CRAWDAD queensu/crowd_temperature

Citation Author(s):
Mohannad A.
Alswailim
Hossam S.
Hassanein
Mohammad
Zulkernine
Submitted by:
CRAWDAD Team
Last updated:
Fri, 11/20/2015 - 08:00
DOI:
10.15783/C7CG65
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License:
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Abstract 

Outdoor temperature data collected by taxis in Rome, Italy.

This dataset is to be used in conjunction with the roma/taxi dataset and provides the outdoor temperature of the areas in Rome where the taxis were located (289 taxicabs over 4 days).

date/time of measurement start: 2012-08-15

date/time of measurement end: 2014-02-04

collection environment: We simulate taxicabs as if they were equipped with temperature sensors attached to their vehicles. The city of Rome is divided into 9 areas and temperate readings were gathered from a weather service, allowing us to simulate each taxicab sending its sensed temperature to a central server every 6 hours for each area.

parent

data name: roma/taxi

note: The original dataset is the CRAWDAD roma/taxi dataset that comprises the position of each taxicab using GPS. This dataset adds the outdoor temperature of the areas that taxicabs visit during their services.

Traceset

 

temperature

Simulated outdoor temperature data collected by taxis in Rome, Italy.

  • files: crowd_temperature.csv
  • methodology: We generate a temperature value for every active taxicab by applying Gaussian distribution. To fill out the parameters of Gaussian function, we need to assign the mean mu; and standard deviation sigma; for every run. Therefore, we assign a ground truth temperature mu; for every period in every grid on every day. We use data from The Weather Network (http://www.theweathernetwork.com/) to assign the right ground truth to the right period and grid. For every taxicab, we assign a fixed error range sigma; that remains the same in all of its contributions. To do so, we randomly classify participant taxicabs into three classes. First class, called "honest", consists of taxicabs that usually sense accurate temperature within a 10% error range from the ground truth. The population of honest class is 145 taxicabs (50% of all participant taxicabs). Second class, called "dishonest", consists of taxicabs that usually sense inaccurate temperature within a 30% error range from the ground truth. The population of the dishonest class is 72 taxicabs (25%). Third class, called "misleading", consists of the rest of the participant taxicabs that is 72 (25%) that usually sense either accurate or inaccurate temperature. The data generator function makes a random decision of generating accurate or inaccurate temperature for each taxicab among the misleading class. The latter class plays a major role in the results of applying the data on a system, such as participants reputation system, since the accuracy of their contributions is not even. As a result, each taxicab has a sensed temperature contribution based on its fixed error range and the ground truth of the day, period and grid of its location.
Instructions: 

The files in this directory are a CRAWDAD dataset hosted by IEEE DataPort. 

About CRAWDAD: the Community Resource for Archiving Wireless Data At Dartmouth is a data resource for the research community interested in wireless networks and mobile computing. 

CRAWDAD was founded at Dartmouth College in 2004, led by Tristan Henderson, David Kotz, and Chris McDonald. CRAWDAD datasets are hosted by IEEE DataPort as of November 2022. 

Note: Please use the Data in an ethical and responsible way with the aim of doing no harm to any person or entity for the benefit of society at large. Please respect the privacy of any human subjects whose wireless-network activity is captured by the Data and comply with all applicable laws, including without limitation such applicable laws pertaining to the protection of personal information, security of data, and data breaches. Please do not apply, adapt or develop algorithms for the extraction of the true identity of users and other information of a personal nature, which might constitute personally identifiable information or protected health information under any such applicable laws. Do not publish or otherwise disclose to any other person or entity any information that constitutes personally identifiable information or protected health information under any such applicable laws derived from the Data through manual or automated techniques. 

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Citation:

Mohannad A. Alswailim, Hossam S. Hassanein, Mohammad Zulkernine, queensu/crowd_temperature, https://doi.org/10.15783/C7CG65 , Date: 20151120

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