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Wireless Sensor Networks and Laboratories. Polly Huang EE NTU http://cc.ee.ntu.edu.tw/~phuang phuang@cc.ee.ntu.edu.tw. Communication Protocols. Diffusion Routing Magnetic Diffusion Cross-Layer Performance Analysis.

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Wireless sensor networks and laboratories

Wireless Sensor Networks and Laboratories

Polly Huang

EE NTU

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

phuang@cc.ee.ntu.edu.tw


Communication protocols

Communication Protocols

Diffusion Routing

Magnetic Diffusion

Cross-Layer Performance Analysis


Directed diffusion largely based on slides from chalermek intanagonwiwat deborah estrin

Directed Diffusionlargely based on slides from Chalermek Intanagonwiwat & Deborah Estrin


In short
In Short

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


Sensor network what

Sensor network, what?

Sensor Networks

Common Features

Challenges

Approach

Why not IP based solution?


Sensors
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

One way

Or another

Sensor Networks


Applications
Applications

Scientific: eco-physiology,

biocomplexity mapping

Infrastructure: contaminant flow monitoring (and modeling)

www.jamesreserve.edu

Engineering: monitoring (and modeling) structures


The real need
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
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
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
Challenges

  • Tight coupling to the physical worldand 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
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 network


In network processing
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
New Design Themes

  • Long-lived systems that can be untetheredand unattended

    • Energy efficient communication

    • Self configuring systems that can be deployed ad hoc


Approach
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 interactionor 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
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

Therefore, Directed Diffusion

Features

Operations

Evaluations


Directed diffusion paradigm
Directed Diffusion Paradigm

  • Data-centric communication

  • Supported with distributed algorithms using localized interactions

  • Application-specific in-network processing


Ip communication

Bob there

Bob there

IP Communication

  • Organize system based on named nodes

  • Per-node forwarding state

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

To Bob

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

To Bob

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

To Bob

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

I am Bob

I am Bob

I am Bob

I am Bob

Chris

Bob

Alice


Data centric communication

Girl info goes there

Girl info goes there

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

Here’s a 19-yr old girl…

Tell me

about girls

Here’s a 19-yr old girl…

Tell me

about girls

Here’s a 19-yr old girl…


Directed diffusion paradigm1
Directed Diffusion Paradigm

  • Data-centric communication

  • Supported with distributed algorithms using localized interactions

  • Application-specific in-network processing


Localized interaction

Girl info goes there

Girl info goes there

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

Here’s a 19-yr old girl…

Tell me

about girls

Here’s a 19-yr old girl…

Tell me

about girls

Here’s a 19-yr old girl…


Directed diffusion paradigm2
Directed Diffusion Paradigm

  • Data-centric communication

  • Supported with distributed algorithms using localized interactions

  • Application-specific in-network processing


Without in network processing

Tell me

about girls

Here’s a 19-yr old girl…

Here’s a 20-yr old girl…

Here’s a 20-yr old girl…

Here’s a 19-yr old girl…

Here’s a 19-yr old girl…

Girl info goes there

Tell me

about girls

Here’s a 20-yr old girl…

Girl info goes there

Girl info goes there

Without In-Network Processing

  • Data are simply passed on

Tell me

about girls

Tell me

about girls

Tell me

about girls


With in network processing

Here’s a 19-yr old girl…

Here’s a 19-yr old girl…

Here’re two 19+ yr old girls…

Girl info goes there

Here’s a 20-yr old girl…

Here’s a 20-yr old girl…

Girl info goes there

Girl info goes there

Application-specific

Aggregation Here!

With In-Network Processing

  • Data are aggregated and then passed on

Here’re two 19+ yr old girls…

Here’re two 19+ yr old girls…


Directed diffusion paradigm3
Directed Diffusion Paradigm

  • Data-centric communication

  • Supported with distributed algorithms using localized interactions

  • Application-specific in-network processing


Example remote surveillance
Example: Remote Surveillance

  • Interrogation:

    • e.g., “Give me periodic reports about animal location in region A every 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 diffusion
Basic Directed Diffusion

Setting up gradients

Source

Sink

Interest = Interrogation

Gradient = Who is interested


Basic directed diffusion1
Basic Directed Diffusion

Sending data and Reinforcing the best path

Source

Sink

Low rate event

Reinforcement = Increased interest


Directed diffusion and dynamics
Directed Diffusion and Dynamics

Source

Sink

Recovering

from node failure

Low rate event

Reinforcement

High rate event


Directed diffusion and dynamics1
Directed Diffusion and Dynamics

Source

Sink

Stable path

Low rate event

High rate event


Local behavior choices

For propagating interests

In this example, flood

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

For data transmission

Multi-path delivery with selective quality along different paths

probabilistic forwarding

single-path delivery, etc.

Local Behavior Choices

  • For setting up gradients

    • data-rate gradients are set up towards neighbors who send an interest.

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

  • For reinforcement

    • reinforce paths, or parts thereof, based on observed delays, losses, variances etc.

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


Initial simulation study of diffusion
Initial simulation study of diffusion

  • Key metric

    • Average Dissipated Energy per event delivered

      • indicates energy efficiency and network lifetime

  • Compare diffusion to

    • flooding

    • centrally computed tree (omniscient multicast)


Diffusion simulation details
Diffusion Simulation Details

  • Simulator: ns-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
Diffusion Simulation

  • Surveillance application

    • 5 sources are randomly selected within a 70m x 70m corner 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 experiments


Average dissipated energy sensor radio energy model
Average Dissipated Energy (Sensor radio energy model)

0.018

0.016

Flooding

0.014

0.012

0.01

0.008

Omniscient Multicast

(Joules/Node/Received Event)

Average Dissipated Energy

0.006

Diffusion

0.004

0.002

0

0

50

100

150

200

250

300

Network Size

Diffusion can outperform flooding and even omniscient multicast. WHY ?


Impact of in network processing
Impact ofIn-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

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


Impact of negative reinforcement
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


Summary of diffusion results
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 level


Average dissipated energy standard 802 11 energy model
Average Dissipated Energy (Standard 802.11 energy model)

0.14

Diffusion

0.12

Flooding

Omniscient Multicast

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


Wireless sensor networks and laboratories

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 first source

    • connect each other source at closest point on existing tree

Late Aggregation

Source 2

Sink

Source 1

Early Aggregation

Source 2

Sink

Source 1


Wireless sensor networks and laboratories

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

E

= 2

Incremental cost

2

E

= 1

message

2

E

= 4

2

E

= 0

2

E

= 2

E

= 3

E

= 5

2

2

2

Source 2

E

= 1

2

Sink

E

= 4

2

E

= 3

2

E

= 2

E

= 2

C

= 2

2

2

2

Source 1

C

= 2

2

C

= 2

2

= 2

C

2

Reinforcement

Source 2

Sink

Source 1


Evaluation
Evaluation

opportunistic

greedy

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



Proof of concept experiment nested queries

Edge Processing

Nested Queries with In-network Processing

Proof-of-Concept Experiment:Nested Queries

  • Edge processing overwhelms power and bandwidth consumption

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


Nested queries experiments @29palms
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

nested

event delivery ratio

end-to-end

ISI Testbed Data: 2-level are nested queries

29Palms Data




Ad hoc network
Ad Hoc Network

  • A collection of wireless mobile nodes

  • Dynamically forming a temporary network


Features
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
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
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




Sensor networks now
Sensor Networks Now

  • Existing sensor network applications

    • Environmental/eco-system monitoring

    • Structural health

    • Agriculture

  • Infrastructure-less environment

  • Main design consideration

    • Energy efficiency


Vision
Vision

  • Anticipated sensor network applications

    • Digital home, smart office

    • Healthcare

    • Workplace safety

  • Mission-critical data

  • Additional design considerations

    • Timely delivery

    • Reliable transmissions


Research objective
Research Objective

  • Data dissemination protocol

    • Timely delivery of data

    • Reliable transmission of data

    • Energy efficiency


Related work
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 diffusion1
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
Gradient-based: Interest Broadcast

4

5

  • Interest: data type, magnetic charge

6

4

5

Sink

7

5

6

6


Gradient based data propagation
Gradient-based: Data Propagation

4

5

  • Sending data according to gradients

Src

6

4

5

Sink

7

5

6

6


Broadcast based interest broadcast
Broadcast-based: Interest Broadcast

4

5

  • No gradients

6

4

5

Sink

7

5

6

6


Broadcast based data propagation
Broadcast-based: Data Propagation

4

5

  • Data: magnetic charge, actual data

Src

6

4

5

Sink

7

5

6

6


Performance evaluation
Performance Evaluation

  • Basic simulation setup

  • Scenarios: static, mobile, on-off


Metrics
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
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

I.Gradient-based vs. Broadcast-based


Overhead and reliability
Overhead and Reliability

  • MDB is more energy-efficient

  • MDB is more reliable

    mobile case


Latency
Latency

  • MDB behaves better in latency

    • No handshake packets

Thus, we adopt MDB for the rest of the comparison



Total overhead
Total Overhead

  • MD being multi-path, the overhead

    • No more than TPP

    • Much less than Flooding

MD

Flooding

TPP

OPP


Reachability
Reachability

  • In dynamic scenarios

    • Multi-paths give more reliable results

    • Multi-paths are not better in the static cases


Reachability with random wait
Reachability with Random Wait

  • Random wait mechanism

    • decreases the probability of collision


Latency in static scenario
Latency in Static Scenario

  • MD performs the best in latency

    • No handshake packets


Latency in mobile scenario
Latency in Mobile Scenario

  • MD is a better solution for applications with restricted latency requirement in dynamic network.



Latency mobile with random wait
Latency - Mobile with Random Wait

  • This technique decreases the probability of collision, and in the meantime, increases transmission delay


System selection guideline
System Selection Guideline

static case dynamic case

  • Static case

    • DD the best

  • Dynamic case

    • MD better overall

  • If 100% reliability is required

    • Flooding


Summary
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
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 live hardware
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 live services
BL-LiveServices

  • BL-Live provides two services:

    • Elevator Report

    • Smart Office


Bl live elevator report
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 live elevator report1
BL-LiveElevator Report

Sensor Networks

Sink


Observations
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
Problem Caused By Asymmetric Links

There exists an asymmetric link!

Sink

A

B

D

C


Problem caused by asymmetric links1
Problem Caused By Asymmetric Links

Interest Broadcast

Sink

8

7

7

A

B

D

C


Problem caused by asymmetric links2
Problem caused by asymmetric links

Interest Broadcast

Sink

8

7

7

A

B

6

D

6

C


Problem caused by asymmetric links3
Problem Caused By Asymmetric Links

Data Propagation

Sink

8

7

7

A

B

6,data

6

D

6

C


Problem caused by asymmetric links4
Problem Caused By Asymmetric Links

Data Propagation

Sink

8

This packet is lost.

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

7,data

7

7

A

B

7,data

7,data

6

D

6

C


Reliable data dissemination
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
Random Wait

  • Before sending the packet, it will wait for a random period of time.

    • Avoid collisions to increase the reachability


Priority
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 Twice

    • Send first copy immediately

      • To shorten the latency

    • Send second copy in a random backoff

      • To avoid the collision


Reliable data dissemination1
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


Wireless sensor networks and laboratories
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
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


Revised interest broadcast1
Revised Interest Broadcast

A

Sink

A

A

B

D

A

C


Revised interest broadcast2
Revised Interest Broadcast

A

Sink

A

A

B

D

A

C


Revised interest broadcast3
Revised Interest Broadcast

A

Sink

A

B

A

B

B

D

A

C


Revised interest broadcast4
Revised Interest Broadcast

A

Sink

8

5,S

7,A

A,D,S

B,C,S

6

7

A

B

6,B

5,D

6,C

5

B

D

6

A

C

Interest broadcast phase is finished!


Revised interest broadcast5
Revised Interest Broadcast

A

The sink receives the data!

Sink

8

7,data

6,data

7,data

A,D,S

B,C,S

6

7

A

B

6,data

5,data

6,data

7,data

5

B

D

6

A

C


Wireless sensor networks and laboratories
MDfd

  • Everything is the same as MD, except…

  • Send data with charge no larger than that of node

  • Like flooding in a smaller area


Wireless sensor networks and laboratories
MDfd

This data is received by sink.

Sink

8

Node A will relay pkt for node B

7,data

7,data

7

7

A

B

7,data

7,data

6,data

7,data

7,data

6

D

6

C


Mdlq v s mdfd
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


Evaluation1
Evaluation

  • We want to see the impact of

    • To counter collision

      • Random wait

      • Priority

        • Two level forwarding

      • Send twice

    • To counter asymmetric link

      • MDlq

      • MDfd

  • All experimental data are collected in BL-Live



Evaluation2
Evaluation

  • Three metrics

    • Reachability

    • Latency

    • Overhead

      • The amount of interest and data packets transmitted

      • Highly related to energy consumption


Impact of random wait
Impact of Random Wait

  • Reachability

The reachability is increased by 5%.


Impact of two level forwarding
Impact of Two Level Forwarding

  • Latency

Latency of high priority packet is slightly shorter.


Impact of send twice
Impact of Send Twice

  • Reachability

With send twice, the reachability is increased by 8%


Impact of mdlq and mdfd
Impact of MDlq and MDfd

  • Reachability

MDfd highly improves the reachability!


Overhead of mdlq and mdfd
Overhead of MDlq and MDfd

  • Overhead

Overhead of MDfd very high.


Wireless sensor networks and laboratories
MDlq+

  • Integrate different mechanisms

    • Increase the reachability

    • Energy efficient

  • MDlq+

    • MDlq with sendtwice


Impact of send twice1
Impact of send twice

  • Reachability

MDlq+ and MDfd is close to Flooding!


Overhead of mdlq mdfd and flooding
Overhead of MDlq+, MDfd and Flooding

  • Overhead

MDlq+ is the most energy efficient.


Latency of mdlq mdfd and flooding
Latency of MDlq+, MDfd and Flooding

Latency of MDfd is as good as Flooding.

MDlq+ is decent.


Summary of the experimental results
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


Summary1
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%




802 11 the standard in wireless network
802.11 – The standard in Wireless Network

  • Contention-based protocol

    • RTS-CTS-DATA-ACK

RTS

DATA

Receiver

Sender

CTS

ACK

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


S mac periodic listen and sleep
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]


S mac schedule synchronization

Node 1

sleep

sleep

listen

listen

Node 2

sleep

sleep

listen

listen

Schedule 1

Schedule 2

S-MAC – Schedule synchronization

  • Schedules can differ

    • Neighboring nodes have same schedule

Border nodes:

two schedules

broadcast twice

(Borrowed from S-MAC)


Scheduling in s mac
Scheduling in S-MAC

  • Unknown neighbors

    • the same schedule

1

1

3

3

Collision

Unicast

Schedule 1

Schedule 2

2

2

4

4

Broadcast


B mac

Carrier sense

Check

Interval

Receiver

Receive data

Sender

Long Preamble

Data Tx

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]

(Borrowed from Z-MAC)


Z mac on top of b mac
Z-MAC – On Top of B-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]

Hybrid (TDMA+CSMA)


Z mac on top of b mac1
Z-MAC – On Top of B-MAC

A

B

  • Problem – hidden terminal collisions

    • Low contention level (LCL)

    • High contention level (HCL)

      • Two-hop contention avoidance

A

C

D

Down


The summarizations
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
Experiments

  • Simulation setup in NS2 simulator


Metrics1
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
Energy Consumption

  • MDB < MDG

  • B-MAC best

  • Z-MAC

    • TDMA scheduling

Unit(J)


Energy consumption the impact of multiple sources
Energy Consumption – The Impact of Multiple sources

MDG+ Z-MAC

  • Energy goes up

    • MDG-ZMAC

    • MDG-BMAC

  • Overhead

    • MDB < MDG

MDB+ Z-MAC

MDG+B-MAC

MDB+B-MAC


Energy consumption summarization
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


Reachability1
Reachability

  • In 802.11

    • MDB < MDG

  • In S-MAC

    • MDB > MDG

Source

5

6

MDG

6

5

7

MDB

Sink

Down


Reachability the impact of multiple sources
Reachability – The Impact of Multiple Sources

MDG+ Z-MAC

  • High traffic load

    • MDG + 802.11

    • MDG + Z-MAC

MDG+ 802.11


Reachability summarization
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


Latency1
Latency

MDB+ B-MAC

MDB+ 802.11

  • MDB-802 best

  • MDB-BMAC

    • Delay < 1 sec

    • 80% < 500ms


Latency summarization
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
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)]


Summary2
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
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
Future Work

  • Extensive set of experiments

  • Various routing protocols

  • Real test-bed