Efficient Implementation on Low-Cost SoC-FPGAs of TLSv1.2 Protocol with ECC_AES Support for Secure IoT Coordinators

Security management for IoT applications is a critical research field, especially when taking into account the performance variation over the very different IoT devices. In this paper, we present high-performance client/server coordinators on low-cost SoC-FPGA devices for secure IoT data collection. Security is ensured by using the Transport Layer Security (TLS) protocol based on the TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA256 cipher suite. The hardware architecture of the proposed coordinators is based on SW/HW co-design, implementing within the hardware accelerator core Elliptic Curve Scalar Multiplication (ECSM), which is the core operation of Elliptic Curve Cryptosystems (ECC). Meanwhile, the control of the overall TLS scheme is performed in software by an ARM Cortex-A9 microprocessor. In fact, the implementation of the ECC accelerator core around an ARM microprocessor allows not only the improvement of ECSM execution but also the performance enhancement of the overall cryptosystem. The integration of the ARM processor enables to exploit the possibility of embedded Linux features for high system flexibility. As a result, the proposed ECC accelerator requires limited area, with only 3395 LUTs on the Zynq device used to perform high-speed, 233-bit ECSMs in 413 µs, with a 50 MHz clock. Moreover, the generation of a 384-bit TLS handshake secret key between client and server coordinators requires 67.5 ms on a low cost Zynq 7Z007S device

Elaborating Transition Interface Sampling Methods

We review two recently developed efficient methods for calculating rate constants of processes dominated by rare events in high-dimensional complex systems. The first is transition interface sampling (TIS), based on the measurement of effective fluxes through hypersurfaces in phase space. TIS improves efficiency with respect to standard transition path sampling (TPS) rate constant techniques, because it allows a variable path length and is less sensitive to recrossings. The second method is the partial path version of TIS. Developed for diffusive processes, it exploits the loss of long time correlation. We discuss the relation between the new techniques and the standard reactive flux methods in detail. Path sampling algorithms can suffer from ergodicity problems, and we introduce several new techniques to alleviate these problems, notably path swapping, stochastic configurational bias Monte Carlo shooting moves and order-parameter free path sampling. In addition, we give algorithms to calculate other interesting properties from path ensembles besides rate constants, such as activation energies and reaction mechanisms.Comment: 36 pages, 5 figure

GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing

Network slicing is a key technology in 5G communications system. Its purpose is to dynamically and efficiently allocate resources for diversified services with distinct requirements over a common underlying physical infrastructure. Therein, demand-aware resource allocation is of significant importance to network slicing. In this paper, we consider a scenario that contains several slices in a radio access network with base stations that share the same physical resources (e.g., bandwidth or slots). We leverage deep reinforcement learning (DRL) to solve this problem by considering the varying service demands as the environment state and the allocated resources as the environment action. In order to reduce the effects of the annoying randomness and noise embedded in the received service level agreement (SLA) satisfaction ratio (SSR) and spectrum efficiency (SE), we primarily propose generative adversarial network-powered deep distributional Q network (GAN-DDQN) to learn the action-value distribution driven by minimizing the discrepancy between the estimated action-value distribution and the target action-value distribution. We put forward a reward-clipping mechanism to stabilize GAN-DDQN training against the effects of widely-spanning utility values. Moreover, we further develop Dueling GAN-DDQN, which uses a specially designed dueling generator, to learn the action-value distribution by estimating the state-value distribution and the action advantage function. Finally, we verify the performance of the proposed GAN-DDQN and Dueling GAN-DDQN algorithms through extensive simulations

Finding Apparent Horizons in Dynamic 3D Numerical Spacetimes

We have developed a general method for finding apparent horizons in 3D numerical relativity. Instead of solving for the partial differential equation describing the location of the apparent horizons, we expand the closed 2D surfaces in terms of symmetric trace--free tensors and solve for the expansion coefficients using a minimization procedure. Our method is applied to a number of different spacetimes, including numerically constructed spacetimes containing highly distorted axisymmetric black holes in spherical coordinates, and 3D rotating, and colliding black holes in Cartesian coordinates.Comment: 19 pages, 13 figures, LaTex, to appear in Phys. Rev. D. Minor changes mad

cuIBM -- A GPU-accelerated Immersed Boundary Method

A projection-based immersed boundary method is dominated by sparse linear algebra routines. Using the open-source Cusp library, we observe a speedup (with respect to a single CPU core) which reflects the constraints of a bandwidth-dominated problem on the GPU. Nevertheless, GPUs offer the capacity to solve large problems on commodity hardware. This work includes validation and a convergence study of the GPU-accelerated IBM, and various optimizations.Comment: Extended paper post-conference, presented at the 23rd International Conference on Parallel Computational Fluid Dynamics (http://www.parcfd.org), ParCFD 2011, Barcelona (unpublished