Mr Tarek Elsaleh
- 'IoT-Lite: A Lightweight Semantic Model for the Internet of Things and its Use with Dynamic Semantics'.
Personal and Ubiquitous Computing,
[ Status: Accepted ]
Over the past few years the semantics community has developed several ontologies to describe concepts and relationships for Internet of Things (IoT) applications. A key problem is that most of the IoT related semantic descriptions are not as widely adopted as expected. One of the main concerns of users and developers is that semantic techniques increase the complexity and processing time and therefore they are unsuitable for dynamic and responsive environments such as the IoT. To address this concern, we propose IoT-Lite, an instantiation of the semantic sensor network (SSN) ontology to describe key IoT concepts allowing interoperability and discovery of sensory data in heterogeneous IoT platforms by a lightweight semantics. We propose 10 rules for good and scalable semantic model design and follow them to create IoT-Lite. We also demonstrate the scalability of IoT-Lite by providing some experimental analysis, and assess IoT-Lite against another solution in terms of round trip time (RTT) performance for query-response times. We have linked IoTLite with Stream Annotation Ontology (SAO), to allow queries over stream data annotations and we have also added dynamic semantics in the form of MathML annotations to IoT-Lite. Dynamic semantics allows the annotation of spatio-temporal values, reducing storage requirements and therefore the response time for queries. Dynamic semantics stores mathematical formulas to recover estimated values when actual values are missing.
- 'A Proof-of-Concept for Semantically Interoperable Federation of IoT Experimentation Facilities'.
SENSORS, 16 (7) Article number ARTN 1006 doi: 10.3390/s16071006
- 'An Internet of Things Platform for Real-World and Digital Objects'.
Scalable Computing: Practice and Experience, 13 (1), pp. 45-57.Repository URL: http://epubs.surrey.ac.uk/531903/
The vision of the Internet of Things (IoT) relies on the provisioning of real-world services, which are provided by smart objects that are directly related to the physical world. A structured, machine-processible approach to provision such real-world services is needed to make heterogeneous physical objects accessible on a large scale and to integrate them with the digital world. The incorporation of observation and measurement data obtained from the physical objects with the Web data, using information processing and knowledge engineering methods, enables the construction of ”intelligent and interconnected things”. The current research mostly focuses on the communication and networking aspects between the devices that are used for sensing amd measurement of the real world objects. There is, however, relatively less effort concentrated on creating dynamic infrastructures to support integration of the data into the Web and provide unified access to such data on service and application levels. This paper presents a semantic modelling and linked data approach to create an information framework for IoT. The paper describes a platform to publish instances of the IoT related resources and entities and to link them to existing resources on the Web. The developed platform supports publication of extensible and interoperable descriptions in the form of linked data.
- 'Handling smart environment devices, data and services at the semantic level with the FI-WARE core platform'.
Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014, , pp. 14-20.
© 2014 IEEE.The development of IoT (Internet of Things) applications poses a number of scientific and technological challenges which stem from the characteristics of the IoT domain itself. These include the huge and increasing number of connected entities (devices, physical objects, people.,) populating the physical environment, the variety of their types which leads to heterogeneity of the data produced or consumed by those connected entities. In this paper we argue that semantic modeling takes up many of these challenges and explain how the core platform developed by the FI-WARE project supports IoT application developers. After a short introduction of the characteristics and requirements of IoT applications we identify the contribution of semantic technologies to address some of them. We describe the FI-WARE platform enablers which support these technologies and illustrate through a real application how these enablers help developers satisfy these requirements.
- 'A validation tool for the W3C SSN ontology based sensory semantic knowledge'.
CEUR Workshop Proceedings, 1401, pp. 83-88.
This paper describes an ontology validation tool that is designed for the W3C Semantic Sensor Networks Ontology (W3C SSN). The tool allows ontologies and linked-data descriptions to be validated against the concepts and properties used in the W3C SSN model. It generates validation reports and collects statistics regarding the most commonly used terms and concepts within the ontologies. An online version of the tool is available at: (http://iot.ee.Surrey.ac.uk/SSNValidation). This tool can be used as a checking and validation service for new ontology developments in the IoT domain. It can also be used to give feedback to W3C SSN and other similar ontology developers regarding the most commonly used concepts and properties from the reference ontology and this information can be used to create core ontologies that have higher level interoperability across different systems and various application domains.
- 'Service continuity for subscribers of the mobile real world Internet'.
IEEE International Conference on Communications,
The Real World Internet or the Web of Things has brought an approach to integrate wireless sensor devices in a manner that is natural to the Web, where sensors are exposed as addressable web resources like any other web resource. Although there is still a clear deficiency with regards to managing the mobility of the sensor devices in this approach, and how it affects the service and the users interacting with it. The work presented here addresses this issue and aims to provide an approach towards maintaining service continuity of migrating sensor devices in a framework that builds upon the concept of the 'Web of Things'. © 2011 IEEE.
- The Knowledge Acquisition Toolkit – KAT (Extracting knowledge from sensor data). ICS/University of Surrey
Knowledge Acquisition Toolkit (KAT) is an open-source software that includes methods to process numerical sensory data. KAT is able to extract and represent human understandable and/or machine interpretable information from raw data. KAT includes a collection of algorithms for each step of the Internet of Things (IoT) data processing workflow ranging from data and signal pre-processing algorithms such as Frequency Filters, dimensionality reduction techniques such as Wavelet, FFT, SAX, and Feature Extraction and Abstraction and Inference methods such as Clustering. Figure 1 shows the steps of the process chain for processing cyber-physical data on the Web. KAT can be used to design and evaluate algorithms for sensor data that aim to extract and find new insights from the data.