International Journal of Computer Systems Science and Engineering

Design of Optimal Elliptic Curve Cryptography by using Partial Parallel Shifting Multiplier with Parallel Complementary

2017-01-17T22:11:45+00:00

High-speed Elliptic Curve Cryptography (ECC) modules implementation with less time, area, devices used is the recent research studies in cryptographic technology. The inclusion of modulus function in ECC contains several computations that lead to more time consumption and less speed. More multipliers and dividers utilization in ECC-based encryption/decryption required huge logic gates and registers. Hence, area and power consumption are more in ECC hardware unit. To overcome these problems, an enhanced ECC proposed in this paper. The framework optimizes the size of the multiplier and divider leads to a reduction of logic gates utilization. In our proposed work, we present a novel design structure for multiplier and divider based on ECC model defined by . During the encryption, the self-multiplication of the input data for n number of times creates the public key. The remainder obtained from modulo division is regarded as the corresponding encrypted form of input data. To perform these multiplication and modulo division, we present a novel Partial Parallel Shifting Multiplier (PPSM) and Parallel Complementary Method (PCM) to reduce the number of logic gates and improve the operational speed. The comparative analysis between the proposed (PPSM-PCM) with the existing ECC architectures regarding the parameters such as number of logic gates, LUTs, FFs, frequency, power consumption, delay rate and latency assures the suitability of high-speed ECC in real-time applications.

Genetic Algorithm Based Energy-Saving ATO Control Algorithm for CBTC

2017-03-10T23:04:56+00:00

To improve their carrying capacities, multiple trains can operate on one line. Urban rail transit employs a Communication-Based Train Control (CBTC) system to realize a movable block, which is applied to decrease the headway. In a CBTC system, trains only know the speed limit within the scope of the Movement Authority Limit (MAL). An energy-saving Automatic Train Operation (ATO) control algorithm based on a genetic algorithm (GA) is proposed to control multi-train movements with incomplete information about speed limits. This algorithm is composed of two layers: a search layer that applies a GA to search for the optimal control solution and a protection layer that helps trains prevent overspeed. The GA in this paper tends to achieve optimal solutions using variable length chromosomes and a novel fitness function. The simulation results indicate that the proposed algorithm achieves optimal energy-saving benefits compared with other control strategies.

An Optimized Adaptive Neuro-Fuzzy Inference System to Estimate Software Development Effort

2016-12-06T21:31:20+00:00

One of the most critical activities in software project management during the project inception phase is to estimate the effort and cost needed to complete the project tasks. Accurate software development effort estimation is crucial to efficient planning of software projects. Due to complex nature of software projects, development effort estimation has become a challenging issue which must be seriously considered at the early stages of project. Insufficient information and uncertain requirements are the main reasons behind unreliable estimations in this area. Although numerous effort estimation models have been proposed during the last decade, accuracy level is not satisfying enough. This paper presents a new model based on a combination of adaptive neuro-fuzzy inference system (ANFIS) and firefly algorithm (FA) to reach more accurate software effort estimations. The proposed hybrid model is an optimized neuro-fuzzy based estimation model which is capable of producing accurate estimations. The proposed model is evaluated using three real data sets (ISBSG, Kemerer and Albrecht). Results show that the proposed model can significantly improve the performance metrics.

A Context-aware Hierarchical Approach for Activity Recognition Based on Mobile Devices

2017-03-26T04:47:24+00:00

Various sensors integrated in the wearable device provide massive data for activity recognition. In this paper, a context-aware hierarchical approach is proposed for the recognition of activities using accelerometers on smartphones and smartwatches. We adopt a simple variance threshold based method and separate the activities into two major categories named body-fixed set and body-unfixed set according to the inherent characteristics of these activities in the first layer. Next, the Support Vector Machine approach is used respectively for the two sets in the second layer. A probability distribution over activity labels instead of a single activity result is generated in this layer. In the third layer, the contextual information is introduced to improve the classification result. Our comparative study with ordinary Support Vector Machines and other alternative methods has shown that our method is more robust and accurate.

Design and Analysis of Control Strategies for a Cyber Physical System

2017-01-03T01:18:57+00:00

Cyber Physical Systems (CPS) use emerging computing, communication, and control methods to monitor and control geographically dispersed critical system components to allow a high level of confidence about their operation. Simulation methods are frequently used in testing such critical system components, however, it might not be adequate to show the absence of errors given the complexity of the system components under test. Failure in detecting errors in safety critical systems can lead to a catastrophic situation. In this paper we propose an approach, based on simulation and formal analysis, for the reliability analysis of CPS. We illustrate this approach on a well-known industrial case study, the four tank process, demonstrating several challenging features in the design and implementation of CPS. The contributions of this research include presenting control strategies for distributed CPS and the proposal of a novel approach for reliability analysis of CPS. Experimental results obtained show that the proposed approach is efficiently used in order to test and verify the four tanks process system, where simulation results show the validity of approximation and abstraction of the system, and formal analysis is used to validate that several design requirements were satisfied in the control strategies proposed.

A MapReduce-based Efficient H-bucket PMR Quadtree Spatial Index

2017-05-13T12:21:50+00:00

Majority of the MapReduce-Hadoop based indexes are based on either non-disjoint decomposition or the data-dependent disjoint decomposition of space. Quadtree index based regular disjoint decomposition in MapReduce takes different forms of spatial data as point data. Lines, curves, polygons and other higher dimensional data are transformed to point data through a mapping process. Though, the mapping makes index-building quite easy, but it is not suitable for answering search queries. This paper proposes H-bucket PMR Quadtree, a parallel implementation of the existing bucket-PMR Quadtree to handle curvilinear or polygonal map data, in MapReduce. The proposed index uses a two-level of indexing: a global index that indexes the decomposed dataset among cluster nodes to support parallel index building and a local bucket-PMR Quadtree index maintained by each participating cluster node. The proposed index is compared with the state-of-the-art MapReduce based R+-tree indexing and the default key-value storage (non-indexed) Hadoop towards index build-time and spatial queries, such as line search and range search queries. The experimental results demonstrate the effectiveness of the proposed index in MapReduce environment.