Sensor Search Techniques for Sensing as a Service Architecture for The Internet of Things

The Internet of Things (IoT) is part of the Internet of the future and will comprise billions of intelligent communicating "things" or Internet Connected Objects (ICO) which will have sensing, actuating, and data processing capabilities. Each ICO will have one or more embedded sensors that will capture potentially enormous amounts of data. The sensors and related data streams can be clustered physically or virtually, which raises the challenge of searching and selecting the right sensors for a query in an efficient and effective way. This paper proposes a context-aware sensor search, selection and ranking model, called CASSARAM, to address the challenge of efficiently selecting a subset of relevant sensors out of a large set of sensors with similar functionality and capabilities. CASSARAM takes into account user preferences and considers a broad range of sensor characteristics, such as reliability, accuracy, location, battery life, and many more. The paper highlights the importance of sensor search, selection and ranking for the IoT, identifies important characteristics of both sensors and data capture processes, and discusses how semantic and quantitative reasoning can be combined together. This work also addresses challenges such as efficient distributed sensor search and relational-expression based filtering. CASSARAM testing and performance evaluation results are presented and discussed.Comment: IEEE sensors Journal, 2013. arXiv admin note: text overlap with arXiv:1303.244

Multimodality in Pervasive Environment

Future pervasive environments are expected to immerse users in a consistent world of probes, sensors and actuators. Multimodal interfaces combined with social computing interactions and high-performance networking can foster a new generation of pervasive environments. However, much work is still needed to harness the full potential of multimodal interaction. In this paper we discuss some short-term research goals, including advanced techniques for joining and correlating multiple data flows, each with its own approximations and uncertainty models. Also, we discuss some longer term objectives, like providing users with a mental model of their own multimodal "aura", enabling them to collaborate with the network infrastructure toward inter-modal correlation of multimodal inputs, much in the same way as the human brain extracts a single self-conscious experience from multiple sensorial data flows