Experiences with GreenGPS – Fuel-Efficient Navigation using Participatory Sensing

Saremi, Fatemeh; Fatemieh, Omid; Ahmadi, Hossein; Wang, Hongyan; Abdelzaher, Tarek F.; Ganti, Raghu K.; Liu, Hengchang; Hu, Shaohan; Li, Shen; Su, Lu

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https://hdl.handle.net/2142/73416 Copy

Description

Title

Experiences with GreenGPS – Fuel-Efficient Navigation using Participatory Sensing

Author(s)

• Saremi, Fatemeh
• Fatemieh, Omid
• Ahmadi, Hossein
• Wang, Hongyan
• Abdelzaher, Tarek F.
• Ganti, Raghu K.
• Liu, Hengchang
• Hu, Shaohan
• Li, Shen
• Su, Lu

Issue Date

2015-03

Keyword(s)

• Application
• Participatory Sensing
• Transportation
• Energy
• Navigation

Abstract

Participatory sensing services based on mobile phones constitute an important growing area of mobile computing. Most services start small and hence are initially sparsely deployed. Unless a mobile service adds value while sparsely deployed, it may not survive conditions of sparse deployment. The paper offers a generic solution to this problem and illustrates this solution in the context of GreenGPS; a navigation service that allows drivers to find the most fuel-efficient routes customized for their vehicles between arbitrary end-points. Specifically, when the participatory sensing service is sparsely deployed, we demonstrate a general framework for generalization from sparse collected data to produce models extending beyond the current data coverage. This generalization allows the mobile service to offer value under broader conditions. GreenGPS uses our developed participatory sensing infrastructure and generalization algorithms to perform inexpensive data collection, aggregation, and modeling in an end-to-end automated fashion. The models are subsequently used by our backend engine to predict customized fuel-efficient routes for both members and non-members of the service. GreenGPS is offered as a mobile phone application and can be easily deployed and used by individuals. A preliminary study of our green navigation idea was performed in [1], however, the effort was focused on a proof-of-concept implementation that involved substantial offline and manual processing. In contrast, the results and conclusions in the current paper are based on a more advanced and accurate model and extensive data from a real-world phone-based implementation and deployment, which enables reliable and automatic end-to-end data collection and route recommendation. The system further benefits from lower cost and easier deployment. To evaluate the green navigation service efficiency, we conducted a user subject study consisting of 22 users driving different vehicles over the course of several months in Urbana-Champaign, IL. The experimental results using the collected data suggest that fuel savings of 21.5% over the fastest, 11.2% over the shortest, and 8.4% over the Garmin eco routes can be achieved by following GreenGPS green routes. The study confirms that our navigation service can survive conditions of sparse deployment and at the same time achieve accurate fuel predictions and lead to significant fuel savings.

Type of Resource

text

Language

en

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http://hdl.handle.net/2142/73416

Sponsor(s)/Grant Number(s)

This research was sponsored in part by IBM Research and NSF Grants CNS 10-59294, CNS 10-40380 and CNS 13-45266.

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