Javier Vales-Alonso Professor of Telematics at Department of Information and Communications, Technical University of Cartagena, Spain Email: javier.vales@upct.es / javier.vales@gmail.com Phone: +34 968 326588 |

- moon optmization WSN,
a package containing all the source codes for optimizing random WSN deployements
following the model developed for the journal paper (please, cite as such):
Parrado et al.,
"Optimal planning of WSN deployments for in situ lunar surveys,"
IEEE Transactions on Aerospace and Electronic Systems, to appear.

- RFID Markov Chain,
a package which implements the Markov Chain developed for the journal paper (please, cite as such):
Vales et al.,
"Analysis of the Power Outage Effects in RFID",
IEEE Communications Letters, vol. 21, issue 2, pp. 306-309, February 2017.

**Note 1:**This chain allows to compute**analitically**several statistics related to the interrogation process: throughput (in tags/slot), probability of full population identification after M frames, and the**mean identification time**(in slots). This chain can be easily adapted to**any FSA policy**as shown in the publication and in the notebooks. A capture effect probability function can be defined (taking the number of contenders as an argument), and also the outage probability in the downlink.

**Note 2:**Provided materials include Mathematica notebooks to performs computations (**IMPORTANT NOTE:**COMPUTATIONS USE CUDA, YOU NEED GPUs TO SUPPORT THESE OPERATIONS, A PREVIOUS VERSION WITHOUT CUDA SUPPORT IS ALSO PROVIDED BUT IT IS**MUCH SLOWER**), and validation scripts for Matlab.

- OSL,
source codes for the model developed for the journal paper (please, cite as such):
J. Vales-Alonso, F.J. Parrado-García, J.J. Alcaraz, OSL: An optimization-based scheduler for RFID Dense-Reader Environments, Ad Hoc Networks, Volume 37, 2016, Pages 512-525

- phi-computation.zip,
a package with the source codes (Mathematica notebooks) and validation scripts (matlab)
to compute the number of identifications in a given time interval for RFID
systems both using FSA and maximum throughput DFSA policies. This corresponds to
developments for the letter (please, cite as such):
Vales et al. "Analytical computation of the mean number of tag identifications during a time interval in FSA," IEEE Communications Letters, vol. 18, issue 11, pp. 1923-1926, November 2014.

**Note 1:**The Mathematica function provides formula PHI, which computes the expected identifications in a time (T), given in 0.01 ms unit, when N tags contend. For example: T=100 means 1 ms, 21 means 0.21 ms, and so on.PHI[4,1100] -> n=4 tags, t = 11 ms 2.10119

**Note 2:**Computation is very demanding and can take days or weeks in some cases. From here you can download precomputed solutions for some cases. Each file contains a matlab matrix with solutions up to a given time T, and number of tags N. Since indices start in 1 a shift is necessary to access the data. For example, you can use them just:load load phi_DFSA_T300ms_N50 >> who Your variables are: Expression1 >> size(Expression1) ans = 51 30001 Expression1(1,1) -> phi(0 tags, 0 ms) Expression1(51, 1001) -> phi(50 tags, 10.00ms)

**Note 3:**Please, see README for further details

- mt-simulated-annealing, a multi-threaded simulated annealing core, implemented in C. It allows to define any optimization program and solve it using multithreading. This development corresponds to the algorithm developed in journal paper (please, cite as such): Vales et al., "A parallel optimization approach for controlling allele diversity in conservation schemes," Mathematical Biosciences, Volume 183, Issue 2, June 2003, Pages 161-173.

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Last updated 17th November, 2017.