Find out how ICT can support biomedical and clinical researchFind out more. From Clever cars to clever farms... Embedded Systems
nsmisc

Miscellaneous ns-2-related things




Patches

Here is a quick list of the various patches for NS-2 available here.

Scripts

  • NsAggregator is a Python object collection (and wrapper script) that can be used to parse NS-2 trace file. This is really the first working version and it still lacks heaps of features, but can be used as is. It basically works for me at the mooment, but may definitely not be suited to your needs unless you have exactly the same as I do. It may be worth the try, though. FIXME: it currently works only with traces from MobiWAN or at least with Hierarchical routing enabled. The line-capturing regexp is too strict. The script expects 2 arguments, and an additional 4 per node to be monitored.
    nsaggregator.py FILENAME|- GRANULARITY NODE EVENT_RE PKT_TYPE_RE WITH_POS [NODE EVENT_RE PKT_TYPE_RE WITH_POS ...]
    For example, to extract both the TCP and DCCP (DCCP_Data and DCCP_DataAck) traffics received (r) by node 1 (but reporting the node's position only once per row), NsAggregator would be used as follows.
    nsaggregator.py dccp-simple.tr .5 r 1 tcp yes 1 r DCCP_Data.* no > dccp-simple.dat
    This will examine tracefile dccp-simple.tr every half a second for tcp traffic to node 1, and report 4 columns being the average bandwidth over the last interval, the cumulative average, the number of packets, and the cumulative number of packets, as well as the geographical location of the node (if available). At the same time traffic which name match regexp DCCP_Data.* to node 1 will also be reported in a similar manner, without the geographical location of the node. Following is a sample result.
    #time   #1 tcp BW (Mbps)        AVG     Pkts    Pkts tot	X	Y	#1 DCCP_Data.* BW (Mbps)        AVG     Pkts Pkts tot
    0.5     6.38000488281   6.38000488281   403     403	  1.00	400.00     0.112060546875  0.112060546875  30      30
    1.0     7.58544921875   6.98272705078   478     881	  1.00	400.00     0.102630615234  0.107345581055  27      57
    1.5     7.58544921875   7.1836344401    478     135	  1.00	400.009    0.0989379882812 0.10454305013   26      83
    2.0     7.58544921875   7.28408813477   478     183	  1.00	400.007    0.1064453125    0.105018615723  28      111
    2.5     7.58544921875   7.34436035156   478     231	  1.00	400.005    0.106262207031  0.105267333984  28      139
    3.0     7.60131835938   7.3871866862    479     279	  1.00	400.004    0.113708496094  0.106674194336  30      169
    3.5     7.60131835938   7.41777692522   479     327	  1.00	400.003    0.117340087891  0.108197893415  31      200
    4.0     7.6171875       7.44270324707   480     375	  1.00	400.003    0.113586425781  0.108871459961  30      230
    4.5     7.60131835938   7.46032714844   479     423	  1.00	400.002    0.106140136719  0.108567979601  28      258

References

  1. A. Baig, L. Libman, and M. Hassan, Performance Enhancement of On-Board Communication Networks using Outage Prediction, In IEEE Journal on Selected Areas in Communications, 24(9), September 2006.