Ubiquitous sensing fuses the concepts of intelligent systems with ubiquitous computing in the development of novel sensor web applications, whereby the interaction of multiple disparate autonomous artefacts is a key requirement. In this paper, we present SIXTH, which is a middleware infrastructure for Ubiquitous Sensing that facilitates, and supports, the development and deployment of Sensor Web applications. SIXTH has been designed to be extensible, with provisions for user definable data retention policies, custom sensor data representations, and custom sensor node representations, whilst still providing a rich set of default behaviours. Within SIXTH, support is provided for the development and interaction of applications that incorporate both physical and cyber (virtual server side) sensors. With a view to supporting intelligent, in network, interaction policies, whereby sensor nodes must negotiate and coordinate their behaviour, the system has been designed to operate in conjunction with Agent Factory Micro Edition (AFME). AFME is a minimised footprint intelligent agent platform designed for resource constrained devices. It is based on the standard Agent Factory platform, which was developed for desktop machines, and is representative of a class of agent systems, which are referred to as Agent Oriented Programming frameworks. The paper discusses a ubiquitous mapping application that was developed using the middleware.

To accommodate the proliferation of heterogeneous network models and protocols, the use of semantic technologies to enable an abstract treatment of networks is proposed. Network adapters are employed to lift network specific data into a semantic representation. Semantic reasoning integrates the disparate network models and protocols into a common data model by making intelligent inferences from low-level network and device details. Automatic discovery of new devices, monitoring of device state, and invocation of device actions in a generic fashion that is agnostic of network types is enabled. A prototype system called SNoMAC is described that employs the proposed approach operating over UPnP, TR-069, and heterogeneous sensors. These sensors are integrated by means of a sensor middleware named SIXTH that augments the capabilities of SNoMAC to allow for intelligent management and configuration of a wide variety of sensor devices. A major benefit of this approach is that the addition of new models, protocols, or sensor types merely involves the development of a new network adapter based on an ontology. Additionally, the semantic representation of the network and associated data allows for a variety of client interfaces to facilitate human input to the management and monitoring of the system.By the year 2021, the Internet of Things (IoT) is expected to encompass 3.5 times as many connected devices as there are people on Earth. 1 In addition to the sheer volume of devices, there is the added complexity of dealing with the unchecked proliferation of new network data models and protocols. To deal with these issues, network and active media applications will not only require high performance and scalability but will also need the means for quickly and dynamically evolving to accommodate the changing universe of devices. Doing this effectively necessitates a new approach for integrating network models and protocols that facilitates the intelligent management of devices across layers and at various levels of abstraction. This allows devices to be handled generically as collections while maintaining the specifics necessary to monitor and control them individually. This paper advocates an approach to solving this problem that leverages the benefits afforded by semantic web technologies, combined with the advantages of intelligent middleware. This automates much of the organization and integration of heterogeneous devices and provides a platform upon which a wide variety of applications can be built.Semantic web technologies enable the definition of formal data models called "ontologies" that provide a number of conceptual and computational benefits. This includes data model alignment, heterogeneous data integration, built-in data abstraction mechanisms, automated inferencing, dynamic meta-modeling, and automatic consistency checking. These ontologies form the base upon which semantic tools such as SPARQL, 2 SWRL, 3 and OWL, 4 or a hybrid of these, can make intelligent inferences about both devices themselves and their networ...

No abstract