Sensors, coupled with transceivers, have quickly evolved from technologies purely confined to laboratory test beds to workable solutions used across the globe. These mobile and connected devices form the nuts and bolt...
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Sensors, coupled with transceivers, have quickly evolved from technologies purely confined to laboratory test beds to workable solutions used across the globe. These mobile and connected devices form the nuts and bolts required to fulfill the vision of the so-called internet of things (IoT). This idea has evolved as a result of proliferation of electronic gadgets fitted with sensors and often being uniquely identifiable (possible with technological solutions such as the use of Radio Frequency Identifiers). While there is a growing need for comprehensive modeling paradigms as well as example case studies for the IoT, currently there is no standard methodology available for modeling such real-world complex IoT-based scenarios. Here, using a combination of complex networks-based and agent-based modeling approaches, we present a novel approach to modeling the IoT. Specifically, the proposed approach uses the cognitive agent-based computing (CABC) framework to simulate complex IoT networks. We demonstrate modeling of several standard complex network topologies such as lattice, random, small-world, and scale-free networks. To further demonstrate the effectiveness of the proposed approach, we also present a case study and a novel algorithm for autonomous monitoring of power consumption in networked IoT devices. We also discuss and compare the presented approach with previous approaches to modeling. Extensive simulation experiments using several network configurations demonstrate the effectiveness and viability of the proposed approach.
Understanding the cognitive evolution of researchers as they progress in academia is an important but complex problem;one that belongs to a class of problems, which often require the development of models to gain furt...
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Understanding the cognitive evolution of researchers as they progress in academia is an important but complex problem;one that belongs to a class of problems, which often require the development of models to gain further understanding of the intricacies of the domain. The research question that we address in this paper is: how to effectively model this temporal cognitive mental development of prolific researchers? Our proposed solution is based on noting that the academic progression and notability of a researcher are linked with a progressive increase in the citation count for the scholar's refereed publications, quantified using indices such as the Hirsch index. We propose the use of an yearly increment of a scholar's cognition quantifiable by means of a function of the scholar's citation index, thereby considering the index as an indicator of the discrete approximation of the scholar's cognitive development. Using validated agent-based modeling, a paradigm presented as part of our previous work aimed at the development of a cognitive agent-based computing framework, we present both formal as well as visual agent-based complex network representations for this cognitive evolution in the form of a temporal cognitive level network model. As proof of the effectiveness of this approach, we demonstrate the validation of the model using historic data of citations.
Our high-level goal is to answer questions concerned with social influence such as: "Who influences whom?", "Who can be influenced?", "Why is an individual attracted to a particular group?&quo...
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Our high-level goal is to answer questions concerned with social influence such as: "Who influences whom?", "Who can be influenced?", "Why is an individual attracted to a particular group?", and "Who is the most influential individual in a particular social network?". To ask these questions we need to define social influence. In this paper we provide a formal definition appropriate to our world of Big Data and automated reasoning. Despite the pervasiveness of influence throughout society and given the vast and disparate literature on the topic, we observe a dearth of work on formalising its semantics. To remedy this, based on the literature, we have categorised and formalised five essential types. To our knowledge this is the first attempt to implement a nuanced representation, and it provides us with a conceptual basis for automated reasoning about social interactions. Crown Copyright (C) 2014 Published by Elsevier B.V. All rights reserved.
Recent work has demonstrated the effectiveness of using complex networks for studying complex biological interactions. The process often involves conversion of biological data such as gene expressions or biochemical i...
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ISBN:
(纸本)9780769549279;9781467349468
Recent work has demonstrated the effectiveness of using complex networks for studying complex biological interactions. The process often involves conversion of biological data such as gene expressions or biochemical interactions into one of numerous complex network file formats. However, the exact selection of an appropriate file format for a particular dataset often poses a non-trivial decision problem;biologists are non-specialist end-users and find it difficult to select a particular format for data storage and conversion. In this paper, we propose a solution to this problem of the selection of a suitable network format by means of a critical evaluation based on performance analysis of empirical experiments on biological data sets of different sizes. Experimental results substantiate the hypothesis of being extra careful in the selection of a suitable complex network format based primarily on the size of the biological dataset.
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