Rule-Based Semantic Sensing

Rule-Based Systems have been in use for decades to solve a variety of problems but not in the sensor informatics domain. Rules aid the aggregation of low-level sensor readings to form a more complete picture of the real world and help to address 10 identified challenges for sensor network middleware. This paper presents the reader with an overview of a system architecture and a pilot application to demonstrate the usefulness of a system integrating rules with sensor middleware.Comment: Proceedings of the Doctoral Consortium and Poster Session of the 5th International Symposium on Rules (RuleML 2011@IJCAI), pages 9-16 (arXiv:1107.1686

Rule-based semantic sensing platform for activity monitoring

Sensors are playing an increasingly important role in our lives, and for these devices to perform to their maximum potential, they need to work together. A single device can provide a single service or a fixed set of services but, when combined with other sensors, different classes of applications become implementable. The vital criterion for this to happen is the ability to bring information from all sensors together, so that all measured physical phenomena can contribute to the solution. Mediation between applications and physical sensors is the responsibility of sensor network middleware (SNM). Rapid growth in the kinds of sensors and applications for sensors/sensor systems, and the consequent importance of sensor network middleware has raised the need to relatively rapidly build engineering applications from those components. A number of SNM exist, each of which attempts to solve the sensor integration problem in a different way. These solutions, based on their ‘closeness’ either to sensors or to applications, can be classified as low-level and high-level. Low-level SNM tends not to focus on making application development easy, while high-level SNM tends to be ‘locked-in’ to a particular set of sensors. We propose a SNM suitable for the task of activity monitoring founded on rules and events, integrated through a semantic event model. The proposed solution is intended to be open at the bottom – to new sensor types; and open at the top – to new applications/user requirements. We show evidence for the effectiveness of this approach in the context of two pilot studies in rehabilitation monitoring – in both hospital and home environment. Moreover, we demonstrate how the semantic event model and rule-based approach promotes verifiability and the ability to validate the system with domain experts

Challenges in Annotation of useR Data for UbiquitOUs Systems: Results from the 1st ARDUOUS Workshop

Labelling user data is a central part of the design and evaluation of pervasive systems that aim to support the user through situation-aware reasoning. It is essential both in designing and training the system to recognise and reason about the situation, either through the definition of a suitable situation model in knowledge-driven applications, or through the preparation of training data for learning tasks in data-driven models. Hence, the quality of annotations can have a significant impact on the performance of the derived systems. Labelling is also vital for validating and quantifying the performance of applications. In particular, comparative evaluations require the production of benchmark datasets based on high-quality and consistent annotations. With pervasive systems relying increasingly on large datasets for designing and testing models of users' activities, the process of data labelling is becoming a major concern for the community. In this work we present a qualitative and quantitative analysis of the challenges associated with annotation of user data and possible strategies towards addressing these challenges. The analysis was based on the data gathered during the 1st International Workshop on Annotation of useR Data for UbiquitOUs Systems (ARDUOUS) and consisted of brainstorming as well as annotation and questionnaire data gathered during the talks, poster session, live annotation session, and discussion session