emane-node-view

The emane-node-view project is a Python Flask web app that publishes EMANE node location and link information using Flask SocketIO and Leaflet.

The emane-node-view-publisher service subscribes to OpenTestPoint measurement probes to determine node location and optionally, link connectivity. Link connectivity is determined by processing OpenTestPoint measurements using embedded Python algorithms specified via XML.

Currently Flask SocketIO and some of its dependencies are not available in all distribution repositories. For those distros, the recommended approach is to install emane-node-view using a Python virtualenv. An installation script that satisfies most use cases is provided (install-virtualenv.sh).

The follow EMANE and OpenTestPoint packages are required:

1. opentestpoint
2. opentestpoint-probe-emane
3. Other probes referenced in link detection algorithms.

Installation Instructions

Node Package Manager (npm) is used to manage JavaScript dependencies. Browserify is used to bundle all dependencies into a single JavaScript file.

Install the latest stable Node LTS

Example shown for Node LTS 10.x:

1. Installing NODE

   On Fedora/CentOS:

   $ curl -sL https://rpm.nodesource.com/setup_14.x | sudo -E bash -
   sudo dnf install nodejs
   

   On Ubuntu:

   $ curl -sL https://deb.nodesource.com/setup_14.x | sudo -E bash -
   $ sudo apt-get install nodejs
   

2. Install Browerify globally

   $ sudo npm install browserify -g
   

Install emane-node-view Dependencies

For distros with flask and flask-socketio packages available, such as Fedora >= 33:

$ sudo dnf install python3-flask python3-flask-socketio

For distros without flask and flask-socketio packages, create a Python virtualenv with flask, flask-socketio and emane-node-view installed. The virtualenv is created with --system-site-packages to allow access to EMANE and OpenTestPoint plugins that have been installed via your system package manager.

For most use cases this will be what you want. More complex system installs are possible, use install-virtualenv.sh as a guide.

The following creates a virtual environment emane-node-view in your chosen install path:

$ ./autogen.sh && ./configure && make
$ sudo ./install-virtualenv.sh  /install/path

Running emane-node-view-publisher

emane-node-view consists of the Python module emane_node_view and the emane-node-view-publisher application.

To execute with default options:

$ /install/path/emane-node-view/bin/emane-node-view-publisher config.xml

See emane-node-view-publisher -h for more information.

To view the Leaflet web front end, point your browser of choice at your_host:5000.

Configuring emane-node-view-publisher

The following XML can be used to visualize node locations and links for EMANE Tutorial demonstration 3.

The top level endpoint attribute corresponds to the OpenTestPoint broker publish endpoint to connect for subscriptions.

For each node specify:

1. nem-id: NEM id of the node.

2. color: Color to display the node. (optional)

3. label: Name of the node to display.

4. tag: The OpenTestPoint tag corresponding the node.

5. link-color: Color to display node links. (optional)

6. link-detection: The name of the link detection algorithm to use. (optional)

A node can proxy report the location of other nodes. This is useful to position nodes that are not running OpenTestPoint or that only exist for generating interference (see emane-jammer-simple). No links are reported for proxy nodes.

<emane-node-view-publisher endpoint='localhost:9002'>
  <nodes>
    <node nem-id='1'
          color='blue'
          label='Radio 1'
          tag='node-1'
          link-color='green'
          link-detection='IEEE80211abg-NeighborMetricTable'/>
    <node nem-id='2'
          color='blue'
          label='Radio 2'
          tag='node-2'
          link-color='green'
          link-detection='IEEE80211abg-NeighborMetricTable'/>
    <node nem-id='3'
          color='blue'
          label='Radio 3'
          tag='node-3'
          link-color='green'
          link-detection='IEEE80211abg-NeighborMetricTable'/>
    <node nem-id='4'
          color='blue'
          label='Radio 4'
          tag='node-4'
          link-color='green'
          link-detection='IEEE80211abg-NeighborMetricTable'/>
    <node nem-id='5'
          color='blue'
          label='Radio 5'
          tag='node-5'
          link-color='green'
          link-detection='IEEE80211abg-NeighborMetricTable'/>
    <node nem-id='6'
          color='blue'
          label='Radio 6'
          tag='node-6'
          link-color='green'
          link-detection='IEEE80211abg-NeighborMetricTable'/>
    <node nem-id='7'
          color='blue'
          label='Radio 7'
          tag='node-7'
          link-color='green'
          link-detection='IEEE80211abg-NeighborMetricTable'/>
    <node nem-id='8'
          color='blue'
          label='Radio 8'
          tag='node-8'
          link-color='green'
          link-detection='IEEE80211abg-NeighborMetricTable'/>
    <node nem-id='9'
          color='blue'
          label='Radio 9'
          tag='node-9'
          link-color='green'
          link-detection='IEEE80211abg-NeighborMetricTable'/>
    <node nem-id='10'
          color='blue'
          label='Radio 10'
          tag='node-10'
          link-color='green'
          link-detection='IEEE80211abg-NeighborMetricTable'>
      <proxy>
        <node nem-id='300' color='white' label='Ghost 300'/>
      </proxy>
    </node>
  </nodes>
  <link-detection>
    <algorithm name='IEEE80211abg-NeighborMetricTable'
               probe='EMANE.IEEE80211abg.Tables.Neighbor'
               measurement='Measurement_emane_ieee80211abg_tables_neighbor'>
      <python>
        <![CDATA[
import time
from otestpoint.emane.ieee80211abg_pb2 import Measurement_emane_ieee80211abg_tables_neighbor

measurement = Measurement_emane_ieee80211abg_tables_neighbor()

measurement.ParseFromString(_blob)

# 0: NEM          7: SINR Avg
# 1: Rx Pkts      8: SINR Stdv
# 2: Tx Pkts      9: NF Avg
# 3: Missed Pkts 10: NF Stdv
# 4: BW Util     11: Rx Rate Avg
# 5: Last Rx     12: Tx Rate Avg
# 6: Last Tx     

if _cookie == None:
    _cookie = {}

for row in measurement.neighbormetrictable.rows:
    remote_id = row.values[0].uValue
    last_rx =  row.values[5].dValue
    _cookie[remote_id] = last_rx

now = time.time()
    
for remote_id,last_rx in _cookie.items():
    if remote_id != 65535:
        if now - last_rx <= 5:
            _links.append(remote_id)
        ]]>
      </python>
    </algorithm>
  </link-detection>
</emane-node-view-publisher>

Link detection algorithms are Python code that is loaded and executed by emane-node-view-publisher. You must specify a unique algorithm name, the OpenTestPoint measurement probe to subscribe, and the measurement within the probe to process.

Six special local variables are used to communicate information to and from link detection code:

1. _blob: Serialized OpenTestPoint measurement specified with the measurement attribute. (in)

2. _name: OpenTestPoint measurement name specified with the measurement attribute. (in)

3. _tag: OpenTestPoint measurement publisher tag. (in)

4. _cookie: A variable that is passed to the link detection code to store any state information. Each instance of the link detection algorithm has a unique _cookie. (in/out)

5. _gcookie: A variable that is passed to the link detection code to store any global state information. All link detection algorithm instances share the same _gcookie. (in/out)

6. _links: A list of NEM ids or 2-tuples (NEM id, link color) that represent the links seen by the node. (out)

For the simple link detection algorithm shown, a node is considered to have a link to a remote node if that node has received an over-the-air message from the remote node in the last 5 seconds.

For heterogeneous experiments, you will need to define link detection algorithms for each radio model since the models have different probes.