International Journal of High Performance Systems Architecture (12 papers in press)
LSTM-based earthquake prediction: Enhanced time feature and data representation
by Asmae BERHICH, Fatima-Zahra BELOUADHA, Mohammed Issam KABBAJ
Abstract: In the last decades, many studies have emerged with different approaches in the field of earthquakes magnitude prediction. Statistical and machine learning approaches have been used. However, the research contributions in this field remain immature. Some among them have not led to a successful prediction. Others have not been able to predict earthquakes so efficiently. Failing in giving a warning or giving false alarms has a negative, even a damaging impact on the socio-economic situation. Consequently, research into more relevant methods, appropriates and adapted to this field will be important, as it would improve accuracy, performance, and dynamicity. In this context, this paper suggests applying the well-known deep learning algorithm long short-term memory (LSTM) to predict earthquakes in the regions of Morocco. The features used in the prediction model takes the most influencing and correlated datasets, and it calculates an appropriate time feature that is simpler and more precise. The optimal hyperparameters values of the applied LSTM models are retrieved by the grid search technique. The performance of our model is examined and compared with deep neural networks, where the final results demonstrate that our model is effective and we strongly recommend it.
Keywords: Earthquake prediction; deep learning; neural networks; LSTM; seismic dataset.
Improving Performance of the Symmetrical Evolutionary Ciphering System SEC
by Mohammed BOUGRINE, Salima TRICHNI, Fouzia OMARY
Abstract: Nowadays computer security becomes, increasingly, an indispensable field. It represents a major challenge for all entities: economic, political, social . However, few are the cryptographic systems that ensure security and still resists the enormous growth in technology. Now, cryptanalysis tools are much more sophisticated and more powerful than before. Hence, the need to design new systems that can be competitive to the old ones. New cryptographic systems with new properties and the ability to support this technology watch.
In this work we have focused on the SEC encryption system uses a new approach of ciphering based on evolutionary algorithms, hence its called: Symmetrical Evolutionary Ciphering. Since the strength of this type of algorithm will be very beneficial in cryptography, we will present in this paper, a new evaluation function that we have developed to improve the performance of this system and improve its resistance against all possible types of attacks.
Keywords: Security; Cryptography; Symmetrical Encryption; Evolutionary Algorithms; Fitness function; Appearance frequency.
New digital signature algorithm based on ECC and its application in Bitcoin and IoT
by Shuai Xiao, Han Wang, Jindan Zhang
Abstract: ECDSA (Elliptic Curve Digital Signature Algorithm) is the simulation of DSA algorithm on elliptic curve. Compared with DSA, ECDSA has higher security and is the only widely accepted elliptic curve digital signature algorithm, which has been adopted by many standardization organizations. Based on the study of the original ECDSA scheme, this paper attempts to propose a new improved scheme. The proposed scheme has one main improvement. That is, considering that the original scheme has a finite field inversion process in the signature equation, the time-consuming inversion operation is completely avoided in the design. The proposed scheme has faster computation speed and reduces the ratio of verifying signature to signature generation time. The algorithm has certain significance for improving the efficiency of elliptic curve cryptography. Our simulation results show that the scheme runs faster and has higher signature and verification efficiency than that of the original scheme without compromising security. What's more, we also explore its application in Bitcoin and IoT.
Keywords: Elliptic curve cryptography (ECC); Bitcoin; IoT; Elliptic curve digital signature (ECDSA).
A Distributed Big Data Analytics Model for People Re-identification Based Dimensionality Reduction
by Abderrahmane Ez-zahout
Abstract: Big data analytics is a vast domain includes intelligent processing systems. Video intelligent surveillance is one of them which have been widely studied, because it generate a huge volume of data, various types of structured and unstructured data and all accompanied treatments require fast processing speed. In recent research areas like Big Data analytics, most of the data involved in the processing comes from video surveillance systems (CCTV systems) are unstructured. In real time CCTV systems, a very big volume of data requires an efficient and secure ingestion, storage and advanced processing. In the process of treatment, all this systems, operate on four phases, detection, tracking, profile analysis and re-identification using various approaches. Intelligent person re-identification process using only the visual information is challenging for several reasons. In the current work, An efficient re-identification algorithm based on SparkMlLib Core is adopted. The Minkowski distance and kmeans algorithms geted from SparkMlLib are used to speed up the features similarity searching. CMC and CDF curves, have functionally explains the importance of this contribution. This function measures the re-identification errors. Then, a principal component analysis is used to reduce the similarity database.
Keywords: (SparkMlLib; CCTV Systems; Re-identification; ROI; Features Similarity; CMC; CDF; PCA).
Research on Frontiers of Space-ground Integration Information Network
by Zhenhua Liu, Linghong Chi, Chuanwen Lin
Abstract: After years of research and development, Space-ground Integration Information Network (SGIN) is still in the initial stage. Although the country has yet established a truly global space-based Internet, the development of frontier technology makes the space-based network feasible. In this paper, we proposed a novel system architecture of SGIN adopting the cutting-edge technologies of new communication transmission, plug and play satellite module, flexible configuration effective carrier, modular network system, and multi-satellite launch. The paper further analyzes the role of new technologies in promoting space-based networks from the aspects of performance and cost and puts forward the development trend of space-based network technology. Finally, we identified and summarized some common misunderstandings and promising trends of SGIN. With the novel architecture design and demonstration of the promising trends, we believe this paper will guide the future research of high-impact research in SGIN.
Keywords: Fontier Technology;Space-Ground Integration Information Network;Flexible Payload of Satellite; Plug and play technology; Space optical Communication; Space Quantum Communication.
A Game Theoretical Approach for a Fair and Effective Pricing Strategy in Cloud Computing
by Abdelkarim Ait Temghart, Driss Ait Omar, Mohamed Baslam, Mbarek Marwan
Abstract: The most proven pricing models for the healthy competition are based on game theory techniques that ensure equal consideration of interests for both clients and providers. However, users preferences and prices sensitivity have a direct influence on the acceptance of new services, an issue that has been relatively neglected by previous research in this area. Thus, we formulate a mathematical model that takes into consideration users' behavior and at the same time increases the marginal profit. The objective of this work is to improve the revenue for cloud providers as well as users satisfaction. Experimental results show that the proposed approach is an optimal solution to obtain a reasonable price strategy in a competitive cloud market. More importantly, clients satisfaction has a considerable influence on pricing policy and the expected payoff. In this context, cloud providers compete with each other to maximize their expected utility even when they offer services at a reasonable price
Keywords: cloud computing; game theory; Nash equilibrium; non-cooperative game; duopoly market; price competition; consumer’s preference; price's sensitivity; profit margin.
A CAD Approach For Power Supply Noise Aware Floorplan In SoC
by PARTHA MITRA, Jaydeb Bhaumik, Angsuman Sarkar
Abstract: This paper deals for reduction of power supply noise with decoupling capacitor estimation and allocation using particle swarm optimization algorithm at the floorplanning stage of the physical design process. Decoupling capacitors are allocated between the power and ground rails in parallel with functional blocks having supply noise effectively reduces rapid fluctuations in supply voltage. Decoupling capacitors placed far away does not have much effect on the power supply noise and hence placement of decoupling capacitors is also a major focus in this work. Excess capacitors increases the delay and power parameters and degrades the overall performance of the integrated circuit. In this work the major focus is on optimizing the decoupling capacitor budget and placement of decoupling capacitors so that the voltage noise margin can be reduced significantly and also the various design parameters remain unaltered as much as possible. In this work peak supply noise has been reduced by up to 69.5%, increment in delay and power parameters is 6.52% and 2.08% with decoupling capacitor allocation. Maximum increment in core area is 6.57% with decoupling capacitor allocation. The decoupling capacitor budget has also been optimized by up to 36.7%. This CAD approach can be used power supply noise reduction for any multi-core architecture.
Keywords: Computer Aided Design(CAD); Decoupling Capacitor(decap); Particle Swarm Optimization(PSO); Power Distribution Network(PDN); System-on-Chip(SoC); White Space(WS).
ACS: An Alternate Coding Scheme to Improve Degrade Read Performance for SSD-based RAID5 Systems
by Yubiao Pan, Mingwei Lin
Abstract: To guarantee high performance and reliability, storage systems require better devices and data redundancy schemes, e.g., SSD-based RAID5. However, failures in the large-scale storage systems are common. In order to serve requests on a failed node, the SSD-based RAID5 causes additional disk I/Os to trigger degraded reads, thus suffering performance degradation. Existing coding methods are not suitable for the SSD-based RAID5. How to maintain the same storage cost as the traditional RAID5 does while obtaining less disk I/Os for degraded reads is an interesting problem. To address this problem via coding aspect, we first come up with an Alternate Coding Scheme (ACS) by using the characteristics of SSD to reduce disk I/Os for boosting degraded reads. To work for realistic workloads, we further propose ACS-W and ACS-DR approaches for write and degraded requests. Our evaluations based on the trace-driven simulator with real-world workloads show that compared to the traditional method, ACS indeed reduces disk I/Os and improves the degraded read performance.
Keywords: Solid-state Drives; RAID; Degrade Read; Disk I/Os; Performance.
Efficient hardware implementation of TEA, XTEA and XXTEA ciphers for low resource IoT applications
by Zeesha Mishra, Bibhudendra Acharya
Abstract: The advancement in network connectivity and data handling capabilitiesrnshows the tremendous growth of Internet of Things (IoT). The number of connected devices in IoT applications has been increasing, leaving serious security concerns. The problem of providing security solutions to resource-constrained devices leads to lightweight cryptographic algorithms. Among instances when the data security in resource constrained environment i.e. smart devices are to be taken into consideration, lightweight encryption algorithms admit in popularity and are deemed to be of great merit. Reduction of both power consumption and area are always a major concern. Tiny Encryption Algorithm (TEA) is one of the lightweight cryptographic algorithm with block size of 64-bit and key size of 128-bit follows Feistel network structure and further this algorithm is modified as Extended Tiny Encryption Algorithm (XTEA) and Corrected tiny encryption algorithm (XXTEA). This paper have proposed highly efficient roundbased architectures of TEA, XTEA and XXTEA to make them suitable for the low area, low power applications. The strategy is to optimize the hardware design for low resource applications. All the three proposed architectures are extensively evaluated and compared on the basis of performance, and area utilization for their implementations in different FPGA platforms.
Keywords: IoT; lightweight cryptography; TEA algorithm; XTEA algorithm; Fiestelrnstructure; FPGA; throughput; slices.
Special Issue on: Advances in Multi-Core and Many-Core Systems
Knowledge-based mining with the game-theoretic rough set approach to handling inconsistent healthcare data
by Abhay Kumar Singh, Muhammad Rukunuddin Ghalib
Abstract: Healthcare data analysis played a crucial role in the medical industries for examining the medical data. Primarily, in smart healthcare applications, a massive volume of data must be handled and processed to make clinical decisions. This clinical analysis process requires more time and complexity due to the high-dimensionality data. Therefore, machine learning and intelligent techniques are introduced in the healthcare data analysis field to improve data processing. This paper focuses on developing knowledge-based mining with a game-theoretic rough set (KM-GTRS) based healthcare data analysis process. The knowledge mining process able to handling the high-dimensional data and providing the data to the application-centric services. Here the introduced game-theoretic rough set algorithm analyzing the medical data and capable of handling the decision regarding the inconsistent and missing data effectively. In addition to this, the method ensures the solutions for medical data analysis with minimum time. The sufficient identification of inconsistent data improves the overall medical analysis recognition accuracy. This process achieves the minimum analysis time, computation complexity, inconsistency, and service latency.
Keywords: Big Data; Healthcare; Knowledge Base; Linear Programming; Machine Learning.
Evaluation of Flexural and Shear Property of High Performance PLA/Bz Composite Filament Printed at Different FDM Parametric Conditions
by Sneha P, Balamurugan K, Kalusuraman G
Abstract: The reinforcement of metal in the Polylactic acid (PLA) matrix provides significant changes in improving the material properties, particularly for 3D-printing. In the present work, 14% of Bronze (Bz) that has the particle size of 10-20
Keywords: PLA-Bz; Fused deposition model; Density; Bend; Shear; Optical microscopy.
Pervasive Hybrid Two-Stage Fusion Model of Intelligent Wireless Network Security Threat Perception
by Feilu Hang, Linjiang Xie, Wei Guo, Yao Lv, Wei Ou, A. Shanthini
Abstract: The rapid development of software, hardware, and communications technologies has helped spread sensors, actuators, and heterogeneous devices connected through the internet, collecting and distributing a large amount of information, opening a unique class of advanced services available at any time. This paper introduced a Pervasive Hybrid two-stage fusion model (PHTSFM) for the cybersecurity situation evaluation and focused on multi-heterogeneous sensors to determine the impact of security threats on a networked system and precisely evaluate system safety. The multisource information\'s characteristics in network security analysis and data fusion security algorithms have been introduced. Multiple correlations are utilized to differentiate the normal event or outlier and abnormal event within the least delay. The simulated network assessment reveals that the proposed solution is appropriate for the network environment, and the findings of the test are precise and effective.
Keywords: Fusion Model; Intelligent Network Security; Threat Detection; Hybrid Two-stage Model Fusion Model.