ENERGY EFFICIENT COMPUTING FOR SMART PHONES IN CLOUD ASSISTED ENVIRONMENT

Nancy Arya¹ Sunita Choudhary¹ and S.Taruna²

¹ Department of Computer Science, Banasthali Vidyapith, Rajasthan, India

² Department of Computer Science, JK Lakshmipat University, Rajasthan, India

Abstract 

In recent years, the employment of smart mobile phones has increased enormously and are concerned as an area of human life. Smartphones are capable to support immense range of complicated and intensive applications results shortened power capability and fewer performance. Mobile cloud computing is the newly rising paradigm integrates the features of cloud computing and mobile computing to beat the constraints of mobile devices. Mobile cloud computing employs computational offloading that migrates the computations from mobile devices to remote servers. In this paper, a novel model is proposed for dynamic task offloading to attain the energy optimization and better performance for mobile applications in the cloud environment. The paper proposed an optimum offloading algorithm by introducing new criteria such as benchmarking for offloading decision making. It also supports the concept of partitioning to divide the computing problem into various sub-problems. These sub-problems can be executed parallelly on mobile device and cloud. Performance evaluation results proved that the proposed model can reduce around 20% to 53% energy for low complexity problems and up to 98% for high complexity problems.

Keywords 

Mobile Cloud Computing, Mobile Computing, Computational Offloading, Dynamic Task Offloading, Energy Optimization.                                 

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ADDRESSING IMBALANCED CLASSES PROBLEM OF INTRUSION DETECTION SYSTEM USING WEIGHTED EXTREME LEARNING MACHINE

Mohammed Awad¹ and Alaeddin Alabdallah²

¹Faculty of E&IT, Dept. of Computer Systems Engineering,

Arab American University, Palestine

²Faculty of E&IT, Dept. of Computer Engineering,

An-Najah National University, Palestine

Abstract 

The main issues of the Intrusion Detection Systems (IDS) are in the sensitivity of these systems toward the errors, the inconsistent and inequitable ways in which the evaluation processes of these systems were often performed. Most of the previous efforts concerned with improving the overall accuracy of these models via increasing the detection rate and decreasing the false alarm which is an important issue. Machine Learning (ML) algorithms can classify all or most of the records of the minor classes to one of the main classes with negligible impact on performance. The riskiness of the threats caused by the small classes and the shortcoming of the previous efforts were used to address this issue, in addition to the need for improving the performance of the IDSs were the motivations for this work. In this paper, stratified sampling method and different cost-function schemes were consolidated with Extreme Learning Machine (ELM) method with Kernels, Activation Functions to build competitive ID solutions that improved the performance of these systems and reduced the occurrence of the accuracy paradox problem. The main experiments were performed using the UNB ISCX2012 dataset. The experimental results of the UNB ISCX2012 dataset showed that ELM models with polynomial function outperform other models in overall accuracy, recall, and F-score. Also, it competed with traditional model in Normal, DoS and SSH classes.

Keywords 

Machine Learning, Weighted Extreme Learning Machine, Intrusion detection system, Accuracy, UNB ISCX2012.

                                                                  

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HISTOGRAM OF NEIGHBORHOOD TRIPARTITE AUTHENTICATION WITH FINGERPRINT-BASED BIOMETRICS FOR IOT SERVICES

S. Kanchana

Department of Computer Science, PSG College of Arts & Science, Coimbatore, India

Abstract 

Internet of Things (IoT) and services is an interesting topic with a wide range of potential applications like smart home systems, health care, telemedicine, and intelligent transportation. Traditionally, key agreement schemes have been evaluated to access IoT services which are highly susceptible to security. Recently, Biometric-based authentication is also used to access IoT services and devices. They are involving a larger amount of memory with increased running time and found to be computationally infeasible. To provide robust authentication for IoT services, Histogram of Neighborhood Tripartite Authentication with Fingerprint Biometrics (HNTA-FB) for IoT services is proposed in this paper. This proposed HNTA-FB method uses binary patterns and a histogram of features to extract the region of interest. To reduce the memory requirements while providing access to IoT services, Histogram of Neighborhood Binary Pattern Pre-processing (HNBPP) model is proposed. The discriminative power of Neighbourhood Binary Pattern Registration (NBPR) is integrated with the normalized sparse representation based on the histogram. Additionally, this work presents a new Tripartite User Authentication model for fingerprint biometric template matching process. When compared with different state-of-the-art methods, the proposed method depicts significantly improved performance in terms of matching accuracy, computational overhead and execution speed and is highly effective in delivering smart home services.

Keywords 

 Binary Patterns, Fingerprint Biometrics, Histogram, Internet of Things, Neighborhood Tripartite Authentication.

                                                                  

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A COMPREHENSIVE STUDY OF DSCP MARKINGS’ IMPACT ON VOIP QOS IN HFC NETWORKS

Shaher Daoud  and Yanzhen Qu

School of Computer Science, Colorado Technical University, Colorado Springs, USA

Abstract 

Various factors can have a significant degrading impact on the residential Voice over Internet Protocol (VoIP) phone services’ quality. Hybrid fibre- coaxial (HFC) networks typically carry three types of traffic that include voice, data, and video. Unlike data and video, some delays or packet loss can result in a noticeable degraded impact on a VoIP’s phone conversation. This paper will analyze and assess VoIP traffic prioritization and its impact on VoIP’s quality of service (QoS) based on the concept of differentiated services code point (DSCP) markings. Call testing examines two types of calls. The first set of tests focus on calls that originate from a VoIP network and terminate on a signalling system 7 (SS7) network. The second experiment focuses on calls that originate from SS7 network and terminate on a VoIP network. The research results provide DSCP markings configurations that can improve phone conversations’ quality.

Keywords 

 QoS , VoIP,  DSCP Marking ,  jitter,  HFC Network, MOS.

                                                                  

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