CS 851Wireless Sensor Networks
Summary Lecture
Professor Jack Stankovic
Department of Computer Science
University of Virginia
November 24, 2003
Goals for Today’s ClassGoals for Today’s Class
• WSN – its niche
• Applications revisited
• Fundamentals - early
• Intriguing Concepts
• Future Research Areas
WSN – Its Niche WSN – Its Niche • Distributed Computing
– Load balancing, group management, distributed OS, middleware, network protocols, …
• Sensor Networks (wired or powerful wireless)– Submarines, automated factories, fleets of ships, …– Real-time systems
• Radio Communications (Wireless)– Radio signal– Sensing signal– DSP
• MANET
How the Problems ChangeHow the Problems Change
• Environment– connect to physical environment (large numbers, dense, real-time)
– massively parallel interfaces (sometimes)
– faulty, highly dynamic, non-deterministic
– wireless – contention, irregular patterns
– power management critical
• Network– structure is dynamically changing
– sporadic connectivity
– new resources entering/leaving
– large amounts of redundancy
– self-configure/re-configure
– individual nodes are unimportant - route/query to AREA
How the Problems ChangeHow the Problems Change
• OS/Middleware– manage aggregate performance
• control the system to achieve required emerging behavior
• How do we know it works?
– self-organizing (self-*)
– fuzzy membership and team formation
– manage power/mobility/real-time/security tradeoffs
– geographical/location based (spatial)
– real-time/real world (temporal)
– data centric
– support new paradigms
ImplicationsImplications
• Fundamental Assumptions underlying distributed systems technology has changed– wired => wireless (limited range, high error
rates)– unlimited power => minimize power– Non-real-time => real-time– fixed set of resources => resources being
added/deleted– each node important => aggregate performance
• New solutions necessary
Applications Applications
• Passive sensing of environment/data collection• Same as above with actuators• Active tracking/target discrimination• Degree of mobility• Interface with the Internet• Handheld PDAs/laptops (seemless integration)• Heterogeneity• Placed versus ad hoc deployment
Any killer apps? Any wild new apps? Impact of cost changes?
CostCost
• 200 nodes at $100 ea. -> $20,000
• 20,000 nodes at $1 ea. -> $20,000
• 20,000 nodes at .10 ea. -> $2,000
Architecture - WSNArchitecture - WSN
• Sensors• Actuators• CPUs/Memory• Omni-dir. Radio
Architecture - WSNArchitecture - WSN
• Fixed Deployment (grid, mesh, …)
TaxonomyTaxonomy
HWCapabilities
ApplicationRequirements
Software/Middleware
FundamentalsFundamentals
• What is truly fundamental about WSN?– Power limitations?
• Solar cells/close down for a time to recharge/plug into wall socket, etc.
• Probably a major problem for a long time and for many applications
– Cpu/memory capacity?• New platforms are being built
– Scale?• Not necessarily for all systems
– Long Lifetimes?
FundamentalsFundamentals
• Interact with the environment – sensing– Consider all the realities of sensing …– Sensor fusion/data aggregation
• Multi-hop wireless radio communication– Consider all the realities of radio comm.
• Ratio of communication/sensing rangesFalse alarm processing
Asymmetry, lost messages, nodes move,nodes sleep or die, etc.
Radio Model in Evaluation Radio Model in Evaluation
Radio ModelDOI = Degree of Irregularity
DOI = 0.05 DOI = 0.2
Sensing versus CommunicationSensing versus Communication
• Sensing/communication range ratio
• Sensing/communication/power tradeoffs
Sensing Range
CommunicationRange
What if the opposite?Required degreeof coverage?
FundamentalsFundamentals
• Self-configure, self-manage, self-heal
• Self-awareness– Space (location/geography), time, energy,
dynamics, security, reliability
• Self-calibrate
• Self-*
• Unattended operation (completely or almost completely) -> difficult physical accessibility
Self-stabilizing algorithms
• A mechanism for discovering spatial
relationships among objects
FundamentalsFundamentals
• Aggregate Behavior – biological metaphors• Simple decentralized algorithms (localized behavior)
– Epidemic/virus type algorithms
– Randomized algorithms
– Develop local rules that yield desired macroscopic behavior
• Uncertainty• Lazy behavior (most of the time/mobility)• Composition
– Functional
– Performance
Epidemic AlgorithmsEpidemic Algorithms
• Final state– Backward links
• The flood extends towards the source
– Stragglers• MAC-level collisions
– High clustering• Most nodes have few
descendants
• A significant few have many children
Fundamentals - EventsFundamentals - Events
• Size of targets/events (point/area)
• Discrete versus continuous
• Probabilistic
Fire
X
Explosion
FundamentalsFundamentals
• Programming Paradigm
Programming EnvironmentProgramming Environment
• OS: cygwin/Win2000 or gcc/Linux
• Software: atmel tools
mote
programming board
mote-PC comms
Code download
nesCnesC
• the nesC model:– interfaces:
• uses
• provides
– components:• modules
• configurations
• application:= graph of components
Component A
Component B
ComponentD
Component C
Application
configurationconfigurationComponent
E
ComponentF
Sensor/Actuator CloudsSensor/Actuator Clouds
HeterogeneousHomogeneous
Resource management, team formation, networking, …
Severe constraints
power, memory, bandwidth, cpu, cost, ...
Make Scripts MobileMake Scripts Mobile
Script can populate/migrate
Language + Run-time Environment = SensorWare
Scripts move NOT due to explicit user
instructions, but due to node’s state
and algorithmic instructions
FundamentalsFundamentals
• Group Management and Consensus
Example: ConsensusExample: Consensus
• Classical consensus: all correct processes agree on one value– No power constraints– No real-time constraints– Does not scale well to dense networks– Approximate agreement (some work here) - on
sets of values (physical quantities)
• New Solutions ?
New Concept of ConsensusNew Concept of Consensus
• Termination: every correct processor eventually decides some value
• Uniform Agreement: no two processors decide differently
• Group Membership: join/leave - everyone knows who is in the group
• Termination: “at least n” correct processors decide some value by time t
• Group Agreement: at least n processors decide the same value within epsilon
• Area/Function Membership: join/leave an area or by function
Classical New Definitions
Examples: Tracking andMap Regions
Examples: Tracking andMap Regions
Base Station
Group Management - APIGroup Management - API
– Create_Group(name,function,criterion,atleast,accuracy) - implicit and explicit
– Destroy_Group(name)– Join()– Leave()– Merge()– Move_COG()– Expand() -- to gain sensing confidence– Shrink() -- to save power– Commit(grp_ID) - to synchronize group re-
configurations
Mobicast FrameworkMobicast Framework
Delivery zone: the area that message should be delivered
Forwarding zone: The area that message should be forwarded, which is some distance ahead of the delivery zone
Headway distance: The physical distance between the forwarding zone its delivery zone
Hold & Forward Zone: The area that receive the message before entering the forwarding zone
Delivery Zone
Future Delivery ZoneForwarding Zone
Headway Distance
Hold & Forward Zone
What’s HardWhat’s Hard
• Multiple targets• Crossing targets• False Alarms
– Depends on (changing) environment, sensors, confidence tradeoffs, noise, lost messages, …)
• Speed of targets• Uniqueness of targets• Classify targets• Proper abstractions• Save power/minimize communication
Fundamentals - SecurityFundamentals - Security• What is the single most important issue that could
prevent WSNs from wide scale deployment? – Security– 2nd issue -> Privacy
• At application level– Authenticity and integrity
• Security of each service (examples)– Routing:
• non-secure if a single node is captured!• Eavesdrop or change message• Flood
• Insidious unintended consequences of collecting data– Monitor oceans for fish migration (data mine location of
submarine fleet)
Fundamentals - AnalysisFundamentals - Analysis
• Control Theory• Markov Processes• Real-time Schedulability Analysis• Optimization Theory• Graph Theory (Random Graphs?)• Information Theory• Phase Transitions• Guarantee Quality of Service• Diffusion Theory?
Intriguing ConceptsIntriguing Concepts
• Space (geography/location)
• Time (deadlines/periods/event lifetime/power lifetime)
• Behavior (emerges versus controls)
SPEEDSPEED
E2E Di stance
j
FS
iD
Actual Speed
Speed todestination(Set Point )
E2E Delay is bound by E2E Distance/Speed SetPoint
USE VELOCITY
Bound ErrorsBound Errors • End-to-end• Real-time• Collisions• Congestion
Destination
Source
ErrorPropagates
Race Ahead
Use TrajectoriesUse Trajectories
Source
Destination
• Trajectory Based Forwarding and Its Applications
Trajectory
BehaviorBehavior
• Flooding – stragglers
• Epidemic algorithms and phase transitions
• Global routing behavior – more emerged than controlled
Feedback Control (FC)Feedback Control (FC)
23
5
9
10
7
DelayBoo
411
6
13
12Packet 1
Packet 1
Beacon
Packet 2
Packet 2
Packet 2
Packet 2
Packet 2
• SPEED: A Stateless Protocol for Real-Time Communication in Sensor Networks.
Use FC – Packet AggregationUse FC – Packet Aggregation
• Adaptive choice of N
• Take into account the output Queue delay
• Delay is used to adjust the output queue push rate and degree of aggregation
MAC
AIDA
Network
PrioritizedOutput Queue
InputQueue
Input Queue
AggregationPool
Aggregator
De-Aggregator
NetworkOutput Queue
IsEmpty
degree
Queuing Delay
AggDegree&
RateController
Counting
Behavior - Integrated SolutionsBehavior - Integrated Solutions
• Routing solutions must be– Power aware– Robust to lost messages, dead motes, voids– Provide real-time QoS– Robust to communication range variations and
asymmetries– Handle moving end points – Scale– Secure
InteractionsInteractions
• Insidious interactions– High density with many motes off to enable long
system lifetime; turn on when activity happens then too many with many collisions and poor response
Future Directions of ResearchFuture Directions of Research
• New platforms/architectures
• Higher level middleware
• Aggregate behavior (algorithms, …)
• Systems implementations/applications
• Systems of systems (pervasive computing)
• Security
• Analysis
• Mobility
• Storage Systems
• Heterogeneous
• Programming Paradigms
The End!The End!