Estudo e desenvolvimento de algoritmos criptográficos para redes de sensores sem fio, utilizando técnicas de programação genética

Cryptography is the main form to obtain security in any network. Even in networks with great energy consumption restrictions, processing and memory limitations, as the Wireless Sensors Networks (WSN), this is no different. Aiming to improve the cryptography performance, security and the lifetime of these networks, we propose a new cryptographic algorithm developed through the Genetic Programming (GP) techniques. For the development of the cryptographic algorithm’s fitness criteria, established by the genetic GP, nine new cryptographic algorithms were tested: AES, Blowfish, DES, RC6, Skipjack, Twofish, T-DES, XTEA and XXTEA. Starting from these tests, fitness functions was build taking into account the execution time, occupied memory space, maximum deviation, irregular deviation and correlation coefficient. After obtaining the genetic GP, the CRYSEED and CRYSEED2 was created, algorithms for the 8-bits devices, optimized for WSNs, i.e., with low complexity, few memory consumption and good security for sensing and instrumentation applications.

Estudo e desenvolvimento de algoritmos criptográficos para redes de sensores sem fio, utilizando técnicas de programação genética

Cryptography is the main form to obtain security in any network. Even in networks with great energy consumption restrictions, processing and memory limitations, as the Wireless Sensors Networks (WSN), this is no different. Aiming to improve the cryptography performance, security and the lifetime of these networks, we propose a new cryptographic algorithm developed through the Genetic Programming (GP) techniques. For the development of the cryptographic algorithm’s fitness criteria, established by the genetic GP, nine new cryptographic algorithms were tested: AES, Blowfish, DES, RC6, Skipjack, Twofish, T-DES, XTEA and XXTEA. Starting from these tests, fitness functions was build taking into account the execution time, occupied memory space, maximum deviation, irregular deviation and correlation coefficient. After obtaining the genetic GP, the CRYSEED and CRYSEED2 was created, algorithms for the 8-bits devices, optimized for WSNs, i.e., with low complexity, few memory consumption and good security for sensing and instrumentation applications.

Análise tribo-termodinâmica da aplicação de nanopartículas de Al2O3 como aditivo lubrificante em compressor hermético de refrigeração

The thermodynamic performance of a refrigeration system can be improved by reducing the compression work by a particular technique for a specific heat removal rate. This study examines the effect of small concentrations of Al2O3 (50 nm) nanoparticles dispersion in the mineral oil based lubricant on the: viscosity, thermal conductivity, and lubrication characteristics as well as the overall performance (based on the Second Law of Thermodynamics) of the refrigerating system using R134a or R600a as refrigerants. The study looked at the influences of variables: i) refrigerant charge (100, 110, 120 and 130 g), ii) rotational speed of the condenser blower (800 and 1100 RPM) and iii) nanoparticle concentration (0.1 and 0.5 g/l) on the system performance based on the Taguchi method in a matrix of L8 trials with the criterion "small irreversibility is better”. They were carried pulldown and cycling tests according to NBR 12866 and NBR 12869, respectively, to evaluate the operational parameters: on-time ratio, cycles per hour, suction and discharge pressures, oil sump temperature, evaporation and condensation temperatures, energy consumption at the set-point, total energy consumption and compressor power. In order to evaluate the nanolubricant characteristics, accelerated tests were performed in a HFRR bench. In each 60 minutes test with nanolubricants at a certain concentration (0, 0.1 and 0.5 g/l), with three replications, the sphere (diameter 6.00 ± 0.05 mm, Ra 0.05 ± 0.005 um, AISI 52100 steel, E = 210 GPa, HRC 62 ± 4) sliding on a flat plate (cast iron FC200, Ra <0.5 ± 0.005 um) in a reciprocating motion with amplitude of 1 mm, frequency 20 Hz and a normal load of 1,96 N. The friction coefficient signals were recorded by sensors coupled to the HFRR system. There was a trend commented bit in the literature: a nanolubricant viscosity reduction at the low nanoparticles concentrations. It was found the dominant trend in the literature: increased thermal conductivity with increasing nanoparticles mass fraction in the base fluid. Another fact observed is the significant thermal conductivity growth of nanolubricant with increasing temperature. The condenser fan rotational speed is the most influential parameter (46.192%) in the refrigerator performance, followed by R600a charge (38.606%). The Al2O3 nanoparticles concentration in the lubricant plays a minor influence on system performance, with 12.44%. The results of power consumption indicates that the nanoparticles addition in the lubricant (0.1 g/L), together with R600a, the refrigerator consumption is reduced of 22% with respect to R134a and POE lubricant. Only the Al2O3 nanoparticles addition in the lubricant results in a consumption reduction of about 5%.