<li><b><code>gain(src, dest)</code></b>: Return the gain value of the
link from <i>src</i> to <i>dest</i>.</li>
- <li><b><code>remove(src, dest)</code></b>: Remove the link from
- <i>src</i> to <i>dest</i>.</li>
+ <li><b><code>threshold()</code></b>: Return the CCA threshold.</li>
+ <li><b><code>setThreshold(val)</code></b>: Set the CCA threshold value in
+ dBm. The default is -72dbM.</li>
+
+ </ul>
- <li><b><code>setNoise(node, mean, variance)</code></b>: Set the noise
- floor at <i>node</i> to be a gaussian distribution with
- <i>mean</i> and <i>variance</i>.</li>
+ <p>The default values for TOSSIM's radio model are based on the CC2420 radio,
+ used in the micaZ, telos family, and imote2. It uses an SNR curve derived
+ from experimental data collected using two micaZ nodes, RF shielding, and
+ a variable attenuator.</p>
+
+ <p>In addition to the radio propagation above, TOSSIM
+ also simulates the RF noise and interference a node hears, both from other
+ nodes as well as outside sources. It uses the Closest Pattern Matching (CPM)
+ algorithm. CPM takes a noise trace as input and generates a statistical model
+ from it. This model can capture bursts of interference and other correlated
+ phenomena, such that it greatly improves the quality of the RF simulation.
+ It is not perfect (there are several things it does not handle, such as
+ correlated interference at nodes that are close to one another), but
+ it is much better than traditional, independent packet loss models. For
+ more details, please refer to the paper "Improving Wireless Simulation through
+ Noise Modeling," by Lee et al.</p>
+
- <li><b><code>sensitivity()</code></b>: Return the receive sensitivity of
- the nodes.</li>
+ <p>To configure CPM, you need to feed it a noise trace. You accomplish this
+ by calling <tt>addNoiseTraceReading</tt> on a Mote object. Once you
+ have fed the entire noise trace, you must call <tt>createNoiseModel</tt>
+ on the node. The directory <tt>tos/lib/tossim/noise</tt> contains
+ sample noise traces, which are a series of noise readings, one per line.
+ For example, these are the first 10 lines of meyer-heavy.txt,
+ which is a noise trace taken from Meyer Library at Stanford University:</p>
- <li><b><code>setSensitivity(val)</code></b>: Set the receive sensitivity
- of nodes to be <i>val</i>. The sensitivity is how much
- stronger a signal must be for it to be received
- uncorrupted. E.g., a sensitivity of 3.0 (the default value)
- means that a packet must be 3dBm greater than the sum of
- noise and concurrent transmissions for it to be received
- uncorrupted.</li>
+ <pre>
+-97
+-98
+-98
+-86
+-90
+-91
+-87
+-87
+-98
+-98
+</pre>
- <li><b><code>threshold()</code></b>: Return the CCA threshold.</li>
+ <p>As you can see, the hardware noise floor is around -98 dBm,
+ but there are spikes of interference around -86dBm and -87dBm.</p>
- <li><b><code>setThreshold(val)</code></b>: Set the CCA threshold value in
- dBm.The default is -95.</li>
-
- </ul>
+ <p>This piece of code will give a node a noise model from a noise trace
+ file. It works for nodes 0-7: you can change the range appropriately:</p>
+ <pre>
+noise = open("meyer-heavy.txt", "r")
+lines = noise.readlines()
+for line in lines:
+ str = line.strip()
+ if (str != ""):
+ val = int(str)
+ for i in range(0, 7):
+ t.getNode(i).addNoiseTraceReading(val)
+
+for i in range(0, 7):
+ t.getNode(i).createNoiseModel()</pre>
+
+
+ <p>CPM can use a good deal of RAM: using the entire meyer-heavy trace as
+ input has a cost of approximately 10MB. You can reduce this overhead
+ by using a shorter trace; this will of course reduce simulation fidelity.</p>
+
<p>The Radio object only deals with physical layer
propagation. The MAC object deals with the data link layer,
packet lengths, and radio bandwidth. The default TOSSIM MAC
t.addChannel("RadioCountToLedsC", sys.stdout)
t.addChannel("Boot", sys.stdout)
+noise = open("meyer-heavy.txt", "r")
+lines = noise.readlines()
+for line in lines:
+ str = line.strip()
+ if (str != ""):
+ val = int(str)
+ for i in range(1, 4):
+ t.getNode(i).addNoiseTraceReading(val)
+
+for i in range(1, 4):
+ t.getNode(i).createNoiseModel()
+
t.getNode(1).bootAtTime(100001);
t.getNode(2).bootAtTime(800008);
t.getNode(3).bootAtTime(1800009);
-r.setNoise(1, -100.0, 5.0)
-r.setNoise(2, -100.0, 5.0)
-r.setNoise(3, -100.0, 5.0)
-
for i in range(0, 100):
t.runNextEvent()
</pre>
DEBUG (1): Received packet of length 2.
</pre>
- <p>If you set the noise to be 30 plus or minus 5 dBm instead
- of 80 plus or minus 5 dBm, then nodes will never transmit, as
- the default CCA threshold is -95 dBm. You'll see something
- like this:</p>
+ <p>If you set node's clear channel assessment to be at -110dBm,
+ then nodes will never transmit, as noise and interference never
+ drop this low. You'll see something like this:</p>
<pre>
1 2 -54.0
2 1 -55.0
file format is
<pre>
-noise n avg std
gain src dest g
</pre>
- where each statement is on a separate line. The <i>noise</i>
- statement defines the noise observed at node <i>n</i> with
- an average of <i>avg</i> and a standard deviation of
- <i>std</i>. The <i>gain</i> statement defines a propagation
+ where each statement is on a separate line.
+ The <i>gain</i> statement defines a propagation
gain <i>g</i> when <i>src</i> transmits to <i>dest</i>. This
is a snippet of python code that will parse this file
format:
if (len(s) > 0):
if (s[0] == "gain"):
r.add(int(s[1]), int(s[2]), float(s[3]))
- elif (s[0] == "noise"):
- r.setNoise(int(s[1]), float(s[2]), float(s[3]))
</pre></p>
<p>TOSSIM has a tool for the second option of generating a
projects / tinyos-2.x.git / commitdiff