CRAWDAD metadata: dartmouth/cenceme (v. 2008-08-13)

CenceMe is a sensing system based on standard and sensor-enabled mobile phones. CenceMe uses the output of the phones' sensors and external data (if such is available) to infer human presence and activity information. This dataset contains movements and inferred activities of participants using CenceMe on their mobile phones.
[xml metadata]

Note: This metadata was prepared by the CRAWDAD team and verified by the data set (or tool) authors. We have made every effort to ensure its accuracy, but urge all users to consider the metadata and data carefully and be sure that their use in research is consistent with the nature and limitations of the data. We welcome any corrections. This metadata was prepared based on the following reference(s):

    CRAWDAD metadata structure[what is CRAWDAD metadata]

    [Dataset] dartmouth/cenceme (v. 2008-08-13)

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    version v. 2008-08-13
    changes 
    the initial version
    bibtex 
    @MISC{dartmouth-cenceme-2008-08-13,
  author = {Mirco Musolesi and Mattia Piraccini and Kristof Fodor and Antonio Corradi and Andrew Campbell},
  title = {{CRAWDAD} data set dartmouth/cenceme (v. 2008-08-13)}, 
  howpublished = {Downloaded from http://crawdad.cs.dartmouth.edu/dartmouth/cenceme},
  month = aug,  
  year = 2008
}
    metadata last modified2010-08-30
    summary
    CenceMe is a sensing system based on standard and sensor-enabled mobile phones.
CenceMe uses the output of the phones' sensors and external data (if such is 
available) to infer human presence and activity information. This dataset 
contains movements and inferred activities of participants using CenceMe on 
their mobile phones.
    release date2008-08-13
    measurement start 2008-07-28
    measurement end 2008-08-11
    authorsMirco Musolesi
    Mattia Piraccini
    Kristof Fodor
    Antonio Corradi
    Andrew Campbell
    web site http://www.crawdad.org/dartmouth/cenceme
    wiki go to the wiki page for this data set
    keywordsensor network, GPS, location
    measurement purposesUser Mobility Characterization
    Location-aware Computing
    Positioning Systems
    Localization
    Social Network Analysis
    Human Behavior Modeling
    Energy-efficient Wireless Network
    Content Distribution Evaluation
    network typesensor network
    environment
    CenceMe is a personal sensing system, which uses sensor
data gathered using mobile devices (e.g. sensor-enabled cell phones) to learn 
about the activities of their carriers.
    network
    The dataset was collected during the deployment of a 
modified version of the CenceMe application, CenceMeLite, that logged 
all the sensed information and high-level inferred activities on the 
phone's on-board flash memory.
    collection
    The phones recorded information about the system and raw 
data from accelerometer and GPS devices.
    limitation
    Some users did not use the phone much and thus did not collect 
useful data.
    tracesets included dartmouth/cenceme/cencemelite (v. 2008-08-13)

    [Traceset] dartmouth/cenceme/cencemelite (v. 2008-08-13)

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    version v. 2008-08-13
    changes 
    the initial version.
    bibtex 
    @MISC{dartmouth-cenceme-cencemelite-2008-08-13,
  author = {Mirco Musolesi and Mattia Piraccini and Kristof Fodor and Antonio Corradi and Andrew Campbell},
  title = {{CRAWDAD} trace set dartmouth/cenceme/cencemelite (v. 2008-08-13)}, 
  howpublished = {Downloaded from http://crawdad.cs.dartmouth.edu/dartmouth/cenceme/cencemelite},
  month = aug,  
  year = 2008
}
    metadata last modified2010-08-30
    summary
    The data were collected by means of 20 Nokia N95 phones carried 
by students and staff members from the departments of Computer Science and 
Biology at Dartmouth College.
    release date2008-08-13
    measurement start 2008-07-28
    measurement end 2008-08-11
    measurement purposesUser Mobility Characterization
    Location-aware Computing
    Positioning Systems
    Localization
    Social Network Analysis
    Human Behavior Modeling
    Energy-efficient Wireless Network
    Content Distribution Evaluation
    methodology
    The dataset includes the following information for 
	each user: accelerometer raw data and GPS location coordinates.
    download urlDownload (252MB directory) from US UK AU
    download urlDownload (75MB rar)
    (MD5 Hash: 997387492cb240088440d99248b82e7f) from US UK AU
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    (MD5 Hash: efdfded11a0712315d35dcd5fbc36583) from US UK AU
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    (MD5 Hash: ea0aa3736467484c77ccb8c29df18213) from US UK AU
    download urlDownload (164KB pdf)
    (MD5 Hash: a84f0973d56c1b76c8907ec1b0a88773) from US UK AU
    parent datadartmouth/cenceme (v. 2008-08-13)
    traces included dartmouth/cenceme/cencemelite/raw (v. 2008-08-13)

    [Trace] dartmouth/cenceme/cencemelite/raw (v. 2008-08-13)

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    version v. 2008-08-13
    changes 
    the initial version
    bibtex 
    @MISC{dartmouth-cenceme-cencemelite-raw-2008-08-13,
  author = {Mirco Musolesi and Mattia Piraccini and Kristof Fodor and Antonio Corradi and Andrew Campbell},
  title = {{CRAWDAD} trace dartmouth/cenceme/cencemelite/raw (v. 2008-08-13)}, 
  howpublished = {Downloaded from http://crawdad.cs.dartmouth.edu/dartmouth/cenceme/cencemelite/raw},
  month = aug,  
  year = 2008
}
    metadata last modified2010-08-30
    summary
    The CenceMeLite traces were collected from 2008-07-28 to 
2008-08-11 by students and staff members at Dartmouth College.
    derivedfalse
    release date2008-08-13
    measurement start 2008-07-28
    measurement end 2008-08-11
    format
    In the files there are both information lines (about system 
configuration) and data lines. Information lines are written when the system is
started. 

All lines (except for the preamble written at the start of the system) start 
with a timestamp followed either by the keyword INFO (for information lines) or
DATA (for data lines).

The preamble written when the system starts consists of the following three
lines:

--------------------------------------------
----------------- NEXT LOG -----------------
-------------------------------------------- 

Data lines can be distinguished with the keywords: ACC, ACT, GPS.

ACC lines contain accelerometer raw data in the format:
Timestamp DATA (0) - ACC: Xacc,Yacc,Zacc*Xacc,Yacc,Zacc*...
where: 
  - Timestamp is the time when the line has been written into the log file. 
  - Xacc, Yacc, Zacc are respectively the accelerations on the three axes. 
    Every accelerometer sample is separated by the symbol *.

ACT lines contain information about inferred activities in the following format:
Timestamp DATA (0) - ACT: AccSamplingStart,AccSamplingEnd, Fact, 
where: 
   - Timestamp is the time when the line has been written into the log file.
   - AccSamplingStart is the time the accelerometer starts the sampling.
   - AccSamplingEnd is the time the accelerometer starts the sampling.
The codes of the different facts ("Fact" field) are the following:

Sitting: 0
Running: 1
Walking: 2
Standing: 5

"AccSamplingStart" and "AccSamplingEnd" are the start and end times of the
interval during which the accelerometer data used for classification of that
particular activity were collected.

GPS lines are of 3 types:
   - No samples 
   - N samples 
   - the string "GPS-Skipped: user sitting".
    parent datadartmouth/cenceme/cencemelite (v. 2008-08-13)

    [Author] Mirco Musolesi

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    emailmirco@cs.st-andrews.ac.uk
    institutionUniversity of St. Andrews
    departmentSchool of Computer Science
    positionLecturer
    addressNorth Haugh St. Andrews, Fife KY16 9SX United Kingdom
    phone+44 (0) 1334 463335
    fax
    web site http://www.cs.st-andrews.ac.uk/~mirco/
    related data/toolsdartmouth/cenceme (v. 2008-08-13)

    [Author] Mattia Piraccini

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    emailmattia.piraccini@studio.unibo.it
    institutionUniversity of Bologna
    departmentDipartimento di Informatica, Elettronica e Sistemistica (DEIS)
    position
    addressV.le Risorgimento, 2 -- 40136 Bologna -- Italy
    phone
    fax
    web site http://pira83.altervista.org/
    related data/toolsdartmouth/cenceme (v. 2008-08-13)

    [Author] Kristof Fodor

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    emailKristof.Fodor@ericsson.com
    institutionEricsson Research
    departmentTraffic Lab
    position
    addressEricsson Research, Traffic Lab, P.O. Box 3, 1300 Budapest, Hungary
    web site
    related data/toolsdartmouth/cenceme (v. 2008-08-13)
    dartmouth/zigbee_radio (v. 2008-01-07)

    [Author] Antonio Corradi

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    emailantonio.corradi@unibo.it
    institutionUniversity of Bologna
    departmentDipartimento di Informatica, Elettronica e Sistemistica (DEIS)
    positionFull professor
    addressV.le Risorgimento, 2 -- 40136 Bologna -- Italy
    phone+39 051 20 93083
    fax+39 051 20 93073
    web site http://www.lia.deis.unibo.it/Staff/AntonioCorradi/
    related data/toolsdartmouth/cenceme (v. 2008-08-13)

    [Author] Andrew Campbell

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    emailcampbell@cs.dartmouth.edu
    institutionDartmouth College
    departmentDepartment of Computer Science
    positionProfessor
    addressDepartment of Computer Science, Dartmouth College, 6211 Sudikoff Laboratory, Hanover, NH 03755-3510 USA
    phone(603)-646-8712
    fax(603)-646-1672
    web site http://www.cs.dartmouth.edu/~campbell
    related data/toolsdartmouth/cenceme (v. 2008-08-13)

    [Paper] musolesi-supporting

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    category inproceedings
    authorsMirco Musolesi
    Mattia Piraccini
    Kristof Fodor
    Antonio Corradi
    Andrew T. Campbell
    titleSupporting Energy-Efficient Uploading Strategies for Continuous Sensing Applications on Mobile Phones
    booktitleProceedings of the 8th International Conference on Pervasive Computing (Pervasive 2010)
    pages355-372
    year2010
    editorP. Floréen and A. Krüger and M. Spasojevic
    volume6030
    seriesLecture Notes in Computer Science
    addressGermany
    month--05--
    publisherSpringer-Verlag
    download urlhttp://www.cs.st-andrews.ac.uk/~mirco/papers/Pervasive10.pdf
    abstract
    Continuous sensing applications (e.g., mobile social networking applications) 
are appearing on new sensor-enabled mobile phones such as the Apple iPhone, 
Nokia and Android phones. These applications present significant challenges to 
the phone's operations given the phone's limited computational and energy 
resources and the need for applications to share real-time continuous sensed 
data with back-end servers. System designers have to deal with a trade-off 
between data accuracy (i.e., application fidelity) and energy constraints in 
the design of uploading strategies between phones and back-end servers. In this 
paper, we present the design, implementation and evaluation of several 
techniques to optimize the information uploading process for continuous sensing 
on mobile phones. We analyze the cases of continuous and intermittent 
connectivity imposed by low-duty cycle design considerations or poor wireless 
network coverage in order to drive down energy consumption and extend the 
lifetime of the phone. We also show how location prediction can be integrated 
into this forecasting framework. We present the implementation and the 
experimental evaluation of these uploading techniques based on measurements 
from the deployment of a continuous sensing application on 20 Nokia N95 phones 
used by 20 people for a period of 2 weeks. Our results show that we can make 
significant energy savings while limiting the impact on the application 
fidelity, making continuous sensing a viable application for mobile phones. For 
example, we show that it is possible to achieve an accuracy of 80\% with 
respect to ground-truth data while saving 60\% of the traffic sent 
over-the-air.
    keywordsmeasurement
    keywordswireless
    keywordsdartmouth_cenceme
    keywordscrawdad
    related data/toolsdartmouth/cenceme