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Data Elevators

Applying the Bundle Protocol in Delay Tolerant Wireless Sensor Networks

Wolf-Bastian Pottner, Felix Busching, Georg von Zengen, Lars Wolf

IEEE MASS 2012, 2012-10-09

Introduction Bundle Protocol Data Elevator Network Capacity Conclusion

Motivation

ZebraNet

tion and type of activity. By selecting andusing these tools, the vineyard workerwould provide the necessary input intothe system naturally and effortlessly. Theconcept of tracking workers’ movementthrough space has already been sug-gested as a useful tool for generatingbilling reports and time studies in custo-dial environments such as hospitals.10

We envision an instantiation of this ideawith the added concept of tagged tools toprovide an indication of workers’ activ-ities. The manager’s need to track activ-ities in the vineyard also suggests thatfocusing attention on developing local-ization algorithms for sensor networks—specifically tracking the location oftagged objects moving through a sensornetwork—is a research direction poten-tially useful for agricultural applications.

System architectureOur study also suggested different

types of interfaces that could be seam-lessly incorporated into the vineyard,including the tagged tools described ear-lier. However, our understanding of theworkflow also suggested some ways thatthe system infrastructure itself could bereorganized to optimize power manage-ment and equipment costs. Our efforts tocreate a working sensor network imple-mentation in a local vineyard gave ussome insight into the interplay betweenpower management, equipment costs,system architecture, and user needs.

Power management is one of the pri-mary issues in the design of sensor net-work systems intended to operate wire-lessly.11 An ideal system would be asensor network made up of devices thathave an extremely long battery life and

are automatically rechargeable or aretiny, disposable, inexpensive, and easilyreplaced. The concepts of Smart Dustand Paintable Computers are two pro-posals of this ideal vision.12,13 Becausewe believe that sensor networks are use-ful in the near term, we must realisti-cally face power management issues toavoid the worst-case scenario where bat-teries must be frequently replaced inhundreds or thousands of individualdevices. We have uncovered opportuni-ties for a system wide approach topower management by designing thesoftware and system architecture tooptimize power management. However,our modest gains could be greatlyimproved if the hardware wereredesigned with these system wide con-figurations in mind.

Self-organizing ad hoc sensor net-works are generally considered thedefault system architecture, in partbecause they present more interestingcomputational problems for computerscientists to tackle. However, this archi-tecture assumes RF connections, oftenusing TDMA (time division multipleaccess, a technology for delivering digi-tal wireless service) between each moteand its neighbors. This arrangement ofsystem components requires enough

equipment to cover a space with a fullyconnected network. It’s an optimal archi-tecture for some types of applicationsbut is by no means the only one oralways the ideal arrangement of the net-work. Specifically, the self-organizingmultihop architecture that forwards datais the only architecture that makes muchsense for sensor network applications inremote, inaccessible environments.

We discovered that other system archi-tectures could be employed in vineyardsbecause they are neither remote nor inac-cessible. For example, one architectureused data mules to collect and transportdata from sensor network motes dis-tributed throughout the vineyard (seeFigure 2).14 From our interviews andobservations, we learned that during thegrowing season, workers move up anddown the rows a lot. In one vineyard,two family dogs also spent a lot of timegoing up and down the rows. Any ofthese moving bodies (even the dogs)could serve as a “data mule” by carry-ing a small device that simply and invis-ibly gathers data wirelessly from the sta-tic, distributed motes.

The data would be transmitted fromthe static mote to the data mule motewhenever the two motes are in physicalproximity and there’s new data to trans-

JANUARY–MARCH 2004 PERVASIVEcomputing 15

Figure 2. Data mule system architecturein the vineyard. (a) The motes recordenvironmental data and vineyard activities. (b) In the course of daily activities, the worker collects more dataonto the shovel. (c) The worker takes thetool back to the shed. (d) Back in the toolshed, the shovels upload their data to thecentral database.

(a)

(b)

(d) (c)

Vineyard Computing SeNDT

ObservationDelay Tolerance is widely used (and needed) in sensor network research

Wolf-Bastian Pottner | Data Elevators | 2


Common Requirements

Measurement

Periodic sampling of sensor values

Networking

Multi-hop data delivery

Disrupted links, changing topologies

Delay is not important, reliability is

9=

; Store, carry and forward

Hardware

Long lifetime

Minimal installation e↵ort

Few maintenance cycles

! Low-power

! Wireless

! Robust



Wireless Sensor Networks (WSNs)

Wireless Sensor Networks

Multi-hop wireless

Battery powered

Wireless Sensor Nodes

Based on microcontrollers

IEEE 802.15.4 radios

App. 16 kB RAM, app. 128 kB ROM

Low-power hardware

Storage (flash, SD, ...)

INGA

T-Mote Sky



Outline

Introduction

Bundle Protocol in Delay Tolerant Wireless Sensor Networks

Data Elevator Application Scenario

Capacity of Delay Tolerant Wireless Sensor Networks

Conclusion



Protocols for Wireless Sensor Networks

Predominant WSN Protocols

6LoWPAN: IPv6 over low-power WPAN

Contiki’s and TinyOS’ proprietary protocols

! Not delay tolerant (not store, carry and forward)

Store, Carry and Forward Protocols

ZebraNET (non-standardized)

Vineyard Computing (non-standardized)

Seal-2-Seal (non-standardized)

Bundle Protocol (RFC 5050)



Benefits and Drawbacks of Standard Protocols

Benefits

Seamless integration

Lower entry barrier

Generic solutions

Drawbacks

Not optimized for use case

Higher overhead

Benefits of the Bundle Protocol

Flexibility: Variable length header fields, extension blocks, etc.

Overlay Protocol: Works on top of heterogeneous technologies

Well suited: Designed for unstable links and changing topologies

Q: Is the Bundle Protocol too heavy for nodes?



Bundle Protocol Overhead Comparison

Overhead [Bytes]

802.15.4

802.15.4

802.15.4

802.15.4

802.15.4

IPv6 TCP

6LoWPAN TCP

6LoWPAN TCP CL

Bundle Protocol

Bundle Protocol

6LoWPAN

6LoWPAN

UDP

UDP

TCP / IPv6

UDP / 6LoWPAN

TCP / 6LoWPAN

BP / UDPCL / 6LoWPANBP / TCPCL / 6LoWPAN

802.15.4 IPv6 UDPUDP / IPv6 57

69

23

38

4461

802.15.4 Bundle ProtocolBP / IEEE 802.15.4 CL 31

802.15.4 RIMEContiki‘s RIME 19

IEEE 802.15.4 maximum frame size is 127 bytes

A: Protocol overhead is higher but manageable



Bundle Protocol Complexity Comparison

160

180

200

220

240

260

280

300

0 10 20 30 40 50 60 70

Pa

rsin

g o

f In

com

ing

Da

ta [

us]

Payload Length [bytes]

6LoWPAN HC06 / UDPBundle Protocol CBHE 0

5000

10000

15000

20000

25000

8 16 24 32SD

NV

Op

era

tion

s p

er

Se

con

d

Integer Size [bit]

DecodeEncode

Run on INGA at 8 MHz

A: Computational complexity is comparable



How can we implement the Bundle Protocol on nodes?

Literature

Bundle Protocol as overlay protocol over 6LoWPAN

Our Approach on the Nodes: µDTN

BP in IEEE 802.15.4 data frames

Cross-layer, avoiding layers 3 and 4

Implementation based on Contiki OS

Our Approach on the PC

IEEE 802.15.4 radio attached to PC

IBR-DTN software extension to handle radio



Data Elevator Application Scenario

Opening QuestionHow can we get temperature readings from the rooftop into our lab?

Concept

Node with sensor on rooftop

Elevator is data mule

Delay tolerant network

Setup

1 sensor, 3 relays, 1 sink

µDTN with RAM storage

Rooftop

#1

#2

#3

#4

Elevator: 1st 14thFloor

#5

Building ABuilding B

15 th Floor

3 rd Floor

14 th Floor

Temperature Sensor



Evaluation: Temperature and Delay (Weekend)

0

1

2

3

4

5

6

7

8

0 2 4 6 8 10 12 14 16 18 20 22 0 0

3

6

9

12

15

18

21

24

Bundle

Dela

y [h

]

Tem

pera

ture

[D

egre

e C

els

ius]

Time of Day [h]

Bundle Delay Temperature



Evaluation: Delay Distribution (Weekend)

0 10 20 30 40 50 60 70 80 90

100

>0-5 >10-15>20-25

>30-35>40-45

>50-55>60-65

>70-75>80-85

>90-95>100

Num

ber

of O

ccurr

ence

s [%

]

Bundle Delay Bins [m]

Delay DistributionAccumulated Delay Distribution



DT-WSN Capacity Model

Storage(Capacity((SCap,Send SCap,Recv

Bundles(in(Storage(SSend,i SRecv,i

Sender( Receiver(

BundleRatei

Channel Capacity:

Transmitted Bundles:

Storage Sender:

Storage Receiver:

Ci ,j = Durationi · BundleRateiTi = min(SSend ,i ,Ci ,j)

SSend ,i = min(SSend ,i�1 � Ti�1 + Ni , SCap,Send)

SRecv ,i = min(SRecv ,i�1 + Ti , SCap,Recv )



Evaluation: Capacity Model

0

10

20

30

40

50

60

70

80

0 50 100 150 200 250 300

Perm

anently

Lost

Bunld

es

[%]

Sample Interval [s]

Storage: 100 BundlesStorage: 300 Bundles

Storage: 500 Bundlesunlimited



Conclusions Wolf-Bastian [email protected]://www.ibr.cs.tu-bs.de/projects/mudtnProtocols

Standard protocols are generic solutions to common problems

BP is de facto standard in DTNs and should be in DT-WSNs

µDTN

Bundle Protocol implementation for Contiki

Overhead is comparable to 6LoWPAN

Integration into existing DTNs via transparent gateway nodes

Data Elevator

Data is delivered with delay but without loss



Evaluation: Temperature and Delay (Weekday)

0

1

2

3

4

5

6

7

8

0 2 4 6 8 10 12 14 16 18 20 22 0 0

3

6

9

12

15

18

21

24

Bundle

Dela

y [h

]

Tem

pera

ture

[D

egre

e C

els

ius]

Time of Day [h]

Bundle Delay Temperature



Evaluation: Delay Distribution (Weekday)

0 10 20 30 40 50 60 70 80 90

100

>0-5 >10-15>20-25

>30-35>40-45

>50-55>60-65

>70-75>80-85

>90-95>100

Num

ber

of O

ccurr

ence

s [%

]

Bundle Delay Bins [m]

Delay DistributionAccumulated Delay Distribution


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