Protocols in Wireless Sensor Networks

1
Protocols in Wireless Sensor Networks
From Vision to Reality
2
ZigBee and 802.15.4
The MAC Layer
3
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

4
The Wireless Market
LAN
802.11b
802.11a/HL2 802.11g
SHORT lt RANGE gt LONG
Bluetooth 2
ZigBee
PAN
Bluetooth1
LOW lt DATA RATE gt HIGH
5
Applications
CONSUMER ELECTRONICS
BUILDING AUTOMATION
security HVAC AMR lighting control access control
TV VCR DVD/CD remote
PC PERIPHERALS
PERSONAL HEALTH CARE
patient monitoring fitness monitoring
ZigBee Wireless Control that Simply Works
mouse keyboard joystick
RESIDENTIAL/ LIGHT COMMERCIAL CONTROL
INDUSTRIAL CONTROL
security HVAC lighting control access
control lawn garden irrigation
asset mgt process control environmental energy
mgt
6
Development of the Standard

• 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

Customer
APPLICATION
ZIGBEE STACK
ZigBee Alliance
SILICON
IEEE 802.15.4
7
(No Transcript)
8
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

9
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 802.2
IEEE 802.15.4 LLC
LLC, Type I
IEEE 802.15.4 MAC
IEEE 802.15.4
IEEE 802.15.4
2400 MHz PHY
868/915 MHz PHY
10
ZigBee Topology Models
Mesh
Star
ZigBee coordinator
Cluster Tree
ZigBee Routers
ZigBee End Devices
11
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!

12
ZigBee and Bluetooth
Optimized for different applications

• ZigBee
• Smaller packets over large network
• Mostly Static networks with many, infrequently
  used devices
• Home automation, toys remote controls
• Energy saver!!!

• Bluetooth
• 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.

13
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 gt3s
• Sleeping slave changing to active 3s typically
• Active slave channel access time 2ms typically

ZigBee protocol is optimized for timing critical
applications
14
Directed DiffusionA Scalable and Robust
Communication Paradigm for Sensor Networks
15
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?

16
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

17
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

18
Interest and Event Naming

• Query/interest
• Typefour-legged animal
• Interval20ms (event data rate)
• Duration10 seconds (time to cache)
• Rect-100, 100, 200, 400
• Reply
• Typefour-legged animal
• Instance elephant
• Location 125, 220
• Intensity 0.6
• Confidence 0.85
• Timestamp 012040
• Attribute-Value pairs, no advanced naming scheme

19
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

20
Interest Propagation

• Flood interest
• Constrained or Directional flooding based on
  location is possible
• Directional propagation based on previously
  cached data

Gradient
Source
Interest
Sink
21
Data Propagation

• Multipath routing
• Consider each gradients link quality

Gradient
Source
Data
Sink
22
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
Reinforcement
Sink
23
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
Reinforcement
Sink
24
Summary of the protocol
25
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

26
Design Considerations
27
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

28

• 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

29
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

30
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.
0.018
0.016
Flooding
0.014
0.012
0.01
0.008
(Joules/Node/Received Event)
Omniscient Multicast
Average Dissipated Energy
0.006
Diffusion
0.004
0.002
0
0
50
100
150
200
250
300
Network Size
31
Impact of In-network Processing
0.025
Diffusion Without Suppression
0.02
0.015
(Joules/Node/Received Event)
Average Dissipated Energy
0.01
Diffusion With Suppression
0.005
0
0
50
100
150
200
250
300
Network Size
32
Impact of Negative Reinforcement
0.012
0.01
Diffusion Without Negative Reinforcement
0.008
Average Dissipated Energy
(Joules/Node/Received Event)
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
33
Average Dissipated Energy (802.11 energy model)
0.14
Diffusion
0.12
Omniscient Multicast
Flooding
0.1
0.08
Average Dissipated Energy
(Joules/Node/Received Event)
0.06
0.04
0.02
0
0
50
100
150
200
250
300
Network Size
Standard 802.11 is dominated by idle energy
34
Failures

• Dynamic failures
• 10-20 failure at any time
• Each source sends different signals
• lt20 delay increase, fairly robust
• Energy efficiency improves
• Reinforcement maintains adequate number of high
  quality paths
• Shouldnt it be done in the first place?

35
Analysis

• Energy gains are dependent on 802.11 energy
  assumptions
• Can the network always deliver at the interests
  requested rate?
• Can diffusion handle overloads?
• Does reinforcement actually work?

36
Conclusions

• Data-centric communication between sources and
  sinks
• Aggregation and duplicate suppression
• More thorough performance evaluation is required

37
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

38
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

39
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

40
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?

41
Geographic Routing for Sensor Networks
42
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

43
Geographic Routing Greedy Routing
S
D

• Find neighbors who are the closer to the
  destination
• Forward the packet to the neighbor closest to
  the destination

44
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

45
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

46
Impact of Sensing Coverage on Greedy Geographic
Routing Algorithms
Guoliang Xing, Chenyang Lu, Robert Pless,
Qingfeng Huang
IEEE Trans. Parallel Distributed System
47
Metrics
b
v
u
c
a
48
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.

49
GF always finds a next-hop node

• Since Rc gtgt 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).

50
Theorem

• In a sensing-covered network, GF can always find
  a routing path between source u and destination v
  no longer than hops.

51
TTDD A Two-tier Data Dissemination Model for
Large-scale Wireless Sensor Networks

• Haiyun Luo
• Fan Ye, Jerry Cheng
• Songwu Lu, Lixia Zhang
• UCLA CS Dept.

52
Sensor Network Model
Stimulus
Source
53
Mobile Sink
Excessive Power Consumption
Increased Wireless Transmission Collisions
State Maintenance Overhead
54
TTDD Basics
Dissemination Node
Data Announcement
Data
Query
Immediate Dissemination Node
55
TTDD Mobile Sinks
Dissemination Node
Trajectory Forwarding
Data Announcement
Immediate Dissemination Node
Data
Immediate Dissemination Node
56
TTDD Multiple Mobile Sinks
Dissemination Node
Trajectory Forwarding
Data Announcement
Immediate Dissemination Node
Data
57
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