Kai Hwang and Zhiwei Xu  McGraw-Hill, Boston, 1998, 802 pp. ISBN 0-07-031798-4, $97.30 This text is an in depth introduction to the concepts of Parallel Computing. Designed for use in university level computer science courses, the text covers scalable architecture and parallel programming of symmetric muli-processors, clusters of workstations, massively parallel processors, and Internet-based metacomputing platforms. Hwang and Xu give an excellent overview in these topics while keeping the text easily comprehensible. The text is organized into four parts. Part I covers scalability and clustering. Part II deals with the technology used to construct a parallel system. Part III pertains to the architecture of scalable systems. Finally, Part IV presents methods of parallel programming on various platforms and languages. The first chapter presents different models on scalability as divided into resources, applications, and technology. It defines three abstract models (PRAM, BSP, and phase parallel models) and five physical models (PVP, SMP, MPP, COW, and MPP systems). Chapter 2 introduces the ideas behind parallel programming including processes, tasks, threads and environments. Chapter 3 introduces performance issues and metrics. As an introduction to Part II, Chapter 4 introduces the history of microprocessor types and their applications in the architectures of current systems. Chapter 5 deals with the issues of distributed memory. It discusses several models such as UMA, NORMA, CC-NUMA, COMA, and DSM. Chapter 6 presents gigabit networks, switched interconnects, and other various high-speed networking architectures to construct clusters. Chapter 7 discusses the overheads created by parallel computing, such as threads, synchronization, and efficient communication between nodes. Part III, Chapter 8, 9 and 11, give comparisons between various types of scalable systems (SMP, CC-NUMA, Clusters, and MPP). The comparisons are based on hardware architecture, the system software, and special features that make each system unique. Chapter 10 compares various research and commercial clusters with an in depth study of the Berkeley NOW, IBM SP2, and Digital TruCluster systems. Chapter 12 introduces the concepts of Part IV with details into parallel programming paradigms. Chapter 13 discusses communications between the processors using message passing programming (such as MPI and PVM libraries). Chapter 14 studies the data parallel approach with an emphasis in Fortran 90 and HPF. With attention to detail through examples, Hwang and Xu have created a well-written introduction to Parallel Computing. The authors are distinguished for their contributions in this field. This text is based on cutting-edge research, providing the current theories that are in use in industry today. Bin Cong, Shawn Morrison and Michael Yorg, Department of Computer Science  California Polytechnic State University at San Luis Obispo

Scalable Parallel Computing: Technology, Architecture, Programming

As the Internet of Things (IoT) and the Web of Things (WoT) are becoming a reality, their interconnection with mobile phone computing is increasing. Mobile phone integrated sensors offer advanced services, which when combined with Web-enabled real-world devices located near the mobile user (e.g., body area networks, radio-frequency identification tags, energy monitors, environmental sensors, etc.), have the potential of enhancing the overall user knowledge, perception and experience, encouraging more informed choices and better decisions. This paper serves as a survey of the most significant work performed in the area of mobile phone computing combined with the IoT/WoT. A selection of over 100 papers is presented, which constitute the most significant work in the field up to date, categorizing these papers into ten different domains, according to the area of application (i.e., health, sports, gaming, transportation, and agriculture), the nature of interaction (i.e., participatory sensing, eco-feedback, actuation, and control), or the communicating actors involved (i.e., things and people). Open issues and research challenges are identified, analyzed and discussed.

Mobile Phone Computing and the Internet of Things: A Survey

Content-based image retrieval (CBIR) uses image content features to search and retrieve digital images from a large database A variety of visual feature extraction techniques have been employed to implement the searching purpose Due to the computation time requirement, some good algorithms are not been used The retrieval performance of a content-based image retrieval system crucially depends on the feature representation and similarity measurements The ultimate aim of the proposed method is to provide an efficient algorithm to deal with the above mentioned problem definition Here the deep belief network (DBN) method of deep learning is used to extract the features and classification and is an emerging research area, because of the generation of large volume of data The proposed method is tested through simulation in comparison and the results show a huge positive deviation towards its performance

Content based image retrieval using deep learning process

Underwater wireless sensor networks (UWSNs) comprise numerous underwater wireless sensor nodes dispersed in the marine environment, which find applicability in several areas like data collection, navigation, resource investigation, surveillance, and disaster prediction. Because of the usage of restricted battery capacity and the difficulty in replacing or charging the inbuilt batteries, energy efficiency becomes a challenging issue in the design of UWSN. Earlier studies reported that clustering and routing are considered effective ways of attaining energy efficacy in the UWSN. Clustering and routing processes can be treated as nondeterministic polynomial-time (NP) hard optimization problems, and they can be addressed by the use of metaheuristics. This study introduces an improved metaheuristics-based clustering with multihop routing protocol for underwater wireless sensor networks, named the IMCMR-UWSN technique. The major aim of the IMCMR-UWSN technique is to choose cluster heads (CHs) and optimal routes to a destination. The IMCMR-UWSN technique incorporates two major processes, namely the chaotic krill head algorithm (CKHA)-based clustering and self-adaptive glow worm swarm optimization algorithm (SA-GSO)-based multihop routing. The CKHA technique selects CHs and organizes clusters based on different parameters such as residual energy, intra-cluster distance, and inter-cluster distance. Similarly, the SA-GSO algorithm derives a fitness function involving four parameters, namely residual energy, delay, distance, and trust. Utilization of the IMCMR-UWSN technique helps to significantly boost the energy efficiency and lifetime of the UWSN. To ensure the improved performance of the IMCMR-UWSN technique, a series of simulations were carried out, and the comparative results reported the supremacy of the IMCMR-UWSN technique in terms of different measures.

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Improved Metaheuristics-Based Clustering with Multihop Routing Protocol for Underwater Wireless Sensor Networks

In recent years, the underwater wireless sensor network (UWSN) has received a significant interest among research communities for several applications, such as disaster management, water quality prediction, environmental observance, underwater navigation, etc. The UWSN comprises a massive number of sensors placed in rivers and oceans for observing the underwater environment. However, the underwater sensors are restricted to energy and it is tedious to recharge/replace batteries, resulting in energy efficiency being a major challenge. Clustering and multi-hop routing protocols are considered energy-efficient solutions for UWSN. However, the cluster-based routing protocols for traditional wireless networks could not be feasible for UWSN owing to the underwater current, low bandwidth, high water pressure, propagation delay, and error probability. To resolve these issues and achieve energy efficiency in UWSN, this study focuses on designing the metaheuristics-based clustering with a routing protocol for UWSN, named MCR-UWSN. The goal of the MCR-UWSN technique is to elect an efficient set of cluster heads (CHs) and route to destination. The MCR-UWSN technique involves the designing of cultural emperor penguin optimizer-based clustering (CEPOC) techniques to construct clusters. Besides, the multi-hop routing technique, alongside the grasshopper optimization (MHR-GOA) technique, is derived using multiple input parameters. The performance of the MCR-UWSN technique was validated, and the results are inspected in terms of different measures. The experimental results highlighted an enhanced performance of the MCR-UWSN technique over the recent state-of-art techniques.

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An Efficient Metaheuristic-Based Clustering with Routing Protocol for Underwater Wireless Sensor Networks

Rapid advancements of sixth-generation (6G) network and Internet of Everything (IoE) supports numerous emerging services and application. Increasing mobile internet traffic and services, on the other hand, presented a number of challenges that could not be addressed with the current network design. The cybertwin is equipped with a variety of capabilities, including communication assistants, network data loggers, and digital asset owners, to address these difficulties. While spectrum resources are limited, effective resource management and sharing are essential in achieving these requirements. With this motivation, this article presents a new metaheuristic with blockchain based resource allocation technique (MWBA-RAT) for cybertwin driven 6G on IoE environment. The incorporation of the blockchain in 6G enables the network to monitor, manage, and share resources effectively. The proposed MWBA-RAT technique designs a new quasi-oppositional search and rescue optimization (QO-SRO) algorithm for the optimal resource allocation process and this shows the novelty of the work. The QO-SRO algorithm involves the integration of the quasi oppositional based learning concept with the traditional SRO algorithm to improve its convergence rate. A wide range of experiments are performed to highlight the enhanced outcomes of the MWBA-RAT technique.

Metaheuristic Optimization-Based Resource Allocation Technique for Cybertwin-Driven 6G on IoE Environment

One motivation of grid computing is to aggregate the power of widely distributed resources, and provide non-trivial services to users. To achieve this goal, an efficient grid fault tolerance system is an essential part of the grid. Rather than covering the whole grid fault tolerance area, this survey provides a review of the subject mainly from the perspective of check point. In this review the challenges for fault tolerance are identified. In grid environments, execution failures can occur for various reasons such as network failure, overloaded resource conditions, or non-availability of required software components. Thus, fault-tolerant systems should be able to identify and handle failures and support reliable execution in the presence of concurrency and failures. In scheduling a large number of user jobs for parallel execution on an open-resource grid system, the jobs are subject to system failures or delays caused by infected hardware, software vulnerability, and distrusted security policy. In this paper we propose a task level fault tolerance. Task-level techniques mask the effects of the execution failure of tasks. Four task level techniques are retry, alternate resource, check point and replication. Check point technique strategy achieves optimal load balance across different grid sites. These fault tolerance task level techniques can upgrade grid performance significantly at only a moderate in extra resources or scheduling delays in a risky grid computing environment.

Fault Tolerance-Genetic Algorithm for Grid Task Scheduling using Check Point

The information about the patients can be maintained with clinical documents. By keeping huge volume of clinical documents we can easily predict the occurrence of any disease in the patients. Dengue is considered to be one of the vital disease which are spreading in more than 110 countries. It is a vector borne disease caused by the mosquito’s of female Aedes Albopictus and Aedes Aegypti which are well suited human environment. We have implemented a data mining technique called ANN which is a well-known technique for classification of data used here to classify diseases. We have analyzed the patients’ dataset for the occurrence of dengue and experimented with Weka and Netbeans IDE and the result is proved to be more accurate than the CART algorithm.

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M. Prakash

Papers

Metaheuristic Optimization-Based Resource Allocation Technique for Cybertwin-Driven 6G on IoE Environment

Fault Tolerance-Genetic Algorithm for Grid Task Scheduling using Check Point

Detection of dengue disease using artificial neural network based classification technique

Design a FPGA, fuzzy based, insolent method for prediction of multi-diseases in rural area

Mitigating eavesdropping by using fuzzy based MDPOP-Q learning approach and multilevel Stackelberg game theoretic approach in wireless CRN