)

References 21 publications

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“…For instance, smart sensors deployed across the city support applications for infrastructure monitoring, asset tracking and maintenance planning [7]. In such a scenario, sensors could indicate whether garbage bins are full [8] or whether street lights need to be turned on or off [9].…”

Section: Introductionmentioning

confidence: 99%

Efficient placement of edge computing devices for vehicular applications in smart cities

Premsankar

Ghaddar

Francesco

et al. 2018

NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium

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The author of this dissertation contributed to the development of the heuristic algorithms, the implementation of the simulator, the evaluation through simulation, the analysis of results, and writing the article.Publication IV empirically evaluates the impact of edge computing on emerging interactive applications and services. At the time of writing, there was no clear consensus on what specifically constitutes the edge of the network in edge computing. Thus, we first survey the state of the art in edge computing architectures and determine different deployment options for edge devices. Next, we experimentally evaluate edge computing when available at different physical locations and with different virtualization technologies. Specifically, we characterize the end-to-end latency (including

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Section: Introductionmentioning

confidence: 99%

Efficient placement of edge computing devices for vehicular applications in smart cities

Premsankar

Ghaddar

Francesco

et al. 2018

NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium

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“…An SSL implementation based on the IoT infrastructure is implemented in [69]. Street lights obtain smart characteristics by adding a light sensor, an IR sensor, GPS and a wireless communication module.…”

Section: Lightsmentioning

confidence: 99%

A Review of Machine Learning and IoT in Smart Transportation

et al. 2019

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With the rise of the Internet of Things (IoT), applications have become smarter and connected devices give rise to their exploitation in all aspects of a modern city. As the volume of the collected data increases, Machine Learning (ML) techniques are applied to further enhance the intelligence and the capabilities of an application. The field of smart transportation has attracted many researchers and it has been approached with both ML and IoT techniques. In this review, smart transportation is considered to be an umbrella term that covers route optimization, parking, street lights, accident prevention/detection, road anomalies, and infrastructure applications. The purpose of this paper is to make a self-contained review of ML techniques and IoT applications in Intelligent Transportation Systems (ITS) and obtain a clear view of the trends in the aforementioned fields and spot possible coverage needs. From the reviewed articles it becomes profound that there is a possible lack of ML coverage for the Smart Lighting Systems and Smart Parking applications. Additionally, route optimization, parking, and accident/detection tend to be the most popular ITS applications among researchers.

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“…Jia et al [17] proposed a Smart Street Lamp (SSL) system based on fog computing for smart cities with the objective of reducing maintenance periods, decreasing energy consumption, providing fine-grain control, and reducing theft. The evaluation demonstrated that the SSL system is capable of self-understanding various static, pre-defined states, and consequently improving issue detection and maintenance.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning: Edge-Cloud Data Analytics for IoT

Ghosh

Grolinger

2019

2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)

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Sensors, wearables, mobile and other Internet of Thing (IoT) devices are becoming increasingly integrated in all aspects of our lives. They are capable of collecting massive quantities of data that are typically transmitted to the cloud for processing. However, this results in increased network traffic and latencies. Edge computing has a potential to remedy these challenges by moving computation physically closer to the network edge where data are generated. However, edge computing does not have sufficient resources for complex data analytics tasks. Consequently, this paper investigates merging cloud and edge computing for IoT data analytics and presents a deep learning-based approach for data reduction on the edge with the machine learning on the cloud. The encoder part of the autoencoder is located on the edge to reduce data dimensions. Reduced data are sent to the cloud where there are used directly for machine learning or expanded to original features using the decoder part of the autoencoder. The proposed approach has been evaluated on the human activity recognition tasks. Results show that 50% data reduction did not have a significant impact on the classification accuracy and 77% reduction only caused 1% change.

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SSL: Smart Street Lamp Based on Fog Computing for Smarter Cities

Cited by 69 publications

References 21 publications

Efficient placement of edge computing devices for vehicular applications in smart cities

Efficient placement of edge computing devices for vehicular applications in smart cities

A Review of Machine Learning and IoT in Smart Transportation

Deep Learning: Edge-Cloud Data Analytics for IoT

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