Driving in the Fog: Latency Measurement, Modeling, and Optimization of LTE-based Fog Computing for Smart Vehicles

Xiao, Y; Krunz, M; Volos, H; Bando, T

Driving in the Fog: Latency Measurement, Modeling, and Optimization of LTE-based Fog Computing for Smart Vehicles

Xiao, Y Krunz, M Volos, H Bando, T

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops, 2019, 2019-June
Issue Date:: 2019-06-01

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Field	Value	Language
dc.contributor.author	Xiao, Y
dc.contributor.author	Krunz, M
dc.contributor.author	Volos, H
dc.contributor.author	Bando, T
dc.date	2019-06-10
dc.date.accessioned	2020-06-13T00:23:04Z
dc.date.available	2020-06-13T00:23:04Z
dc.date.issued	2019-06-01
dc.identifier.citation	Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops, 2019, 2019-June
dc.identifier.isbn	9781728112077
dc.identifier.issn	2155-5486
dc.identifier.issn	2155-5494
dc.identifier.uri	http://hdl.handle.net/10453/141398
dc.description.abstract	© 2019 IEEE. Fog computing has been advocated as an enabling technology for computationally intensive services in connected smart vehicles. Most existing works focus on analyzing and optimizing the queueing and workload processing latencies, ignoring the fact that the access latency between vehicles and fog/cloud servers can sometimes dominate the end-to-end service latency. This motivates the work in this paper, where we report a five-month urban measurement study of the wireless access latency between a connected vehicle and a fog computing system supported by commercially available multi-operator LTE networks. We propose AdaptiveFog, a novel framework for autonomous and dynamic switching between different LTE operators that implement fog/cloud infrastructure. The main objective here is to maximize the service confidence level, defined as the probability that the tolerable latency threshold for each supported type of service can be guaranteed. AdaptiveFog has been implemented on a smart phone app, running on a moving vehicle. The app periodically measures the round-trip time between the vehicle and fog/cloud servers. An empirical spatial statistic model is established to characterize the spatial variation of the latency across the main driving routes of the city. To quantify the performance difference between different LTE networks, we introduce the weighted Kantorovich-Rubinstein (K-R) distance. An optimal policy is derived for the vehicle to dynamically switch between LTE operators' networks while driving. Extensive analysis and simulation are performed based on our latency measurement dataset. Our results show that AdaptiveFog achieves around 30% and 50% improvement in the confidence level of fog and cloud latency, respectively.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops
dc.relation.ispartof	Annual IEEE International Conference on Sensing, Communication, and Networking
dc.relation.isbasedon	10.1109/SAHCN.2019.8824922
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Driving in the Fog: Latency Measurement, Modeling, and Optimization of LTE-based Fog Computing for Smart Vehicles
dc.type	Conference Proceeding
utslib.citation.volume	2019-June
utslib.location.activity	Boston, MA, USA
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-06-13T00:23:02Z
pubs.finish-date	2019-06-13
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-06-10
pubs.volume	2019-June
dc.location	Piscataway, USA

Abstract:

© 2019 IEEE. Fog computing has been advocated as an enabling technology for computationally intensive services in connected smart vehicles. Most existing works focus on analyzing and optimizing the queueing and workload processing latencies, ignoring the fact that the access latency between vehicles and fog/cloud servers can sometimes dominate the end-to-end service latency. This motivates the work in this paper, where we report a five-month urban measurement study of the wireless access latency between a connected vehicle and a fog computing system supported by commercially available multi-operator LTE networks. We propose AdaptiveFog, a novel framework for autonomous and dynamic switching between different LTE operators that implement fog/cloud infrastructure. The main objective here is to maximize the service confidence level, defined as the probability that the tolerable latency threshold for each supported type of service can be guaranteed. AdaptiveFog has been implemented on a smart phone app, running on a moving vehicle. The app periodically measures the round-trip time between the vehicle and fog/cloud servers. An empirical spatial statistic model is established to characterize the spatial variation of the latency across the main driving routes of the city. To quantify the performance difference between different LTE networks, we introduce the weighted Kantorovich-Rubinstein (K-R) distance. An optimal policy is derived for the vehicle to dynamically switch between LTE operators' networks while driving. Extensive analysis and simulation are performed based on our latency measurement dataset. Our results show that AdaptiveFog achieves around 30% and 50% improvement in the confidence level of fog and cloud latency, respectively.

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http://hdl.handle.net/10453/141398