Empirical Performance Analysis of Hyperledger LTS for Small and Medium Enterprises.

The massive success of blockchain technology in Bitcoin is taking the world by storm by attracting vast acceptance from both the public and private sectors. Blockchain allows digital transactions between two parties without a third party as a broker. Blockchain is now applicable beyond fintech to various other industries. Among these, Hyperledger fabric has emerged as the most popular blockchain-based open-source permissioned platform targeting business applications. It has been used in over 400 proofs-of-concept blockchain and is well proven in applications, such as supply chain, healthcare, and so on. Despite the many obvious benefits observed in blockchain-enhanced platforms, there still exist technical challenges in scalability, causing performance deficiency, which includes latency and throughput. There is an exigent need to improve the current blockchain-based applications to have the blockchain nodes be scalable without compromising the blockchain performance. In this study, we present the impact of workload variance of up to 1000 transactions with the setup of 20 blockchain nodes in the Hyperledger LTS platform. The evaluation metrics are transaction success and failure rate, throughput, and latency in the blockchain. The transaction throughput was found to be consistent with the increasing workload on a constant number of nodes. However, it showed a declining trend with an increasing number of nodes. As far as the latency, it was in tandem with the increased workload and the number of nodes. We, therefore, conclude that the LTS version is suitable for small and medium enterprises that do not scale up.

An analytical model to minimize the latency in healthcare internet-of-things in fog computing environment.

Fog computing (FC) is an evolving computing technology that operates in a distributed environment. FC aims to bring cloud computing features close to edge devices. The approach is expected to fulfill the minimum latency requirement for healthcare Internet-of-Things (IoT) devices. Healthcare IoT devices generate various volumes of healthcare data. This large volume of data results in high data traffic that causes network congestion and high latency. An increase in round-trip time delay owing to large data transmission and large hop counts between IoTs and cloud servers render healthcare data meaningless and inadequate for end-users. Time-sensitive healthcare applications require real-time data. Traditional cloud servers cannot fulfill the minimum latency demands of healthcare IoT devices and end-users. Therefore, communication latency, computation latency, and network latency must be reduced for IoT data transmission. FC affords the storage, processing, and analysis of data from cloud computing to a network edge to reduce high latency. A novel solution for the abovementioned problem is proposed herein. It includes an analytical model and a hybrid fuzzy-based reinforcement learning algorithm in an FC environment. The aim is to reduce high latency among healthcare IoTs, end-users, and cloud servers. The proposed intelligent FC analytical model and algorithm use a fuzzy inference system combined with reinforcement learning and neural network evolution strategies for data packet allocation and selection in an IoT-FC environment. The approach is tested on simulators iFogSim (Net-Beans) and Spyder (Python). The obtained results indicated the better performance of the proposed approach compared with existing methods.

On fuzzy semantic similarity measure for DNA coding.

A coding measure scheme numerically translates the DNA sequence to a time domain signal for protein coding regions identification. A number of coding measure schemes based on numerology, geometry, fixed mapping, statistical characteristics and chemical attributes of nucleotides have been proposed in recent decades. Such coding measure schemes lack the biologically meaningful aspects of nucleotide data and hence do not significantly discriminate coding regions from non-coding regions. This paper presents a novel fuzzy semantic similarity measure (FSSM) coding scheme centering on FSSM codons׳ clustering and genetic code context of nucleotides. Certain natural characteristics of nucleotides i.e. appearance as a unique combination of triplets, preserving special structure and occurrence, and ability to own and share density distributions in codons have been exploited in FSSM. The nucleotides׳ fuzzy behaviors, semantic similarities and defuzzification based on the center of gravity of nucleotides revealed a strong correlation between nucleotides in codons. The proposed FSSM coding scheme attains a significant enhancement in coding regions identification i.e. 36-133% as compared to other existing coding measure schemes tested over more than 250 benchmarked and randomly taken DNA datasets of different organisms.