On the dynamic reconfigurable implementations of MISTY1 and KASUMI block ciphers.

Novel hardware architectures for dynamic reconfigurable implementation of 64-bit MISTY1 and KASUMI block ciphers are proposed to enhance the performance of cryptographic chips for secure IoT applications. The SRL32 primitive (Reconfigurable Look up Tables-RLUTs) and DPR (Dynamic Partial Reconfiguration) are employed to reconfigure single round MISTY1 / KASUMI algorithms on the run-time. The RLUT based architecture attains dynamic logic functionality without extra hardware resources by internally modifying the LUT contents. The proposed adaptive reconfiguration can be adopted as a productive countermeasure against malicious attacks with the added advantage of less reconfiguration time (RT). On the other hand, the block architecture reconfigures the core hardware by externally uploading the partial bit stream and has significant advantages in terms of low area implementation and power reduction. Implementation was carried out on FPGA, Xilinx Virtex 7. The results showed remarkable results with very low area of 668 / 514 CLB slices consuming 460 / 354 mW for RLUT and DPR architectures respectively. Moreover, the throughput obtained for RLUT architecture was found as 364 Mbps with very less RT of 445 nsec while DPR architecture achieved speed of 176 Mbps with RT of 1.1 msec. The novel architectures outperform the stand-alone existing hardware designs of MISTY1 and KASUMI implementations by adding the dynamic reconfigurability while at the same achieving high performance in terms of area and throughput. Design details of proposed unified architectures and comprehensive analysis is described.

Convergence Analysis of Path Planning of Multi-UAVs Using Max-Min Ant Colony Optimization Approach.

Unmanned Aerial Vehicles (UAVs) seem to be the most efficient way of achieving the intended aerial tasks, according to recent improvements. Various researchers from across the world have studied a variety of UAV formations and path planning methodologies. However, when unexpected obstacles arise during a collective flight, path planning might get complicated. The study needs to employ hybrid algorithms of bio-inspired computations to address path planning issues with more stability and speed. In this article, two hybrid models of Ant Colony Optimization were compared with respect to convergence time, i.e., the Max-Min Ant Colony Optimization approach in conjunction with the Differential Evolution and Cauchy mutation operators. Each algorithm was run on a UAV and traveled a predetermined path to evaluate its approach. In terms of the route taken and convergence time, the simulation results suggest that the MMACO-DE technique outperforms the MMACO-CM approach.

Collective Motion and Self-Organization of a Swarm of UAVs: A Cluster-Based Architecture.

This study proposes a collective motion and self-organization control of a swarm of 10 UAVs, which are divided into two clusters of five agents each. A cluster is a group of UAVs in a dedicated area and multiple clusters make a swarm. This paper designs the 3D model of the whole environment by applying graph theory. To address the aforesaid issues, this paper designs a hybrid meta-heuristic algorithm by merging the particle swarm optimization (PSO) with the multi-agent system (MAS). First, PSO only provides the best agents of a cluster. Afterward, MAS helps to assign the best agent as the leader of the nth cluster. Moreover, the leader can find the optimal path for each cluster. Initially, each cluster contains agents at random positions. Later, the clusters form a formation by implementing PSO with the MAS model. This helps in coordinating the agents inside the nth cluster. However, when two clusters combine and make a swarm in a dynamic environment, MAS alone is not able to fill the communication gap of n clusters. This study does it by applying the Vicsek-based MAS connectivity and synchronization model along with dynamic leader selection ability. Moreover, this research uses a B-spline curve based on simple waypoint defined graph theory to create the flying formations of each cluster and the swarm. Lastly, this article compares the designed algorithm with the NSGA-II model to show that the proposed model has better convergence and durability, both in the individual clusters and inside the greater swarm.

Fuzzy-Based Hybrid Control Algorithm for the Stabilization of a Tri-Rotor UAV.

In this paper, a new and novel mathematical fuzzy hybrid scheme is proposed for the stabilization of a tri-rotor unmanned aerial vehicle (UAV). The fuzzy hybrid scheme consists of a fuzzy logic controller, regulation pole-placement tracking (RST) controller with model reference adaptive control (MRAC), in which adaptive gains of the RST controller are being fine-tuned by a fuzzy logic controller. Brushless direct current (BLDC) motors are installed in the triangular frame of the tri-rotor UAV, which helps maintain control on its motion and different altitude and attitude changes, similar to rotorcrafts. MRAC-based MIT rule is proposed for system stability. Moreover, the proposed hybrid controller with nonlinear flight dynamics is shown in the presence of translational and rotational velocity components. The performance of the proposed algorithm is demonstrated via MATLAB simulations, in which the proposed fuzzy hybrid controller is compared with the existing adaptive RST controller. It shows that our proposed algorithm has better transient performance with zero steady-state error, and fast convergence towards stability.