Sensor Networks

1
Sensor Networks

• ?????
• ??????????????
• ??????????

2
Sources

• Comm n Sense Research Challenges in Embedded
  Networked Sensing, D. Estrin, http//lecs.cs.ucl
  a.edu
• A Survey on Sensor Network,I.F. Akyildiz, W.
  Su, Y. Sankarasubramaniam, E. Cayirci, Georgia
  Institute of TechnologyIEEE Communications
  Magazine, Aug. 2002

3
Introduction

• Mark Weiser envisioned a world in which computing
  is pervasive
• What we need is to instrument the physical world
  with pervasive networks of sensor-rich, embedded
  computation
• Such systems fulfill two of Weisers objectives
• Ubiquity by inject computation into the physical
  world with high spatial density
• Invisibility by having the nodes and collective
  of nodes operate autonomously

4
Introduction

• What is required is the ability to easily deploy
  flexible sensing, computation, and actuation
  capabilities into our physical environments such
  that the devices themselves are general-purpose
  and can organize and adapt to support several
  application types

5
Vision

• Embed numerous distributed devices to
  monitor/interact with physical world
• Exploit spatially and temporally dense, in situ,
  sensing and actuation

• Network these devices so that they can coordinate
  to perform higher-level tasks.
• Requires robust distributed systems of hundreds
  or thousands of devices.

6
Sensor Nodes and Networks

• Sensor nodes sensing, data processing, and
  communicating capacity
• Sensor network a large number of sensor nodes
  that are densely deployed either inside the
  phenomenon or very close to it
• Sensor node position not engineered or
  predecided?protocols or algorithms must be
  self-organizing
• Cooperative effort of sensor nodes with in
  network processing

7
Applications
Scientific eco-physiology, biocomplexity mapping
Infrastructure Contaminant flow monitoring
www.jamesreserve.edu
Engineering adaptive structures
8
Other Applications (I)

• Environmental
• Forest fire detection, biocomplexity mapping of
  the environment, flood detection, precision
  agriculture
• Healthy
• Telemonitoring of human physiological data,
  tracking and monitoring doctors and patients
  inside a hospital, drug administration in
  hospitals
• Military
• Monitoring friendly forces, equipment and
  ammunition battlefield surveillance
  reconnaissance of opposing forces and terrain
  targeting battle damage assessment nuclear,
  biological and chemical attack detection and
  reconnaissance

9
Other Applications (II)

• Home
• Home automation
• Smart environment
• Commercial
• Environmental control in office buildings
• Interactive museums
• Detecting and monitoring car thefts
• Managing inventory control
• Vehicle tracking and detection
• Monitoring product quality
• Monitoring disaster areas
• .

10
Challenges

• Tight coupling to the physical world and embedded
  in unattended control systems
• Different from traditional Internet, PDA,
  mobility applications that interface primarily
  and directly with human users
• Untethered, small form-factor, nodes present
  stringent energy constraints
• Living with small, finite, energy source is
  different from fixed but reusable resources such
  as BW, CPU, storage
• Communications is primary consumer of energy
• Sending a bit over 10 or 100 meters consumes as
  much energy as thousands/millions of operations

11
New Design Themes

• Long-lived systems that can be untethered and
  unattended
• Low-duty cycle operation with bounded latency
• Exploit redundancy
• Tiered architectures (mix of form/energy factors)
• Self-configuring systems that can be deployed ad
  hoc
• Measure and adapt to unpredictable environment
• Exploit spatial diversity and density of
  sensor/actuator nodes

12
Approach

• Leverage data processing inside the network
• Exploit computation near data to reduce
  communication
• Achieve desired global behavior with adaptive
  localized algorithms (i.e., do not rely on global
  interaction or information)
• Dynamic, messy (hard to model), environments
  preclude pre-configured behavior
• Cant afford to extract dynamic state information
  needed for centralized control or even
  Internet-style distributed control

13
Why cant we simply adapt Internet protocols and
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 cant
  tolerate communication overhead of indirection

14
vs. Ad Hoc Networks

• Large number of sensor nodes (several orders of
  magnitude higher)
• Densely deployed
• Prone to failures
• Network topology changes very frequently
• Mainly use a broadcast paradigm vs.
  point-to-point in ad hoc networks
• Limited in power, computational capacities, and
  memory
• May not have global identification (ID)

15
Communication Architecture

• Factors of design consideration
• Transmission media
• Production costs
• Power consumption
• Fault tolerance
• NW topology
• HW constraints
• Environment
• Scalability

16
Fault Tolerance

• The ability to sustain sensor network
  functionalities without any interruption due to
  sensor node failures
• The reliability Rk(t) or fault tolerance of a
  sensor node can be modeled with the Poisson
  distribution to capture the probability of not
  having a failure within the time interval (0,t)
• Rk(t) exp(-?kt) , for node k

17
Scalability

• The number of sensor nodes
• 10 -gt 100 -gt 1000 -gt 10000 -gt .
• Depending on the application
• New schemes must be able to utilize the high
  density
• The density
• µ(R) (N . p R2)/A
• A region area
• R radio transmission range
• N the number of scattered sensor nodes

18
Production Costs

• The cost of a single node is very important to
  justify the overall cost of the network
• The cost of a sensor node should be much less
  than US1
• The state-of-art technology allows a Bluetooth
  radio system to be less than US10
• 10 times more expensive the the targeted price

19
Hardware

• 4 basic units sensing unit, processing unit,
  transceiver unit, power unit
• Sensing sensors, Analog-to-digital converters
  (ADCs)
• Additional application-dependent units
• Location finding system, power generator,
  mobilizer.

20
Hardware Constraints

• Constraints
• Size
• Power
• Operate in very high densities
• Low cost
• Dispensable
• Autonomous
• Adaptive to environment

21
Sensor Network Topology

• Topology maintenance and change in 3 phases
• Predeployment and deployment phase
• Be thrown in as a mass or placed one by one
• Post-deployment phase
• Change in sensor nodes position, reachability,
  available energy, malfunctioning, and task
  details
• Redeployment of additional nodes phase
• Additional sensor nodes can be redeployed

22
Environment

• Nodes are densely deployed either very close or
  directly inside the phenomenon to be observed
• Usually work unattended in remote geographic
  areas
• in the interior of large machinery
• at the bottom of an ocean
• in a biologically or chemically contaminated
  field
• in a battlefield beyond the enemy lines
• in a home or large building
• .

23
Transmission Media

• Often by wireless medium
• Radio
• Used by most sensors
• µAMPS sensor uses a Bluetooth-compatible 2.4 GHz
  transceiver with an integrated frequency
  synthesizer
• Infrared
• License-free, robust to interference from
  electrical devices
• cheaper and easier to build
• Optical Smart Dust mote
• Both infrared and optical require line of sight

24
Power Consumption

• In some application scenarios, replenishment of
  power resources might be impossible
• Battery lifetime
• In a multihop ad hoc sensor network, each node
  plays dual role of data originator and data
  router
• cause significant topological changes
• require rerouting of packets and reorganization
  of the network
• Power consumption
• sensing, communication, and data processing

25
Design Issues According to Protocol Stack

• Physical layer
• Simple, robust modulation, transmission,
  receiving
• MAC protocol
• power-aware minimize collision with neighbors
  broadcasts
• Network layer
• routing data supplied by transport layer
• Transport layer
• maintain flow of data

26
Three Management Planes

• The power management plane, e.g.
• Turn off its receiver after receiving a message
• Broadcasts low in power and cannot participate in
  routing messages
• The mobility management plane
• Detects and registers movement of sensor nodes
• maintain route back to the user, keep track of
  their neighbor
• The task management plane
• balances and schedules sensing tasks for a
  specific region
• They are needed for sensor nodes to work
  power-efficiently, route data in a mobile
  network, share resources between sensor nodes

27
Physical Layer

• Responsibility
• Frequency selection, carrier frequency
  generation, signal detection, modulation, and
  data encryption.
• 915 MHz industrial, scientific, and medical (ISM)
  band has been widely used
• Long distance wireless communication can be
  expensive in terms of power
• A good modulation is critical for reliable comm.
• Binary and M-ary modulation schemes
• Ultra wideband (UWB) or impulse radio (IR) are
  promising

28
Physical Layer Open Issues

• Modulation schemes
• Simple and low-power modulation schemes
• Strategies to overcome signal propagation effects
• Hardware design
• Tiny, low-power, low-cost transceiver, sensing,
  and processing units
• Power-efficient hardware management strategies

29
Data Link Layer

• Responsibility
• Multiplexing of data streams, data frame
  detection, medium access and error control
• Reliable point-to-point and point-to-multipoint
• Medium Access Control protocol
• creation of the network infrastructure
• fairly and efficiently share communication
  resources
• Existing MAC protocols cannot be used
• Cellular system infrastructure-based
• Bluetooth and mobile ad hoc network (MANET)
• much larger number, power and radio range,
  frequent topology change, power conservation
  needed

30
Some Proposed MAC Protocols
31
Example MAC Protocols

• Self-Organizing Medium Access Control for Sensor
  Networks (SMACS) and the Eavesdrop-And-Register
  (EAR) Algorithm
• Nodes to discover their neighbors and establish
  communication without the need for any local or
  global master nodes
• No necessity for networkwide synchronization
• using a random wake-up schedule during connection
  phase and turning the radio off during idle time
  slots
• EAR attempts to offer continuous service to the
  mobile nodes

32
Data Link Open Issues

• MAC for mobile sensor networks
• more extensive mobility in the sensor nodes and
  targets
• Determination of lower bounds on the energy
  required for sensor network self-organization
• Error control coding schemes
• Power-saving modes of operation

33
Network Layer

• Design principles
• Power efficiency
• Sensor networks are mostly data-centric
• Data aggregation is useful only when it does not
  hinder the collaborative effort of the sensor
  nodes.
• An ideal sensor network has attribute-based
  addressing and location awareness
• Also providing internetworking with external
  networks

34
Energy-Efficient Route

• Available powerPA
• Energy required (a)
• Maximum minimum PA node route
• Min PA is larger thanthe min PAs
• Maximum PA route
• Minimum energy route
• Minimum hop route

35
Data Centric Route

• Use interest dissemination
• Sinks broadcast the interest, or
• Sensor nodes broadcast an advertisement and wait
  for a request
• Often require attribute-based naming
• Query by using attributes of phenomenon
• Data aggregation
• Solve the implosion and overlap problems

36
Proposed Schemes

• Flooding
• Implosion (duplicated message), overlap (both
  sensors detect the same event), resource
  blindness (not considering resource constraints)
• Gossiping
• Relay packets to randomly selected neighbor
• Negotiation (SPIN)

37
More Schemes

• Small minimum energy communication network
• Sequential assignment routing
• Low-energy adaptive clustering hierarchy
• Directed diffusion

38
Protocol Summary
39
Application Layer Protocols

• Sensor management
• nodes do not have global identifications and are
  infrastructureless
• Providing administrative tasks
• Introducing the rules related to data
  aggregation, attribute-based naming, and
  clustering to the sensor nodes
• Exchanging data related to the location finding
  algorithms
• Time synchronization of the sensor nodes
• Moving sensor nodes
• Turning sensor nodes on and off
• Querying the sensor network configuration and the
  status of nodes, and reconfiguring the sensor
  network
• Authentication, key distribution, and security in
  data communications

40
Application Layer Protocols

• Task assignment and data advertisement
• interest dissemination
• Advertisement of available data
• Sensor query and data dissemination
• issue queries, respond to queries and collect
  incoming replies
• Sensor query and tasking language (SQTL) supports
  3 types of events
• Receive defines events generated by a sensor node
  when the sensor node receives a message
• every defines events occurring periodically due
  to timer timeout
• expire defines events occurring when a timer is
  expired
• Different types of SQDDP can be developed for
  various applications. The use of SQDDPs may be
  unique to each application

41
(No Transcript)
42
Research Areas

• Constructs for in network distributed
  processing
• system organized around naming data, not nodes
• programming large collections of distributed
  elements
• Localized algorithms that achieve system-wide
  properties
• Time and location synchronization
• energy-efficient techniques for associating time
  and space with data to support collaborative
  processing
• Experimental infrastructure

43
Constructs for in NW Processing

• Nodes pull, push, store named data (using tuple
  space) to create effic. processing points in NW
• e.g. duplicate suppression, aggregation,
  correlation
• Nested queries reduce overhead relative to edge
  processing
• Complex queries support collaborative signal
  proc.
• propagate function describing desired
  locations/nodes/data (e.g. ellipse for tracking)

44
Self-organization with Localized Alg.

• Self-configuration and reconfiguration essential
  to lifetime of unattended systems in dynamic,
  constrained energy, environment
• Efficient, multi-hop topology formation node
  measures neighborhood to determine participation,
  duty cycle, and/or power level
• Beacon placement candidate beacon measures
  potential reduction in localization error
• Requires large solution space not seeking unique
  optimal
• Investigating applicability, convergence, role of
  selective global information

45
Time and Location Synchronization

• Common time coordinate for in situ processing,
  correlation of events
• Developing methods that balance communication
  (energy) cost with other variables (e.g.,
  precision, scope, lifetime, cost, form factor)
• Post facto pulse synchronization
• Common spatial coordinate for 3-space related
  tasks and network operation (e.g., geo-routing)
• Methods not rely on GPS or RF RSSI (due to
  envir.)
• Multi-modal localization using acoustic time of
  flight measurements, RF synchronization, and
  imaging to identify bad data sources (NLOS)

46
Experimental Infrastructure
PC-104(off-the-shelf)

• Software
• Directed Diffusion
• TinyOS (UCB/Culler)
• Measurement, Simulation

UCB Mote (Pister/Culler)
47
Berkeley Motes TinyOS

• ???

48
Berkeley Motes

• 1st generation
• 2nd generation

49
System of MICA Motes
50
MICA Motes

• Processor and radio board -
• MPR300
• Sensor board
• MTS310
• Base station/interface board -
• MIB300

51
MICA Motes
52
MICA Motes
53
Sensor Board
54
Processor/Radio Board
55
Processor/Radio Board
56
TinyOS

• TinyOS application/binary image, executable on
  an ATmega processor
• event-driven, 2-level scheduling, single-shared
  stack
• no kernel, no process management, no memory
  management,no virtual memory
• simple FIFOscheduler, partof the main

57
TinyOS

• f\avrgcc
• \cygwin
• \tinyos-1.x\apps cnt_to_leds, cnt_to_rfm,
  sense,
• \docs connector.pdf, tossim.pdf,
• \tools toscheck, inject,
  verify,
• \tos shared/system
  components,
• ..

58
Programming Model

• Application
• Component
• 2 types modules and configurations.
• Module
• Configuration
• A configuration is a component that "wires" other
  components together. Every NesC application has a
  single top-level configuration.
• Interface

59
Programming Model
60
Reference

• Crossbow
• http//www.xbow.com
• MICA Motes http//www.xbow.com/Products/Wireless_
  Sensor_Networks.htm
• TinyOS
• http//today.cs.berkeley.edu/tos/
• TinyOS support
• http//today.cs.berkeley.edu/tos/support.html
• TinyOS tutorial
• http//today.cs.berkeley.edu/tos/tinyos-1.x/doc/t
  utorial/index.html
• PADSFTP/TinyOS

61
Directed Diffusion A Scalable and Robust
Communication Paradigm for Sensor Networks

• Chalermek Intanagonwiwat (USC/ISI)
• Ramesh Govindan (USC/ISI)
• Deborah Estrin (USC/ISI and UCLA)

62
The Goal

• Embed numerous devices to monitor and interact
  with physical world
• Network these devices so that they can coordinate
  to perform higher-level tasks
• Requires robust distributed systems of tens of
  thousands of devices

63
The Challenge Dynamics!

• The physical world is dynamic
• Dynamic operating conditions
• Dynamic availability of resources
• particularly energy!
• Devices must adapt automatically to the
  environment
• Too many devices for manual configuration
• Environmental conditions are unpredictable
• Unattended and un-tethered operation is key to
  many applications

64
Energy Is the Bottleneck Resource

• Communication VS Computation Cost
• E ? R4
• 10 m 5000 ops/transmitted bit
• 100 m 50,000,000 ops/transmitted bit
• Short distance communication gt multi-hop
• Cannot assume global knowledge, cannot
  pre-configure networks
• Get desired global behavior thru localized
  interactions
• Empirically adapt to observed environment
• Can leverage data processing/aggregation inside
  the network

65
Research Theme

• What communication primitives can be employed in
  such unattended sensor networks?
• Assume no structured sensor fields, but
  task-specific
• A user of the network contact one of the sensors
  in the field and pose queries (interrogation)
• e.g., Give me periodic reports about animal
  location in region A every t seconds
• Interrogation propagated to sensor nodes in
  region A
• Sensor nodes in region A are tasked to collect
  data
• Data are sent back to the users every t seconds
• Dissemination mechanisms for tasks and events?

66
Issues to Be Addressed

• Scalable to thousands of sensor nodes
• Sensor nodes may fail, lose battery power, be
  temporarily unable to communication, gt
  communication mechanisms must be robust
• Minimize energy usage
• gt a data dissemination mechanism for
  sensorsDirected Diffusion

67
Directed Diffusion

• In-network data processing (aggregation, caching)
• Distributed algorithm with localized interaction
• Application-aware communication primitives
• expressed in terms of named data (not in terms of
  the nodes generating or requesting data)gt
  data-centric
• Data generated by sensors named by
  attribute-value
• Sensor nodes need not have globally unique
  address, but need to distinguish between neighbors

68
Basic Ideas

• A node requests data by sending interests for
  named data (diffusion)
• Gradients are set up in network to draw events
• Data matching the interest is drawn towards that
  node along multiple reverse paths
• The network reinforces one or more paths
• Intermediate nodes can cache, transform, or
  aggregate data, and may direct interests based on
  previously cached data
• Interest/data propagation, aggregation decided by
  localized interactions (with local naming)

69
Naming

• Task descriptions are named by a list of
  attribute-value pairs
• This specifies an interest for data matching the
  attributes

70
Basic Directed Diffusion
Setting up gradients (flooding)
Source
Data rate 1ms
Sink
Interest Interrogation
Gradient Who is interested
71
Basic Directed Diffusion
Sending data and reinforcing the best path
Source
Sink
Low rate event
Reinforcement Increased interest e.g. 1st
neighbor sending the event
72
Multiple Sources and Sinks
73
Directed Diffusion and Dynamics
Source
Sink
Recovering from node failure
Low rate event
Reinforcement
High rate event
74
Directed Diffusion and Dynamics
Source
Sink
Stable path
Low rate event
High rate event
75
Local Behavior Choices

• For propagating interests
• In our example, flood
• More sophisticated behaviors possible e.g. based
  on cached information, GPS

• For data transmission
• Multi-path delivery with selective quality along
  different paths
• probabilistic forwarding
• single-path delivery, etc.

• For setting up gradients
• data-rate gradients are set up towards neighbors
  who send an interest.
• Others possible probabilistic gradients, energy
  gradients, etc.

• For reinforcement
• reinforce paths, or parts thereof, based on
  observed delays, losses, variances etc.
• other variants inhibit certain paths because
  resource levels are low

76
Simulation Study of Diffusion

• Key metric
• Average Dissipated Energy per event delivered
• indicates energy efficiency and network lifetime
• Compare diffusion to
• flooding
• centrally computed tree (omniscient multicast)

77
Diffusion Simulation Details

• Simulator ns-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

78
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 experiments

79
Average Dissipated Energy(Standard 802.11 Energy
Model)
0.14
Diffusion
0.12
Omniscient Multicast
Flooding
0.1
0.08
Average Dissipated Energy
(Joules/Node/Received Event)
0.06
0.04
0.02
0
0
50
100
150
200
250
300
Network Size
Standard 802.11 is dominated by idle energy
80
Average Dissipated Energy(Sensor Radio Energy
Model)
0.018
0.016
Flooding
0.014
0.012
0.01
0.008
(Joules/Node/Received Event)
Omniscient Multicast
Average Dissipated Energy
0.006
Diffusion
0.004
0.002
0
0
50
100
150
200
250
300
Network Size
Diffusion can outperform flooding and even
omniscient multicast. WHY ?
81
Impact of In-network Processing
0.025
Diffusion Without Suppression
0.02
0.015
(Joules/Node/Received Event)
Average Dissipated Energy
0.01
Diffusion With Suppression
0.005
0
0
50
100
150
200
250
300
Network Size
Application-level suppression allows diffusion to
reduce traffic and to surpass omniscient
multicast.
82
Impact of Negative Reinforcement
0.012
0.01
Diffusion Without Negative Reinforcement
0.008
Average Dissipated Energy
(Joules/Node/Received Event)
0.006
0.004
Diffusion With Negative Reinforcement
0.002
0
0
50
100
150
200
250
300
Network Size
Reducing high-rate paths in steady state is
critical
83
Summary of Diffusion Results

• 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 but also MAC and application
  level
• Experimentation on our testbed in progress

84
More Information

• 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

85
(No Transcript)