Free air traffic control software for linux

IP Traffic Meter is a traffic counter for IPv4 addresses. It uses the DB4 database from Berkeley to keep its counters, the pcap library for monitoring, and the gd library from Boutel to create graphics. The results are displayed in JPEG graphics on an HTML webpage.
Enhancements:
- With ipmeter you can monitor the traffic made by some IPs. It produces daily, weekly, monthly and yearly statistics into jpg graphics. It uses db4 database from Berkeley to keep its internal counters, and gd library from Boutell to create jpg graphic.

Useful Traffic Accounting Dragon is a traffic accounter that actually gives useful information about traffic instead of stupidly counting incoming/outgoing packets.
Dragon accounts traffic by user, program and timestamp and puts this information into useful correlation.
Dragon is implemented as a Perl script for the backend, using /etc/passwd to automatically get information about user accounts, /proc/net/ to get information about open connections and /proc/ to get information about running processes and which processes currently use which network connection.
All this information can be combined so you can actually see what user caused which traffic using what program and when it happened.
All this information is acquired automatically so the minimal configuration necessary is to tell dragon where it can find its MySQL database.
Enhancements:
Features:
- cleanup on CTRL-C
- add patch for Linux Kernel 2.6.17.7
- for now, graphs are disabled in the webfrontend
- webfrontend has now Total, User and Program tabs
Bugfixes:
- forgot to close a filedescriptor fixed
- use leading zeroes on port numbers in messages about connections that no longer exist
- really fix wait for next minute

Slimserver Control is a Amarok script that can control your Slimserver (play/stop) from Amarok.

Usage:

Run the script. On first run, it will ask for the Slimserver IP address, port and the Squeezebox MAC address. If in doubt for the answers, browse to your Slimserver home page, then, while playing a song, move your mouse over the "Play" button. You should see in the status bar of your browser a URL similar to this one:

http://localhost:9000/status_header.html?p0=play&player=00%3A04%3A20%3A06%3A9c%3Aaa

In this example:

Slimserver IP adddress = localhost

Slimserver port = 9000

Squeezebox MAC address = 00%3A04%3A20%3A06%3A9c%3Aaa


Form more information about Slimserver and Squeezebox, look at: www.slimdevices.com

Traffic Control Super Script implements traffic shaping for IP traffic passing through a NAT/bridge box with a single configuration file with one line per host.
Traffic Control Super Script can manage bandwidth to user-specified speeds based on the u32 classifier, and can identify traffic by source, destination, source and destination port, protocol, and ToS field. It then limits the rate of connection in either a single or a bidirectional fashion.
Enhancements:
- This release adds multiple interface support.
- It adds an option to choose between flat file and MySQL rules databases.
- It fixes bug #1469742 (duplicate group breaking child / parent relationships when direction=bi on group definitions).
- Various minor bugs have been fixed.
- There are major code cleanups, and major documentation updates on the Web site.

Wallpaper Control is a graphical wallpaper manager.

Wallpaper Control is a non-window manager-specific wallpaper manager for the X Window System.

It aims to be a full featured utility for managing and setting your images you use as wallpaper.

Features include a preview window, directory listing, and deletion of graphics from hard drive.

Linux Advanced Routing & Traffic Control HOWTO is a document concerning iproute2, traffic control (shaping), and more!

The LARTC-HOWTO describes many advanced ways of routing and shaping traffic.

Using the HOWTO, you will be able to shape traffic in myriad ways, which will help you lower latency for interactive use when others are downloading or uploading.

It will also teach you how to limit certain hosts/protocols bandwidth usage. It also explains many aspects of routing using the ip tool from the iproute2 suite.

Python Traffic Analyzer is a Python base class and sample driver script written to retrieve and manipulate images from the TrafficLand cameras and calculate a numeric value representing the current traffic flow.

PyTrAn, an example driver script, an image collector and an image mask creator are available for download from the link shown at the bottom. To use the PyTrAn package begin by choosing a camera that you wish to analyze, for this example well use the camera captioned above.

We want to construct a mask over the area of the image that we are interested in, namely the road. In this particular example the road takes up the majority of the image but that is not always the case.

We will apply the mask over captured images to fine tune the area over which we are looking for movement. To create the mask we will first need to collect a sequential series of snapshots from the target camera. The image_collector.py script was written for this task:

$ mkdir mask_200003
$ cd mask_200003
$ ../image_collector.py 200003 30
Collecting 30 images...
30

Done.

The script is hard coded to capture images on a 2-second delay. The delay is necessary to ensure the image has changed. I believe 2-seconds to be the absolute minimum. Once complete, 30 images numbered 1 through 30 will be created in the current directory.

We construct a mask from these captured images by creating a diff-image for each sequential image pair and then adding each diff-image together. Naturally, a script was written to automate this task as well:

$ ../mask_maker.py 1 30
Creating a diff for each sequential image pair.
Diffing 29

Creating the initial mask from the first image pair.

Adding the rest of the diffs to the mask.
Masking 29

Done.

A number of .diff files are generated in this process. These files repesent the movement between individual sequence pairs.

The .diff files are simply intermediary files, the important bit is the mask file, which is generated as the sum of all differences.

The mask file may be dirty (as in this case) and require manual cleanup. The basic shape of the road however is clearly visible, evidence that we can with minimal effort automate the mask generation process. Also, this run was conducted at night, day-time images yield better results.

There are a few final steps we need to take before we can use the example PyTrAn driver script. First we need to convert the mask to ASCII (noraw) format:

$ pnmnoraw mask > mask_200003.ascii

Then we need to open an ImageMagick display window and get its X-window-ID using xwininfo. Finally, update camera_id and window_id in pytran_sampling.py and launch the driver:

$ ../pytran_sampling.py
DEBUG> grabbing frame from camera 200003
DEBUG> rotating image: pytran.this > pytran.last
DEBUG> refreshing image in 3 secs
taking a 5 minute sample at various thresholds.
DEBUG> grabbing frame from camera 200003
DEBUG> generating frame diff on pytran.last, pytran.this
DEBUG> displaying image: pytran.diff
DEBUG> converting pytran.diff to ascii
DEBUG> calculating traffic ratio...
ratio[5]: 55%
DEBUG> calculating traffic ratio...
ratio[10]: 52%
...
...
5 minute sample[5]: 67.88
5 minute sample[10]: 42.66
5 minute sample[15]: 30.57
5 minute sample[20]: 23.03
5 minute sample[25]: 18.39
5 minute sample[30]: 14.79
5 minute sample[35]: 12.42
5 minute sample[40]: 10.53
5 minute sample[45]: 9.06
5 minute sample[50]: 7.85

The sampling script will take 5 minute samples at varying color thresholds. The optimal threshold must be manually chosen. Furthermore, you will need to sample the traffic ratios during both heavy and light traffic times to get a good feel for your acceptable range. Also, keep in mind that the traffic ratio value is simply the percent change detected, or in other words the movement detected within the masked region. This means that a completely empty road will register similar values to a road so congested it looks like a parking lot. The time of day can be combined with the traffic ration to determine the logical truth.

With this task implemented and abstracted more complex systems can be built. When I find the time Id like to create a system that will take multiple potential travel routes and times, and during the travel time e-mail the traveler with the best route to take. Another idea I had would be to record the traffic flow values for each camera, for each day and for each half hour interval. Travelers and other interested parties can then analyze traffic patterns to determine the fastest route dependant on date/time.