3/13/2002cse 581 - sensor-network schemes1 sensor-network schemes presented by: charles ‘buck’...

3/13/2002 CSE 581 - Sensor-Network Schemes 1

Sensor-Network Schemes

Presented by: Charles ‘Buck’ KrasicSlides adapted from original authors’


Paper List

1. C. Intanagonwiwa, R. Govindan, D. Estrin, (USC/ISI, UCLA) “Directed Diffusion: A Scalable and Robust Communications Paradigm for Sensor Networks”. MobiCOMM 2000

2. 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

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


Disaster ResponseCirculatory Net

EmbedEmbed 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.

The long term goal


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


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


Overview

• Motivation and goals

• Approach to sensor communication:– Meta-data exchanges– Data aggregation– “Resource-Adaptive” applications

• Implementation using ns

• Experiments


Sensor Networks• New research area• Advantages:

– Improved accuracy

– Fault tolerance

• Characteristics:– Wireless network

• No high-powered central base-station

• Distribution network

– Energy-limited nodes


System Parameters

• Quality– Accuracy of result

• Deadline– Time result required

• Energy EnergyDeadline

Qua

lity

Goal: Setup framework for analyzing trade-offs


Classic Network Approaches

• Flooding– Redundant data transmission

• Multi-hop routing– Large routing tables– Frequent updates– Complexity

Question: Are there better approaches?


Negotiation Protocol

• ADV- advertise data

• REQ- request specific data

• DATA- requested data

A B

ADV

A B

REQ

A B

DATA

Meta-Data <=> Data Naming


B

A

• Sensor A sends meta-data to neighbor

ADV


• Sensor B requests data from Sensor A

REQB

A


• Sensor A sends data to Sensor B

DATA

B

A


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

ADV

AD

VADV

ADV

AD

V ADV

B

A


• All but 1 neighbor request data

REQ

RE

Q

REQ

RE

Q

REQB

A


• Sensor B sends requested data to neighbors

DATA

DA

TA

DATA

DA

TA

DATA

B

A


ns Software Architecture

RCApplication

Resource Manager

Network Interface

RCAgent

Network Neighbor Energy

Link Link Link

Meta-DataData

Meta-DataData

Resource-AdaptiveNode


Resource-Adaptive Application

• Communication protocol implementation– Internal state– ADV/REQ/DATA algorithm

• Resource-adaptive decision-making– Application-specific

• Computation

• Communication


Other Simulation Tools

• Wireless topology generation

• Radio energy models

• Statistics collection– Data acquired– Energy dissipated– Redundant data received– Meta-data exchanged


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...


25-Node Wireless Test Network

70 meters

70

meters

Diameter = 152 meters

Node reach = 10 meters

Average degree = 4.7 neighbors59 edges


Limited DeadlineTotal Data Acquired Energy Dissipated

Time (ms)Time (ms)

% T

otal

Dat

a A

cqui

red

Tot

al E

nerg

y D

issi

pate

d (J

)

Negotiating

Flooding

Gossipping

Sleeping

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.450.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.450

5

10

15

20

25

30

35

40


Limited Energy

0 0.02 0.04 0.06 0.08 0.1 0.12 0.140.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Total Data Acquired

Time (ms)

% T

otal

Dat

a A

cqui

red

Flooding

GossippingNegotiating Sleeping

0.02 0.04 0.06 0.08 0.1 0.12 0.14

1

1.5

2

2.5

3

3.5

4

4.5

5

Energy Dissipated

Time (ms)T

otal

Ene

rgy

Dis

sipa

ted

(J)

0.50


Data Acquired/Energy Dissipated

0.5 1 1.5 2 2.5 3 3.5 4 4.5 50.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Flooding

GossippingNegotiating Sleeping

Total Energy Dissipated (Joules)

% T

otal

Dat

a A

cqui

red


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!


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


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


Directed Diffusion Concepts (cont’d)

• 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


Illustrating Directed Diffusion

Sink

Source

Setting up gradients

Sink

Source

Sending data

Sink

Source

Recoveringfrom node failure

Sink

Source

Reinforcingstable path


Local Behavior Choices1. For propagating interests

In our example, floodMore sophisticated behaviors

possible: e.g. based on cached information, GPS

2. For setting up gradientsHighest gradient towards

neighbor from whom we first heard interest

Others possible: towards neighbor with highest energy

3. For data transmissionDifferent local rules can result in

single path delivery, striped multi-path delivery, single source to multiple sinks and so on.

4. For reinforcementreinforce one path, or part

thereof, based on observed losses, delay variances etc.

other variants: inhibit certain paths because resource levels are low


Initial simulation studies(Intanago, Estrin, Govindan)

• 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

CENTRALIZED

DIFFUSION

FLOODING

DIFFUSION

FLOODING

CENTRALIZED


Experiments on PC104 testbed

• Initial experimental measurements of diffusion (e.g., for comparison with simulation)– Compare bytes sent by diffusion with and without aggr

egation (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 eve

ry 6 seconds)


Experimental Results

Diffusion with suppression

Diffusion without suppression

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


Comparison to Simulation

Diffusion with suppression

Diffusion without suppression

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


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 didn’t 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)


In network processing: Nested Queries

• Edge processing overwhelms power and bandwidth consumption

• Nested queries where low-energy sensors trigger high-energy sensors

Edge Processing

Nested Queries with In-network Processing


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

1-level query

Nested query


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.


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.


Gateway Architecture

Mote-NIC

Serial

Device Driver

LINUX

DIFFUSION

QueryData Sink

AcousticData Source

MOTE

TINYOS

TinyDiffusion

PhotoData Source

Data Sink

TINYOS

Transceiver

RFM

MOTEATMEL 8586 4MHz MCU8K program memory512 Bytes Data MemoryRFM Radio 900 MHz

PC104AMD Elan™SC40066MHz CPU16MB RAMForm Factor: 3.6" x 3.8" x 0.6"


Tiered Testbed

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

PC/104Tag

UCB Mote


“Shoebox Testbed v2”Featuring:

• PC-104+ w/Pentium 266 • Mote-NIC• Ethernet fordebugging andmeasurement• Linux 2.4.2w/glibc 2.1.3• Plasticshoeboxesfrom local drugstore


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


Directed Diffusion Summary (cont’d)

• Disadvantages– Design doesn’t deal with congestion or loss

• Future Work– Sensor networks today are analogous to the

Internet 3 decades ago


Sensor Card• The sensor card is a small (2”x4”)

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.


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 deli

vering aggregated data is approximately minimized

3/13/2002cse 581 - sensor-network schemes1 sensor-network schemes presented by: charles ‘buck’...

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