Fog Computing: Towards Minimizing Delay in the Internet of Things

Yousefpour, Ashkan; Ishigaki, Genya; Jue, Jason P.

doi:10.1109/ieee.edge.2017.12

Cited by 228 publications

(156 citation statements)

References 12 publications

Supporting

Mentioning

154

Contrasting

Unclassified

Order By: Relevance

“…Few other topics related to the fog application in this context are worth mentioning. An implementation to minimize the services delay is presented in [41]. This work proposes a policy for fog nodes considering queue length and different request types with variant processing times.…”

Section: Fogmentioning

confidence: 99%

A Framework for Analyzing Fog-Cloud Computing Cooperation Applied to Information Processing of UAVs

Pinto

Marcato

Melo

et al. 2019

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAVs) are a relatively new technology. Their application can often involve complex and unseen problems. For instance, they can work in a cooperative-based environment under the supervision of a ground station to speed up critical decision-making processes. However, the amount of information exchanged among the aircraft and ground station is limited by high distances, low bandwidth size, restricted processing capability, and energy constraints. These drawbacks restrain large-scale operations such as large area inspections. New distributed state-of-the-art processing architectures, such as fog computing, can improve latency, scalability, and efficiency to meet time constraints via data acquisition, processing, and storage at different levels. Under these amendments, this research work proposes a mathematical model to analyze distribution-based UAVs topologies and a fog-cloud computing framework for large-scale mission and search operations. The tests have successfully predicted latency and other operational constraints, allowing the analysis of fog-computing advantages over traditional cloud-computing architectures.

show abstract

Section: Fogmentioning

confidence: 99%

A Framework for Analyzing Fog-Cloud Computing Cooperation Applied to Information Processing of UAVs

Pinto

Marcato

Melo

et al. 2019

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Bonomi et al made provision for resource constrained IoT devices in their Fog Computing platform [28]. In [29], the authors propose a framework to minimize service delays in Fog applications by load sharing. The authors in [30] use a multi-tier Fog and Cloud computing approach for a pervasive brain monitoring system that can reliably estimate brain states and adapt to track users' brain dynamics.…”

Section: Fog Roboticsmentioning

confidence: 99%

A Fog Robotics Approach to Deep Robot Learning: Application to Object Recognition and Grasp Planning in Surface Decluttering

Tanwani

Mor

Kubiatowicz

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

The growing demand of industrial, automotive and service robots presents a challenge to the centralized Cloud Robotics model in terms of privacy, security, latency, bandwidth, and reliability. In this paper, we present a 'Fog Robotics' approach to deep robot learning that distributes compute, storage and networking resources between the Cloud and the Edge in a federated manner. Deep models are trained on non-private (public) synthetic images in the Cloud; the models are adapted to the private real images of the environment at the Edge within a trusted network and subsequently, deployed as a service for lowlatency and secure inference/prediction for other robots in the network. We apply this approach to surface decluttering, where a mobile robot picks and sorts objects from a cluttered floor by learning a deep object recognition and a grasp planning model. Experiments suggest that Fog Robotics can improve performance by sim-to-real domain adaptation in comparison to exclusively using Cloud or Edge resources, while reducing the inference cycle time by 4× to successfully declutter 86% of objects over 213 attempts.

show abstract

“…The master node manages the interaction among fog nodes in its domain. In [32], the centralized mode was used to analyze the delay of fog computing regarding several policies. Specifically, each fog node transmitted its estimated request time to the master node for the latter to track the network condition (i.e., queue time) among fog nodes inside its domain.…”

Section: Interaction Modesmentioning

confidence: 99%

Toward Reliable Fog Computing Architecture for Industrial Internet of Things

Ramli¹,

Bhardwaj²,

Kim³

2019

Preprint

View full text Add to dashboard Cite

Reliability is essential in industrial networks. In addition, most of the data from nodes of industrial Internet of Things (IIoT) are generated in real time. Thus, those data are mainly used for the time-sensitive applications. Furthermore, device failures should be considered when modeling reliable fog computing for IIoT. In this paper, we provide fundamental aspects to model reliable fog computing for IIoT. First, existing models of fog computing are compared. Then, the most feasible communication type to achieve a reliable system is determined from model analysis. Interaction modes are elaborated to study the advantages and drawbacks when communication is deployed in fog computing for IIoT, and challenges and solutions for reliable fog computing are discussed.

show abstract

Fog Computing: Towards Minimizing Delay in the Internet of Things

Cited by 228 publications

References 12 publications

A Framework for Analyzing Fog-Cloud Computing Cooperation Applied to Information Processing of UAVs

A Framework for Analyzing Fog-Cloud Computing Cooperation Applied to Information Processing of UAVs

A Fog Robotics Approach to Deep Robot Learning: Application to Object Recognition and Grasp Planning in Surface Decluttering

Toward Reliable Fog Computing Architecture for Industrial Internet of Things

Contact Info

Product

Resources

About