SensorNetwork Schemes

1
Sensor-Network Schemes

• Presented by Charles Buck Krasic
• Slides adapted from original authors

2
Paper List

• C. Intanagonwiwa, R. Govindan, D. Estrin,
  (USC/ISI, UCLA) Directed Diffusion A Scalable
  and Robust Communications Paradigm for Sensor
  Networks. MobiCOMM 2000
• J. Heidemann, F. Silva, C. Intanagonwiwat, R.
  Govindan, D. Estrin, D. Ganesan, (USC/ISI,UCLA)
  Building Efficient Wireless Sensor Networks with
  Low-Level Naming. SOSP 2001
• J. Kulik, W. Heinzelman, H. Balakrishnan, (MIT)
  Negotiation-based Protocols for Disseminating
  Information in Wireless Sensor Networks.
  MobiCOMM 1999

3
The long term goal
Embed numerous distributed devices to monitor and
interact with physical world in work-spaces,
hospitals, homes, vehicles, and the environment
(water, soil, air)
Network these devices so that they can coordinate
to perform higher-level tasks. Requires robust
distributed systems of tens of thousands of
devices.
4
Resource-Adaptive Protocols for Networks of
Sensors

• J. Kulik, W. Heinzelman, H. Balakrishnan, (MIT)
  Negotiation-based Protocols for Disseminating
  Information in Wireless Sensor Networks.
  MobiCOMM 1999

5
SPIN Sensor Protocols fro Information via
Negotiation

• J. Kulik, W. Heinzelman, H. Balakrishnan, (MIT)
  Negotiation-based Protocols for Disseminating
  Information in Wireless Sensor Networks.
  MobiCOMM 1999

6
Overview

• Motivation and goals
• Approach to sensor communication
• Meta-data exchanges
• Data aggregation
• Resource-Adaptive applications
• Implementation using ns
• Experiments

7
Sensor Networks

• New research area
• Advantages
• Improved accuracy
• Fault tolerance
• Characteristics
• Wireless network
• No high-powered central base-station
• Distribution network
• Energy-limited nodes

8
System Parameters

• Quality
• Accuracy of result
• Deadline
• Time result required
• Energy

Goal Setup framework for analyzing trade-offs
9
Classic Network Approaches

• Flooding
• Redundant data transmission
• Multi-hop routing
• Large routing tables
• Frequent updates
• Complexity

Question Are there better approaches?
10
Negotiation Protocol
Meta-Data ltgt Data Naming
ADV
A
B

• ADV- advertise data
• REQ- request specific data
• DATA- requested data

REQ
A
B
DATA
A
B
11

• Sensor A sends meta-data to neighbor

A
ADV
B
12

• Sensor B requests data from Sensor A

A
B
REQ
13

• Sensor A sends data to Sensor B

A
DATA
B
14

• Sensor B aggregates data and sends meta-data for
  A and B to neighbors

A
ADV
ADV
B
ADV
ADV
ADV
ADV
15

• All but 1 neighbor request data

A
REQ
REQ
B
REQ
REQ
REQ
16

• Sensor B sends requested data to neighbors

A
DATA
DATA
B
DATA
DATA
DATA
17
ns Software Architecture
Resource-Adaptive Node
RCApplication
Meta-Data Data
Resource Manager
RCAgent
Network Neighbor
Energy
Meta-Data Data
Network Interface
Link
Link
Link
18
Resource-Adaptive Application

• Communication protocol implementation
• Internal state
• ADV/REQ/DATA algorithm
• Resource-adaptive decision-making
• Application-specific
• Computation
• Communication

19
Other Simulation Tools

• Wireless topology generation
• Radio energy models
• Statistics collection
• Data acquired
• Energy dissipated
• Redundant data received
• Meta-data exchanged

20
Test Algorithms

• Flooding -- Each node floods new data to all of
  its neighbors.
• Gossipping -- Each node floods all its data to
  one, randomly selected neighbor.
• Negotiating -- nodes decide what data to send
  based on meta-data advertisements.
• Sleeping -- Same as negotiating, except that
  nodes stop sending messages when energy is low.

Zzz...
21
25-Node Wireless Test Network
Diameter 152 meters
Node reach 10 meters
70 meters
70 meters
59 edges
Average degree 4.7 neighbors
22
Limited Deadline
Energy Dissipated
Total Data Acquired
1
40
0.9
35
0.8
30
0.7
Total Data Acquired
25
Total Energy Dissipated (J)
0.6
20
0.5
15
0.4
10
0.3
5
0.2
0.1
0
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
Time (ms)
Time (ms)
23
Limited Energy
Total Data Acquired
Energy Dissipated
0.8
5
4.5
0.7
4
0.6
3.5
0.5
Total Data Acquired
Total Energy Dissipated (J)
3
2.5
0.4
2
0.3
1.5
0.2
1
0.5
0.1
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
Time (ms)
Time (ms)
24
Data Acquired/Energy Dissipated
0.8
Flooding
0.7
Gossipping
Negotiating
0.6
Sleeping
0.5
Total Data Acquired
0.4
0.3
0.2
0.1
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Total Energy Dissipated (Joules)
25
SPIN Summary

• Contribution
• Sensor networks should be more data-centric
  (meta-data driven)
• Simulation results
• Advantages Seems better than flooding
• Disadvantages communication still excessive?
• Future Work lots!

26
Directed Diffusion

• C. Intanagonwiwa, R. Govindan, D. Estrin,
  (USC/ISI, UCLA) Directed Diffusion A Scalable
  and Robust Communications Paradigm for Sensor
  Networks. MobiCOMM 2000
• J. Heidemann, F. Silva, C. Intanagonwiwat, R.
  Govindan, D. Estrin, D. Ganesan, (USC/ISI,UCLA)
  Building Efficient Wireless Sensor Networks with
  Low-Level Naming. SOSP 2001

27
Directed Diffusion Concepts

• Application-aware communication primitives
• expressed in terms of named data (not in terms of
  the nodes generating or requesting data)
• Consumer of data initiates interest in data with
  certain attributes
• Nodes diffuse the interest towards producers via
  a sequence of local interactions

28
Directed Diffusion Concepts (contd)

• This process sets up gradients in the network
  which channel the delivery of data
• Reinforcement and negative reinforcement used to
  converge to efficient distribution
• Intermediate nodes opportunistically fuse
  interests, aggregate, correlate or cache data

29
Illustrating Directed Diffusion
Setting up gradients
Source
Sink
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Local Behavior Choices

• 1. For propagating interests
• In our example, flood
• More sophisticated behaviors possible e.g. based
  on cached information, GPS
• 2. For setting up gradients
• Highest gradient towards neighbor from whom we
  first heard interest
• Others possible towards neighbor with highest
  energy

• 3. For data transmission
• Different local rules can result in single path
  delivery, striped multi-path delivery, single
  source to multiple sinks and so on.
• 4. For reinforcement
• reinforce one path, or part thereof, based on
  observed losses, delay variances etc.
• other variants inhibit certain paths because
  resource levels are low

31
Initial simulation studies(Intanago, Estrin,
Govindan)
FLOODING

• Compare diffusion to a)flooding, and b)centrally
  computed tree (ideal)
• Key metrics
• total energy consumed per packet delivered
  (indication of network life time)
• average pkt delay

DIFFUSION
CENTRALIZED
CENTRALIZED
DIFFUSION
FLOODING
32
Experiments on PC104 testbed

• Initial experimental measurements of diffusion
  (e.g., for comparison with simulation)
• Compare bytes sent by diffusion with and without
  aggregation (simple in network processing)
• Measurement Setup
• A 5-hop network of 14 nodes on 2 ISI floors
  (testbed is actually 30 nodes and growing)
• Radio 13kbps radiometrix
• 1 sink and 1-4 sources (each source sends 112
  bytes every 6 seconds)

33
Experimental Results

• Bytes sent by diffusion per event vs. Number of
  sources

Diffusion without suppression
Diffusion with suppression
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Comparison to Simulation

• Bytes sent by diffusion per event vs. Number of
  sources

Diffusion without suppression
Diffusion with suppression
35
Differences between Simulations and Experiments

• MAC differences
• Modified 802.11 for simulations to represent
  hybrid TDMA-Contention
• Radiometrix MAC for experiments
• Channel differences
• No obstacles used in ns-2 simulations
• Note we have added ability to include simple
  terrain but didnt try to replicate indoor exp
  terrain in sims
• More packet losses and collisions in experiments
• Collisions in experiments act as unintentional
  suppression (make no suppression look better than
  it will with better mac)

36
In network processing Nested Queries

• Edge processing overwhelms power and bandwidth
  consumption
• Nested queries where low-energy sensors trigger
  high-energy sensors

37
Experimental Validation Testbed Measurements

• Higher delivery ratio for nested query indicates
  that localizing data traffic benefits
  performance.
• Audio Events Successfully Delivered vs. Number
  of light sensors

38
TinyDiffusion

• Implementation of Diffusion on resource
  constrained UCB motes
• 8bit CPU, 8K program memory, 512 bytes data
  memory
• Subset of full system
• retains only gradients, and condenses attributes
  to a single tag.
• Entire System runs for less than 5.5 KB memory
• TinyOS adds 3.5K and 144 bytes of data. (incl.
  support for Radio and Photo Sensor)
• Diffusion adds 2K code and 110 bytes of data to
  TinyOS.

39
TinyDiffusion Functionality

• Resource Constraints
• Limited cache size currently 10 entries of
  2bytes each
• Limited ability to support multiple traffic
  streams. Currently supports 5 concurrently active
  gradients.
• Tiered Deployment
• PC104s running diffusion interface with mote
  clusters using TinyDiffusion.
• Motes enable dense sensor deployment but can
  support limited in-network processing
• Logical Header format of TinyDiffusion is
  compatible with the Diffusion header.

40
Gateway Architecture
MOTE ATMEL 8586 4MHz MCU 8K program memory 512
Bytes Data Memory RFM Radio 900 MHz
Mote-NIC
MOTE
PC104 AMD ElanSC400 66MHz CPU 16MB RAM Form
Factor 3.6"  x  3.8"  x  0.6"
Serial
41
Tiered Testbed

• PC-104(linux) with MoteNIC
• Tags, Sensor Card
• UCB Motes w/TinyOS
• Yet to come SmartDust (highly specialized nodes)

PC/104
Tag
UCB Mote
42
Shoebox Testbed v2

• Featuring
• PC-104 w/Pentium 266
• Mote-NIC
• Ethernet fordebugging andmeasurement
• Linux 2.4.2w/glibc 2.1.3
• Plastic
• shoeboxes
• from local drugstore

43
Directed Diffusion Summary

• Main contributions
• Description of new networking paradigm
• Interests, gradients, reinforcement
• MobiCOMM simulation results
• SOSP empirical results
• Advantages
• Benefits of in-network processing
• Aggregation and nested-queries

44
Directed Diffusion Summary (contd)

• Disadvantages
• Design doesnt deal with congestion or loss
• Future Work
• Sensor networks today are analogous to the
  Internet 3 decades ago

45
Sensor Card

• The sensor card is a small (2x4)
  microcontroller board with several on-board
  sensors and emitters
• Microphone
• Light sensor
• Accelerometer
• Designed to perform simple sensing tasks at low
  power.
• Currently it is connected to the PC-104 platform
  by serial.
• Data is preprocessed on the sensor board and fed
  back to the PC-104 for analysis and
  communication.
• The next version of the PC-104 platform will have
  the capability to be awakened by a peripheral
  such as the sensor card.

46
Reinforced Aggregation

• Promote In-network Data Aggregation near the
  Sources for Better Energy Savings
• Two Approaches for Reinforced Aggregation
• Greedy Tree Approach
• Incremental approach -- Adds minimum number of
  links on the existing tree
• Iterative Approach
• Selects aggregation points such that energy
  dissipation for delivering aggregated data is
  approximately minimized