1 / 6

Engaging Participants for Collaborative Sensing of Human Mobility

This research discusses the challenge of collecting data for human mobility analysis, exploring the Epi application that uses people as sensors to suggest sustainable paths and make decisions on new computing infrastructures. Preliminary results show user activities and the impact of advertisement periods. The study aims to improve users' awareness of collaborative data collection and ensure privacy and integrity.

saburo
Télécharger la présentation

Engaging Participants for Collaborative Sensing of Human Mobility

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Engaging participants for collaborative sensing of human mobilityHelena Rodrigues, Maria JoãoNicolau, Rui José and Adriano MoreiraCentro Algoritmi, Universidade do Minho, Portugal 1st International Workshop on Ubiquitous Mobile Instrumentation, 8 September, 2012

  2. Outline • The problem • The Epi application • Preliminary results • Discussion

  3. The problem • How to collect data for human mobility analysis • Where do people spend more time during the day? • Which are the most frequent routes used by people? • It is possible to suggest alternative and more sustainable paths for daily trips • Which could be the best decisions concerning new computing infrastructures deployments? • Large data sets • Extended periods of time

  4. The Epi application • Explore people as sensors • Laptop vs mobile phone • Social application • Text messages sharing between users • Collects information about nearby APs • Privacy policy • Preserves normal spatio-temporal behaviour of users • Text messages sharing as a service reward • Non-personalised advertisements through social services (in July and October 2010) to attract users

  5. Preliminary results Web site: unique visitors per day Web site: number of downloads per day Application: number of active users per week. Collected data: number of records uploaded per week

  6. Discussion • Users are more active close to advertisement periods • Scalable solution for advertisement and distribution? • Improve users’ awareness about the value of the data collected collaboratively • Which should be the main characteristics of a mobile sensing application? • Open access/publication to/of the data • How to ensure privacy, trust and integrity?

More Related