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INT598 Sensor Networks. Silvia Nittel Spatial Information Science & Engineering University of Maine Fall 2006. IGERT. Overview. Motivation & Applications Platforms, Operating Systems, Power Networking Protocols, naming, routing Data Collection and Aggregation. Motivation. Trends :

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int598 sensor networks

INT598Sensor Networks

Silvia Nittel

Spatial Information Science & Engineering

University of Maine

Fall 2006

  • Motivation & Applications
  • Platforms, Operating Systems, Power
  • Networking
    • Protocols, naming, routing
  • Data Collection and Aggregation
  • Trends:
    • Developments of new sensor materials
    • Miniaturization of microelectronics
    • Wireless communication
  • Consequences:
    • Embedding devices into almost any man-made and some natural devices, and
    • connecting the device to an infinite network of other devices, to perform tasks, without human intervention.
    • Information technology becomes omnipresent.
  • ”Pervasive Computing”:The idea that technology is to move beyond the personal computer to everyday devices with embedded technology and connectivity as computing devices become progressively smaller and more powerful.
Embedded Networked Sensing Potential
  • Micro-sensors, on-board processing, and wireless interfaces all feasible at very small scale
    • can monitor phenomena “up close” in non-intrusive way
  • Will enable spatially and temporally dense environmental monitoring
  • Embedded & Networked Sensing will reveal previously unobservable phenomena

Habitat Monitoring

Storm petrels on Maine’s Great Duck Island

Contaminant Transport

Marine Microorganisms

Vehicle Detection

multiscale observation and fusion example regional or greater scale to local scale
Multiscale Observation and Fusion: Example, Regional (or greater) scale to local scale
  • Satellite, airborne remote sensing data sets at regular time intervals
  • coupled to regional-scale “backbone” sensor network for ground-based observations
  • fusion, interpolation tools based on large-scale computational models


Sensor network

images from Susan Ustin, UC Davis

  • Motivation & Applications
  • Platforms, Operating Systems, Power
  • Networking
    • Protocols, naming, routing
  • Data Collection and Aggregation
    • In-network data aggregation
emergence of wisenets
Emergence of WiSeNets
  • 1994 Pottie and Kaiser propose Low Power Wireless Integrated Microsensors
  • DARPA Sensit Program (Sensor Information Technology)
  • Late 97-98 handhelds emerge
    • Palm platform
    • ITSY, BWRC PicoRadio, etc.
    • Matchbox PCs
    • Bluetooth promised
  • Berkeley SmartDust
    • 1999 WeC mote offshoot
  • 2000 Mote/TinyOS platforms
  • WINS finally appears in Linux for Darpa’s Sensit
  • 2002 Mica NEST OEP creates de facto platform
  • 2003 Bluetooth revival
  • 2004 Telos, lowest power mote, supports IEEE 802.15.4
  • Sensit
    • Darpa’s Program “Sensor Information Technology”
  • WINS
    • Wireless Integrated Network Sensor Platforms
    • Developed by Sensoria Corporation for Darpa’s Sensit program
  • NEST
    • Network Embedded Systems
  • OEP
    • Open Experimental Platform (a middleware for sensor networks)
sensor network
Sensor Network
  • “Sensor Node”:
    • Tiny vanilla computer with operating system, on-board sensor(s) and wireless communication (“PC on a pin tip”)
      • Trend towards low-cost, micro-sized sensors
      • Use of wireless low range RF communication
      • Batteries as energy resource
  • “Sensor Network”
    • Massive numbers of “sensors” in the environment that measure and monitor physical phenomena
      • Local interaction and collaboration of sensors
      • Global monitoring
      • Tightly coupled to the physical world to sense and influence it
mica2 and mica2dot
Mica2 and Mica2Dot

1 inch

  • Processor:
    • ATmega128 CPU
  • RAM/Storage:
    • Chipcon CC1000
      • Manchester encoding
      • Tunable frequency
      • Byte spooling
  • Power usage scales with range
basic sensor board
Basic Sensor Board
  • Light (Photo)
  • Temperature
  • Prototyping space for new hardware designs
mica sensor board
Light (Photo)



2 axis

Resolution: ±2mg


Resolution: 134mG


Tone Detector



Mica Sensor Board
mica weather board
Total Solar Radiation

Photosynthetically Active Radiation

Resolution: 0.3A/W

Relative Humidity

Accuracy: ±2%

Barometric Pressure

Accuracy: ±1.5mbar


Accuracy: ±0.01oC


2 axis

Resolution: ±2mg

Designed by UCB w/ Crossbow and UCLA

Mica Weather Board

Revision 1.5

Revision 1.0

telos new oep mote
Telos: New OEP Mote
  • Single board philosophy
    • Robustness, Ease of use, Lower Cost
    • Integrated Humidity & Temperature sensor
  • First platform to use 802.15.4
    • CC2420 radio, 2.4 GHz, 250 kbps (12x mica2)
    • 3x RX power consumption of CC1000, 1/3 turn on time
    • Same TX power as CC1000
  • Motorola HCS08 processor
    • Lower power consumption, 1.8V operation,faster wakeup time
    • 40 MHz CPU clock, 4K RAM
  • Package
    • Integrated onboard antenna +3dBi gain
    • Removed 51-pin connector
    • Everything USB & Ethernet based
    • 2/3 A or 2 AA batteries
    • Weatherproof packaging
  • Support in upcoming TinyOS 1.1.3 Release
  • Co-designed by UC Berkeley and Intel Research
  • Available from Moteiv (
robomote usc
Robomote (USC)
  • Less than 0.000047m3
  • $150 each
  • Platform to test algorithms for adaptive wireless networks with autonomous robots
a network
A Network

S. Madden, UBerkeley

wireless sensor networks
Wireless Sensor Networks
  • They present a range of computer systems challenges because they are
    • closely coupled to the physical world with
    • all its unpredictable variation, noise, and asynchrony;
    • they involve many energy-constrained, resource-limited devices operating in concert;
    • they must be largely self-organizing and self-maintaining; and
    • they must be robust despite significant noise, loss, and failure.
sensor network objectives
Sensor Network Objectives
  • Several classes of systems:
    • Mote herds
    • Collaborative processing arrays (32 bit, 802.11, linux)
    • Networked Info-Mechanical Systems: Autonomy
  • Achieve longevity/autonomy, scalability, performance with:
    • heterogeneous systems
    • in-network processing, triggering, actuation
  • Algorithm/Software challenges
    • Characterizing sensing uncertainty
    • Error resiliency, integrity
    • Statistical and information-theoretic foundations for adaptive sampling, fusion
    • Programming abstractions, Common services, tools
    • Data modeling, informatics


Mote Clusters




Collaborative processing arrays (imaging, acoustics)

sensor network design topics
Sensor Network Design Topics
  • Long-lived systems that can be untethered (wireless) and unattended
    • Communication will be the persistent primary consumer of scarce energy resources (MICA Mote: 720nJ/bit xmit, 4nJ/op)
    • Autonomy requires robust, adaptive, self-configuring systems
  • Leverage data processing inside the network
    • Exploit computation near data to reduce communication, achieve scalability
    • Collaborative signal processing
    • Achieve desired global behavior with localized algorithms (distributed control)
  • “The network is the sensor”(Manges&Smith, Oakridge Natl Labs, 10/98)
    • Requires robust distributed systems of hundreds of physically-embedded, unattended, and often untethered, devices.

Application layer

Application: Events, Reactions

Data model, Declarative queries


DB layer

Data aggregation, Query processing

Adaptive topology, Geo-Routing

Network layer

MAC, time, location

Physical layer

Phy: comm, sensing, actuation


Deborah Estrin, UCLA

  • Motivation & Applications
  • Platforms, Operating Systems, Power
  • Networking
    • Protocols, naming, routing
  • Data Collection and Aggregation
communication using radio
Communication using Radio

Listening &

receiving signals


radio signals

picoradio and radio propagation
PicoRadio and Radio propagation
  • Energy required to transmit signals in distance d
    • Communication is huge battery drain
    • Indoor has lots of other complications
  • Small energy consumption => short range comm
    • Multihop routing required to achieve distance
    • Routes around obstacles
    • Requires discovery, network topology formation, maintenance
      • may dominate cost of communication
  • Energy to receive ~ E*t at short range
    • Dominated by listening time (potential receive)
    • Radio must be OFF most of the time!
iso osi protocol stack





Data Link


ISO/OSI Protocol Stack

The End Computer

System View

7 Layer ISO/OSI Reference Model



Transport Control

Protocol (TCP)

The Internet


Internet Protocol


The Network


*) International Standard Organization's Open System Interconnect

low level networking
Low-level Networking
  • Physical Layer
    • Low-range radio broadcast/receive
    • Wireless (wiSeNets)
  • MAC: Media Access Control
    • Controls when and how each node can transmit in the wireless channel (“Admission control”)
    • Objectives:
      • Channel utilization
        • How well is the channel used? (bandwidth utilization)
      • Latency
        • Delay from sender to receiver; single hop or multi-hop
      • Throughput
        • Amount of data transferred from sender to receiver per time unit
      • Fairness
        • Can nodes share the channel equally?
mac design decisions
Dominant factorMAC Design Decisions
  • Energy is primary concern in sensor networks
  • What causes energy waste?
    • Collisions
    • Control packet overhead
    • Overhearing unnecessary traffic
    • Long idle time
      • bursty traffic in sensor-net apps
      • Idle listening consumes 50—100% of the power for receiving (Stemm97, Kasten)
  • Network Architecture: Can we adapt the Internet protocols and the “end to end” architecture to SN?
    • Internet routes data using IP Addresses in Packets and Lookup tables in routers
      • Many levels of indirection between data name and IP address, but basically address-oriented routing
      • Works well for the Internet, and for support of Person-to-Person communication
    • Embedded, energy-constrained (un-tethered, small-form-factor), unattendedsystems cannot tolerate communication overhead of indirection
    • Our sensor network architecture needs
      • Minimal overhead
      • Data centric routing
data centric routing
Data-centric Routing
  • Named-data as a way of tasking motes, expressing data transport request (data-centric routing)
  • Basically:
    • “send the request to sensors that can deliver the data, I do not care about their address”
  • Two initial approaches in literature:
    • Derived from multicast-routing perspective where you name a logical group of sensor nodes (Diffusion)
    • Derived from database query language (TinyDB) with stronger semantics on data delivery, timing, sequencing
  • Commonality is tree-based routing
    • Query sent out from microserver to motes
    • Sink-Tree built to carry data from motes to microserver
tree routing







Tree Routing

Parent Node

Children Nodes

tree building
Tree building
  • Queries/Request
    • What goes in query?
    • Where does query go?
  • Neighbor selection
    • How does mote select upstream neighbor for data?
    • Asymmetric links
    • Unidirectional links
    • Route characterization (like ETX)
  • Multiple microservers
    • What about multiple microservers?
    • How does mote select a microserver?
tree building1
Tree building
  • Dynamics
    • How often do you send out a new query?
    • How often do you select a new upstream path
  • Design Tree building protocol
    • From query source to data producer(s) and back
    • Multihop ad-hoc routing
  •  reliable routing is essential!
basic primitives
Basic Primitives
  • Single Hop packet loss characteristics
    • Environment, distance, transmit power, temporal correlation, data rate, packet size
  • Services for High Level Protocols/Applications
    • Link estimation
    • Neighborhood management
    • Reliable multihop routing for data collection
basic neighborhood of devices
Basic Neighborhood of Devices
  • Services for High Level Protocols/Applications
    • Link estimation
    • Neighborhood management
    • Reliable multihop routing for data collection
  • Direct Reception
  • Large variation in affinity
    • Asymmetric links
    • Long, stable high quality links
    • Short bad ones
  • Link quality varies with traffic load
    • Collisions
    • Distant nodes raise noise floor
  • Many poor “neighbors”
neighborhood management
Neighborhood Management
  • Maintain link estimation statistics and routing information of each neighboring sensor node
  • How large should this table be?
    • O(cell density) * meta-data for each neighbor
  • Issue:
    • Density of nodes can be high but memory of each node is limited
    • At high density, many links are poor or asymmetric
  • Neighborhood Management
    • Question: when table becomes full,
      • should we add new neighbor?
      • If so, evict which old neighbor?
    • Similar to
      • frequency estimation of data streams, or
      • classical cache policy
reliable routing
Reliable Routing
  • 3 core components for Routing
    • Neighbor table management
    • Link estimation
    • Routing protocol
routing protocols
Routing Protocols
  • Ad-hoc routing, Geographic routing
  • Topology Formation
  • Directed Diffusion
  • Rumor/Gossip Routing
  • Motivation & Applications
  • Platforms, Operating Systems, Power
  • Networking
    • Physical layer, MAC, Protocols
    • Routing
  • Adaptable, Configurable Systems
  • Data Collection and Aggregation