Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)transmission.Vehicular networks give a safe and more effective driving experience by presenting time-sensitive and location-aware data.The communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with time.Therefore,the scheme of an effectual routing protocol for reliable and stable communications is significant.Current research demonstrates that clustering is an intelligent method for effectual routing in a mobile environment.Therefore,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in VANETS.The FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the VANET.To accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust level.For the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR method.The experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.
Since 2016,the National Institute of Standards and Technology(NIST)has been performing a competition to standardize post-quantum cryptography(PQC).Although Falcon has been selected in the competition as one of the standard PQC algorithms because of its advantages in short key and signature sizes,its performance overhead is larger than that of other lattice-based cryptosystems.This study presents multiple methodologies to accelerate the performance of Falcon using graphics processing units(GPUs)for server-side use.Direct GPU porting significantly degrades performance because the Falcon reference codes require recursive functions in its sampling process.Thus,an iterative sampling approach for efficient parallel processing is presented.In this study,the Falcon software applied a fine-grained execution model and reported the optimal number of threads in a thread block.Moreover,the polynomial multiplication performance was optimized by parallelizing the number-theoretic transform(NTT)-based polynomial multiplication and the fast Fourier transform(FFT)-based multiplication.Furthermore,dummy-based parallel execution methods have been introduced to handle the thread divergence effects.The presented Falcon software on RTX 3090 NVIDA GPU based on the proposed methods with Falcon-512 and Falcon-1024 parameters outperform at 35.14,28.84,and 34.64 times and 33.31,27.45,and 34.40 times,respectively,better than the central processing unit(CPU)reference implementation using Advanced Vector Extensions 2(AVX2)instructions on a Ryzen 95900X running at 3.7 GHz in key generation,signing,and verification,respectively.Therefore,the proposed Falcon software can be used in servers managing multiple concurrent clients for efficient certificate verification and be used as an outsourced key generation and signature generation server for Signature as a Service(SaS).
