Techniques for Building Long-Lived Wireless Sensor Networks

Jeremy Elson and Deborah EstrinUCLA Computer Science

DepartmentAnd

USC/Information Sciences Institute

Collaborative work with R. Govindan, J. Heidemann, and

SCADDS of other grad students

What might make systems long-lived?

Consider energy the scarce system resource Minimize communication (esp. over long

distances)Computation costs much less, so:In-network processing: aggregation, summarization

Adaptivity at fine and coarse granularityMaximize lifetime of system, not individual nodesExploit redundancy; design for low duty-cycle operation

Exploit non-uniformities when you have themTiered architecture

New metrics

What might make systems long-lived?

Robustness to dynamic conditions: Make system self-configuring and self-reconfiguring Avoid manual configuration Empirical adaptation (measure and act)

Localized algorithms prevent single points of failure and help to isolate scope of faults Also crucial for scaling purposes!

The Rest of the Talk

Some of our initial building blocks for creating long-lived systems: Directed diffusion - a new data

dissemination paradigm Adaptive fidelity Use of small, randomized identifiers Tiered architecture Time synchronization

Directed DiffusionA Paradigm for Data Dissemination

Key features name data, not nodes interactions are

localized data can be aggregated

or processed within the network

network empirically adapts to best distribution path, the correct duty cycle, etc.

2. Reinforcement

1. Low data rate

3. High data rate

Diffusion: Key Results

Directed diffusion Can provide significantly

longer network lifetimes than existing schemes

Keys to achieving this: In-network aggregation Empirical adaptation to

path Localized algorithms and

adaptive fidelity There exist simple,

localized algorithms that can adapt their duty cycle

… they can increase overall network lifetime

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Network Size (nodes)

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0 50 100 150 200 250 300

Diffusion without suppression

flooding

Diffusion with suppression

Omniscient multicast

Adaptivity I: Robustness in Data Diffusion

A primary goal of data diffusion is robustness throughempirical adaptation: measuring and reacting to theenvironment.

no failures

20% node failure

10% node failureBecause of this adaptation,mean latency (shown here)for data diffusiondegrades only mildlyeven with10%-20% node failure.

Adaptivity II:Adaptive Fidelity

extend system lifetime while maintaining accuracy

approach: estimate node density

needed for desired quality automatically adapt to

variations in current density due to uneven deployment or node failure

assumes dense initial deployment or additional node deployment

zzz

zzz

zzz

zzz

Adaptive Fidelity Status

applications: maintain consistent latency or bandwidth in

multihop communication maintain consistent sensor vigilance

status: probablistic neighborhood estimation for ad hoc

routing30-55% longer lifetime with 2-6sec higher initial delay

currently underway: location-aware neighborhood estimation

Small, Random Identifiers

Sensor nets have many uses for unique identifiers(packet fragmentation, reinforcement, compression codebooks...)

It’s critical to maximize usefulness of every bit transmitted; each reduces net lifetime (Pottie)

Low data rates + high dynamics = no space to amortize large (guaranteed unique) ids or claim/collide protocol

So: use small, random, ephemeral transaction ids? Locality is key: random ids much smaller than guaranteed

unique ids if total net size large and transaction density small

ID collisions lead to occasional losses; persistent losses avoided because the identifiers are constantly changing

Marginal cost of occasional losses is small compared to losses from dynamics, wireless conditions, collisions…

AFF Allows us to optimize # bits used for identifiers

Fewer bits = fewer wasted bits per data bit, but high collision rate; vs.

More bits = less waste due to ID collisions but many bits wasted on headers

Address-Free Fragmentation

Data Size=16 bits

Consider a memory hierarchy: registers, cache, main memory, swap space on disk

Due to locality, provides the illusion of a flat memory that has speed of registers but size & price of disk space

Similar goal in sensor nets: we want a spectrum of hardware within a network with the illusion of CPU/memory, range, scaling properties of large

nodes Price, numbers, power consumption, proximity

to physical phenomena of the smallest

Exploit Non-Uniformities I:Tiered Architecture

We are implementing a sensor net hierarchy: PC-104s, tags, motes, ephemeral one-shot sensors

Save energy by Running the lower power and more numerous

nodes at higher duty cycles than larger ones Having low-power “pre-processors” activate

higher power nodes or components (Sensoria approach)

Components within a node can be tiered too Our “tags” are a stack of loosely coupled boards Interrupts active high-energy assets only on

demand

Exploit Non-Uniformities I:Tiered Architecture

Exploit Non-Uniformities II:Time Synchronization

Time sync is critical at many layers; some affect energy use/system lifetime TDMA guard bands Data aggregation & caching Localization

But time sync needs are non-uniform Precision Lifetime Scope & Availability Cost and form factor

No single method optimal on all axes

Exploit Non-Uniformities II:Time Synchronization

Use multiple modes “Post-facto” synchronization pulse NTP GPS, WWVB Relative time “chaining”

Combinations can (?) be necessary and sufficient, to minimize resource waste Don’t spend energy to get better sync than

app needs Work in progress…

Conclusions

Many promising building blocks exist, butLong-lived often means highly vertically

integrated and application-specific Traditional layering often not possible

Challenge is creating reusable components common across systems

Create general-purpose tools for building networks, not general purpose networks