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Noise data production and analysis

This project was developed as main assignment for the course of Middleware Technologies for Distributed Systems at Politecnico di Milano during academic year 2021-22.

Index

Documents

Project-related documents are available at the following links:

Tested configuration

The project has been carefully tested under the following configuration; all the system setup information, thus, refers to the configuration which is mentioned hereby:

  • A virtual machine for the ContikiNG part (running on a Linux machine)
  • A Windows computer to run the MPI simulation
  • A Windows computer to host Node-RED, Spark and Kafka sections.

System setup

Contiki-NG

Contiki-NG was run on a Linux virtual machine and can be easily downloaded from the neslab repository; the official repository is partially supported, however the newest versions might not be compatible, especially with the introduction of MQTT v.5.

Additional required software

  • Mosquitto, to access a remote broker
  • Apache Ant
  • JAVA 11 or newer versions
  • Gcc compiler
  • Make tool on linux

Mosquitto configuration

  • Modify the configuration file of mosquitto with sudo nano /etc/mosquitto/mosquitto.conf by adding:
    • connection bridge-01
    • address mqtt.neslab.it:3200
    • topic # out 0
    • topic # in 0
  • Save configuration file
  • Restart the service typing sudo service mosquitto restart

Launching the simulation

  • Move the content of Project1-Contiki inside your contiki installation folder (from now on referenced as CONTIKI)
  • Type in the shell CONTIKI/tools/cooja && ant run to start the Cooja simulator
  • Load the CONTIKI/Project1-Contiki/project_simulation_1.csc and compile the motes
  • Right-click on the border-router mote or mote_1 and click on "mote tools for contiki -> Serial Socket (server)"; on the window that pops up check that the listen port is 60001 then press start
  • Start the simulation
  • Open a new terminal and in the CONTIKI/Project1-Contiki/rpl-border-router directory insert the command make TARGET=cooja connect-router-cooja; this will connect the simulated border router.

After about 30-40 seconds the motes will start to connect and send data.

Advice: we recommend having the Node-RED flows already up and running and to attempt the connection of the border router once the mote is actually started in order to avoid bugs.

MPI

There are several open source implementation; for instance, the project was developed using Open MPI which is open source and can be downloaded from its official website. In order to compile and run program for MPI on Windows, WSL is needed. Then follow the instruction on the official website. To launch the program:

  • Compile the code using the command: mpic++ -o main Main.cpp Simulator.cpp Simulator.h SimulationParameters.h -lmosquitto
  • Run the program with the requested parameters, for example: mpirun -np 2 ./main 1000 1000 16 16 60 80 2 4 5.0 14.0 15 100 41.903641 12.466195

The compiling process requires the download and installation of the mosquitto library from the official site website and its linking during compilation.

To shut down the system, simply press Ctrl + C in the WSL terminal.

Node-RED

Node-RED was run natively on a Windows machine; to install it, follow the instructions provided on the Node-RED website. You should be able to run it, then, just by typing the node-red command in the cmd shell. To stop the program, just press Ctrl + C from the shell in which Node-RED is running.

Spark

Due to this technology's limitations, Spark was run on WSL, instead of natively on the machine. A good introduction to Apache Spark on WSL can be found here. To launch the Spark program from the WSL, head to Spark's directory, then:

  • Launch a master with the command: ./sbin/start-master.sh;
  • Launch a worker with the command: ./sbin/start-worker.sh spark://localhost:70771;
  • Submit the project jar with the command:

./bin/spark-submit --packages org.apache.spark:spark-sql-kafka-0-10_2.12:3.2.1 --class it.polimi.mwtech2022.project1.DataAnalysis /path/to/project/target/project1-spark-1.0.jar2

To shut the system down:

  • Press Ctrl + C in the WSL terminal from which you submitted the project;
  • Stop the worker with the command: ./sbin/stop-worker.sh spark://localhost:70771;
  • Stop the master with the command: ./sbin/start-master.sh;

Kafka

Apache Kafka broker, instead, was run on a Docker environment as the project uses Kafka's log compaction feature, which on Windows tends to throw errors upon cleaning. A full guide to its installation can be found here. Following the aforementioned guide, the broker was launched starting the powershell in the downloaded directory and providing the command

docker-compose -f .\zk-single-kafka-single.yml up

The program can be then gently shut down pressing Ctrl + C.

Troubleshooting

  • In case you haven't deployed the Spark master with the default configuration, the project couldn't be submitted; that's due to the fact that the project gets the path to the Spark master from its own configuration file, called settings.json. To fix this issue, just replace the Spark master's location in the configuration file with your actual deployment location.

Contributors

Footnotes

  1. Spark master's location depends on your Spark settings; here the default settings were used, so the master was started at localhost:7077 ↩ ↩2

  2. Replace /path/to/project/ with the actual path of the project directory ↩