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Demo: how long to wait?: predicting bus arrival time with mobile phone based participatory sensing

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TLDR
A bus arrival time prediction system based on bus passengers' participatory sensing that achieves outstanding prediction accuracy compared with those bus operator initiated and GPS supported solutions and is more generally available and energy friendly.
Abstract
The bus arrival time is primary information to most city transport travelers. Excessively long waiting time at bus stops often discourages the travelers and makes them reluctant to take buses. In this paper, we present a bus arrival time prediction system based on bus passengers' participatory sensing. With commodity mobile phones, the bus passengers' surrounding environmental context is effectively collected and utilized to estimate the bus traveling routes and predict bus arrival time at various bus stops. The proposed system solely relies on the collaborative effort of the participating users and is independent from the bus operating companies, so it can be easily adopted to support universal bus service systems without requesting support from particular bus operating companies. Instead of referring to GPS enabled location information, we resolve to more generally available and energy efficient sensing resources, including cell tower signals, movement statuses, audio recordings, etc., which bring less burden to the participatory party and encourage their participation. We develop a prototype system with different types of Android based mobile phones and comprehensively experiment over a 7 week period. The evaluation results suggest that the proposed system achieves outstanding prediction accuracy compared with those bus company initiated and GPS supported solutions. At the same time, the proposed solution is more generally available and energy friendly.

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Mobile Crowd Sensing and Computing: The Review of an Emerging Human-Powered Sensing Paradigm

TL;DR: The unique features and novel application areas of MCSC are characterized and a reference framework for building human-in-the-loop MCSC systems is proposed, which clarifies the complementary nature of human and machine intelligence and envision the potential of deep-fused human--machine systems.
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Smartphones Based Crowdsourcing for Indoor Localization

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A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

TL;DR: A survey on existing works in the MCS domain is presented and a detailed taxonomy is proposed to shed light on the current landscape and classify applications, methodologies, and architectures to outline potential future research directions and synergies with other research areas.
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Mobility Increases Localizability: A Survey on Wireless Indoor Localization using Inertial Sensors

TL;DR: This article surveys this new trend of mobility enhancing smartphone-based indoor localization and discusses how mobility assists localization with respect to enhancing location accuracy, decreasing deployment cost, and enriching location context.
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Free Market of Crowdsourcing: Incentive Mechanism Design for Mobile Sensing

TL;DR: Three online incentive mechanisms, named TBA, TOIM and TOIMAD, based on online reverse auction are designed, designed to pursue platform utility maximization, while toIM and ToIM-AD achieve the crucial property of truthfulness.
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Proceedings ArticleDOI

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