[R] Temperature Prediction Model
Aneeta
anestays at cs.sunysb.edu
Thu Oct 29 21:21:00 CET 2009
Thank you Clint for your response. I am happy to know that you have gotten
interested in this analysis.:-)
Let me give you some details about sensor networks that would help you
understand my goal better. Sensor nodes run on small batteries which have
limited life. So communication amongst various nodes is kept at a minimum to
preserve battery life. Hence, I am working on a localized analysis which
each node could perform on its own.
The disturbances caused by the malicious nodes would be abrupt and for a
short span. But please note that the data set that I have supplied is from a
perfectly working sensor network which is not under any attack. The attack
model will be simulated separately. I apologize if I have given you the idea
that the data set consists of noisy data.
Ideally I would first like to build a model for the normal behaviour of each
node from the data set which is at hand. Once we are able to define the
normal behaviour I could introduce some gorillas and see if we can detect
that some node is under attack. I have this part figured out.
I am not looking at a day-of-week dependence. Rather I am looking for a
time-of-day dependence. We could take into consideration more than 7 days of
data if that gives us a stronger model. What I was hoping to do was to plot
the data for the past 7-days on top of each other to get a general idea on
how the temperature varies throughout the day and thus build an equation
which would calculate the temperature given the time of the day.
Thank you again for your help.
Best Regards,
Aneeta
Clint Bowman wrote:
>
> 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: clint at ecy.wa.gov
> Air Quality Modeler INTERNET: clint at 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: clint at ecy.wa.gov
>>> Air Quality Modeler INTERNET: clint at 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
>>>>>>
>>>>>> ______________________________________________
>>>>>> R-help at r-project.org mailing list
>>>>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>>>>> PLEASE do read the posting guide
>>>>>> http://www.R-project.org/posting-guide.html
>>>>>> and provide commented, minimal, self-contained, reproducible code.
>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>>
>>> ______________________________________________
>>> R-help at r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>> PLEASE do read the posting guide
>>> http://www.R-project.org/posting-guide.html
>>> and provide commented, minimal, self-contained, reproducible code.
>>>
>>>
>>
>>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
>
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