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

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.