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README.md

WMEWMA link quality estimator

Window mean with exponentially weighted moving average (WMEWMA) link quality estimator proposed by A. Woo et al. in paper Taming the Underlying Challenges of Reliable Multihop Routing in Sensor Networks implemented as a simple Python script.

Components

Different types of WMEWMA link quality estimator are implemented in the following fashion:

  • wmewma_real_time.py - Simulation of WMEWMA estimator in real time.
  • wmewma_static.py - WMEWMA estimator that works on static data (e.g. data files).
  • wmewma_trace1_py - Parallel real time simulation of WMEWMA estimator on Rutgers dataset (trace 1).

Getting Started

These instructions will get you a copy of the WMEWMA link quality estimator up and running on your local machine for development and testing purposes. The rest of this section assumes a Linux (namely Linux Mint) machine is being used.

Prerequisites

WMEWMA link quality estimator requires Pyton 2.7. One way of installing Python 2.7:

sudo apt-get install python

Real-time WMEWMA

Simulation of WMEWMA estimator in real time.

Configuration

Real-time WMEWMA can have the following parameters altered (in the script):

  • ALPHA - Controls the history of the estimator, range [0, 1].
  • MIN_DATA_FREQUENCY - Minimum message rate, one packet per x miliseconds, e.g. 10000 (1 packet / 10 seconds).
  • ESTIMATE_TO_DATA_RATIO - Defines time window, e.g. 20 means estimation is performed every 20 times MIN_DATA_FREQUENCY.

Usage

Running the script:

python wmewma_real_time.py

The script will now read packets' sequence numbers from standard input and calculate the estimations. We can connect the input of WMEWMA estimator to output of some other command via a pipeline.

Static WMEWMA

WMEWMA estimator that works on static data (e.g. data files).

Configuration

Static WMEWMA can have the following parameters altered (in the script):

  • ALPHA - Controls the history of the estimator, range [0, 1].
  • PACKETS_PER_WINDOW - A number of lines to read from a file at once.
  • FILE - Input file with data.
Input file format

Each row of the input file must have two values separated by a blank space: a sequence number and a measurement, e.g.:

0 -55
1 -55
3 -56
5 -55

Usage

Running the script:

python wmewma_static.py

The script will now read packets' sequence numbers from the specified file and calculate the estimations.

Trace 1 WMEWMA

Parallel real time simulation of WMEWMA estimator on Rutgers dataset (trace 1).

Configuration

Trace 1 WMEWMA can have the following parameters altered (in the script):

  • ALPHA - Controls the history of the estimator, range [0, 1].
  • MIN_DATA_FREQUENCY - Minimum message rate, one packet per x miliseconds, e.g. 10000 (1 packet / 10 seconds).
  • ESTIMATE_TO_DATA_RATIO - Defines time window, e.g. 20 means estimation is performed every 20 times MIN_DATA_FREQUENCY.
  • SLEEP - Time in seconds between packet "transmissions".

Usage

Running the script:

python wmewma_trace1.py

The script will now simulate real time transmission of packets to the estimator, calculate the estimations and output the estimations to a file. We can run multiple instances of this script simultaneously.

Author and license

WMEWMA link quality estimator scripts were written by Timotej Gale, timotej.gale@gmail.com.

Copyright (C) 2017 SensorLab, Jožef Stefan Institute http://sensorlab.ijs.si

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses

Acknowledgement

The research leading to these results has received funding from the European Horizon 2020 Programme project eWINE under grant agreement No. 688116.