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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
Sink
Detection of events
Observation of environments
Relaying of third party
messages
Information is generally
Sink
gathered at sinks
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
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
Seismic
Magnetic
Thermal
Visual
Infrared
Acoustic
Radar
Pressure
…
Sensor Tasks
Periodic sampling
Event-based sampling
Movement detection
Direction of movement
Object detection
Object classification
Chemical composition
Mechanical stress
…
Sensor Network Applications
General applications are geared towards
Command, Control, Communications, Computing, Intelligence,
Surveillance, Reconnaissance, Targeting (C4ISRT)
Example military applications
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
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
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
Soft Real Time
• Provides uniform speed delivery across the network
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
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
Possible enhancement
• Advertising state changes (rather than on fixed intervals) may
reduce the number of beacons transmitted
On-demand beacons
• Delay estimation
• Back pressure
Fields in beacon
•
•
•
•
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)
Neighbor set of node I
• NSi = {n | d(n,i) < range(i)}
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.
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.
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)
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
Data lookup
Robustness
Data Caching
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
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
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
energy-aware
real-time scheduling
Event Detection Services
Event Hierarchy
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
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
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
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
Simulation
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
Save energy since communication cost dominates the energy consumption
Baseline causes severe traffic congestion
Number of missing report around 1 or 2 out of 100 nodes
Reaction Time
Completeness
Impact of Node Density
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
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
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
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
Zero stand-by power consumption and timely wake-up
capability