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    Disaster management and profile modelling of IoT objects: conceptual parameters for interlinked objects in relation to social network analysis

    Zelenkauskaite, A. and Bessis, N. and Sotiriadis, Stelios and Asimakopoulou, E. (2012) Disaster management and profile modelling of IoT objects: conceptual parameters for interlinked objects in relation to social network analysis. In: UNSPECIFIED (ed.) 2012 Fourth International Conference on Intelligent Networking and Collaborative Systems. IEEE, pp. 509-514. ISBN 9781467322799.

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    Abstract

    In recent years, the emergence of ubiquitous and pervasive computing suggests the radical transformation of the Internet to incorporate physical objects. The transformation suggests the enabling of a new form of communication and interaction style that incorporates people, their smart devices and their physical objects through the utilization of distributed sensors spread in the environment. In this study, we propose extending Internet of Things (IoT) by modelling an IoT enabled smart environment as a whole, representing the dynamic communication and interaction among all objects (users, devices, physical objects and sensors). This is by recognizing and categorizing objects' properties in the form of a generic profile. We also reflect this in a disaster management context. That is by identifying which of the parameters of these smart objects are fixed, constant and persistent over time and which parameters are actually change over time, i.e. those characterized by their transient and dynamic nature. Thus, through the process of communication and interaction of the objects, we analyze parameters by demonstrating their static and/or dynamic properties as well as those supporting context-aware variables which are evident in disaster scenarios. To achieve these goals, we designed the persistent or temporal relationships to encompass internal information of smart-objects, along with their characteristics that actually depict their capacity to offer services to users by properties' matchmaking. The interlinked relationships represent a 'social network' providing a terrain of flexible scenarios that would lead to tailored parameters to fit user preferences. To enable communication among them in a dynamic dimension we utilized a distributed topology in which communication could occur indirectly between objects. Finally, we detailed a generic - but equally applicable for disaster management - case scenario in which we used graph theory to demonstrate how embedded intelligence to real-life objects will be able to assist the smart-resource pairing, thus improving resource discovery and harvesting process by taking into consideration user needs and preferences.

    Metadata

    Item Type: Book Section
    Keyword(s) / Subject(s): Internet, business continuity, distributed sensors, emergency services, graph theory, social networking (online), ubiquitous computing, Internet of things, IoT Objects, IoT enabled smart environment, context-aware variables, disaster management, distributed sensors, distributed topology, dynamic communication, dynamic dimension, embedded intelligence, graph theory, harvesting process improvement, incorporate physical objects, interlinked objects, object property categorization, object property recognition, parameter analysis, pervasive computing, profile modelling, property matchmaking, radical transformation, real-life objects, resource discovery improvement, smart devices, smart-objects, smart-resource pairing, social network analysis, tailored parameters, ubiquitous computing, Context, Disaster management, Internet, Object recognition, Organizations, Sensors, Social network services, Disaster management, Graphs, Interlinked objects, Internet of things, Resource discovery, Social network analysis
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centre: Birkbeck Knowledge Lab
    Depositing User: Stelios Sotiriadis
    Date Deposited: 27 Jun 2018 14:55
    Last Modified: 30 Jul 2019 01:00
    URI: http://eprints.bbk.ac.uk/id/eprint/21838

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