1. 1 SATWARE: A Semantic Middleware for Multi Sensor Applications Sharad Mehrotra

2. 2 SAFIRE Stream Middleware Writing sensor applications is hard: -Continuous data -Sensor heterogeneity -Diversity of platforms -Tolerance to failures Powerful programming abstractions to ease application development Hide heterogeneity, failures, concurrency Core Services alerting, triggering, data & stream management, queries. Mediation application needs with resource constraints of devices & networks Sensor FICB FiltersAlerts Analysis Networks SA Applications Middleware – glue between H/w, networks, OS and applications Networks Stream Middleware Goals

3. 3 Key Concepts Driving SATWARE Semantic Level: Entities -- people, appliances, and buildings, rooms; Relationships – interactions. Infrastructure Level: sensing devices, computing devices, network devices. Virtual Sensors: maps data captured by sensors into events in the semantic world. Event Logs: evolution of physical world as observed by the sentient system 3 SATware models sensor embedded spaces at two levels sentient Applications Virtual Sensor High level stream language like CQL

4. 4 Key Concept: Virtual Sensors Provide the “bridge” between sensors & the semantic “real” world concepts. L, Room12, t> Filter [L=Room1] AP Readings Listener AP Readings to location Translate Location to Lon./Lat. Finger print DB Location Virtual Sensor WiFi fingerprints, t>

5. 5 Virtual Sensors: Multi-Sensor Fusion to improve quality AP Readings Listener AP Readings to location Finger print DB Signal strength Listener Signal strength triagulation AP locations Merge <Person, L, Room12, t> Location Virtual Sensor Using fingerprints Location Virtual Sensor Using signal Strength triangulation

6. 6 Virtual Sensors: Speech illustrating how semantics can help improve quality speech DB Acoustic analysis Location Virtual Sensor Using speech recognition Location Virtual Sensor Using acoustic analysis Audio listeners Audio stream Speech recognizer Data Cleaning using semantics Merge

7. 7 Building Applications using Semantic Model Virtual Sensors “hide” complexity of sensor programming from application developers  Convert heterogeneous sensor streams into semantic event streams  Hide sensor failures / imprecision through Noise reduction (e.g., averaging over multiple samples) multi-sensor fusion (e.g., multiple location sensing technologies provide more accurate location assessment) Semantics (e.g., speech sensors exploit word correlation to improve on ASR) Applications can view the system as consisting of high level concepts such as entities, events, artifacts, spaces, etc. SATWARE supports high level query languages for implementing queries & triggers:  SQL style stream language (at design stage – not yet implemented)  Event graph based language

8. 8 Event Graphs in SATWARE Triggers/continuous queries are converted into an event graph network. SATWARE Deployer submits the resulting event graph into an executable pipeline based on available resources, machines and networks.  Mediates with resources to guarantee application needs are met  Multiple optimizations possible in executing such networks. Loc operator [FF1] Join [t] Filter [L=first floor] Loc operator [FF2] { } Near [5 Rooms] Detect when Fire Fighter 1 is on the 1 st floor Detect when FF1 & FF2 are near each other

9. 9 Demo 5/27/09

10. 10 SATWARE Summary Middleware to ease multi-sensor applications  provides a powerful semantic interface for complex multi-sensor applications this feature used extensively in building SAFIRE SA Applications  Supports core services Alerts, triggers, storage, archival, & replay capabilities.  Mediation between application needs & system resources E.g., sensor stream scheduling based on application quality requirement 5/27/09