Automatic Collection of Fuel Prices from a Network of Mobile Cameras

Abstract

Abstract. It is an undeniable fact that people want information. Unfortunately, even in today's highly automated society, a lot of the information we desire is still manually collected. An example is fuel prices where websites providing fuel price information either send their workers out to manually collect the prices or depend on volunteers manually relaying the information. This paper proposes a novel application of wireless sensor networks to automatically collect fuel prices from camera images of road-side price board (billboard) of service (or gas) stations. Our system exploits the ubiquity of mobile phones that have cameras as well as users contributing and sharing data. In our proposed system, cameras of contributing users will be automatically triggered when they get close to a service station. These images will then be processed by computer vision algorithms to extract the fuel prices. In this paper, we will describe the system architecture and present results from our computer vision algorithms. Based on 52 images, our system achieves a hit rate of 92.3% for correctly detecting the fuel price board from the image background and reads the prices correctly in 87.7% of them. To the best of our knowledge, this is the first instance of a sensor network being used for collecting consumer pricing information.

The lord of the sense: A privacy preserving reputation system for participatory sensing applications

“…Although there are many participatory sensing applications [3]- [13], only some of them deal with the problem of anonymous and private data reporting. Furthermore, the absence of reputation schemes that would create a rank for each user, based on their reports and the votes that each report receives from the community is even bigger.…”

Privacy-Enhanced Participatory Sensing with Collusion Resistance and Data Aggregation

“…Participatory sensing is a novel paradigm for data collection using smartphones or other mobile devices with applications ranging from sensing of environmental conditions like traffic [29], urban air [37] and noise level [39,18], or earth quakes [11] to environmental impact measurements [34], sport activities [21], market aspects like fuel prices [20], or concerns of personal health like diets [40]. They all leverage the high and increasing distribution of mobile phones, whose number of subscriptions surpassed 6 billion including a high share of smartphones with sufficient computation power for a variety of sensing tasks.…”

Anonymous Sensory Data Collection Approach for Mobile Participatory Sensing

“…An example is the OpenStreetMap [7] project, which collects user contributed GIS data (such as GPS traces and POIs) and constructs an online map from mobile data, open for the public to use. Other examples of participatory sensing include cooperative collection of gas prices [5], monitoring of traffic conditions [3], and photo sharing through mobile applications such as Facebook and Instagram.…”