Wireless Sensor Networks and Laboratories Polly Huang EE NTU phuang [email protected].

Wireless Sensor Networks and Laboratories

Polly Huang

EE NTU

http://cc.ee.ntu.edu.tw/~phuang

[email protected]

Communication Protocols

Diffusion Routing

Magnetic Diffusion

Cross-Layer Performance Analysis

Directed Diffusionlargely based on slides from

Chalermek Intanagonwiwat & Deborah Estrin

In Short

• A data dissemination mechanism fitting into the data-centric communication paradigm for sensor networks

Sensor network, what?

Sensor Networks

Common Features

Challenges

Approach

Why not IP based solution?

Sensors

• Devices to sense the situation about physical objects or environments

• The situations– Location, motion, visual, sound, vital signs,

temperature, brightness, etc

• The sensors– Could be placed at close proximity of the sensing target– Could be tagged physically on to the sensing target

Sensor Networks

Or anotherOne way

ApplicationsScientific: eco-physiology,biocomplexity mapping

Infrastructure: contaminant flow monitoring (and modeling)

Engineering: monitoring (and modeling) structures

www.jamesreserve.edu

The Real Need

• Specialized communication in a wild wide space– Specialized: application dependent– Wild: little or no infrastructure– Wide: expensive to build/use communication

infrastructure

Applications: A Longer List

• Science: monitoring temperature change on a volcanic island

• Engineering: monitoring power use of industrial district

• Infrastructure: monitoring passenger traffic at MRT stations

• Military: tracking enemy migration in a dessert• Disaster: emergency relief after Gozzila taking a

short tour of Tokyo

Common Vision

• Embed numerous distributed devices to monitor and interact with physical world

• Exploit spatially and temporally dense, in situation, sensing and actuation

• Network these devices so that they can coordinate to perform higher-level tasks

• Requires robust distributed systems of hundreds or thousands of devices

Challenges• Tight coupling to the physical world and embedded in

unattended systems– Different from traditional Internet, PDA, Mobility applications that

interface primarily and directly with human users– But solutions might be applicable to the Internet, PDA, Mobility

applications as well

• Untethered, small form-factor, nodes present stringent energy constraints – Living with small, finite, energy source is different from traditional

fixed but reusable resources such as BW, CPU, Storage

• Communications is primary consumer of energy in this environment– R4 drop off dictates exploiting localized communication and in-network

processing whenever possible

Energy the Bottleneck Resource• Communication VS Computation Cost [Pottie 2000]

– E α R4

– 10 m: 5000 ops/transmitted bit– 100 m: 50,000,000 ops/transmitted bit

• Avoid communication over long distances• Cannot assume global knowledge, cannot pre-

configure networks– Achieve desired global behavior through localized

interactions – Empirically adapt to observed environment

• Can leverage data processing/aggregation inside the Can leverage data processing/aggregation inside the networknetwork

In-Network Processing

• Sensor technology is advancing steadily• Situations detected by the sensors can be

surprisingly rich• For example, all these at once

– Detecting a speech– Inferring the location and identity of the speaker

• These information can be used to facilitate efficient dissemination of the recorded speech– Suppressing speech coming from the same speaker– Forwarding towards the likely listeners

New Design Themes

• Long-lived systems that can be untethered and unattended

– Energy efficient communication– Self configuring systems that can be deployed

ad hoc

Approach• Leverage data processing inside the network

– Exploit computation near data to reduce communication

• Achieve desired global behavior with adaptive localized algorithms (i.e., do not rely on global interaction or information)– Dynamic, messy (hard to model), environments preclude

pre-configured behavior

– Can’t afford to extract dynamic state information needed for centralized control or even Internet-style distributed control

Why can’t we simply adapt Internet protocols and “end to end” architecture?

• Internet routes data using IP Addresses in Packets and Lookup tables in routers– Humans get data by “naming data” to a search engine

– Many levels of indirection between name and IP address

– Works well for the Internet, and for support of Person-to-Person communication

• Embedded, energy-constrained (un-tethered, small-form-factor), unattended systems can’t tolerate communication overhead of indirection

Therefore, Directed Diffusion

Features

Operations

Evaluations

Directed Diffusion Paradigm

• Data-centric communication • Supported with distributed algorithms using

localized interactions• Application-specific in-network processing

IP Communication

• Organize system based on named nodes

• Per-node forwarding state

• Senders need to push data to the node address of sink

BobAlice

To Bob

My name is Alice. I am a 19-yr old girl…

Chris

I am BobI am Bob

Bob there

I am Bob

Bob there

I am Bob

To Bob


To Bob


Data-Centric Communication

• Organize system based on named data

• Per-data diffusion state

• Sinks need to be specific about what data they’d pull

Tell me about girls

Tell me about girls

Girl info goes there

Tell me about girls


Tell me about girls

Here’s a 19-yr old girl…






Localized Interaction

• Diffuse requests/interest across network• Set up gradients to guide responses/data• Diffuse responses/data based on the gradients• (Pretty much the same as in the IP routing)

Tell me about girls

Tell me about girls


Tell me about girls


Tell me about girls







Without In-Network Processing

• Data are simply passed on

Tell me about girls

Tell me about girls


Tell me about girls



Tell me about girls

Tell me about girls







With In-Network Processing

• Data are aggregated and then passed on


Here’re two 19+ yr old girls…












Application-specificAggregation Here!




Example: Remote Surveillance

• Interrogation:– e.g., e.g., ““Give me periodic reportGive me periodic reportss about animal location in region A e about animal location in region A e

very t secondsvery t seconds””

• Interrogation is propagated to sensor nodes in region A

• Sensor nodes in region A are tasked to collect data

• Data are sent back to the users every t seconds

Basic Directed DiffusionSetting up gradients

Source

Sink

Interest = Interrogation

Gradient = Who is interested

Basic Directed Diffusion

Source

Sink

Sending data and Reinforcing the best path

Low rate event Reinforcement = Increased interest

Directed Diffusion and Dynamics

Recoveringfrom node failure

Source

Sink

Low rate event

High rate eventReinforcement

Directed Diffusion and Dynamics

Source

Sink

Stable path

Low rate event

High rate event

Local Behavior Choices

• For propagating interests– In this example, floodIn this example, flood

– More sophisticated behaviors possible: e.g. based on cached information, GPS

• For data transmission– Multi-path delivery with Multi-path delivery with

selective quality along selective quality along different pathsdifferent paths

– probabilistic forwarding

– single-path delivery, etc.

• For setting up gradients• data-rate gradients are set data-rate gradients are set

up towards neighbors who up towards neighbors who send an interestsend an interest..

• Others possible: probabilistic gradients, energy gradients, etc.

• For reinforcement• reinforce paths, or parts reinforce paths, or parts

thereof, based on observed thereof, based on observed delaysdelays, losses, variances etc.

• other variants: inhibit certain paths because resource levels are low

Initial simulation study of diffusion

• Key metric– Average Dissipated Energy per event delivered

• indicates energy efficiency and network lifetime

• Compare diffusiondiffusion to – floodingflooding– centrally computed tree (omniscient multicastomniscient multicast)

Diffusion Simulation Details

• Simulator: ns-2ns-2• Network Size: 50-250 Nodes• Transmission Range: 40m• Constant Density: 1.95x10-3 nodes/m2 (9.8 nodes in radius)• MAC: Modified Contention-based MAC• Energy Model: Mimic a realistic sensor radio [Pottie 2000]

– 660 mW in transmission, 395 mW in reception, and 35 mw in idle

Diffusion Simulation

• Surveillance application– 5 sources are randomly selected within a 70m x 70m co

rner in the field– 5 sinks are randomly selected across the field– High data rate is 2 events/sec– Low data rate is 0.02 events/sec– Event size: 64 bytes– Interest size: 36 bytes– All sources send the same location estimate for base exAll sources send the same location estimate for base ex

perimentsperiments

Average Dissipated Energy (SensSensor radioor radio energy model)

0

0.002

0.004

0.006

0.008

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0.018

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Network Size

DiffusionDiffusion

Omniscient MulticastOmniscient Multicast

FloodingFlooding

Diffusion can outperform flooding and even omniscient multicast. Diffusion can outperform flooding and even omniscient multicast. WHY ?WHY ?

Impact of In-network Processing

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Network Size

Diffusion With Diffusion With SuppressionSuppression

Diffusion Without Diffusion Without SuppressionSuppression

Application-level suppression allows diffusion to reduce traffic Application-level suppression allows diffusion to reduce traffic and to surpass omniscient multicast.and to surpass omniscient multicast.

Impact of Negative Reinforcement

0

0.002

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Network Size

Diffusion With Negative Diffusion With Negative ReinforcementReinforcement

Diffusion Without Diffusion Without Negative ReinforcementNegative Reinforcement

Reducing high-rate paths in steady state is criticalReducing high-rate paths in steady state is critical

Summary of Diffusion Results

• Under the investigated scenarios, diffusion outperformed omniscient multicast and flooding

• Application-level data dissemination has the potential to improve energy efficiency significantly– Duplicate suppression is only one simple example out of

many possible ways. – Aggregation (next)

• All layers have to be carefully designed– Not only network layer but also MAC and application lev

el

Average Dissipated Energy (StanStandard 802.11dard 802.11 energy model)

0

0.02

0.04

0.06

0.08

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Network Size

DiffusionDiffusion

Omniscient MulticastOmniscient MulticastFloodingFlooding

Standard 802.11 is dominated by idle energyStandard 802.11 is dominated by idle energy

Source 1

Source 2Sink

Source 1

Source 2Sink

Late Aggregation

Early Aggregation

Greedy Aggregation

• Low-latency tree might be inefficient (late aggregation)

• Bias path selection to increase early sharing of paths (early aggregation)

• Construct greedy incremental tree (GIT)– establish t shortest path for firs

t source

– connect each other source at closest point on existing tree

Mechanisms• Path Establishment

– Propagate energy cost with events

– On-tree incremental cost message for finding closest point on existing tree

– Path selection based on lowest energy cost (events and incremental cost messages)

• Path maintenance– Use greedy heuristic of weight

ed set-covering problem to compute energy cost of an outgoing aggregate

Source 1

Source 2Sink

E2 = 0

E2 = 2

E2 = 1

E2 = 1

E2 = 2

E2 = 2 E2 = 3

E2 = 4

E2 = 2E2 = 3

E2 = 4

E2 = 5

C2 = 2C2 = 2

C2 = 2

C2 = 2

Source 1

Source 2Sink

Incremental costmessage

Reinforcement

Evaluation

Greedy aggregation appears to outperform opportunistic aggregation only in very high-density networks

opportunistic

greedy

Testbed Experiments

Proof-of-Concept Experiment: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

Nested Queries Experiments @29Palms

• Used BAE-Austin’s signal processing– Live, Multiple-target, real-vehicle detections

• SITEX’02 validates previous lab experiments– Reduces network traffic/Improves event delivery

ISI Testbed Data: 2-level are nested queries 29Palms Data

nested

end-to-end

even

t del

iver

y ra

tio

Questions?

Ad Hoc Network Routing

Ad Hoc Network

• A collection of wireless mobile nodes

• Dynamically forming a temporary network

Features

• Without the use of any existing network infrastructure or centralized administration– Infrastructure-less networking

• Little or no communication infrastructure• Expensive or inconvenient to establish/use

infrastructure

– No central administration• Some overlay network• Some peer-to-peer networks

Ad Hoc Routing

• Finding a path from the source to the destination in ad hoc networks

• Multi-hop exchange

• Each host is also a router

Temporally-Ordered Routing Algorithm (TORA)

• Presented INFOCOM ’97 by Park and Carson• Designed to Minimize overhead and discover

routes on demand• Think about it as water flowing through tubes on

its way to a destination• Node broadcasts a QUERY packet, recipient

broadcasts an UPDATE packet• Uses IMEP as transport

– Reliable, in-order transmission

Route Creation Example

Magnetic Diffusion

Sensor Networks Now

• Existing sensor network applications– Environmental/eco-system monitoring– Structural health– Agriculture

• Infrastructure-less environment• Main design consideration

– Energy efficiency

Vision

• Anticipated sensor network applications– Digital home, smart office– Healthcare– Workplace safety

• Mission-critical data• Additional design considerations

– Timely delivery – Reliable transmissions

Research Objective

• Data dissemination protocol– Timely delivery of data– Reliable transmission of data– Energy efficiency

Related Work

• Energy-efficient data dissemination– Cluster based– Probability based: random walk– Geographical based: location-aware

• Reliable data dissemination– Passive approaches

• error recovery– Active approaches

• Avoid congestion, selecting less lossy path

• This work aims at achieving timely delivery, reliability, and energy efficiency.

Magnetic Diffusion• Consider the sink as a magnet

• Consider the data as metallic nails

• Two strategies of data propagation– Gradient-based (MDG)– Broadcast-based (MDB)

Gradient-based: Interest Broadcast• Interest: data type, magnetic charge

6

6

6

5

54

5

4

7

Sink

Gradient-based: Data Propagation• Sending data according to gradients

6

6

6

5

54

5

4

7

Sink

Src

Broadcast-based: Interest Broadcast• No gradients

6

6

6

5

54

5

4

7

Sink

Broadcast-based: Data Propagation• Data: magnetic charge, actual data

6

6

6

5

54

5

4

7

Sink

Src

Performance Evaluation

• Basic simulation setup

• Scenarios: static, mobile, on-off

Metrics

• Overhead– The amount of interest and data packet transmitted

• Reachability– The probability that the sink receives data

successfully

• Latency– The data transmission time from the source to the

sink

Two Sets of Comparisons

• I. Gradient-based vs. Broadcast-based– Which mode is better?

• II. MD vs. DD vs. Flooding– Is MD really better in terms of latency,

reliability, and overhead?– Directed diffusion (DD)

• Two phase pull (TPP) and One phase pull (OPP)

I. Gradient-based vs. Broadcast-based

Overhead and Reliability• MDB is more energy-efficient• MDB is more reliable

mobile case

MDG MDBInterest # 9943 9943

Data # 6010 3943Total # 15953 13886

Reachability 80.67% 86.27%

Latency• MDB behaves better in latency– No handshake packets

Thus, we adopt MDB for the rest of the comparisonThus, we adopt MDB for the rest of the comparison

II. MD vs. DD vs. Flooding

Total Overhead• MD being multi-path, the overhead

– No more than TPP– Much less than Flooding

OPP

TPP

MDFlooding

Reachability• In dynamic scenarios

– Multi-paths give more reliable results– Multi-paths are not better in the static cases

Reachability with Random Wait• Random wait mechanism

– decreases the probability of collision

Latency in Static Scenario• MD performs the best in latency

– No handshake packets

Latency in Mobile Scenario• MD is a better solution for applications with

restricted latency requirement in dynamic network.

Latency in On-Off Scenario

Latency - Mobile with Random Wait• This technique decreases the probability of collision,

and in the meantime, increases transmission delay

System Selection Guideline

static case dynamic case

• Static case– DD the best

• Dynamic case– MD better overall

• If 100% reliability is required– Flooding

Summary

• MD achieves in– Timely delivery– Reliability– Energy effectiveness – for dynamic sensor networks– An effective solution to mission-critical applications

• Simulation-based performance evaluation– Guidelines – for selecting the suitable mechanisms – for different application requirements

BL-Live: The TestBed

BL-Live

– A mid-size sensor network testbed, 70+ sensor nodes

– Transform BL Hall into a lively smart office building

– Obtain practical experience and discover problems

BL-LiveHardware

• Two kinds of sensor nodes– Crossbow Micaz and Moteiv Telos.

• The placement– 1 sink node in Lab 621– 2 sensor nodes with accelerometers in the elevators– 72 relay nodes from the 4th floor to the 6th floor

BL-LiveServices

• BL-Live provides two services:– Elevator Report– Smart Office

BL-LiveElevator Report

• Two slow paced elevators located on two opposite sides in BL Hall • What if we can know the status of the elevator before we move to take?

BL-LiveElevator Report

Sensor Networks

Sink

Observations

• The reachability of MD is not good! (70+%)• The reasons

– Collisions• Deployment is too dense• MD broadcasts packets in multipath

– Asymmetric links

Link quality difference of A and B = |Rab-Rba|

Problem Caused By Asymmetric Links

Sink

A B

C

D

There exists an asymmetric link!


8

7 7

Sink

A B

C

D

Interest Broadcast

Problem caused by asymmetric links

8

7 7

6

6

Sink

A B

C

D

Interest Broadcast


8

7 7

6

6

Sink

A B

C

D

Data Propagation

6,data


8

7 7

6

6

Sink

A B

C

D

This packet is lost.

Data Propagation

Node A won’t relay this pkt for node B.

7,data7,data

7,data

Reliable Data Dissemination

• To improve the reliability of MD• Counter two problems

– Collision• Random wait• Priority

– Two level forwarding

• Send Twice

– Asymmetric Link• MDlq• MDfd

Random Wait

• Before sending the packet, it will wait for a random period of time. – Avoid collisions to increase the reachability

Priority• Random wait increases the delay

– Critical data need short latency

• Classify packets into two types– High priority and low priority

• Two-level Priority Forwarding– Send high priority packets first!

• To save queuing delay of high priority data

Send Twice

• Send Twice– Send first copy immediately

• To shorten the latency

– Send second copy in a random backoff• To avoid the collision

Reliable Data Dissemination

• To improve the reliability of MD• Counter two problems

– Collision• Random wait• Priority


• Send Twice

– Asymmetric Link• MDlq• MDfd

MDlq

• MD with revised interest broadcast method

• lq stands for link quality

• To set proper charge value for every node according to link quality

Revised Interest Broadcast

• Two phases– Link quality estimation

• CC2420 provides an indicator to estimate the link quality.

– Interest Broadcast• Specify the charge value and destination node id in the

interest packets


Sink

A B

C

D

A

A

A


Sink

A B

C

D

A

A

A


Sink

A B

C

D

A

A

A

B

B


8

7 6

6

5

Sink

A B

C

D

A

A,D,S

A

B

B,C,S

7,A

6,B

6,C

5,S

5,D

Interest broadcast phase is finished!


8

7 6

6

5

Sink

A B

C

D

A

A,D,S

A

B

B,C,S

The sink receives the data!

5,data6,data

6,data

6,data

7,data

7,data

7,data

MDfd

• Everything is the same as MD, except…

• Send data with charge no larger than that of node

• Like flooding in a smaller area

MDfd

8

7 7

6

6

Sink

A B

C

D

6,data

7,data

7,data

7,data

Node A will relay pkt for node B

7,data

7,data

7,data

This data is received by sink.

MDlq V.S. MDfd

• MDlq– Advantage:

• Set proper charge value

– Disadvantage:• Overhead on revised interest broadcast

• MDfd– Advantage

• More paths

– Disadvantage• Overhead on new paths

Evaluation

• We want to see the impact of– To counter collision

• Random wait• Priority


• Send twice– To counter asymmetric link

• MDlq• MDfd

• All experimental data are collected in BL-Live

Experiment Setup

Sink node 1

Source node 6

Relay node 66

Period of interest broadcast

2 min.

Period of critical data 3 sec.

Period of status data 30 sec.

Evaluation time 80 min.

Evaluation

• Three metrics– Reachability– Latency– Overhead

• The amount of interest and data packets transmitted

• Highly related to energy consumption

Impact of Random Wait• Reachability

The reachability is increased by 5%.

Impact of Two Level Forwarding

• Latency

Latency of high priority packet is slightly shorter.

Impact of Send Twice• Reachability

With send twice, the reachability is increased by 8%

Impact of MDlq and MDfd

• Reachability

MDfd highly improves the reachability!

Overhead of MDlq and MDfd

• Overhead

Overhead of MDfd very high.

MDlq+

• Integrate different mechanisms– Increase the reachability– Energy efficient

• MDlq+– MDlq with sendtwice

Impact of send twice

MDlq+ and MDfd is close to Flooding!

• Reachability

Overhead of MDlq+, MDfd and Flooding

• Overhead

MDlq+ is the most energy efficient.

Latency of MDlq+, MDfd and Flooding

Latency of MDfd is as good as Flooding.

MDlq+ is decent.

Summary of the Experimental Results

• Impact of– Random wait:

• increasing 5%– Two-level forwarding:

• Slightly shorten latency– Send twice

• Increasing 8%– Revised interest broadcast method

• Increasing 15%– MDfd and MDlq+

• Close to Flooding(96%)• More energy efficient

Summary

• Two Contributions– BL-Live

• Establish the testbed

• Manage the networking of sensor nodes

– Reliable Data Dissemination• Evaluate several mechanisms

• Improve the reachability to 95%

Questions?

Cross-Layer Analysis

802.11 – The standard in Wireless Network

• Contention-based protocol– RTS-CTS-DATA-ACK

RTS

CTS

Sender

Receiver

DATA

ACK

[Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specification, IEEE Std. 802.11-1999 edition]

S-MAC - Periodic Listen and Sleep

• Contention-based protocol– RTS-CTS-DATA-ACK

• Listen interval– Send packets– Receive packets

[W. Ye et al., “An energy-efficient MAC protocol for wireless sensor

networks”, in INFOCOM 2002]

sleeplisten listen sleepsleeplistenlisten listenlisten sleep

S-MAC – Schedule synchronization

• Schedules can differ– Neighboring nodes have same schedule

Node 1

Node 2

sleeplisten listen sleep

sleeplisten listen sleep

Schedule 2

Schedule 1Border nodes: two schedules broadcast

twice

(Borrowed from S-MAC)

1 3

2 4

Scheduling in S-MAC• Unknown neighbors

– the same schedule

2

3

4

Schedule 2Schedule 1

Collision1

Unicast

Broadcast

B-MAC

• Contention-based protocol– No RTS/CTS, optional ACK

• Low Power Listening (LPL)– Preamble > Check-Interval

[J. Polastre et al., Versatile low power media access for wireless sensor networks, Proceedings of the Second ACM Conference on Embedded Networked Sensor Systems (SenSys) 2004]

Receive data

Carrier sense

Receiver

Long Preamble Data TxSender

CheckInterval

(Borrowed from Z-MAC)

• Low power listening (LPL)• no RTS/CTS, optional ACK

• Schedule-based (TDMA ) Contention-based (CSMA)• TDMA scheduling

– Owners– non-owners

[Injong Rhee, Ajit Warrier, Mahesh Aia and Jeongki Min, “Z-MAC: a Hybrid MAC for Wireless Sensor Networks”, ACM Sensys 2005]

Z-MAC – On Top of B-MAC

Hybrid (TDMA+CSMA)

Z-MAC – On Top of B-MAC

• Problem – hidden terminal collisions– Low contention level (LCL)– High contention level (HCL)

• Two-hop contention avoidance

A B

C DA

Down

The Summarizations• Non-energy efficient MAC

– 802.11• RTS-CTS-DATA-ACK

• Energy efficient MACs– S-MAC

• Periodic listen and sleep

– B-MAC• LPL, no RTS/CTS

– Z-MAC: • LPL

• TDMA + CSMA

• no RTS/CTS

• LCL/HCL

Experiments• Simulation setup in NS2 simulator

Metrics

• Energy consumption– The amount of energy consumed in the network

• Reachability– The probability that the sink receives data

successfully

• Latency– The data transmission time from the source to the

sink

Energy Consumption

• MDB < MDG

• B-MAC best

• Z-MAC– TDMA scheduling

802.11

MDB 26800

MDG 26804Unit(J)

Energy Consumption – The Impact of Multiple sources

• Energy goes up– MDG-ZMAC

– MDG-BMAC

• Overhead– MDB < MDG MDG + B-MAC

MDG + Z-MAC

MDB+ Z-MAC

MDB + B-MAC

Energy Consumption - Summarization

• Energy consumption – MDB < MDG– B-MAC < S-MAC < Z-MAC < 802.11– Best - MDB + BMAC

• LPL is sensitive to the traffic load

• Routing and MAC– Critical to the energy consumption

Reachability• In 802.11

– MDB < MDG

• In S-MAC– MDB >

MDG5 6

5

6

7

Sink

Source

MDG

MDB

Down

Reachability – The Impact of Multiple Sources

• High traffic load– MDG + 802.11

– MDG + Z-MAC

MDG + Z-MAC

MDG + 802.11

Reachability - Summarization• The relative performance of routing protocols changes

– When run over different MACs

• In dense network– S-MAC is bad

• Reachability– Retransmission

– Two-hop collision avoidance

– MDG + 802.11 and MDG + Z-MAC

Latency

• MDB-802 best• MDB-BMAC

– Delay < 1 sec

– 80% < 500ms

MDB + 802.11

MDB + B-MAC

Latency - Summarization

• Generally speaking, MDB is better

• The relative performance is not obvious

• Latency– MDB + 802 is the best– MDB + B-MAC is surprisingly good– Delay can be short

• In an energy-efficient MAC

System Selection Guidline

• The selection of protocol combination depends on– Application

– Deployment environment

• Elevator application in BL-Live

[Seng-Yong Lau et al., “Sensor Networks for Everyday Use: The BL-Live Experience“, IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC 2006)]

Summary

• The interactions between routing and MAC– Relative performance might change– Both are critical to energy consumption– No one wins in every case

• High reliability in an energy-efficient MAC– Retransmission– Two-hop collision avoidance

Contribution

• We achieves in– Cross-layer performance evaluation

• Relative performance might change

• The interaction between routing and MAC

• In wireless sensor network

– System selection guidelines

Future Work

• Extensive set of experiments

• Various routing protocols

• Real test-bed

Questions?

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Wireless Sensor Networks and Laboratories Polly Huang EE NTU phuang [email protected].

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