Opportunistic WiFi offloading in a vehicular environment: Waiting or downloading now?

Wang, Ning; Wu, Jie

doi:10.1109/infocom.2016.7524495

Cited by 70 publications

(30 citation statements)

References 13 publications

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“…We regard the rewards of task offloading as the task utility, which decays along with the service latency and grows along with the image resolution. According to [21], we use a linear function to model the task utility for offloading an image with resolution level q i when the fog node workload state is l i :…”

Section: Pomdp Formulationmentioning

confidence: 99%

Chameleon: Latency and Resolution Aware Task Offloading for Visual-Based Assisted Driving

Zhu

Chiang

Mehrabi

et al. 2019

IEEE Trans. Veh. Technol.

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Emerging visual-based driving assistance systems involve time-critical and data-intensive computational tasks, such as real-time object recognition and scene understanding. Due to the constraints on space and power capacity, it is not feasible to install extra computing devices on all the vehicles. To solve this problem, different scenarios of vehicular fog computing have been proposed, where computational tasks generated by vehicles can be sent to and processed at fog nodes located for example at 5G cell towers or moving buses. In this paper, we propose Chameleon, a novel solution for task offloading for visual-based assisted driving. Chameleon takes into account the spatiotemporal variation in service demand and supply, and provides latency and resolution aware task offloading strategies based on partially observable Markov decision process (POMDP). To evaluate the effectiveness of Chameleon, we simulate the availability of vehicular fog nodes at different times of day based on the bus trajectories collected in Helsinki, and use the real-world performance measurements of visual data transmission and processing. Compared with adaptive and random task offloading strategies, the POMDP-based offloading strategies provided by Chameleon shortens the average service latency of task offloading by up to 65% while increasing the average resolution level of processed images by up to 83%. Index Terms-Vehicular fog computing, task offloading, assisted driving, POMDP. I. INTRODUCTION E MERGING visual-based assisted driving applications, such as see-through and cooperative lane-change, involve time-critical and data-intensive computational tasks, such as real-time object recognition and scene understanding from images/video. Obviously, processing visual data demands for a lot more computing power, compared with other sensor data like Manuscript

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Section: Pomdp Formulationmentioning

confidence: 99%

Chameleon: Latency and Resolution Aware Task Offloading for Visual-Based Assisted Driving

Zhu

Chiang

Mehrabi

et al. 2019

IEEE Trans. Veh. Technol.

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show abstract

“…Furthermore, the authors in [16], [18]- [20] proposed prediction techniques based on a table that the vehicle has to maintain and update while connecting to different APs. The table was used to store the position of different APs the vehicle meets.…”

Section: A Related Workmentioning

confidence: 99%

Prediction Model for Offloading in Vehicular Wi-Fi Network

Alawi¹,

Alsaqour²,

Sundararajan³

et al. 2016

International Journal on Advanced Science, Engineering and Information Technology

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It cannot be denied that, the inescapable diffusion of smartphones, tablets and other vehicular network applications with diverse networking and multimedia capabilities, and the associated blooming of all kinds of data-hungry multimedia services that passengers normally used while traveling exert a big challenge to cellular infrastructure operators. Wireless fidelity (Wi-Fi) as well as fourth generation long term evolution advanced (4G LTE-A) network are widely available today, Wi-Fi could be used by the vehicle users to relieve 4G LTE-A networks. Though, using IEE802.11 Wi-Fi AP to offload 4G LTE-A network for moving vehicle is a challenging task since it only covers short distance and not well deployed to cover all the roads. Several studies have proposed the offloading techniques based on predicted available APs for making offload decision. However, most of the proposed prediction mechanisms are only based on historical connection pattern. This work proposed a prediction model which utilized historical connection pattern, vehicular movement, and driver profile to predict the next available AP. The proposed model is compared with the existing models to evaluate its practicability.

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“…In [14], auction-based WiFi offloading in vehicular environments is proposed to improve the utility of vehicle users and increase the revenue of cellular network operator. In [15], the opportunistic decision-making algorithm is designed to maximize the vehicle user's satisfaction, which is modeled as the downloading cost and downloading delay. In [16], a seamless WiFi-based Internet access scheme is proposed to overcome high packet loss rate and frequent connection disruptions.…”

Section: Introductionmentioning

confidence: 99%

Game-Theoretic Scalable Offloading for Video Streaming Services over LTE and WiFi Networks

Park

Song

2018

IEEE Trans. on Mobile Comput.

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Abstract-This paper presents a game-theoretic scalable offloading system that provides seamless video streaming services by effectively offloading parts of video traffic in all video streaming services to a WiFi network to alleviate cellular network congestion. The system also consolidates multiple physical paths in a cost-effective manner. In the proposed system, the fountain encoding symbols of compressed video data are transmitted through long term evolution (LTE) and WiFi networks concurrently to flexibly control the amount of video traffic through the WiFi network as well as mitigate video quality degradation caused by wireless channel errors. Furthermore, the progressive second price auction mechanism is employed to allocate the limited LTE resources to multiple user equipment in order to maximize social welfare while converging to the ε-Nash equilibrium.Specifically, we design an application-centric resource valuation that explicitly considers both the realistic wireless network conditions and characteristics of video streaming services. In addition, the scalability and convergence properties of the proposed system are verified both theoretically and experimentally. The proposed system is implemented using network simulator 3. Simulation results are provided to demonstrate the performance improvement of the proposed system.

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Opportunistic WiFi offloading in a vehicular environment: Waiting or downloading now?

Cited by 70 publications

References 13 publications

Chameleon: Latency and Resolution Aware Task Offloading for Visual-Based Assisted Driving

Chameleon: Latency and Resolution Aware Task Offloading for Visual-Based Assisted Driving

Prediction Model for Offloading in Vehicular Wi-Fi Network

Game-Theoretic Scalable Offloading for Video Streaming Services over LTE and WiFi Networks

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