107 research outputs found

    The Internet of Everything

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    In the era before IoT, the world wide web, internet, web 2.0 and social media made people’s lives comfortable by providing web services and enabling access personal data irrespective of their location. Further, to save time and improve efficiency, there is a need for machine to machine communication, automation, smart computing and ubiquitous access to personal devices. This need gave birth to the phenomenon of Internet of Things (IoT) and further to the concept of Internet of Everything (IoE)

    The Internet of Everything

    Get PDF
    In the era before IoT, the world wide web, internet, web 2.0 and social media made people’s lives comfortable by providing web services and enabling access personal data irrespective of their location. Further, to save time and improve efficiency, there is a need for machine to machine communication, automation, smart computing and ubiquitous access to personal devices. This need gave birth to the phenomenon of Internet of Things (IoT) and further to the concept of Internet of Everything (IoE)

    Enabling and Understanding Failure of Engineering Structures Using the Technique of Cohesive Elements

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    In this paper, we describe a cohesive zone model for the prediction of failure of engineering solids and/or structures. A damage evolution law is incorporated into a three-dimensional, exponential cohesive law to account for material degradation under the influence of cyclic loading. This cohesive zone model is implemented in the finite element software ABAQUS through a user defined subroutine. The irreversibility of the cohesive zone model is first verified and subsequently applied for studying cyclic crack growth in specimens experiencing different modes of fracture and/or failure. The crack growth behavior to include both crack initiation and crack propagation becomes a natural outcome of the numerical simulation. Numerical examples suggest that the irreversible cohesive zone model can serve as an efficient tool to predict fatigue crack growth. Key issues such as crack path deviation, convergence and mesh dependency are also discussed

    Metaheuristics Techniques for Cluster Head Selection in WSN: A Survey

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    In recent years, Wireless sensor communication is growing expeditiously on the capability to gather information, communicate and transmit data effectively. Clustering is the main objective of improving the network lifespan in Wireless sensor network. It includes selecting the cluster head for each cluster in addition to grouping the nodes into clusters. The cluster head gathers data from the normal nodes in the cluster, and the gathered information is then transmitted to the base station. However, there are many reasons in effect opposing unsteady cluster head selection and dead nodes. The technique for selecting a cluster head takes into factors to consider including residual energy, neighbors’ nodes, and the distance between the base station to the regular nodes. In this study, we thoroughly investigated by number of methods of selecting a cluster head and constructing a cluster. Additionally, a quick performance assessment of the techniques' performance is given together with the methods' criteria, advantages, and future directions

    Mobile Oriented Future Internet (MOFI)

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    This Special Issue consists of seven papers that discuss how to enhance mobility management and its associated performance in the mobile-oriented future Internet (MOFI) environment. The first two papers deal with the architectural design and experimentation of mobility management schemes, in which new schemes are proposed and real-world testbed experimentations are performed. The subsequent three papers focus on the use of software-defined networks (SDN) for effective service provisioning in the MOFI environment, together with real-world practices and testbed experimentations. The remaining two papers discuss the network engineering issues in newly emerging mobile networks, such as flying ad-hoc networks (FANET) and connected vehicular networks

    Enhanced delay-aware and reliable routing protocol for wireless sensor network

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    Wireless Sensor Networks (WSN) are distributed low-rate data networks, consist of small sensing nodes equipped with memory, processors and short range wireless communication. The performance of WSN is always measured by the Quality of Service (QoS) parameters that are time delay, reliability and throughput. These networks are dynamic in nature and affect the QoS parameters, especially when real time data delivery is needed. Additionally, in achieving end-to-end delay and reliability, link failures are the major causes that have not been given much attention. So, there is a demanding need of an efficient routing protocol to be developed in order to minimize the delay and provide on time delivery of data in real time WSN applications. An efficient Delay-Aware Path Selection Algorithm (DAPSA) is proposed to minimize the access end-to-end delay based on hop count, link quality and residual energy metrics considering the on time packets delivery. Furthermore, an Intelligent Service Classifier Queuing Model (ISCQM) is proposed to distinguish the real time and non-real time traffic by applying service discriminating theory to ensure delivery of real time data with acceptable delay. Moreover, an Efficient Data Delivery and Recovery Scheme (EDDRS) is proposed to achieve improved packet delivery ratio and control link failures in transmission. This will then improve the overall throughput. Based on the above mentioned approaches, an Enhanced Delay-Aware and Reliable Routing Protocol (EDARRP) is developed. Simulation experiments have been performed using NS2 simulator and multiple scenarios are considered in order to examine the performance parameters. The results are compared with the state-of-the-art routing protocols Stateless Protocol for Real-Time Communication (SPEED) and Distributed Adaptive Cooperative Routing Protocol (DACR) and found that on average the proposed protocol has improved the performance in terms of end-to-end delay (30.10%), packet delivery ratio (9.26%) and throughput (5.42%). The proposed EDARRP protocol has improved the performance of WSN

    Personality Identification from Social Media Using Deep Learning: A Review

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    Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed
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