1. University of Virginia Data Services in Real-Time Systems and Sensor Networks: Timeliness, Freshness, and QoS Sang H. Son Department of Computer Science University of Virginia Charlottesville, Virginia 22904

2. University of Virginia Input –current state (view) update –tasks to be performed by real-time systems Output –actions to change real world situation –information to be used to support decision-making Real World Real-Time (Embedded) System Input Output

3. University of Virginia Real-Time Systems Real-time systems –timeliness and predictability –typically embedded in a large complex system –dependability (reliability) is crucial –explicit timing constraints (soft, firm, hard) A large number of applications –aerospace and defense systems, nuclear systems, robotics, process control, agile manufacturing, stock exchange, network and traffic management, multimedia computing, databases, medical systems, wireless sensor networks Rapid growth in research and development –workshops, symposia, journals –standards (RT-Linux, RT-Java, RT-COBRA, …)

4. University of Virginia Time Constraints dt v(t) v0v0 d2d2 t v0v0 d1d1

5. University of Virginia Trends in Real-Time Systems Applications Soft real-time requirements rather than hard ones –much wider applications –relates well with the notion of QoS –soft is harder to deal with than hard ones Operate in unpredictable environments –WCET too pessimistic or high variance –unbounded arrival rate; overload unavoidable Need to support multi-dimensional requirements –real-time, power, size, security, and fault-tolerance –conflicting resource requirements and system architecture Embedded and interacting with physical world

6. University of Virginia Key Issues (Part of a Long List) Real-time services in embedded networked systems –flexible and adaptable (self-configurable) –interaction with physical/distributed environment - sensors/actuators in mobile nodes using WSN –group-based aggregation and confidence management –scalability Multi-dimensional constraints –real-time, location-dependence, power, mobility, wireless, size, cost, fault-tolerance, security and privacy Timely management of real-time data (QoD/QoS) –large volume with temporal properties –robust real-time data and event services

7. University of Virginia Current Real-Time/WSN Projects QoS management in real-time data services: NSF Event services for emergency response in WSN : NSF Network virtual machine for RT services: DARPA Undersea sensor systems: ONR/Migma Systems Real-time data services using feedback: Swedish Gov’t Real-time image rendering in VR: Korean Gov’t Collaboration: Stankovic, Brogan, Song, Shu (Taiwan), Hansson (CMU), Andler (Sweden), Park (Korea), Hur (Korea), Lam (Hong Kong), Lee (Hong Kong)

8. University of Virginia Current Research Work QoS management in real-time data services –freshness and timeliness –distributed real-time data services using replication –imprecision and differentiated service Service middleware for sensor networks –event detection + query management services –formal event description language –data aggregation/dissemination –undersea surveillance RT transaction management in mobile environments Time-constrained recovery techniques for RTDB

9. University of Virginia QoS Management in Real-Time Data Motivation –increasing demands for real-time data/event services web-based information services and e-business sensor networks interactive rendering location-aware services in mobile networks –temporary overload and service degradation inevitable Service quality: QoS parameters –timeliness –data freshness –degree of imprecision –behavior in transient state: overshoot and settling time

10. University of Virginia Feedback Control Controller Actuator Process Sensor feedback reference (set point) controlled variable control input

11. University of Virginia Timeliness Specification Settling time Overshoot Miss ratio Time Reference  % Steady StateTransient State Steady state error

12. University of Virginia Data Freshness Database Freshness: Set of continuous data Perceived Freshness: Set of continuous data accessed by timely transactions Database

13. University of Virginia WSN Application Spectrum

14. University of Virginia Data/Event Services in Sensor Networks Recent advances in low-cost low-power devices –large scale sensor networks (ad hoc mobile networks) –each node consists of sensors/actuators/processors Issues in wireless sensor networks –how to collect and disseminate real-time data –QoS management under resource constraints –how to conserve energy while satisfying application requirements –efficient real-time localization –consensus, aggregation, in-network processing, confidence, security

15. University of Virginia Event Services for Emergency Response Explosion Atomic event reports Detect compound event & dispatch emergency rescue team Gas Leak Evacuate people ahead of leak Dynamic Deployment of Wireless Sensor Network (self-organizing) Technology and Research Multidisciplinary Impact Confidence levels in data Multi-level events Real-Time Minimize false alarms Actual implementation Sensor design Application to emergency response services Save lives Minimize damage Improve response to natural disasters or terrorist attacks

16. University of Virginia Undersea Surveillance Cluster-based surveillance project by Navy Acoustic communication in undersea Experiments performed in 2003 in Florida Three sensor nodes in a cluster –Each had 3-dimensional magnetic sensor One submarine at a time moved through the network Data was gathered during experiments and analyzed later (not real-time)

17. University of Virginia Navy Experiments DADS node X2, with 500- meter range circle Radar range circles, at 0.5 nmi intervals from control station Gateway Buoy and Telesonar Repeaters DADS node X3, with 500- meter range circle

18. University of Virginia Navy Experiments (cont)

19. University of Virginia Issues in Undersea Surveillance Feature extraction from magnetic/acoustic sensors to mitigate false alarms Multi-sensor fusion System trade-off analysis Identify system objectives and key performance parameters –System configuration (# and type of sensor nodes in each cluster, cluster deployment, …) –System parameters (sensing/communication ranges, duty cycles, data aggregation ratio, …)