Wireless Sensor Networks

Wireless Sensor Networks Download Area

Below you can find various material (software, experimental data, documentation, etc.) related to our research on Wireless Sensor and Actuator Networks.


RSSI data sets for multichannel RSSI-based ranging

Type: Experimental data

Description: This archive contains five matlab files that collect the RSSI measurements performed with TmoteSky sensors in different scenarios, namely Aisle, Desk, Lab, outdoor and Room. A PDF documents describes the scenarios and the data format.

Credits: SIGNET lab: Andrea Bardella, Andrea Zanella, Francesco Zorzi

Download: Archive


Getting started with TinyOS

Type: TinyOS v1 – tutorial
Description: Here some useful links for the newbies…


SYNAPSE++: a System for Reprogramming Wireless Sensor Networks based on Fountain Codes


TinyOS v2 – source code


Below you can find the TinyOS 2 source code of our SYNAPSE++ reprogramming system for wireless sensor networks (WSNs). SYNAPSE++ is a system for reprogramming over the air WSNs. In contrast to previous solutions, which implement plain NACK-based ARQ strategies, SYNAPSE++ adopts a more sophisticated error recovery approach exploiting rateless Fountain Codes (FCs). This allows it to scale considerably better in dense networks and to better cope with noisy environments. In order to speed up the decoding process and decrease its computational complexity, we engineered the FC encoding distribution through an original genetic optimization approach. Further, novel channel access and pipelining techniques have been jointly designed so as to fully exploit the benefits of Fountain Codes, mitigate the hidden terminal problem and reduce the number of collisions. All of this makes it possible for SYNAPSE++ to recover data over multiple hops through overhearing by limiting, as much as possible, the number of explicit retransmissions. We finally created new bootloader and memory management modules so that SYNAPSE++ could disseminate and load program images written using any language.

A previous version of this software is described in our IEEE SECON 2008 paper. However, SYNAPSE++ has many additional features with respect to its predecessor. In detail:

  • It implements an advanced pipelining strategy (for improved performance over multiple hops), optimized in conjunction with the selected fountain code.
  • Fountain codes and pipelining were jointly optimized so that data blocks can be recovered (with high probability) through overhearing; thus limiting as much as possible the need for explicit retransmissions.
  • The dissemination protocol was re-designed according to a novel pseudo-TDMA scheme, integrated with pipelining and rateless codes. This reduces the hidden terminal problem, limits the number of collisions among control messages and facilitates advancement towards unexplored portions of the network.
  • The bootloader was modified so that SYNAPSE++ can disseminate and load applications written in any language or O.S.

Published Papers:

  • Michele Rossi, Nicola Bui, Giovanni Zanca, Luca Stabellini, Riccardo Crepaldi and Michele Zorzi, SYNAPSE++: Code Dissemination in Wireless Sensor Networks using Fountain Codes, IEEE Transactions on Mobile Computing. Accepted for Publication.





CCMR (Cost and Collision Minimizing Routing)

Type: TinyOS v1 – source code

Description: CCMR is a lightweight integrated MAC/routing scheme for wireless sensor networks. This version of the software was written for the EYESIFXv2 sensor nodes, developed by Infineon Technologies. The full description of the protocol as well as of the mathematical analysis behind it can be found here: INFOCOM 2007 paper.

Credits: SIGNET lab: Nicola Bui, Riccardo Crepaldi, Michele Rossi

Download Download source in tar.gz format


RSSI measurements for indoor WSN

Type: Experimental data

Description: We collected a large set of experimental RSSI measurements for our sensor nodes (IFX-Eyes and Tmote-Sky) in different environments. You can download all such data, together with the description of the experimental settings and a short description of the platforms.

Credits: SIGNET lab: Giovanni Zanca, Francesco Zorzi, Andrea Bardella, Andrea Zanella