sensor networks february 6, 2003 class meeting 8 (images from prof. deborah estrin, usc)

Sensor Networks

February 6, 2003

Class Meeting 8

(Images from Prof. Deborah Estrin, USC)

Objectives

• Embedded Sensor Networks– How to coordinate distributed sensor nodes?

Once We Have a Blanket/Field CoverageHow Do We Handle Sensor Data?

• Lots of sensors distributed over wide area, each with only local information

Example: Intruder Detection UsingDistributed Acoustic Sensor Network

Distributed Acoustic Sensing Algorithm:While (forever)• Communicate my volume heard (h) to my nearest

neighbors;• Receive V[1..n] volumes from my n nearest

neighbors;• If h > V[i], for all i, then:

– Broadcast my position as nearest to the detected target

Embedded Networked Sensing (ENS):A Transforming Technology

• Imagine if: – High-rise buildings in Los Angeles were able to detect their own structural faults

(e.g., weld cracks or plumbing infrastructure)

– Buoys along the coast could alert surfers, swimmers, and fisherman to dangerous bacterial levels

– An earthquake-rubbled building could be infiltrated with robots and sensors to locate signs of life and evaluate structural damage

– We could infuse complex and endangered ecosystems with a plethora of chemical, physical, acoustic, and image sensors to track global change parameters continuously.

– Dangerous bacterial and contaminant levels could be detected “on the farm” through dense sampling, instead of “in the market” through sparse sampling

(Slide adapted from Prof. Deborah Estrin, USC)

Embedded Networked Sensing Potential

• Micro-sensors, on-board processing, and wireless interfaces all feasible at very small scale:– Can monitor

phenomena “up close”– Enables spatially and

temporally dense environmental monitoring

– Reveals previously unobservable phenomena

Seismic Structure Response

Contaminant Transport

Marine MicroorganismsEcosystems,

Biocomplexity


Disaster ResponseCirculatory Net

EmbedEmbed numerous distributed devices to monitor and interact with physical world: work-spaces, hospitals, homes, vehicles, and “the environment”

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

Requires robust distributed systems of hundreds or thousands of devices.

More Examples…


Enabling Technologies

Embedded Networked

Sensing

Control system with small form factor, untethered nodes

Exploit collaborative sensing, action

Tightly coupled to physical world

Embed numerous distributed devices to monitor and interact with physical world

Network devices to coordinate and perform higher-level tasks

Exploit spatially and temporally dense, in situ, sensing and actuation


“The network is the sensor”

(Manges & Smith, Oak Ridge National Lab, 1998)


Requires robust distributed systems of:

–thousands of –physically-embedded, –unattended, –and often untethered,

devices.

New Design Themes

• Long-lived systems that can be untethered and unattended – Low-duty cycle operation with bounded latency– Exploit redundancy and heterogeneous tiered systems

• Leverage data processing inside the network– Thousands or millions of operations per second can be done using energy of sending

a bit over 10 or 100 meters (Pottie00)– Exploit computation near data to reduce communication

• Self-configuring systems that can be deployed ad hoc– Un-modeled physical world dynamics makes systems appear ad hoc– Measure and adapt to unpredictable environment– Exploit spatial diversity and density of sensor/actuator nodes

• Achieve desired global behavior with adaptive localized algorithms– Can’t afford to extract dynamic state information needed for centralized control


From Embedded Sensing to Embedded Control

• Embedded in unattended “control systems”– Different from traditional Internet, PDA, Mobility applications – More than control of the sensor network itself

• Critical applications extend beyond sensing to control and actuation– Transportation, Precision Agriculture, Medical monitoring and drug

delivery, Battlefield applications– Concerns extend beyond traditional networked systems

• Usability, Reliability, Safety

• Need systems architecture to manage interactions– Current system development: one-off, incrementally tuned, stove-piped– Serious repercussions for piecemeal uncoordinated design:

insufficient longevity, interoperability, safety, robustness, scalability...


Embedded Network Sensors Architecture Drivers

Varied and variable environments

Energy and scalability

Heterogeneity of devices

Smaller component size and cost

Adaptive Self-ConfiguringSystems

Distributed Signal and Information Processing

Sensor Coordinated Actuation

Embeddable Microsensors

Drivers Research Areas


Long-Lived, Self-Configuring Systems

Local sensors

• Irregular configurations

• Network topology changes over time

• Hand configuration will fail -- scale, and variability

• Solution: local adaptation and redundancy

• Challenges:– Localization– Time Synchronization– Calibration– Information aggregation and storage– Event detection– Programming model!


Programming Challenge

• How do we task a 1000+ node dynamic sensor network to conduct complex, long-lived tasks ??– Identify Spatio-temporal, multi-modal, events– Scalability– Energy constrained…Communication constrained


Exploiting Redundancy example

• Efficient, multi-hop topology formation goal: exploit redundancy provided by high density to extend system lifetime while providing communication and sensing coverage.

– If too many sensors active at the same time, increase energy consumption and competition for communication resources.

– If too few nodes active, then lack of communication and/or sensing coverage.– Central control/configuration requires too much communication– Nodes should self-configure to find the right trade-off– Ultimately should adapt based on desired “fidelity”


Robustness and Scalability through Adaptation

• Adaptive mechanisms increase complexity but enable self-configuration for robustness and scalability

• Self calibration to adapt to variations in sensor response and placement• Adjust duty cycle and transmit range as a function of node density and measured range

(adaptive fidelity)– Balance increased system life-time with increased resolution

• Challenge: develop and evaluate localized adaptive algorithms

• We hope adaptive functions will go beyond “connectivity”…e.g., tracking


Why can’t we simply adapt Internet protocols and the “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– Name the data, not the nodes; even at the lowest levels of the

system.

• ENS systems raise many new technical challenges


Its NOT just an Internet:Directed Diffusion: Data Centric Routing

• Basic idea– Name data (not nodes) with externally relevant attributes

• Data type, time, location of node, SNR, etc– Diffuse requests and responses across network using application driven routing

(e.g., geo sensitive or not)– Optimize path with gradient-based feedback– Support in-network aggregation and processing

• Data sources publish data, Data clients subscribe to data– However, all nodes may play both roles

• A node that aggregates/combines/processes incoming sensor node data becomes a source of new data

• A sensor node that only publishes when a combination of conditions arise, is a client for the triggering event data

– True peer to peer system

• Implementation defines namespace and simple matching rules with filters– Linux (32 bit proc) and TinyOS (8 bit proc) implementations


Diffusion as a construct for in-network processing

• Nodes pull, push, and store named data (using tuple space) to create efficient processing points in the network– e.g. duplicate suppression, aggregation, correlation

• Nested queries reduce overhead relative to “edge processing”• Complex queries support

collaborative signal processing– Propagate function

describing desired locations/nodes/data (e.g. ellipse for tracking (Zhao et al))

– Interesting analogs to emergingpeer-to-peer architectures

• Build on a data-centric architecturefor queries and storage


A more general look at A more general look at Data Centric vs. Address Centric approachData Centric vs. Address Centric approach

(Krishnamachari et al.)(Krishnamachari et al.)

• Address Centric• Distinct paths from each source to sink.

• Data Centric• Support aggregation in the network where paths/trees overlap• Essential difference from traditional IP networking

• Building efficient trees for Data centric model• Aggregation tree: On a general graph if k nodes are sources and one is a sink, the aggregation tree that minimizes the number of transmissions is the minimum Steiner tree. • NP-complete….Approximations:

– Center at Nearest Source (CNSDC): All sources send through source nearest to the sink.– Shortest Path Tree (SPTDC): Merge paths.– Greedy Incremental Tree (GITDC): Start with path from sink to nearest source. Successively add next nearest source to the existing tree.


Comparison of energy costsComparison of energy costs

Address CentricShortest path data centricGreedy tree data centricNearest source data centricLower Bound

Data centric has many fewer transmissions than does Address Centric; independent on the tree building algorithm.


System Architecture: Current state of the art and community “consensus”…

• “It’s a Database!”…• “NO, it’s a wireless Ad Hoc Network!”…• “NO, it’s an Internet!”…• “NO, it’s a Neural Net!”…• “NO, it’s an Parallel computer!”…• “NO, it’s an Distributed system!”…


Theme: New Constraints

• Tight coupling to the physical world– Need better physical models– More experimentation

• Designing for energy constraints• Coping with “apparent” loss of layering


Theme: New Design Goals

• Designing for long-lived (and often energy-constrained) systems– Exploiting redundancy – Low-duty cycle operation– Tiered architectures

• Self configuring systems – Measure and adapt to unpredictable environment– Exploit spatial diversity of sensor/actuator nodes– Localization and Time synchronization are key building blocks


Implications for Wireless Sensor Network Design

• Achieve desired global behavior through localized interactions, without global state– Avoid communication over long distances [Pottie 2000]

– Energy propagation loss: E α R4 (10 m: 5000 ops/transmitted bit; 100 m: 50,000,000 ops/transmitted bit)

• Empirically adapt to observed environment– Dynamic, messy, environments preclude pre-configured behavior

• Leverage data processing/aggregation inside the networkLeverage data processing/aggregation inside the network


Example: Directed Diffusion

• In-network data processing (e.g., aggregation, caching)

• Application-aware communication primitives– Expressed in terms of named data (not in terms of the nodes generating or

requesting data)

• Distributed algorithms using localized interactions and measurement based adaptation


Basic Directed Diffusion

Setting up gradients

Source

Sink

Interest = Interrogation in terms of data attributes

Gradient = direction and strength(Slide adapted from Prof. Deborah Estrin, USC)

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 our example, floodIn our example, flood– More sophisticated behaviors

possible: e.g. based on cached information, GPS

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

along different pathsalong different paths

– probabilistic forwarding– single-path delivery, etc.

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

towards neighbors who send towards neighbors who send an interestan 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 diffusion 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 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 experimentsAll sources send the same location estimate for base experiments


Average Dissipated Energy (Standard 802.11Standard 802.11 energy model)

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DiffusionDiffusion

Omniscient MulticastOmniscient MulticastFloodingFlooding

Standard 802.11 is dominated by idle energyStandard 802.11 is dominated by idle energy(Slide adapted from Prof. Deborah Estrin, USC)

Average Dissipated Energy (Sensor radioSensor radio energy model)

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DiffusionDiffusion

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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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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

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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(Slide adapted from Prof. Deborah Estrin, USC)

• 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 (in progress)

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

• Experimentation on our testbed in progress

Summary of Diffusion Results


Implied direction: Hierarchical Queries

• Create processing points in the network– High level interests/queries for activity triggers lower level local queries

for particular data modalities and signatures (e.g. acoustic and vibration patterns that are mapped to the activity of interest)

– As opposed to generating detailed queries at sink points and relying on opportunistic aggregation alone.

Source

Sink

Large animal?

Acoustic?


Self-configuration

• Each node assesses its connectivity and signals or actuates when it detects a depleted (BW/fidelity) region.

• 'Healing' is collaborative self-organized deployment of nodes – Activate more/fewer nodes– Mobilize more/fewer nodes– Adjust duty cycle/power level of existing nodes…

• Assumptions:– No centralized processing; all nodes act based on locally available

information.– A very large solution space; not seeking unique optimal solution.– Some links have high packet loss..


Wrapping up…Tiered Architecture

• USC: 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


Tiered Platform for experimentation

• Embedded PC:– COTS PC104 CPU module

• AMD ELANSC400, 16MB RAM+16MB FlashDisk, 4 serial/1 parallel ports

– Phasing out current radio: 418Mhz RPC from Radiometrix

– Moving to RFM– OS: Slimmed Redhat 6.1. (2.2.x/Libc6)– Incoporating PC104+ for higher end

processing, image capture, etc• Tags and Motes:

– 8 bit proc (ATMEL/PIC)– RFM Radio– Mote nicely packaged– Tag for more experimentation– Culler’s TOS

UCB Mote (Pister)UCLA Tag (Girod)

ISI PC-104


Technical challenges

• Ad hoc, self organizing, adaptive systems with predictable behavior• Collaborative processing, data fusion, multiple sensory modalities • Data analysis/mining to identify collaborative sensing, triggering

thresholds, etc • Combining experimentation, simulation, and analysis• Engaging theory community (Algorithms? Controls?)


Enormous Potential Impact

Networked Embedded Systems

Disaster Recoveryand Urban RescueEarth Science

Exploration

Wearable computing

Smart spaces

EnvironmentalMonitoring

Medical monitoring

Transportation

Condition Based Maintenance

Active Structures

Strand Stand

Algae

Biological Monitoring

-scaledTetheredRobot

Bio-Tank

2 meters

Sensors(Slide adapted from Prof. Deborah Estrin, USC)

More information

• UCLA Laboratory for Embedded Collaborative Systems (LECS) – http://lecs.cs.ucla.edu

• UCLA Distributed Embedded Systems Program (DESP)– http://desp.cs.ucla.edu (joint EE and CS)

• SCADDS project– http://www.isi.edu/scadds

• ns-2: network simulator (with diffusion supports)– http://www.isi.edu/nsnam/dist/ns-src-snapshot.tar.gz

• Our testbed and software– http://www.isi.edu/scadds/testbeds.html


Some Other Related Work(NOT complete)

• Sensor networks– www.isi.edu/scadds– www.janet.ucla.edu/WINS– wins.rsc.rockwell.com

– wind.lcs.mit.edu/~hari– www.nesl.ee.ucla.edu/people/mbs– tinyos.millennium.berkeley.edu

• Smart Matter– www.parc.xerox.com/spl/projects/smart-matter– www-swiss.ai.mit.edu/projects/amorphous

• Internet design inspiration– irl.cs.ucla.edu/AWC/– www-mash.cs.berkeley.edu/mash


Preview of Next Class

• Communication and Communications Networks

sensor networks february 6, 2003 class meeting 8 (images from prof. deborah estrin, usc)

Documents