I didn't understand how to configure the network topology for TOSSIM.
The snippet of Python code create seven nodes.
for i in range(0, 7):
t.getNode(i).createNoiseModel()
How can I relate this nodes with the network topology?
Should I set the Channel, Radio and Topology parameters in the Python?
Thanks all
On Thu, Jun 5, 2008 at 12:14 PM, Tal Rusak <[EMAIL PROTECTED]> wrote:
> Hi,
> There are two components to the link model in TOSSIM 2.0.2: noise and
> gain. Noise is the external interference and noise from sources other
> than the TinyOS network, which gain is the strength at which nodes in
> the network can hear packets that other nodes send. The probability
> of receiving a packet is given by the signal to noise ratio, which is
> gain - noise in the dB scale considered here. This is explained in
> http://sing.stanford.edu/pubs/ipsn07-noise.pdf
> TOSSIM currently considers that gain is constant per node pair, and
> network topologies can be constructed using the tool discussed at
> http://www.tinyos.net/tinyos-2.x/doc/html/tutorial/usc-topologies.html
> The TOSSIM tutorial shows how to use python to parse this tool's output
> and to input the gain values to the simulator. Search for "topo.txt" in
> this entry:
> http://docs.tinyos.net/index.php/TOSSIM#Configuring_a_Network
> Regarding the noise values, you can collect noise samples from your
> environment using
> tinyos-2.x-contrib/stanford-sing/apps/RssiSample
> To create an artificial "low noise" environment, you can create a
> text file with very low values. If your values in the noise file are
> all at least 14 below the values used for gain, then all packets are
> received. Just be sure to have at least 100 entries in the file.
>
> Thanks,
> Tal
>
>> Hello
>>
>> How can I use a topology file in TOSSIM
>> (http://www.tinyos.net/dist-2.0.0/tinyos-2.x/doc/html/tutorial/usc-topologies.html)
>> once I have the .out
>> files?
>>
>> The Tossim wiki show how to integrate a text file noise model like
>> heavy-meyer but I didn't find how to use this outpouts.
>>
>> best regards
>>
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>>
>
>
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