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Wireless Sensor Networks Seminar Research Trends in Distributed Systems Florian Schaub Ulm University 19. Nov. 2007 Florian Schaub (Ulm University) Wireless Sensor Networks 19. Nov. 2007 1 / 37 Overview 1 Introduction 2 Characteristics 3

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SLIDE 1

Wireless Sensor Networks

Seminar Research Trends in Distributed Systems Florian Schaub

Ulm University

  • 19. Nov. 2007

Florian Schaub (Ulm University) Wireless Sensor Networks

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SLIDE 2

Overview

1 Introduction 2 Characteristics 3 System Software

TinyOS

4 Middleware

Databases Mobile Agents Events

5 Applications

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SLIDE 3

Overview

1 Introduction 2 Characteristics 3 System Software

TinyOS

4 Middleware

Databases Mobile Agents Events

5 Applications

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SLIDE 4

What is Sensing?

Measuring real-world phenomena

Temperature, humidity, vibration, velocity, light conditions, sound, orientation, weight, gas, chemicals, . . . Many applications

  • Environmental monitoring (forest vitality)
  • Weather observation (rain fall in a certain region)
  • Animal tracking (herd movements)
  • Warning systems (flood warnings, avalanche warnings)
  • Industrial sensing (production lines, quality control)
  • Military applications
  • . . .

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SLIDE 5

Tradtitional Sensing

  • Autonomous sensor stations
  • Often expensive and heavy equipment
  • Wired to infrastructure, or
  • Manual data collection from stations

(e.g. every week) Problems

  • Cumbersome deployment
  • Limited coverage
  • Low sensor density
  • No real-time data

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SLIDE 6

Wireless Sensor Networks

  • Network of tiny sensor nodes
  • Battery-powered
  • Processing capabilites
  • Cheap
  • Wireless communication
  • Self-organizing ad hoc network

Advantages

  • Automatic data collection and reports
  • Easy deployment in large numbers (e.g. via plane)
  • Suitable for harsh/hostile environments (e.g. jungle)
  • Higher sensor density, data accuracy and consistency
  • Collaborative sensing

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SLIDE 7

Problems with Sensor Nodes

Restricted ressources

  • Limited energy (typically 2 AA batteries)
  • Limited storage capacity
  • Limited processing capabilites

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SLIDE 8

Problems with Sensor Nodes

Restricted ressources

  • Limited energy (typically 2 AA batteries)
  • Limited storage capacity
  • Limited processing capabilites

Trade-off

Autonomous operation for long periods of time vs. limited energy ressources

  • Strong impact on how sensor nodes operate
  • Switching off components (incl. radio unit) most of the time
  • Energy-awareness as the important design factor for WSNs

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SLIDE 9

Overview

1 Introduction 2 Characteristics 3 System Software

TinyOS

4 Middleware

Databases Mobile Agents Events

5 Applications

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SLIDE 10

Characteristics of Wireless Sensor Networks

Diverse range of application scenarios and requirements

But: common tasks for WSNs

1 Sensing real-world phenomena 2 Processing sensor data 3 Communicating with other nodes to share data

Resulting in general characteristics for (almost) all WSNs

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SLIDE 11

Size and Costs

Moore’s Law

“Processing power of chips doubles roughly every two years.” Gordon E. Moore, 1965

  • Desktop computers and servers
  • More transistors
  • Constant physical size
  • Sensor nodes and embedded systems
  • Smaller chips
  • Almost constant computing capabilites
  • Integration of additional functionality (A/D converters, . . . )
  • Minaturization

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SLIDE 12

Sensor Nodes dissected

Components of a sensor node

  • Micro-processor chip
  • One or more microsensors
  • Radio unit
  • Data storage
  • Analog-digital converters
  • Batteries and antenna

Network sensor platforms

  • Commercially available off-the-shelf products
  • Cheap production
  • Low per-unit prices (ca. $150)

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SLIDE 13

Network Sensor Platforms

Several platforms and vendors

  • Berkeley Motes Family (UC Berkeley), information from www.xbow.com

Mote MICA MICA2(DOT) MICAz TelosB Year 2001 2002 2004 2005 CPU ATmega163 ATmega128 ATmega128 TI MSP430 RAM 1 KB 4 KB 4 KB 10 KB Memory 16 KB 128 KB 128 KB 48 KB Flash 32 KB 512 KB 512 KB 1024 KB Bandwidth 40 kbps 38.4 kbps 250 kbps 250 kbps

  • Other platforms
  • ESB/ScatterWeb (FU Berlin)
  • MIT Cricket (MIT)
  • Imote 2 (Intel Research)

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SLIDE 14

Energy Restrictions

Energy resources usually restricted (batteries)

Lifetime Maximization

  • Switching off components
  • Sleep Modes
  • Communication is most expensive operation
  • In-node data processing to reduce Communication

TelosB energy consumption: Operation Consumption Standby 5.1µA Idle 54.5µA Active 1800µA Send/Receive 20,000µA 2.5 years lifetime with 1% sensing, communication every 3 min.

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SLIDE 15

Wireless Communication

  • IEEE802.11 and Bluetooth too energy expensive
  • Only short range communication (10–15 meters) needed
  • ZigBee (IEEE802.15.4) more energy-efficient

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SLIDE 16

Wireless Communication

  • IEEE802.11 and Bluetooth too energy expensive
  • Only short range communication (10–15 meters) needed
  • ZigBee (IEEE802.15.4) more energy-efficient
  • Ad hoc communication and self-organization
  • Neighbor discovery (periodic beaconing)
  • Negotiation of listening/beaconing intervalls
  • Additional control communication (e.g. for routing)
  • Piggybacking control information onto data packets
  • Gateway nodes
  • Connected to infrastructure (e.g. satellite, directed radio links)
  • Nodes route results to gateway nodes

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SLIDE 17

Data Aggregation and Dissemination

  • Simple approach: broadcasting raw data
  • Network congestion
  • Nodes have to be sending constantly
  • Efficient approach: data aggregation
  • Nodes aggregate received data and own data
  • Less traffic, less communication
  • Reduce results to level of interest (compression)
  • Always process as much data as possible in the network
  • Utilize collaborative processing power in networks

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SLIDE 18

Fault Tolerance and Scalability

Fault tolerance

  • Dynamic network changes (failing nodes, moved nodes, obstacles, . . . )
  • Adapt to changes, network reorganization
  • Data-centric communication (address functionality, not individual nodes)

Scalability

  • WSNs scale from 10 to 1,000 nodes
  • Efficient routing and communication algorithms
  • Continous deployment to extend network lifetime
  • Automatic discovery of new nodes (ad hoc comm., data-centric comm.)

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SLIDE 19

Real-time and Security Requirements

Real-time Requirements

  • Time-critical applications rely on real-time data and results
  • Military applications
  • Warning systems
  • QoS assertions (response time, delays)

Security

  • Ensure data authenticity and integrity
  • Protection against data injection, replay attacks
  • Tamper resistance of nodes
  • Protection against eavesdropping or unobtrusive communication
  • Privacy issues (when nodes are linked to persons)

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SLIDE 20

Overview

1 Introduction 2 Characteristics 3 System Software

TinyOS

4 Middleware

Databases Mobile Agents Events

5 Applications

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SLIDE 21

WSN System Software

Operating system requirements

  • High concurrency (sensing, processing, communicating)
  • Handle data streams (sensor data, communication data)
  • Energy efficiency
  • Modularity (keep system small)

De facto standard: TinyOS

  • www.tinyos.net

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SLIDE 22

TinyOS

Operating system features

  • Component-based
  • Event-based programming model
  • Lightweight multithreading support
  • System language: nesC

extended C with support for network embedded systems

TinyOS components

  • Command handlers (to send commands to lower layers)
  • Event handlers (to handle lower layer events)
  • Tasks (specifying program logic)
  • A fixed-sized memory frame

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SLIDE 23

TinyOS System Configuration

  • Component graph (sometimes called wiring)
  • Tiny scheduler
  • No kernel (!)

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SLIDE 24

Overview

1 Introduction 2 Characteristics 3 System Software

TinyOS

4 Middleware

Databases Mobile Agents Events

5 Applications

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SLIDE 25

Middleware for Wireless Sensor Networks

  • Abstraction from underlying system (hardware platform, sensors, . . . )
  • Efficient (network-wide) resource management
  • Easier development of WSN systems
  • Energy-awareness
  • Scalability and fault tolerance
  • Self-organization
  • Efficient communication and data aggregation
  • Security aspects and real-time services

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SLIDE 26

WSN middleware characteristics

Concentration on global behavior

  • See the network as a whole
  • Better scalability
  • No need to write low-level software for individual nodes
  • Utilize a priori application knowledge
  • Limited number of applications
  • Optimize middleware for one application domain

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SLIDE 27

Databases

TinyDB (UC Berkeley)

  • Sensor network as one virtual table Sensors
  • Rows = sensor readings of one node at a given time
  • Columns = node attributes (ID, location, . . . )
  • SQL-like query language
  • Acquisitional query processing
  • Queries are disseminated with controlled flooding (spanning tree)
  • Query is only forwarded to nodes that are relevant for query
  • Aggregation of the results on return path
  • Sensors table is only constructed during query processing

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SLIDE 28

TinyDB Query Example

SELECT AVG(temp), room FROM sensors WHERE floor=2 GROUP BY room HAVING AVG(temp) > 25C SAMPLE PERIOD 30s

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SLIDE 29

TinyDB Query Example

SELECT AVG(temp), room FROM sensors WHERE floor=2 GROUP BY room HAVING AVG(temp) > 25C SAMPLE PERIOD 30s

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SLIDE 30

Mobile Agents and Mobile Code

Impala (Princeton University)

  • Modular programs (agents) to task WSN
  • Agents can
  • Collect data locally
  • Statefully migrate to other nodes
  • Replicate and communicate with replicates
  • Automated distribution of updates and code
  • Supports fault tolerance and network

self-organization

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Events and Event Notification

DSWare (University of Virginia)

  • Applications register interest in real-world events in certain area
  • Nodes send event notifications to application when event occurs
  • Real-time support with event reporting deadlines
  • Compound events
  • Set of basic events B
  • Confidence function f assigns weights to basic events
  • Minimum confidence threshold cmin
  • Time window t (interval for event occurence)
  • Compound event is triggered when all basic events B occur in t and

f(B) ≥ cmin

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SLIDE 32

Compound Event Example

Explosion event

  • Three basic events B:
  • Light event Elight(bright flash)
  • Audio event Eaudio (loud bang)
  • Temperature event Etemp(sudden peak)
  • Time window t = 15 sec.
  • Confidence f(B) = 0.6· B(Etemp)+ 0.5· B(Elight)+ 0.4· B(Eaudio)
  • Minimum confidence cmin = 0.8

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SLIDE 33

Overview

1 Introduction 2 Characteristics 3 System Software

TinyOS

4 Middleware

Databases Mobile Agents Events

5 Applications

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SLIDE 34

ZebraNet: Wildlife Tracking

ZebraNet (Princeton University and Mpala Research Centre, Kenya)

  • Monitor animal activity for a certain time period (≥1 year)
  • Animals are equipped with tracking nodes
  • Nodes are equipped with GPS and light sensor
  • Impala middleware (mobile agents) part of project
  • Data flooding
  • Sensor data is stored locally
  • When animals pass each other, sensor nodes exchange data
  • Mobile base stations (plane, car) collect data by passing only a few animals

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SLIDE 35

Argo: Ocean Water Monitoring

Argo (50 international partners from 26 countries)

  • Battery-powered autonomous free-drifting floats
  • Gather temperature and salinity data of upper ocean layer
  • Float sinks to certain depth (up to 2,000 meters deep)
  • Rises in 10 day intervals to transmit data to satellites
  • Lifetime of floats is 4-5 years
  • Deployment started 2000, reaching 3,000 nodes in November 2007
  • Daily updated float positions and data: http://www.argo.ucsd.edu

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SLIDE 36

Argo: Ocean Water Monitoring

Active Argo floats (by country)

source: http://wo.jcommops.org/cgi-bin/WebObjects/Argo

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SLIDE 37

SensEye: Visual Sensor Networks

SensEye (University of Massachusetts)

  • Sensor nodes are equipped with visual sensors (e.g. webcams)
  • Forming a visual sensor network (SVN)
  • Collaborative signal processing for efficient in-network application of

computer vision techniques

  • Object detection
  • Object localization
  • Object tracking
  • Object recognition
  • Can be used for intrusion detection and automated surveillance tasks

(e.g. in airports)

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SLIDE 38

SHM: Self-Healing Minefield

SHM (DARPA)

  • Anti-tank landmines equipped with radio and positioning modules
  • Nodes establish wireless network and estimate positions of other nodes
  • Link quality is measured periodically to detect failing nodes
  • If node is missing, each node computes appropriate response to ensure

best mine coverage of the area

  • Mines have 8 rocket thrusters to catapult themselves into the breach

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SLIDE 39

Other Applications

There are many more:

  • Cold chain management
  • Rescue of avalanche victims
  • Patient monitoring
  • Cattle herding with virtual fence lines
  • Grape monitoring
  • Glacier monitoring
  • Sniper localization
  • . . .

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SLIDE 40

Conclusion

  • Large scale networks of wireless sensor nodes
  • Autonomous operation for long time periods
  • Restricted ressources (energy, processing, bandwidth)
  • Collaborative sensing
  • Different approaches to ease development
  • Diverse application scenarios
  • Growing number of projects and first commercial products

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