Sensor Information Representation for the Internet of Things

Transcription

1 Sensor Information Representation for the Internet of Things Jiehan Zhou University of Oulu, Finland Carleton University, Canada

2 Agenda Internet of Things and Challenges Application example and representation requirements Review on Information Representation for IoT Sensors Conclusion

3 The Internet of Things

4 future computing would depend on more data captured by computer-enabled objects or things, rather than on data originated by people. Thus he brought the idea of the Internet of Things (IoT), in which objects are identified and are able to perceive or understand surrounding data. Objects are connected and are able to interact with servers over the Internet. Objects are also able to make queries and change their states and or their information programs. [Ashton 1999] IoT is about embedding intelligence so that things become smarter and do more than they did before. [Negroponte 2012]

5 We are heading into a new era of ubiquity, where the users of the Internet will be counted in billions, and where humans may become the minority as generators and receivers of traffic. Changes brought about by the Internet will be dwarfed by those prompted by the networking of everyday objects UN report

6 Four steps to the Internet of Things * The Internet of Things viewed as a network for communicating devices and based upon four degrees of sophistication, involving: Purely passive devices (RFID) that yield fixed data output when queried Devices with moderate processing power to format carrier messages, with the capability to vary content with respect to time and place Sensing devices that are capable of generating and communicating information about environment or item status when queried Devices with enhanced processing capability that facilitate decisions to communicate between devices without human intervention introducing a degree of intelligence into networked systems * European Commission (2007) From RFID to the Internet of Things Pervasive networked systems

7 Principal challenges: 1. Disconnection between logical and physical worlds, how to make the sensing data meaningful to the real life 2. Lack of interoperability structural and semantic heterogeneity, Limitations of Enterprise application integration (EAI) for enterprise-wide and inter-enterprise integration 3. Developments in information integration schema integration, semantic mediation and ontology merging more intelligent search engines

8 Application examples and representation requirements

9 Ongoing study on IoT [Mammoth project 2012]

10 The ubiquitous city Oulu towards IoT-enabled city

11 11 An example: IoT-enabled SmartCities

12 Microsoft's KinEtre Microsoft's KinEtre Animates Household Objects New Kinect technique lets your body control virtual versions of anything mail&utm_campaign=080912

13 Internet of Things visions (networked smart things) =related elmfu Understanding the data interoperability sensor involves different types of data, whether small and static, or big and dynamic. Fusion of data from multiple sensors leads to the extraction of knowledge that cannot be inferred from using individual sensors alone. It is necessary that the context terminology is commonly understood by all participating devices. Sensor ontology (i.e. representation scheme) is what facilitates the data fusion and increases interoperability

14 A semantic perspective on the IoT Sensor data querying and (pre-)processing Data heterogeneity Data quality New inference capabilities required to deal with sensor information Sensor data model representation and management For data publication, integration and discovery Bridging between sensor data and ontological representations for data integration Ontologies: Observations and measurements, time series, etc. Event models User interaction with sensor data

15 Sensor data representation requirements The scheme needs to be generic and extensible Able to describe technical information Able to describe sensor locations Able to describe physical sensor information Able to describe operational information on process and results

16 Reviews

17 Existing efforts for representing sensor data Name W3C SSN ontology (W3C) ontology for adaptive sensor networks (Avancha et al. 2004) Description Ten modules, consisting of 41 concepts and 39 object properties, built on four perspectives of sensor, observation, system, and property. Consisting of the main concepts of processor CPU and memory, power supply, and radio and sensor modules. sensor network modeling framework (Jurdak et al. 2004) ontology for sensor-rich information system (Jie Liu & Feng Zhao 2005) The ontology describes the network topology and settings, sensor description, and data flow. UML-based representation for a vehicle/human detection ontology. sensor data ontology (Alamri et al. 2006) Sensor data s pedigree ontology (Matheus et al. 2005) SensorML (Sensor Modeling Language) The initial class taxonomy includes two main concepts: data and sensor. The refined pedigree ontology consists of concepts of sensor, system, human, setting, software, InfoSource, and report data etc. It aims to describe the geometric, dynamic and observational properties of dynamic sensors. OntoSensor (Russomanno et al. 2005) W3C Efficient XML Interchange (Efficient XML Interchange) DPWS (DPWS) It includes definitions of concepts and properties adopted from SensorML, extensions to IEEE SUMO and references to ISO and constructs of the Web Ontology Language (OWL). IoT exploits the EXI format to mitigate the size and parsing complexity of the XML while maintaining most of its capability. It defines a minimal set of implementation constraints to enable secure Web Service messaging, discovery, description, and eventing on resource-constrained IoT devices.

18 Conclusion Internet of Things and challenges Example examinations and requirements Survey of the start of the art