Real-Time Systems
and Sensor Networks
Sang H. Son
Department of Computer Science
University of Virginia
Charlottesville, Virginia 22904
University of Virginia
Input
Real
Real-Time (Embedded)
System
World
Output
• 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
University of Virginia
Real-Time Systems
• Real-time systems
– typically embedded in a large complex system
– timeliness and dependability (reliability) are crucial
– explicit/implicit timing constraints (soft, firm, hard)
• A large number of applications
– aerospace and defense systems, nuclear systems,
robotics, process control, agile manufacturing, stock
trading, network and traffic monitoring and control,
multimedia computing, databases, medical systems,
wireless sensor networks
• Rapid growth in research and development
– workshops, symposia, journals
– standards (RT-Linux, RT-Java, RT-COBRA, …)
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Time Constraints
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t
v(t)
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d1
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t
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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
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
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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
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Feedback Control
control
input
Controller
feedback
Actuator
Process
Sensor
controlled
variable
reference
(set point)
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Timeliness Specification
Miss ratio
Overshoot
Steady state error
%
Reference
Transient State
Steady State
Time
Settling time
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Data Freshness
Database
Database Freshness:
Set of continuous data
Perceived Freshness:
Set of continuous data
accessed by timely transactions
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WSN Application Spectrum
Interactive VR
Game
Environmental Monitoring
Wearable
Computing
Disaster Recovery
Earth Science &
Exploration
Immerse
Environments
Wireless Sensor
Networks
Hazard
Detection
Military Surveillance
Context-Aware
Computing
Biological
Monitoring
Smart
Environment
Linear Structure
Protection
Urban Warfare
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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
University of Virginia
Event Services for Emergency Response
Technology and Research
• Confidence levels in data
• Multi-level events
• Real-Time
• Minimize false alarms
• Actual implementation
Multidisciplinary
• Sensor design
• Application to
emergency
response services
Impact
• Save lives
• Minimize damage
• Improve response
to natural disasters
or terrorist attacks
Dynamic Deployment of Wireless Sensor Network
(self-organizing)
Evacuate people
ahead of leak
Explosion
Gas Leak
Simple event reports
Detect compound event &
dispatch emergency rescue
team
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Undersea Surveillance
• Automated real-time undersea 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)
University of Virginia
Issues in Undersea Surveillance
• Feature extraction from magnetic/acoustic
sensors to mitigate false alarms
• Confidence to reduce false positives/negatives
• System architecture and trade-off analysis
• Identify system objectives and key performance
parameters
– System configuration (# and type of sensor
nodes, surveillance coverage, deployment, …)
– System parameters (sensing/communication
ranges, duty cycles, data aggregation ratio, …)
– Adaptation under uncertainty
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Undergrad Research Assistant
• NSF REU (Research Experience for Undergrads)
• What is promised
– challenging problems
– rewarding research experience
• Offers will not last long
– call toll-free number 1-800-982-2205
– e-mail to son@cs.virginia.edu
University of Virginia