A vehicle-based edge computing platform for transit and human mobility analytics

Qi, Bozhao; Kang, Lei; Banerjee, Suman

doi:10.1145/3132211.3134446

Cited by 33 publications

(12 citation statements)

References 27 publications

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“…4.13.3 Data Analytics. Another study to make use of edge computing is the work in [354] that introduces a Wi-Fi-based in-bus monitoring and tracking system that observes mobile devices and provides analytics about people both within and outside the vehicle. The system can further use the data that is collected by the vehicle-mounted wireless device to track passenger movements, detect pedestrian flows, and evaluate how external factors impact human mobility, which provides useful analytics to transit operators.…”

Section: Bandwidthmentioning

confidence: 99%

All one needs to know about fog computing and related edge computing paradigms: A complete survey

Yousefpour

Fung

Nguyen

et al. 2019

Journal of Systems Architecture

1,082

613

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With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of securitycritical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.

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Section: Bandwidthmentioning

confidence: 99%

All one needs to know about fog computing and related edge computing paradigms: A complete survey

Yousefpour

Fung

Nguyen

et al. 2019

Journal of Systems Architecture

1,082

613

View full text Add to dashboard Cite

show abstract

“…5 The process of study inclusion into the present study Wireless Networks movement continues to develop. Simultaneously, the advent of a slew of new applications, like smart cities and unmanned driving, raises the stakes for delay [45]. Some interlinked devices or sensors can gather and exchange data using a contemporary communication network as an infrastructure that numerous IoT nodes have linked.…”

Section: Data Replication and Placement Techniques In Iotmentioning

confidence: 99%

Replica selection and placement techniques on the IoT and edge computing: a deep study

Shao

Huang

2021

Wireless Netw

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Internet of Things (IoT) has lately been presented as a new technological transformation in which things are connected via the Internet. Several sensors and devices create data and send vital signals constantly over sophisticated networks that allow machine-to-machine interactions and monitor and manage key smart-world infrastructures. Since huge amounts of data are generated, reducing the data access costs is a critical issue. Edge computing has been developed as a novel paradigm for solving IoT demands to reduce the rise in resource congestion. One of the most significant data management challenges in the IoT is selecting suitable replication things that minimize reaction time and cost. Therefore, our goal is to examine replica selection and placement techniques in IoT and edge computing. The findings revealed that the edge computing environment might significantly enhance system performance regarding access response time, prediction accuracy, effective network, and increased data availability. Furthermore, the results illustrate that data provenance is necessary to raise the accuracy of the data by. Also, the results showed that the most important challenge in data replication and placement techniques in IoT and edge computing was the availability of data and access response time.

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“…However, we argue that data collection on such a big scale would not be possible without edge computing, due to necessary pre-processing steps (e.g., encoding a video to a lower bitrate or aggregating measurements). Qi et al [204]* have introduced an edge computing platform using onboard computers on public transport vehicles. The platform collects data (e.g., by intercepting WiFi probe packets from phones) to gain insights that help public transport operators to devise better plans (e.g., by identifying popular stations and assessing vehicle occupancy).…”

Section: ) Smart Transportation and Connected Carsmentioning

confidence: 99%

What the Fog? Edge Computing Revisited: Promises, Applications and Future Challenges

et al. 2019

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Edge computing brings computing and storage resources closer to (mobile) end users and data sources, thus bypassing expensive and slow links to distant cloud computing infrastructures. Often leveraged opportunistically, these heterogeneous resources can be used to offload data and computations, enabling upcoming demanding applications such as augmented reality and autonomous driving. Research in this direction has addressed various challenges, from architectural concerns to runtime optimizations. As of today, however, we lack a widespread availability of edge computing-partly because it remains unclear which of the promised benefits of edge computing are relevant for what types of applications. This article provides a comprehensive snapshot of the current edge computing landscape, with a focus on the application perspective. We outline the characteristics of edge computing and its postulated benefits and drawbacks. To understand the functional composition of applications, we first define common application components that are relevant w.r.t. edge computing. We then present a classification of proposed use cases and analyze them according to their expected benefits from edge computing and which components they use. Furthermore, we illustrate existing products and industry solutions that have recently surfaced and outline future research challenges. INDEX TERMS Edge computing, heterogeneous networks, next generation networking mobile applications, Internet of Things, ubiquitous computing.

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A vehicle-based edge computing platform for transit and human mobility analytics

Cited by 33 publications

References 27 publications

All one needs to know about fog computing and related edge computing paradigms: A complete survey

All one needs to know about fog computing and related edge computing paradigms: A complete survey

Replica selection and placement techniques on the IoT and edge computing: a deep study

What the Fog? Edge Computing Revisited: Promises, Applications and Future Challenges

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