wireless, self-organizing network research at …celio/obraczka.pdfuc santa cruz research interests...

UNIVERSITY OF CALIFORNIASANTA CRUZ

Wireless, Self-Organizing Network Research at UCSC’s InternetworkResearch Group

Katia ObraczkaComputer Engineering, Baskin School of Engineering

University of California, Santa Cruz

[email protected]

http://www.cse.ucsc.edu/~katia

http://inrg.cse.ucsc.edu/

UC Santa Cruz

Research Interests

� Protocol design, development, evaluation, testing, and deployment.

� Wired networks.� Wireless networks.

� Self-organizing networks:� Ad-hoc, � Sensor,� Disruption-tolerant networks.

� Different layers of the stack.� Mainly MAC, network, and transport.� Cross-layer issues.

UC Santa Cruz

Current Projects

� Medium access for wireless, self-organizing networks.

� First scheduled-access energy efficient, traffic adaptive MAC framework.

� Higher data rates, e.g., UWB.

� Routing in networks with intermittent connectivity (a.k.a, DTNs).

� Message delivery in heterogeneous networked environments.

� With INRIA Sophia-Antipolis.

UC Santa Cruz

Current Projects

� Tool for rapid prototyping of wireless network protocols.� With Lip6 (Paris VI).

� Sensor networks:� Localization.

� With Lip6 (Paris VI).

� Systems for monitoring and surveillance.� With UCSC biologists and USGS geologists.

� Mobility models for wireless networks.� New approach to modeling mobility in wireless networks using statistical equivalent models (SEMs).

� With UCSC Applied Math.

UC Santa Cruz

Today

� Routing in disruption-tolerant networks (DTNs).

� Sensor network systems for monitoring and surveillance.


Routing in Disruption-Tolerant Networks (DTNs)

Work with Jay Boice (UCSC MSc, May 2007) and J.J. Garcia-Luna.

UC Santa Cruz

References

� "On-Demand Routing in Disrupted Environments," Jay Boice, J.J. Garcia-Luna-Aceves, and Katia Obraczka, Best

Paper Award, IFIP Networking 2007, May 2007.

� “Disruption-Tolerant Routing with Scoped Propagation of Control”, Jay Boice, J.J. Garcia-Luna-Aceves, and Katia Obraczka, IEEE ICC 2007, June 2007.

UC Santa Cruz

What are DTNs?

� DTNs or disruption-tolerant networks.

� Aka,

� Delay-tolerant,

� Episodically-connected,

� Intermittently-connected networks.

Networks where end-to-end connectivity is NOT guaranteed.

UC Santa Cruz

Why DTNs?

� Applications:

� Emergency response and disaster relief.

� Special (military) operations.

� Environmental and wild-life monitoring.

� Vehicular networks.

� Internet to remote communities.

Technological Advances:smaller, cheaper, wireless

devices

Emerging Applications

UC Santa Cruz

DTN Applications: Emergency Response

UC Santa Cruz

DTN Applications: Environmental Monitoring

CARNIVORES Project at UCSC: Monitoringcoyotes in the SantaCruz mountains.

UC Santa Cruz

DTN Applications: Connecting Remote Communities

� Rural “kiosks”:� Shared among locals.

� Selling/buying agricultural products.

� Banking and other transactions.

UC Santa Cruz

DTN Summary

� Normal operation in disconnectedmode.

� End-to-end connectivity may neverexist!

UC Santa Cruz

What’s the big deal?

� Routing protocols have always assumed end-to-end connectivity.� Table-driven (proactive) protocols (e.g., Internet routing) can recover from infrequent topology changes.

� On-demand (reactive) protocols (e.g., MANET routing) can recover from frequent but short-lived outages.

� But what if “outages” are frequent and long-lived?� MANET routing simply drops packets!

UC Santa Cruz

DTN’s Routing Paradigm Shift

� Before DTNs:

� Space dependency.

� Network routing: given graph G(V,E), find shortest path between source-destination.

UC Santa Cruz

Before DTNs: “Space” Dependency

�Assume links have no dependence on time.�N = G(V,E).

� V nodes.� E links with some associated cost (bandwidth, delay, energy, etc.)

� Find path between S-D such that cost minimized.

Examples:�Flooding.�Proactive routing.�On-demand routing.

UC Santa Cruz

Proactive Routing

S

D

Proactive Routing(DSDV, OLSR)

� Send periodic topology updates.

� S learns next hop to D

� But, topology updatesdon’t go past partitions.

UPD

UPD UPDUPD

UPD

UC Santa Cruz

Reactive Routing

S

DReactive Routing(DSR, AODV)

� Flood Route Request (RREQ).

� S waits for reply from D.

�RREQ reaches only same cluster as S!

REQREQREQ

REQ

UC Santa Cruz

DTN’s Routing Paradigm Shift

� Before DTNs:

� Space dependency.

� Network routing: given graph G(V,E), find shortest path between source-destination.

� After DTNs:

� Space and time dependency.

� Network as a time-varying graph G(V,E(t)).

� Links are a function of time.

� Links as “contacts”.

UC Santa Cruz

Types of Contacts

� Scheduled contacts

� E.g. satellite links, message ferry.

� All info known.

� Probabilistic contacts� Statistics about contacts known.

� E.g., bus, sensors with random wake-up schedule.

� Opportunistic contacts

� Not known a priori.

� E.g., tourist car that happens to drive by.

UC Santa Cruz

Designing DTN Routing/Forwarding Protocols

�What information is available?� Oracles.

� Contacts, contact statistics, queuing, traffic, buffer capacity, etc.

�How much information is known?� No knowledge.

� Partial knowledge.

� Complete knowledge.

�Trade-offs?

UC Santa Cruz

Routing/Forwarding under Intermittent Connectivity

Scheduled/, (partially) known contacts (e.g., buses).

Enforced contacts with specialized nodes (e.g., ferries).

What about unknown contacts?� Contacts not known in advance.� No specialized nodes. i.e., only mobility of the nodes themselves is available.

Opportunistic (mobility-assisted) routing

UC Santa Cruz

Opportunistic Routing

� Graph disconnected and/or time-varying.

� Set of contacts C: unknown.

� Set of nodes V: often unknown too.

UC Santa Cruz

Opportunistic Routing

A

C

B

D

D

Tx Range

(B,D) = ??

(C,D) = ??

D

WHERE IS D?

D

WHERE IS D?

D

WHERE IS D?

UC Santa Cruz

Opportunistic Routing Paradigm

� At every hop, node decides whether to:

�Forward and/or

�Store-and-carry.

� Store-carry-and-forward paradigm.

UC Santa Cruz

Opportunistic Routing Primitives

� Copy replication.

� Copy forwarding.

� Coding.

UC Santa Cruz

Copy Replication

�When node i carrying message mencounters node j.

�If j doesn’t have copy of m, i decides whether to spawn a new copy of m and forward it to j.

�Message vector.

�Summary of messages being carried by node.

UC Santa Cruz

Replication Strategies: Greedy

A

C

B

D

D

EF

D

D

D

D

i gives a copy of m to j,i j doesn’t yet have a copy of m.

Epidemic Routing

UC Santa Cruz

Copy Forwarding

�Single-copy scheme.

�When i encounters j, i may decide to pass its copy of message m to j.

UC Santa Cruz

Forwarding Strategies: Direct Transmission

� Forward message only to its destination.� Minimizes transmissions.

UC Santa Cruz

Forwarding Strategies: Direct Transmission

S

C

B

D

D

EF

D

UC Santa Cruz

2-Hop Forwarding

� Source gives copy to any relay encountered.

� Relays can only give copy to destination.

UC Santa Cruz

2-Hop Forwarding

Src

C

B

Dst

D

EF

D

D

D

Relay C cannot FWD to B

Relay C can FWD to Dst

UC Santa Cruz

Coding

�Processing messages end-to-end or hop-by-hop in order to:

�Increase reliability (e.g., erasure coding).

�Increase throughput, decrease number of transmissions, etc. (e.g., network coding, data fusion/aggregation).

UC Santa Cruz

Our Approach

� Combines on-demand (intra-partition) with opportunistic (inter-partition) routing.

� Use of relays, or stewards, to deliver data to partitioned destinations.

UC Santa Cruz

Outline

� Steward Assisted Routing (StAR).

� StAR components and operation.

� Performance evaluation.

� Conclusions.

UC Santa Cruz

Steward Assisted Routing

� StAR

� Goal:

Routing mechanism that works well in both connected networks as well as networks prone to frequent, long-lived disconnections.

UC Santa Cruz

StAR

� No a-priori topological knowledge.

� Use past connectivity information to predict future communication opportunities.

� Scopes temporal and spatial dissemination of routing information.

UC Santa Cruz

StAR Components

� Routing table maintenance.

� Routing information scoping.

� Steward selection.

� Data forwarding.

UC Santa Cruz

Routing Table Maintenance

� Periodic exchanges between nodes.

� Node incorporates advertised routing information based on most recentdestination sequence number.

Di sad had nad

At node a:⇒Routing table indexed by destination (Di).⇒Sad: sequence number for Di known by a.⇒had: number of hops taken by sad to reach a.⇒nad: a’s steward for Di.

UC Santa Cruz

SCIP

� Scoped Contact and Interest Propagation.

� Limits scope of routing information.

� Nodes get routing info for destinations of interest.

� Nodes only keep info for d if they are on the path from s to d.

Example: s1 and s2 interested in d.

UC Santa Cruz

Steward Selection

� Steward selected for given destination.

� Use sequence numbers and number of hops to select local steward for destination d.� Steward has most recent sequence number.

� If sequence numbers are equal, choose node with lowest number of hops to destination.

One steward per destinationper partition.

UC Santa Cruz

Data Forwarding

� Messages forwarded till destination (if network is connected) or steward.

� Steward stores message till route to other steward with more recent sequence number.

UC Santa Cruz

Performance Evaluation

� Simulations using QualNet.

� Different mobility scenarios.

� Random waypoint.

� Grid mobility.

� Campus laptop trace.

� Scheduled bus routes.

� Different replication schemes:

� No replication, i.e., single copy.

� Source buffering.

� Controlled replication.

UC Santa Cruz

Simulation Setup

� Number of nodes: 100

� Simulation time: 2000 seconds.

� Number of runs/seed: 10.

� Traffic: 25 randomly selected CBR flows at 1 packet/sec.

� Performance metrics:� Packet delivery ratio.

� Delay.

� Overhead (routing table size, number of messages transmitted, etc).

UC Santa Cruz

Well-Connected Topologies

� 100 nodes in 3600x500m area with full connectivity.

� Static and random waypoint mobility.

� Comparison against AODV and OLSR.

StAR performs well with full connectivity and under short-lived disconnections.

UC Santa Cruz

As Connectivity Decreases…

Decreasing connectivity

PDR

AODV

Epidemic

StAR

⇒ Gridded mobility.

⇒ Decrease connectivity byincreasing grid dimension.

UC Santa Cruz

Campus Laptop Trace

Number of nodes

PDR

StAR

Epidemic

Additional connectivity improves performance…

UC Santa Cruz

Campus Laptop Trace

Number of nodes Number of nodes

PDR

Total messagesper deliveredpacket

StAR

Epidemic

StAR

Epidemic

Additional connectivity improves performance; but StAR isable to keep overhead low.

UC Santa Cruz

Summary

� StAR as routing framework that operates well in both well-connected networks as well as networks prone to episodic connectivity.

� No a-priori knowledge, e.g., node schedules, location, etc.

� Combines on-demand (intra-partition) with opportunistic (inter-partition) routing.

� Use of relays, or stewards, to deliver data to partitioned destinations.

UC Santa Cruz

Future Work

� Use other sources of information to improve performance.

� Full/partial node schedules, GPS, etc.

� Investigate other metrics for steward selection.

� Explore different message replication strategies.


CARNIVORES Project

Joint with Roberto Manduchi, Terrie Williams, Dan Costa, Pat Mantey, Cyrus Bazeghi, Vladi Petkov (PhD student), and Matt Ruttinshauser (MSc student).

UC Santa Cruz

Motivation

� Need to investigate behavior of large predators by:

� Monitoring their location.

� More importantly, monitoring their activity patterns to draw up in depth energy budgets (activities such as walking, trotting, galloping and eating will be identified).

� With this information several questions can be answered:

� Can predators assimilate food and run simultaneously?

� Do they conserve their energy when hunting to prolong hunting duration?

� What are the impacts on environment/human populations?

UC Santa Cruz

Case Study

� Coyotes: local inhabitants of the Santa Cruz mountains.

UC Santa Cruz

Methodology

� Develop a heterogeneous, multi-tiered sensor network consisting of:

� Mobile, animal-bourne sensors.

� Static sensors.

UC Santa Cruz

Coyote Network Infrastructure

Coyote-coyote data exchange

Coyote-tower data exchange

Coyote-coyote data exchange Coyote-tower

data exchange

UC Santa Cruz

Challenges

� Animal-bourne sensors limited capabilities:

� Power,

� Storage,

� Bandwidth/coonectivity.

UC Santa Cruz

Collar Sensor Package

Trimble Lassen SQ GPS module

• Low power: current consumption including antenna is 40.3mA

• Not mounted on board for more freedom of placement

Off the shelf, high capacity, lithium batteries providing approximately 3000mA hours at 3V input.

Sensor Package

• Made up of two boards, the main board underneath and the sister-board on top.

• Details on next slide.

UC Santa Cruz

Bottom sideTop side

Sensor Package Main Board

MSP430F1611 microcontroller• 10 KB RAM, 48 KB ROM

• Peripherals include:– 2 Universal synchronous/asynchronous

receive/transmit units– 12-bit Analog to Digital converter

– 2 Timer peripherals that facilitate heavily periodic tasks

– 3 channel DMA controller

• Power consumption in µA range

Freescale MMA7260Q Accelerometer• 3 orthogonal axes

• 500µA current consumption when active

• Selectable sensitivity: ±1.5/2/4/6g

• One analog output for each axis• Small form factor

32,768Hz watch crystal• Stable, low frequency crystal

• Used as a reference for the higher frequency Digitally Controlled Oscillator of the MSP430 to keep it stable

• Also used by one of the system timers to trigger the periodic tasks that the software system relies on to function

• Keeps an accurate Real-Time Clock, periodically synchronized to GPS time from the GPS module. This allows synchronization between all the collars in the system.

Board-to-Board Connector• 20 pins that are used to carry power to

the sister-board and data to and from the sister board

• Small form factor

Step-up Switching regulator• 8-pin part (other two parts are an

inductor and schottky diode that the regulator needs to function)

• Makes output voltage ≥ 3.3V out of an input voltage that can be as low as 1.5V -- battery source remains usable until almost fully drained

• 60 µA quiescent current

Dual Linear Regulator• 8-pin part

• Regulates voltage coming from step-up regulator to a stable 3.3V for the electronics

• Dual part -- has two separate regulators, each one can be individualyshut off to control power to separate parts of the system

• This regulator powers the GPS on one output and the microcontroller and accelerometer on the second

UC Santa CruzTop side

Telegesis ETRX1 ZigBee Transceiver• Integrated Ember EM2420 radio and

Atmel Atmega 128L microcontroller• Surface mount gigaAnt microstrip small

form factor antenna

• Serial interface (top baud rate: 38,400)• FCC approved

Bottom side

Sensor Package Sister Board

Board-to-board connector• Fits into connector on main board to

establish connectivity of power and data between the two boards

Dual Linear Regulator• This regulator powers the SD card and

ZigBee radio• The two devices can be individually

shut down

Socketed Secure Digital(SD) Card• Interfaced to the MSP430 using SPI

serial bus• SD card is formatted with FAT16 file

system

• FAT16 chosen due to its implementation and run-time simplicity (it does not require too many system resources to maintain)

• Although a file system is not required in order to use the SD card, it makes movement of data among collars manageable

UC Santa Cruz

Preliminary Tests

� Pippin, a friendly and well trained dog, was used to study correlations between behavior and acceleration.

� Next 4 slides show freeze frames of Pippin running at different speeds with acceleration graphs overlaid.

� Different gaits (walk, trot, gallop) clearly affect acceleration graphs.

� Higher speeds also identifiable by higher amplitudes of acceleration.

� Z-axis is the up down axis, and the one used for the brief annotations on the graphs.

UC Santa Cruz

Pippin: Treadmill 3mph walk

Period = 360 msAmplitude (peak to peak) = 800 mg

UC Santa Cruz

Pippin: Treadmill 6mph trot

Period = 200 ms

Amplitude (peak to peak) = 1750 mg

UC Santa Cruz

Pippin: Alongside cart 10mph gallop

Period = 400 ms


UC Santa Cruz

Pippin: Alongside cart 15mph gallop

Period = 400 ms


UC Santa Cruz

Low Power Considerations

� Texas Instruments MSP430 microcontroller is very low-power versatile.

� ZigBee radio was designed for sensor applications with low power in mind and will not be on at all times.

� GPS module will be turned on only long enough to acquire a fix; off interval will be large compared to fix-acquisition-interval.

� SD card consumes significant power only during read/write operations which happen very quickly and as infrequently as possible.

� Virtually all system functions are duty cycled allowing peripherals to remain on only as long as they are needed.

UC Santa Cruz

Data Handling Considerations

�Network is not always connected!

�Not all coyotes guaranteed to come in close proximity to base station.

�Collars copy data bundles of other collars in proximity to ensure timely transmission to tower (messenger coyotes).

� In absence of intelligent routing, all data is copied to all collars.

�Better routing decision methods based on metrics appropriate to this system, a la RIDE, are being explored.

UC Santa Cruz

Ongoing Work

� Data analysis algorithm(s) to extract behavior information from raw acceleration data.

� Network protocol stack.

� Detailed system power consumption analysis.

� Trial runs in controlled environment.


SEA-LABS

Joint with Matt Bromage (PhD student) and Donald Potts.


Sensor Exploration Apparatus utilizing

Low–power Aquatic Broadcasting System

UC Santa Cruz

References

� Matt Bromage, Katia Obraczka, “SEA-LABS: A Wireless Sensor Network for Sustained Monitoring of Coral Reefs”, poster at IFIP/TC6 NETWORKING 2007.

UC Santa Cruz

Goal

• Real-time, low-cost, low-power, environmental monitoring system for use in shallow-water reef habitats.

• Measure several important physical and chemical variables for studying the growth and calcification of corals and coralline algae.

UC Santa Cruz

Scientific Contribution

� Coral reefs are extremely vulnerable to both atmospheric and ocean conditions.

� By measuring physical and chemical variables with adequate resolution and in real time, SEA-LABS enables scientists to:

� Observe large scale changes for monitoring the growth and metabolic rate of the coral.

� Variation on shorter (lunar, tidal, even daily) scales for monitoring climatic effects.

� Variations of environmental conditions among different reef habitats.

UC Santa Cruz

Overview

RF

Sensors

UC Santa Cruz

Overview

UC Santa Cruz

Sensors

� Temperature, light, salinity and pressure sensors.

� MCU powers sensors with MOSFET driver on POD.

� Communicate with POD over Category 5e cable.

UC Santa Cruz

POD and RF

� T.I. MSP430F1611

� Onboard flash memory.

� Collects sensor data.

� One antenna for both transmit and receive

� Transmit & receive sensed data.

POD

RF

UC Santa Cruz

Basic Operation

� Data packets generated on POD sent wirelessly to base station through helical.

� Base station parses packets.

� Uploads sensor information to online database.

� Download data from browser into excel spreadsheet.

UC Santa Cruz

Deployment

� Test Deployments.

� Monterey Bay.

� Final Deployment:

� Midway Atoll Spring 2006.


Sensor Network Systems for Monitoring and Surveillance


Meerkats: Wireless Camera Network for Surveillance and Monitoring

Work with Roberto Manduchi, CintiaMargi, Gefan Zhang, and Xiaoye Lu

UC Santa Cruz

References

� Cintia B. Margi, Xiaoye Lu, Gefan Zhang, Roberto Manduchi, Katia Obraczka, “Meerkats: A Power-Aware, Self-Managing Wireless Camera Network for Wide Area Monitoring”, Workshop on Distributed Smart Cameras (DSC), 2006.

� Cintia B. Margi, Roberto Manduchi, Katia Obraczka; "Energy Consumption Tradeoffs in Visual Sensor Networks", in Proceedings of the 24th Brazilian Symposium on Computer Networks (SBRC) 2006.

� Cintia B. Margi, Vladislav Petkov, Katia Obraczka, Roberto Manduchi; "Characterizing Energy Consumption in a Visual Sensor Network Testbed", in Proceedings of the 2nd International IEEE/Create-Net Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities (TridentCom), 2006.

UC Santa Cruz

References (Cont’d)

� Cintia B. Margi, Katia Obraczka, Roberto Manduchi; "Characterizing System Level Energy Consumption in Mobile Computing Platforms", in

Proceedings of the IEEE WirelessCom, 2005.

� Cintia B. Margi and Katia Obraczka, "Instrumenting Network Simulators for Evaluating Energy Consumption in Power-Aware Ad-Hoc

Network Protocols", in Proceedings of the IEEE/ACM MASCOTS, 2004.

� Marcelo M. Carvalho, Cintia B. Margi, Katia Obraczka, and J.J. Garcia-Luna-Aceves, "Modeling Energy Consumption in Single-Hop IEEE 802.11 Ad Hoc Networks", in Proceedings of the IEEE ICCCN, 2004.

UC Santa Cruz

Motivation

� High-level sensors like cameras provide richer information and wider monitoring range.

� But, pose new challenges.

� E.g., more power-hungry than traditional sensors.

� How to balance application-level requirements (e.g., high event detection rate) with maximizing network operational lifetime?

UC Santa Cruz

Meerkats

� Goal: novel resource management strategies to balance trade-off between energy efficiency and performance.

UC Santa Cruz

Meerkats Node

UC Santa Cruz

Software

Meerkats Visual Sensor Node Hardware

ResourceManager

Visual Processing

Communication

UC Santa Cruz

Visual Processing

Background Newly acquired image

Foreground detection

Image to becompressedand transmitted

UC Santa Cruz

Node Operation

� Duty-cycle based.

� E.g., node wakes up; takes picture; processes it; if event, transmits; else, sleeps.

� Understand trade-off between always sending versus on-board processing.

� Perform triggering by “edge” nodes.

UC Santa Cruz

Meerkats Testbed

� Currently, 8 camera nodes plus information sink.

� Experiments:

� Node-sink interaction.

� Node-to-node interaction.

� Energy profiles.

UC Santa Cruz

Experiments

Node-to-sink Node-to-node

UC Santa Cruz

Node-to-Node Interaction

� Master-slave.

� Master periodically wakes up, acquires and processes image; if event, then alert slave.

� Slave periodically wakes up, listens for messages; if alert, then takes image.

Master

Slave

UC Santa Cruz

Node-to-Node Interaction

UC Santa Cruz

That’s it…

� Questions?

wireless, self-organizing network research at …celio/obraczka.pdfuc santa cruz research interests...

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