Transcript Document

Protocols in Wireless Sensor
Networks
From Vision to Reality
1
ZigBee and 802.15.4
The MAC Layer
2
The ZigBee Alliance Solution
• Targeted at home and building automation
and controls, consumer electronics, toys etc.
• Industry standard (IEEE 802.15.4 radios)
• Primary drivers are simplicity, long battery
life, networking capabilities, reliability, and
cost
• Short range and low data rate
3
The Wireless Market
HI-FI
AUDIO
STREAMING
VIDEO
DIGITAL MULTI-CHANNEL
VIDEO
VIDEO
>
LONG
TEXT GRAPHICS INTERNET
RANGE
802.11b
802.11a/HL2 & 802.11g
Bluetooth 2
<
SHORT
LAN
ZigBee
PAN
Bluetooth1
LOW
<
DATA RATE
>
HIGH
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Applications
security
HVAC
AMR
lighting control
access control
BUILDING
AUTOMATION
patient
monitoring
fitness
monitoring
CONSUMER
ELECTRONICS
TV
VCR
DVD/CD
remote
ZigBee
PERSONAL
HEALTH CARE
asset mgt
process
control
environmental
energy mgt
Wireless Control that
Simply Works
INDUSTRIAL
CONTROL
RESIDENTIAL/
LIGHT
COMMERCIAL
CONTROL
PC &
PERIPHERALS
mouse
keyboard
joystick
security
HVAC
lighting control
access control
lawn & garden
irrigation
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Development of the Standard
APPLICATION
Customer
ZIGBEE STACK
SILICON
ZigBee
Alliance
IEEE
802.15.4
• ZigBee Alliance
– 50+ companies
– Defining upper layers of
protocol stack: from network
to application, including
application profiles
• IEEE 802.15.4 Working Group
– Defining lower layers : MAC
and PHY
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IEEE 802.15.4 Basics
• 802.15.4 is a simple packet data protocol:
– CSMA/CA - Carrier Sense Multiple Access with
collision avoidance
– Optional time slotting and beacon structure
– Three bands, 27 channels specified
• 2.4 GHz: 16 channels, 250 kbps
• 868.3 MHz : 1 channel, 20 kbps
• 902-928 MHz: 10 channels, 40 kbps
• Works well for:
– Long battery life, selectable latency for
controllers, sensors, remote monitoring and
portable electronics
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IEEE 802.15.4 standard
• Includes layers up to and including Link Layer
Control
– LLC is standardized in 802.1
• Supports multiple network topologies including
Star, Cluster Tree and Mesh
ZigBee Application Framework
• Low complexity:
26 service primitives
versus
131 service primitives
for 802.15.1
(Bluetooth)
Networking App Layer (NWK)
Data Link Controller (DLC)
IEEE 802.15.4 LLC
IEEE 802.2
LLC, Type I
IEEE 802.15.4 MAC
IEEE 802.15.4
868/915 MHz PHY
IEEE 802.15.4
2400 MHz PHY
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ZigBee Topology Models
Mesh
Star
Cluster Tree
ZigBee coordinator
ZigBee Routers
ZigBee End Devices
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IEEE 802.15.4 Device Types
• Three device types
– Network Coordinator
• Maintains overall network knowledge; most
memory and computing power
– Full Function Device
• Carries full 802.15.4 functionality and all features
specified by the standard; ideal for a network
router function
– Reduced Function Device
• Carriers limited functionality; used for network
edge devices
• All of these devices can be no more complicated than
the transceiver, a simple 8-bit MCU and a pair of AAA
batteries!
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ZigBee and Bluetooth
Optimized for different applications
• ZigBee
• Bluetooth
– Smaller packets
over large network
– Mostly Static
networks with many,
infrequently used
devices
– Home automation,
toys remote
controls
– Energy saver!!!
– Larger packets over small
network
– Ad-hoc networks
– File transfer; streaming
– Cable replacement for items
like screen graphics,
pictures, hands-free audio,
Mobile phones, headsets,
PDAs, etc.
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ZigBee and Bluetooth
Timing Considerations
ZigBee:
• Network join time = 30ms typically
• Sleeping slave changing to active = 15ms typically
• Active slave channel access time = 15ms typically
Bluetooth:
• Network join time = >3s
• Sleeping slave changing to active = 3s typically
• Active slave channel access time = 2ms typically
ZigBee protocol is optimized for
timing critical applications
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Directed Diffusion:
A Scalable and Robust
Communication Paradigm for Sensor
Networks
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Motivation
• Properties of Sensor Networks
–
–
–
–
–
–
Data centric
No central authority
Resource constrained
Nodes are tied to physical locations
Nodes may not know the topology
Nodes are generally stationary
• How can we get data from the sensors?
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Directed Diffusion
• Data centric
– Individual nodes are unimportant
• Request driven
– Sinks place requests as interests
– Sources satisfying the interest can be found
– Intermediate nodes route data toward sinks
• Localized repair and reinforcement
• Multi-path delivery for multiple sources,
sinks, and queries
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Motivating Example
• Sensor nodes are monitoring animals
• Users are interested in receiving data
for all 4-legged creatures seen in a
rectangle
• Users specify the data rate
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Interest and Event Naming
• Query/interest:
1. Type=four-legged animal
2. Interval=20ms (event data rate)
3. Duration=10 seconds (time to cache)
4. Rect=[-100, 100, 200, 400]
• Reply:
1. Type=four-legged animal
2. Instance = elephant
3. Location = [125, 220]
4. Intensity = 0.6
5. Confidence = 0.85
6. Timestamp = 01:20:40
• Attribute-Value pairs, no advanced naming
scheme
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Directed Diffusion
• Sinks broadcast interest to neighbors
– Initially specify a low data rate just to find sources
for minimal energy consumptions
• Interests are cached by neighbors
• Gradients are set up pointing back to where
interests came from
• Once a source receives an interest, it routes
measurements along gradients
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Interest Propagation
• Flood interest
• Constrained or Directional flooding based on
location is possible
• Directional propagation based on previously cached
data
Gradient
Source
Interest
Sink
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Data Propagation
• Multipath routing
– Consider each gradient’s link quality
Gradient
Source
Data
Sink
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Reinforcement
• Reinforce one of the neighbor after receiving initial
data.
– Neighbor who consistently performs better than others
– Neighbor from whom most events received
Gradient
Source
Data
Reinforcemen
t
Sink
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Negative Reinforcement
• Explicitly degrade the path by re-sending interest with lower
data rate.
• Time out: Without periodic reinforcement, a gradient will be
torn down
Gradient
Source
Data
Reinforcemen
t
Sink
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Summary of the protocol
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Sampling & forwarding
• Sensors match signature waveforms from codebook
against observations
• Sensors match data against interest cache, compute
highest event rate request from all gradients, and (re)
sample events at this rate
• Receiving node:
– Find matching entry in interest cache
• If no match, silently drop
– Check and update data cache (loop prevention,
aggregation)
– Resend message along all the active gradients,
adjusting the frequency if necessary
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Design Considerations
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Evaluation
• ns2 simulation
• Modified 802.11 MAC for energy use calculation
– Idle time: 35mW
– Receive: 395mw
– Transmit: 660mw
• Baselines
– Flooding
– Omniscient multicast: A source multicast its event to all
sources using the shortest path multicast tree
– Do not consider the tree construction cost
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• Simulate node failures
• No overload
• Random node placement
– 50 to 250 nodes (increment by 50)
– 50 nodes are deployed in 160m * 160m
• Increase the sensor field size to keep the density
constant for a larger number of nodes
– 40m radio range
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Metrics
• Average dissipated energy
– Ratio of total energy expended per node to number of
distinct events received at sink
– Measures average work budget
• Average delay
– Average one-way latency between event transmission and
reception at sink
– Measures temporal accuracy of location estimates
• Both measured as functions of network size
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Average Dissipated Energy
They claim diffusion can outperform omniscient multicast due to
in-network processing & suppression. For example, multiple
sources can detect a four-legged animal in one area.
Average Dissipated Energy
(Joules/Node/Received Event)
0.018
0.016
Flooding
0.014
0.012
0.01
0.008
Omniscient Multicast
0.006
0.004
Diffusion
0.002
0
0
50
100
150
200
Network Size
250
300
30
Average Dissipated Energy
(Joules/Node/Received Event)
Impact of In-network Processing
0.025
Diffusion Without
Suppression
0.02
0.015
0.01
Diffusion With
Suppression
0.005
0
0
50
100
150
200
250
300
Network Size
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Average Dissipated Energy
(Joules/Node/Received Event)
Impact of Negative Reinforcement
0.012
0.01
Diffusion Without
Negative Reinforcement
0.008
0.006
0.004
Diffusion With Negative
Reinforcement
0.002
0
0
50
100
150
200
250
300
Network Size
Reducing high-rate paths in steady state is critical 32
Average Dissipated Energy
(802.11 energy model)
Average Dissipated Energy
(Joules/Node/Received Event)
0.14
Diffusion
0.12
Flooding
Omniscient Multicast
0.1
0.08
0.06
0.04
0.02
0
0
50
100
150
200
250
300
Network Size
Standard 802.11 is dominated by idle energy
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Failures
• Dynamic failures
– 10-20% failure at any time
• Each source sends different signals
• <20% delay increase, fairly robust
• Energy efficiency improves:
– Reinforcement maintains adequate number of
high quality paths
– Shouldn’t it be done in the first place?
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Analysis
• Energy gains are dependent on 802.11
energy assumptions
• Can the network always deliver at the
interest’s requested rate?
• Can diffusion handle overloads?
• Does reinforcement actually work?
35
Conclusions
• Data-centric communication between
sources and sinks
• Aggregation and duplicate suppression
• More thorough performance evaluation
is required
36
Extensions
• Push diffusion
– Sink does not flood interest
– Source detecting events disseminate
exploratory data across the network
– Sink having corresponding interest
reinforces one of the paths
• One-phase pull
– Propagate interest
– A receiving node pick the link that
delivered the interest first
– Assumes the link bidirectionality
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TEEN (Threshold-sensitive Energy Efficient
sensor Network protocol)
• Push-based data centric protocol
• Nodes immediately transmit a sensed
value exceeding the threshold to its
cluster head that forwards the data to
the sink
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LEACH [HICSS00]
• Proposed for continuous data gathering
protocol
• Divide the network into clusters
• Cluster head periodically collect &
aggregate/compress the data in the cluster
using TDMA
• Periodically rotate cluster heads for load
balancing
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Discussions
• Criteria to evaluate data-centric routing
protocols?
– Or, what do we need to try to optimize?
Energy consumption? Data timeliness?
Resilience? Confidence of event detection?
Too many objectives already? Can we pick
just one or two?
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Geographic Routing for Sensor
Networks
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Motivation
• A sensor net consists of hundreds or thousands of nodes
– Scalability is the issue
– Existing ad hoc net protocols, e.g., DSR, AODV, ZRP, require
nodes to cache e2e route information
– Dynamic topology changes
– Mobility
• Reduce caching overhead
– Hierarchical routing is usually based on well defined, rarely
changing administrative boundaries
– Geographic routing
• Use location for routing
• Assumptions
– Every node knows its location
• Positioning devices like GPS
• Localization
– A source can get the location of the destination
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Geographic Routing: Greedy
Routing
Closest
to D
S
A
D
- Find neighbors who are the closer to the destination
- Forward the packet to the neighbor closest to the
destination
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Greedy Forwarding does NOT always
work
GF fails
 If the network is dense enough that each
interior node has a neighbor in every 2/3
angular sector, GF will always succeed
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Dealing with Void
 Apply the right-hand rule to traverse the
edges of a void
Pick the next anticlockwise edge
Traditionally used to get out of a maze
45
Impact of Sensing Coverage on Greedy
Geographic Routing Algorithms
Guoliang Xing, Chenyang Lu, Robert Pless, Qingfeng Huang
IEEE Trans. Parallel Distributed System
46
Metrics
b
v
u
a
c
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Theorem.
• Definition: A network is sensing-covered if
any point in the deployment region of the
network is covered by at least one node.
• In a sensing-covered network, GF can always
find a routing path between any two nodes.
Furthermore, in each step (other than the last
step arriving at the destination), a node can
always find a next-hop node that is more than
Rc-2Rs closer (in terms of both Euclidean and
projected distance) to the destination than
itself.
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GF always finds a next-hop node
• Since Rc >> 2Rs, point a
must be outside of the
sensing circle of si.
• Since a is covered, there
must be at least one
node, say w, inside the
circle C(a, Rs).
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Theorem
• In a sensing-covered network, GF can
always find a routing path between
source u and destination v no longer
than
hops.
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TTDD: A Two-tier Data
Dissemination Model for Largescale Wireless Sensor Networks
Haiyun Luo
Fan Ye, Jerry Cheng
Songwu Lu, Lixia Zhang
UCLA CS Dept.
51
Sensor Network Model
Sink
Stimulu
s
Source
Sink
52
Mobile Sink
Excessive
Power
Consumption
Increased Wireless
Transmission
Collisions
State
Maintenance
Overhead
53
TTDD Basics
Dissemination Node
Data Announcement
Source
Data
Sink
Immediate Query
Dissemination
Node
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TTDD Mobile Sinks
Dissemination Node
Trajectory
Forwarding
Data Announcement
Source
Immediate
Dissemination
Node
Data
Sink
Immediate
Dissemination
Node
Trajectory
Forwarding
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TTDD Multiple Mobile Sinks
Dissemination Node
Trajectory
Forwarding
Data Announcement
Source
Data
Immediate
Dissemination
Node
Source
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Conclusion
• TTDD: two-tier data dissemination Model
– Exploit sensor nodes being stationary and
location-aware
– Construct & maintain a grid structure with low
overhead
• Proactive sources
– Localize sink mobility impact
• Infrastructure-approach in stationary sensor
networks
– Efficiency & effectiveness in supporting mobile
sinks
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