Aneeta,
My "gorilla and mouse" analogies were referring to the magnitude of
the disturbance and also to its time signature. Are you only
interested in the large disturbance which is abrupt (the gorilla)?
Or do you also want to be able to detect the more surreptitious
attack which may be quite gradual (the mouse)?
You will want to define the magnitude (and perhaps the associated
duration) of the smallest disturbance that would be important. I
would look at the entire data set to see what would be the
likelihood of detecting such a change given the noise in the
temperature data. Or alternatively, use the global analysis to
help define the minimum disturbance that could be detected.
Then see what can be done with just the first 7 days of data (or
for matter the past 7 days regardless of when they occur).
I applaude your goal of looking at each sensor without referring to
other nodes but I think I would develop the analysis by looking for
anomalies in one sensor's data when compared with other sensors and
then focusing on those periods to determine an approach for
detecting a disturbance.
Because you are looking at 7 days, should we assume that you expect
a day-of-week dependence? If so, I'd be more comfortable if you
used more than one week to develop it.
I fear that you've gotten me quite interested in this analysis,
good luck.
Clint
--
Clint Bowman INTERNET: cl...@ecy.wa.gov
Air Quality Modeler INTERNET: cl...@math.utah.edu
Department of Ecology VOICE: (360) 407-6815
PO Box 47600 FAX: (360) 407-7534
Olympia, WA 98504-7600
On Sun, 25 Oct 2009, Aneeta wrote:
Thank you everyone for all the responses.
Clint you are correct in assuming that the problem deals with sensors in a
lab setup which can be assumed to be isolated from outside temperature
changes. And, I am only dealing with temperature so the other parameters are
not important.
There will be no gorillas or mouses in the picture but rather some malicious
attacker who would try to cause disturbances in the normal readings. That is
why it is important to have an equation that defines 'normal behaviour'.
The data-sets contain readings for multiple days. I want to take the first 7
days for each node and establish a relationship between time(column 2) and
temperature(column 4).
My objective is not to model temperature variation throughout the year and
take into consideration climatic changes. Rather, it is to define a model
for the given data which happens to be temperature recorded by nodes. In a
simple way we may look at it as a set of X(time) and Y(temperature) values
where I am trying to define Y in terms of X.
How should I approach this problem?
Many Thanks,
Aneeta
Clint Bowman wrote:
Aneeta,
If I understand the figure at
<http://db.csail.mit.edu/labdata/labdata.html> this problem deals
with sensors in a lab that is probably isolated from outdoor
temperature changes.
I assume the predictive model must detect when a "rampaging 800
pound gorilla" messes with a sensor. Do we also have to detect the
pawing of a "micro-mouse" as well?
The collected data also seem to have other parameters which would
be valuable--are you limited to just temperature?
Clint
--
Clint Bowman INTERNET: cl...@ecy.wa.gov
Air Quality Modeler INTERNET: cl...@math.utah.edu
Department of Ecology VOICE: (360) 407-6815
PO Box 47600 FAX: (360) 407-7534
Olympia, WA 98504-7600
On Thu, 22 Oct 2009, Thomas Adams wrote:
Aneeta,
You will have to have a seasonal component built into your model, because
the
seasonal variation does matter, particularly -where- you are
geographically
(San Diego, Chicago, Denver, Miami are very different). Generally, there
is a
sinusoidal daily temperature variation, but frontal passages and
thunderstorms, etc., can and will disrupt this nice pattern. You may have
to
tie this into temperature predictions from a mesoscale numerical weather
prediction model. Otherwise, you will end up with lots of misses and
false
alarms…
Regards,
Tom
Aneeta wrote:
The data that I use has been collected by a sensor network deployed by
Intel.
You may take a look at the network at the following website
http://db.csail.mit.edu/labdata/labdata.html
The main goal of my project is to simulate a physical layer attack on a
sensor network and to detect such an attack. In order to detect an
attack
I
need to have a model that would define the normal behaviour. So the
actual
variation of temperature throughout the year is not very important out
here.
I have a set of data for a period of 7 days which is assumed to be the
correct behaviour and I need to build a model upon that data. I may
refine
the model later on to take into account temperature variations
throughout
the year.
Yes I am trying to build a model that will predict the temperature just
on
the given time of the day so that I am able to compare it with the
observed
temperature and determine if there is any abnormality. Each node should
have
its own expectation model (i.e. there will be no correlation between
the
readings of the different nodes).
Steve Lianoglou-6 wrote:
Hi,
On Oct 21, 2009, at 12:31 PM, Aneeta wrote:
Greetings!
As part of my research project I am using R to study temperature
data
collected by a network. Each node (observation point) records
temperature of
its surroundings throughout the day and generates a dataset. Using
the
recorded datasets for the past 7 days I need to build a prediction
model for
each node that would enable it to check the observed data against
the
predicted data. How can I derive an equation for temperature using
the
datasets?
The following is a subset of one of the datasets:-
Time Temperature
07:00:17.369668 17.509
07:03:17.465725 17.509
07:04:17.597071 17.509
07:05:17.330544 17.509
07:10:47.838123 17.5482
07:14:16.680696 17.5874
07:16:46.67457 17.5972
07:29:16.887654 17.7442
07:29:46.705759 17.754
07:32:17.131713 17.7932
07:35:47.113953 17.8324
07:36:17.194981 17.8324
07:37:17.227013 17.852
07:38:17.809174 17.8618
07:38:48.00011 17.852
07:39:17.124362 17.8618
07:41:17.130624 17.8912
07:41:46.966421 17.901
07:43:47.524823 17.95
07:44:47.430977 17.95
07:45:16.813396 17.95
I think you/we need much more information.
Are you really trying to build a model that predicts the temperature
just given the time of day?
Given that you're in NY, I'd say 12pm in August sure feels much
different than 12pm in February, no?
Or are you trying to predict what one sensor readout would be at a
particular time given readings from other sensors at the same time?
Or ... ?
-steve
--
Steve Lianoglou
Graduate Student: Computational Systems Biology
| Memorial Sloan-Kettering Cancer Center
| Weill Medical College of Cornell University
Contact Info: http://cbio.mskcc.org/~lianos/contact
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