Suparna De

Knowledge Representation in the Internet of Things: Semantic Modelling and its Applications

Semantic modelling provides a potential basis for interoperating among different systems and applications in the Internet of Things (IoT). However, current work has mostly focused on IoT resource management while not on the access and utilisation of information generated by the “Things”. We present the design of a comprehensive and lightweight semantic description model for knowledge representation in the IoT domain. The design follows the widely recognised best practices in knowledge engineering and ontology modelling. Users are allowed to extend the model by linking to external ontologies, knowledge bases or existing linked data. Scalable access to IoT services and resources is achieved through a distributed, semantic storage design. The usefulness of the model is also illustrated through an IoT service discovery method.

Semantic enablers for dynamic digital-physical object associations in a federated node architecture for the Internet of Things

Abstract The Internet of Things (IoT) paradigm aims to realize heterogeneous physical world objects interacting with each other and with the surrounding environment. In this prospect, the automatic provisioning of the varied possible interactions and bridging them with the digital world is a key pertinent issue for enabling novel IoT applications. The introduction of description logic-based semantics to provide homogeneous descriptions of object capabilities enables lowering the heterogeneity and a limited set of interactions (such as those with stationary objects with fixed availability) to be deduced using classical reasoning systems. However, the inability of such semantics to capture the dynamics of an IoT system as well as the scalability issues that reasoning systems encounter if too many descriptions have to be processed, necessitate that such approaches should be used in conjunction with others. Towards this aim, this paper proposes an automated rule-based association mechanism for integrating the digital IoT components with physical entities along temporal–spatial–thematic axes. To address the scalability issue, this mechanism is distributed over a federated network of nodes, each embodying a set of objects located in the same geographical area. Nodes covering nearby geographical areas can share their object descriptions while all nodes are capable of deducing interactions between the descriptions that they are aware of.

An experimental study on geospatial indexing for sensor service discovery

A scalable platform supporting large-scale sensor service discovery.A discovery approach more resilient to dynamicity of sensor services and environment.Accurate computation of the geographical features of gateways and sensor services.Superior performance over existing methods for response time and throughput. The Internet of Things enables human beings to better interact with and understand their surrounding environments by extending computational capabilities to the physical world. A critical driving force behind this is the rapid development and wide deployment of wireless sensor networks, which continuously produce a large amount of real-world data for many application domains. Similar to many other large-scale distributed technologies, interoperability and scalability are the prominent and persistent challenges. The proposal of sensor-as-a-service aims to address these challenges; however, to our knowledge, there are no concrete implementations of techniques to support the idea, in particular, large-scale, distributed sensor service discovery. Based on the distinctive characteristics of the sensor services, we develop a scalable discovery architecture using geospatial indexing techniques and semantic service technologies. We perform extensive experimental studies to verify the performance of the proposed method and its applicability to large-scale, distributed sensor service discovery.

Search Techniques for the Web of Things: A Taxonomy and Survey

The Web of Things aims to make physical world objects and their data accessible through standard Web technologies to enable intelligent applications and sophisticated data analytics. Due to the amount and heterogeneity of the data, it is challenging to perform data analysis directly; especially when the data is captured from a large number of distributed sources. However, the size and scope of the data can be reduced and narrowed down with search techniques, so that only the most relevant and useful data items are selected according to the application requirements. Search is fundamental to the Web of Things while challenging by nature in this context, e.g., mobility of the objects, opportunistic presence and sensing, continuous data streams with changing spatial and temporal properties, efficient indexing for historical and real time data. The research community has developed numerous techniques and methods to tackle these problems as reported by a large body of literature in the last few years. A comprehensive investigation of the current and past studies is necessary to gain a clear view of the research landscape and to identify promising future directions. This survey reviews the state-of-the-art search methods for the Web of Things, which are classified according to three different viewpoints: basic principles, data/knowledge representation, and contents being searched. Experiences and lessons learned from the existing work and some EU research projects related to Web of Things are discussed, and an outlook to the future research is presented.

Ontology-based context inference and query for mobile devices

The vision of service personalization for mobile communication environments entails context sensitive service provisioning. The realization of such customizable smart spaces necessitates acquisition and processing of modality context information from a variety of devices in the ambient environment. The heterogeneity of available device capabilities and description formats brings new challenges for a context reasoning engine that formulates content delivery decisions. Specifically, to ensure interoperability with existing application logic, the enabling components should support semantic queries. Secondly, situations where variously formatted context input may not provide enough information to answer queries, should be intelligently handled. Towards this aim, this paper discusses a context reasoning and query interface component as part of a service context manager (SCM) framework that supports semantic querying and handles incomplete context information through a rule-based mechanism. The validation of the approach is provided by showing the mapping of disparate UAProf and UPnP descriptions into the framework and querying of supported modality services.

A framework for mobile, context-aware applications

The heterogeneous, dynamic nature of current mobile environments necessitates that all system components that form part of a personalization framework should be context aware. This necessitates the development of a framework of distributed context handlers to derive meaning from context and combine it with application logic. Towards this aim, this paper presents a Service Context Manager (SCM) framework that handles all the stages of context gathering, processing and reasoning to enable personalized service presentation. The validation of the approach is shown through application use cases.

Test-Enabled Architecture for IoT Service Creation and Provisioning

The information generated from the Internet of Things (IoT) potentially enables a better understanding of the physical world for humans and supports creation of ambient intelligence for a wide range of applications in different domains. A semantics-enabled service layer is a promising approach to facilitate seamless access and management of the information from the large, distributed and heterogeneous sources. This paper presents the efforts of the IoT.est project towards developing a framework for service creation and testing in an IoT environment. The architecture design extends the existing IoT reference architecture and enables a test-driven, semantics-based management of the entire service lifecycle. The validation of the architecture is shown though a dynamic test case generation and execution scenario.

A ranking method for sensor services based on estimation of service access cost

The concept of sensing-as-a-service is proposed to enable a unified way of accessing and controlling sensing devices for many Internet of Things based applications. Existing techniques for Web service computing are not sufficient for this class of services that are exposed by resource-constrained devices. The vast number of distributed and redundantly deployed sensors necessitate specialised techniques for their discovery and ranking. Current research in this line mostly focuses on discovery, e.g., designing efficient searching methods by exploiting the geographical properties of sensing devices. The problem of ranking, which aims to prioritise semantically equivalent sensor services returned by the discovery process, has not been adequately studied. Existing methods mostly leverage the information directly associated with sensor services, such as detailed service descriptions or quality of service information. However, assuming the availability of such information for sensor services is often unrealistic. We propose a ranking strategy by estimating the cost of accessing sensor services. The computation is based on properties of the sensor nodes as well as the relevant contextual information extracted from the service access process. The evaluation results demonstrate not only the superior performance of the proposed method in terms of ranking quality measure, but also the potential for preserving the energy of the sensor nodes.

Device discovery in future service platforms through SIP

This paper proposes an extension to Session Initiation Protocol (SIP) for contextualized service delivery in a service delivery platform (SDP) that enables device specific multimedia delivery. SIP separates between session establishment and description and is thus, amenable to be extended for advanced implementations which make it an ideal platform for service creation. Device specific multimedia delivery needs rich and flexible device descriptions, and our approach proposes advanced device descriptions through semantic technologies. The proposed SIP extensions have been implemented on a SIP Application Server which functions as SDP in IP Multimedia Subsystem (IMS). The validation of the proposed extensions is shown through an Android SIP client application that acts as a device browser and recommender for different multimedia services to users. An example device user agent (UA) application has also been implemented on a laptop.

Device and service descriptions in Personal Distributed Environments

The current research on context-aware systems in ubiquitous environments opens a number of interlinked research challenges. On the lowest level of such systems, discovery mechanisms and flexible semantic descriptions of available devices and services form the basis for end-user service personalization. The challenge is to design a description model that leverages implicit semantic information obtained, while being cognizant of resource constraints of a device. To encounter the different device characteristics and personalization challenges, this paper proposes a device and service description approach that provides a high level contextual view of device information. The work has been performed as part of the Personal Distributed Environment concept, also described in the paper. Further, a user-centric view of multiple user interface devices to access services in a heterogeneous and dynamic networked environment has been implemented by extending UPnP device discovery. A comparison with existing state of the art approaches concludes the work.