Transcript Document 7385104

Real-Time Communication in Wireless Sensor
Networks
Richard Arps, Robert Foerster, Jungwoo Lee, Hui Cao
 SPEED
 Routing
 RAP
 Event Detection
 Power Management
Introduction
 Wireless sensor networks (WSN)
 Small sensor devices
 Equipped with wireless communication interfaces
 In very large numbers
 The distances between nodes are in the order of
meters
 The network density is very high, sometimes as
high as tens of nodes / m2
Common Network Architecture
 Sensor nodes are responsible
for
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Sink
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Detection of events
Observation of environments
Relaying of third party
messages
 Information is generally
Sink
gathered at sinks
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Source
Event
Sinks are responsible for
higher level processing and
decision making
Sensor Node Hardware
 Components:
Processor unit
 Memory
 Sensor unit(s)
 Transceiver
 Power Unit
 Optional Components:
 Mobilizers
 Localization hardware
 Power generators
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Limited processing capability
Limited storage space
Simple sensing devices
Limited range and rate
Limited power supplies
Example Sensor Nodes
JPL Sensor Webs
Rockwell WINS
UC Berkeley Dust
weC
MICA Motes
Rene
Sensor Types and Tasks
 Sensor Types
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Seismic
Magnetic
Thermal
Visual
Infrared
Acoustic
Radar
Pressure
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 Sensor Tasks
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Periodic sampling
Event-based sampling
Movement detection
Direction of movement
Object detection
Object classification
Chemical composition
Mechanical stress
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Sensor Network Applications
 General applications are geared towards
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Command, Control, Communications, Computing, Intelligence,
Surveillance, Reconnaissance, Targeting (C4ISRT)
 Example military applications
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Monitoring friendly forces, equipment, and ammunition
Battlefield surveillance
Reconnaissance of opposing forces and terrain
Targeting
Battle damage assessment
Nuclear, biological and chemical (NBC) attack detection and
reconnaissance
Sensor Network Applications
 Example military applications
 Intrusion detection (mine fields)
 Detection of firing gun (small arms) location
 Chemical (biological) attack detection
 Targeting and target tracking systems
 Enhanced navigation systems
 Battle damage assessment system
 Enhanced logistics systems
Sensor Network Applications
 Environmental applications
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Habitat monitoring
Monitoring environmental conditions for farming
Irrigation, Precision agriculture
Earth monitoring and planetary exploration
Biological, Earth, and environmental monitoring in marine, soil,
and atmospheric contexts
Meteorological or geophysical research
Pollution study
Biocomplexity mapping of the environment
Flood detection and forest fire detection
Sensor Network Applications
 Health applications
 Providing interfaces for the disabled
 Integrated patient monitoring
 Diagnostics
 Telemonitoring of human physiological data
 Tracking and monitoring doctors and patients inside a
hospital
 Drug administration in hospitals
Sensor Network Applications
 Commercial applications
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Smart homes and office spaces
Interactive toys
Monitoring disaster areas
Machine diagnosis
Interactive museums
Inventory control
Environmental control in office buildings
Detecting and monitoring car thefts
Vehicle tracking and detection
Parking lot management
Factors Affecting
Sensor Network Design
 Fault Tolerance (Reliability)
 Scalability
 Production Costs
 Hardware Constraints
 Sensor Network Topology
 Operating Environment
 Transmission Media
 Power Consumption
SPEED
 Goals
 Stateless
• Information regarding only the immediate neighbors
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Soft Real Time
• Provides uniform speed delivery across the network
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Minimum MAC layer support
Traffic load balancing
Localized behavior
Void Avoidance
SPEED
 Soft real-time guarantees
 “SPEED aims at providing a uniform packet delivery speed
across the sensor network, so that the end-to-end delay
of a packet is proportional to the distance between the
source and the destination. With this service, real-time
applications can estimate end-to-end delay before making
admission decisions.”
SPEED
 Neighbor beacon exchange
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Periodically broadcasts a beacon to neighbors to exchange
location information
• In order to reduce traffic we can piggyback the information
• Assume all neighbors fit in the neighborhood table
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Possible enhancement
• Advertising state changes (rather than on fixed intervals) may
reduce the number of beacons transmitted
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On-demand beacons
• Delay estimation
• Back pressure
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Fields in beacon
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Neighbor ID
Position
Send to delay
TTL
SPEED
 Delay estimation
 Due to scarce bandwidth, cannot use probe packets
 Delay is measured at the sender as the round trip time
minus the processing time at the receiver.
 Exponential weighted moving average is used to keep a
running estimation
 Delay estimation beacon is used to communicate
estimated delay to neighbors
SPEED
 Stateless non-deterministic geographic forwarding
(SNGF)
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Neighbor set of node I
• NSi = {n | d(n,i) < range(i)}
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Forwarding candidate set
• FSi(destination) =
{n e NSi| L-Lnext >0 }
– Where
L = d(i, destination) and
Lnext = d(next,destination)
SPEED
 Back pressure rerouting
SPEED
 Void avoidance
SPEED
 Last mile processing
 Since SPEED is targeted at sensor networks where the
ID of a node is not important, SPEED only cares about
the location.
 Called “last mile” since this function will only be invoked
when the packet enters the destination area
 Area-multicast, area-anycast
SPEED- results
E2E delay under different congestion
SPEED results (2)
Deadline Miss ratio under different congestion
Routing in Sensor Networks
 Different than regular network routing
 Power
 Mobility
 Congestion
Parametric Probabilistic Routing
 Partial flooding
 When a node receives a packet it calculates if it is
closer or further from the destination.
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If closer, probability of retransmission goes up
If farther, probability goes down
Parametric Probabilistic Routing
 Test of probability of retransmission with origin
at (0,0) and destination at (1,0)
Parametric Probabilistic Routing
 Pro’s
 Allows for dynamic network topology.
 Completely stateless.
 Reduced transmission load at sensors close to base
station.
 Simple to impliment.
 Con’s
 Wasted power.
 Flooding doesn’t utilize bandwidth very well.
 Possible packet loss.
Packet Priority Routing
 Packets in sensor networks have deadlines.
 Hard deadlines can give priority to those who don’t
need it.
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Packets originating farther from the base station need
to travel more hops but have the same time to do it.
 A new protocol is needed to address the issues of
late packets
 RAP protocol suite
RAP Protocol Suite
 Lightweight set of protocols aimed to reduced the
percentage of missed deadlines.
 Velocity Monotonic Scheduling (VMS)
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Designates packet’s velocity instead of hard deadline
If a packet travels through the network at this velocity
it will make its deadline.
Velocity can be static or dynamic.
– Static
– Dynamic
Vel=distance(origin, dest)/deadline
Vel=distance(current, dest)/(deadline-elapsed time)
VMS
 Simulations
 Miss ratio Vs. packet throughput
 Overall miss ratio
 Miss ratio from far corner
RAP
 RAP can reduce deadline miss ratio from 90% to
17.9% for packets originating far from the
destination.
Wireless Sensor Networks
 Event Detection Services
 Radio-Triggered Wake-Up Capability
Event Detection Services Using Data Service
Middleware in Distributed Sensor Networks
 Data Service Middleware (DSWare):
 Exists between the application layer and the network layer
 Integrates various real-time data services
 Provides data service abstractions
 Event Detection: dig meaningful information out of the huge volume
of data produced
Framework of DSWare
 Data Storage
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Data lookup
Robustness
 Data Caching
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provides multiple copies of the data
monitors current usages of copies
determines whether to increase or reduce the number
Framework of DSWare (Cond.)
 Group Management
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provides localized cooperation among sensor nodes to
accomplish a more global objective
nodes decides whether to join this group by checking the
criterion
 Event Detection
 Data Subscription
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places copies of the data at some intermediate nodes to
minimize the total amount of communication scheduling
changes the data feeding paths when necessary
 Scheduling
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energy-aware
real-time scheduling
Event Detection Services
 Event Hierarchy
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Event: activity that can be monitored or detected in the
environment and is of interest to the application
Atomic event and compound event
 Confidence, Confidence Function and Phase
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Confidence: return value of the confidence function
Confidence > 1.0 , confirmed , event actually occurred
Confidence function: specifies the relationships among subevents of a compound event (relative importance, sensing
reliability, historic data, statistical model, fitness of a known
pattern, proximity of detection)
Phase: there is a set of events that are likely to occur
Event Detection Services (Cond.)
 Real-Time Semantics
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AVI: absolute validity interval
Temporal consistency btw environment and its measurement
Preserve a time window to allow all possible reports of subevent to arrive to the aggregating node
 Registration and Cancellation
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Registration: application submits a request in SQL-like
statement
Subevent_Set defines a set of sub-events and their timing
constrains
Cancellation: similar to event detection, only needs to specify
the event’s id instead of describing an event’s cirteria
Evaluation of Real-Time Event Detection
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Simulation
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Detection of Explosion: temp. light and acoustic event
Baseline: sensor detect atomic event, report to the registrant
registrant decide whether there is a compound event happening
Communication cost
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Save energy since communication cost dominates the energy consumption
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Baseline causes severe traffic congestion
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Number of missing report around 1 or 2 out of 100 nodes
Reaction Time
Completeness
Impact of Node Density
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400 node experiment
Low density →Low missing rate,
high density →high energy consumption, reaction time
Conclusions
 Sensor Network should be able to provide the
abstraction of data services to applications
 DSWare
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Hide unattractive characteristics of sensor network
(Unreliability, Complexity and necessity of group
coordination)
Present a more general data service interface to
applications
Accommodates the data semantics of real-life compound
events and tolerates the uncertainty and unreliability
Radio-Triggered Wake-Up Capability for
Sensor Networks
 Power Management Scheme
 High power running mode
 Low-power sleep mode
 Problem
 Network node has its CPU halted
 Unaware of the external events
 Periodical wake up
Basic Radio-Triggered Power management
 Aims to avoid the useless wake-up periods
 Special radio signal wakes up the sleeping node
 Saves energy spent in wake-up listen intervals
 Requirements
 Wake up almost instantly when it receives a wake-up
packet
 Use approximately the same amount of energy in sleep
mode as in power mag. protocol without radio-triggered
support
 Should not wake up when the event of interest does not
happen
 Should not miss wake-up calls
Design of the Basic Radio-Triggered circuit
 Essential Tasks
 Collect energy from radio signals
 Distinguish trigger signal from other radio signals
 Basic radio triggered circuit
 Antenna provide suitable selectivity and efficiency
 Reacts to electromagnetic wave and generates an input
voltage
Effectiveness of the circuit
 Electric signal of 0.6V is sufficient to trigger an
interrupt
 Berkeley Mica2 mote
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Wake up logic is implemented as an interrupt caused by a
timer
Wake up logic can work with the radio-triggered
interrupt
 SPICE simulation
 SPICE is a circuit level simulator developed by Berkeley
 Output voltage, Vout > 0.6
 Simulation shows Vout is 0.62V
Evaluation of the potential power saving
 Tracking application system
 Berkeley Mica2 mote
 Total 1,000 nodes randomly deployed
 10 events/day, Each event lasts 2 minutes
 Each network node uses two 1600mAh AA batteries
 Average wake up current: 20 mA, sleep mode: 100uA
 Comparison
 Energy saving
• 98% saved to always-on scheme
• 70% saved to rotation-based scheme
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Lifespan
• 3.3 days (always-on), 49.5 days (rotation –based), 178 days
(radio-triggered)
Conclusions
 Extracting energy from the radio signals
 Hardware provides wake-up signals to the network
node without using internal power supply
 Adequate antenna : does not respond to normal
data communication, not prematurely wake up
 highly flexible and efficient
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Zero stand-by power consumption and timely wake-up
capability