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Top1. Introduction
The convergence of smart devices and wireless sensors with the Internet has ushered in a new revolution. Today, a whole new environment of “Machine-to-Machine (M2M)” interaction (How Machine-to-Machine Communication Works, n.d.; Pinto, n.d.; Gronbaek, 2008) is emerging focused on the issues of how machines communicate, how they are managed, how the data within them are managed, and perhaps most importantly, how the world (humans, businesses and society) can deal with them. The term “Pervasive Internet” (Pinto, n.d.) refers to the convergence of machine-to-machine communications, Internet connectivity, enterprise-level data-management applications, and Web-based smart services. The phenomenon arises from the connection of smart devices to the Internet, enabling fully automated global communication, data-collection and control. Within the next few years, more machines will be connected via the Internet than humans, eventually reaching tens of billions of connections (Pinto, n.d.; Gronbaek, 2008).
M2M allows a transition from a data-driven communication model to a knowledge-driven model (Pinto, n.d.). M2M provides communications between people, devices, and systems and sometimes turns data into information that manufacturers can subsequently use to develop new services-based business models, with the potential to drive growth. M2M networks can be opened for use in both public and private architectures over general-purpose backbones, using a standard method of data communication, namely, the Internet protocol, with the proposed extensions, via wired or wireless networks. This would allow M2M networks to provide ubiquitous services. Connections between people, devices, systems, machines, and business processes allow interactions among devices that talk with one another, figure out the appropriate action, and execute upon that information. Interactions on such a scale require a common and unified view of data (Gronbaek, 2008) and a precise definition of shared interfaces. Such a shared interface lifts the semantics out of the application logic as a separate, independent layer (Gronbaek, 2008; Gronbaek, 2007). Ontology is a representation form that captures the semantics.
Ontologies (Chandrasekaran, Josephson, & Benjamins, 1999; Lenat & Guha, 1990; Lenat, Guha, Pittman, Pratt, & Shepherd, 1990; Pirlein, 1993) are used in Knowledge Management and Artificial Intelligence to solve semantic issues. They have also been successfully used (Lopez de Vergara, Villagra, Asensio, & Berrocal, 2003; Lopez de Vergara, Villagra, & Berrocal, 2004; Wong, Ray, Parameswaran, & Strassner, 2005; Su, Alapnes, & Shiaa, 2008; Al’ipio, Neves, & Carvalho, 2006; Sensoy & Yolum, 2007; Chen, Perich, Finin, & Joshi, 2004; Roman, Hess, Cerqueira, Ranganathan, Campbell, & Gaia, 2002; Gu, Wang, Pung, & Zhang, 2004) to solve similar semantic problems in other domains such as Semantic Web (Owl Guide, n.d.), where these knowledge-based techniques provide the semantics to web pages and web services. This paper explores how ontologies can be useful for network and service management of ubiquitous M2M networks, as a way to unify heterogeneous definitions of management information and service functionality under one common framework to reach the desired semantic interoperability among multiple components of these networks, anywhere and everywhere.