Biblio

We use potential fields tuned by genetic algorithms to dynamically reconFigure unmanned aerial vehicles networks to serve user bandwidth needs. Such flying network base stations have applications in the many domains needing quick temporary networked communications capabilities such as search and rescue in remote areas and security and defense in overwatch and scouting. Starting with an initial deployment that covers an area and discovers how users are distributed across this area of interest, tuned potential fields specify subsequent movement. A genetic algorithm tunes potential field parameters to reposition UAVs to create and maintain a mesh network that maximizes user bandwidth coverage and network lifetime. Results show that our evolutionary adaptive network deployment algorithm outperforms the current state of the art by better repositioning the unmanned aerial vehicles to provide longer coverage lifetimes while serving bandwidth requirements. The parameters found by the genetic algorithm on four training scenarios with different user distributions lead to better performance than achieved by the state of the art. Furthermore, these parameters also lead to superior performance in three never before seen scenarios indicating that our algorithm finds parameter values that generalize to new scenarios with different user distributions.

We propose to use a genetic algorithm to evolve novel reconfigurable hardware to implement elliptic curve cryptographic combinational logic circuits. Elliptic curve cryptography offers high security-level with a short key length making it one of the most popular public-key cryptosystems. Furthermore, there are no known sub-exponential algorithms for solving the elliptic curve discrete logarithm problem. These advantages render elliptic curve cryptography attractive for incorporating in many future cryptographic applications and protocols. However, elliptic curve cryptography has proven to be vulnerable to non-invasive side-channel analysis attacks such as timing, power, visible light, electromagnetic, and acoustic analysis attacks. In this paper, we use a genetic algorithm to address this vulnerability by evolving combinational logic circuits that correctly implement elliptic curve cryptographic hardware that is also resistant to simple timing and power analysis attacks. Using a fitness function composed of multiple objectives - maximizing correctness, minimizing propagation delays and minimizing circuit size, we can generate correct combinational logic circuits resistant to non-invasive, side channel attacks. To the best of our knowledge, this is the first work to evolve a cryptography circuit using a genetic algorithm. We implement evolved circuits in hardware on a Xilinx Kintex-7 FPGA. Results reveal that the evolutionary algorithm can successfully generate correct, and side-channel resistant combinational circuits with negligible propagation delay.