Sensor Networks

1
Sensor Networks Trend, Applications, Design

• Polly Huang
• iSpace Labs
• EE GINM, NTU
• http//cc.ee.ntu.edu.tw/phuang

2
Road Map

• Overview
• What a sensor network is?
• Applications
• How can the sensor network be useful?
• Problems Designs
• How should the sensor network work?

3
Sensor Network
4
Sensors?

• Also the biomedical sensors EMG, EKG, pulses,
  emotions, etc

camera
mic
gyro
accelerometer
pressure
GPS
thermal
5
Sensor Nodes Today
MICA, 2001-2002 5.7cm X 3.18cm 4 MHz CPU 128K
ROM 512K RAM 40kbps Radio range x00 feet Sensors,
battery not included
6
Embedded Sensor Node
Intel Xscale CPU Analog and digital radio Flash
and SRAM memory Sensors
7
Embedded Sensor Network
8
Applications

• Home, Office, Healthcare,
• Science Nature, Agriculture

9
Intel Digital Home
10
UCSB Bren School of Environmental Science and
Management

• Day-lighting controls
• Operable windows control
• taking advantage of on-site ocean breezes
• Airflow controls based on CO2 level

11
Long-Term Nursing Home
Camera

• Fall detection
• Vital sign monitoring
• Dietary/exercise monitoring

Orientation sensor
Pressure sensor
Accelerometers
Muscle activity
12
The Channel Islands Fox
13
Fancy Californian Winemaking

• Temperature
• Soil moisture
• Pest/disease

14
Taiwan Value!

• Home Security (Crime Rate)
• Industry IC (Energy crises)
• Healthcare Elder Care (?? Burden)
• Agriculture Orchid Growing (Big Export Business)

15
Our Applications

• Emergency Alarm (chronic disease)
• Location Tracking (cognitive declined)
• Background Music Player (just lonely)

16
The Elders Companion
MicaZ Sensor Node
Xscale-based Embedded System
Full-Fledged Server
17
Idea of Emergency Detection
Activity Level
Normal
18
Prototype
Front
Back
Courtesy Steven Chiu
19
Neck

1. The value level does not imply the level of
   activity
2. Going for the difference to distinguish active
   vs. inactive periods
3. Microphone is useful detecting conversation

Microphone
Accelerometer - x
Accelerometer - y
20
Converting to Activity Level
Wavelet decomposition to extract the difference
f(x)
Activity_level(xk) avg(f(xi), ik-4,k)
x
21
Sensor Network Design

• Major Difference to other Networks
• 1. Wireless Technology
• 2. Communication Model

22
Wireless Technologies
Mote PicoNet IEEE 802.15.4 bluetooth WLAN
Range x m x m 10 m 100 m
Max BW 40 kbps 224 kbps 723 kbps 11Mbps
Band 310, 433, 868/916MHz 868-928 MHz, 2.4GHz 2.4GHz 2.4GHz
Power (data mode) (1mA) (10mA) 30mA 250mA
23
Communication Models

• Address centric (for example IP)
• Name the nodes
• Data disseminated by the destination address
• Data centric
• Nodes desiring data expressing interest of
  content
• Data disseminated based on the content

24
IP Communication (Address Centric)

• Organize system based on named nodes
• Per-node forwarding state
• Senders need to push data to the node address of
  sink

To Bob My name is Alice. I am a 19-yr old girl
To Bob My name is Alice. I am a 19-yr old girl
To Bob My name is Alice. I am a 19-yr old girl
To Alice I am Bob Tell me about you
To Alice I am Bob Tell me about you
To Alice I am Bob Tell me about you
Like to meet girls
I am Bob
I am Bob
I am Bob
I am Alice
I am Alice
I am Bob
I am Alice
Web search -gt Alice
Bob
Chris
Alice
25
Data-Centric Communication

• Organize system based on named data
• Per-data diffusion state
• Sinks need to be specific about what data theyd
  pull

Tell me about girls
Tell me about girls
Tell me about girls
Heres a 19-yr old girl
Heres a 19-yr old girl
Tell me about girls
Heres a 19-yr old girl
26
The Need to Go Data Centric

• Node addressing not scalable
• Difficulty of configuring/tracking small nodes
  scattering around
• Cost of re-configuration of nodes moving around
• Server infrastructure not efficient
• Difficulty of deployment
• DNS and search engines in sensor networks?
• The sum of maintenance traffic (energy)
• Additional delay
• Information might be outdated by the time of
  communication

27
Research Topics

• Data dissemination (Routing)
• Load balancing
• Service differentiation
• Naming and forwarding
• Data aggregation
• Time synchronization
• Energy efficient MAC

28
Data Dissemination

• The problem
• Setting up content-based states of interest to
  direct data to the rightful destination
• Naive dissemination may result in unreliable or
  long-delay in delivery
• Challenge
• Medical data are mission critical
• The approach
• Shortest multiple paths
• Magnets and nails

29
Our Design

• Magnetic Diffusion

30
Medical Applications

• Mission-critical data
• Timeliness
• Reachability
• Average user
• Wireless sensor nodes a must
• Energy efficiency wireless communication

31
The Idea Magnetic Diffusion
32
Establishing Magnetic Field
33
Data Attracted by the Magnet
3
4
4
3
2
1
2
3
34
Reachability
35
Overhead
36
Latency-Base
37
Latency-Mobility
38
Latency-On Off
39
Major Findings

• Going for timeliness
• Magnetic diffusion, flooding, opp
• Going for reachability
• Flooding, magnetic diffusion, opp
• Going for energy efficiency
• Opp, magnetic diffusion, flooding

40
Best for Medicare Applications

• Healthcare applications
• Mission-critical data
• Require timeliness, reachability
• Average user
• Energy efficiency
• Magnetic diffusion
• The solution to offer all the QoS required

41
Load Balancing

• The problem
• Setting up content-based states of interest to
  direct data to the rightful destination
• Naive dissemination may result in short network
  lifetime
• Uniqueness
• Co-existence of static and mobile sensor nodes
• The approach
• Mobility and power aware data diffusion
• Mobile, power limited nodes avoid propagating
  interests

42
Service Differentiation

• The problem
• The network delay could be long when the demand
  is high
• The reliability might not be perfect
• Unique problem to health care
• Some data could be life-death critical
• The approach
• 2 class differentiation
• Urgent data going flooding and high priority in
  forwarding
• Non-urgent data going magnetic diffusion and
  regular forwarding

43
System Architecture
Urgent Data Dissemination Service
Non-urgent Data Dissemination Service
Services
Broadcast
Adaptive Diffusion
Regular
Priority
Core
Routing
Forwarding
44
Naming and Forwarding

• The problem
• Addressing data by the content
• Looking up all possible states of interest to
  find the matching entry to direct data
• A real problem to sensor network for
• Different data types
• If not handled well, could be a serious
  performance problem
• The approach
• Efficient string matching algorithms for limited
  device

45
Two Algorithms
Searching Time Preprocessing Time Space
Suffix tree O(P) O(S) O(S)
TST O(log(N)P) Nlog(N)O(S) O(S)
Note N means the number of words S
means the total length of words P means
the length of input testing string
46
Research Topics

• Data dissemination (Routing)
• Load balancing
• Service differentiation
• Naming and forwarding
• Data aggregation
• Time synchronization
• Energy efficient MAC

47
Data Aggregation

• Concept
• Intermediate nodes have the computation power to
  process and aggregate the data passing through
• Achieve data reduction
• Why do we need to aggregate?
• Energy is a critical problem in sensor networks

48
Without In-Network Processing

• Data are simply passed on

Tell me about girls
Tell me about girls
Tell me about girls
49
With In-Network Processing

• Data are aggregated and then passed on

Herere two 19 yr old girls
Herere two 19 yr old girls
50
Potential 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.
51
Our Design

• Data Aggregation for Resource Inventory
  Applications

52
Resource Inventory

• Ecologist tracking the amount of animals over a
  certain area
• Retailers tracking the amount of in-stock
  merchandizes
• Factories tracking the amount of parts

53
The Old vs. New Way

• Sampling inferencing
• Sampling
• Inferencing the population statistically
• Issues
• Bias
• Never sure what the real population is
• Sensor network monitoring
• Each sensor reporting location of objects
• Count the number of distinct locations

54
Our Strategy

• Minimizing the energy consumption
• Send object counts within each sensors sensing
  range
• Aggregate location data right at the beginning of
  transmission
• One data per sensor, it scales well with
  increasing object population

55
Energy Dissipation

• When the population increases

56
Population Estimation

• A problem still exists
• how to get the population from the object count
• Cannot just sum up counts
• We could find the tight lower and upper bounds
• Given the location of the sensor
• Given the radius of the sensing range

57
Lower Bound Computation

• Find sets of disjoint regions
• Sum the counts
• The population is at least this much
• Get the maximal of these total counts
• Equivalent to the
• maximal independent set problem in graph algorithm

58
Upper Bound Computation

• Similar,
• But finding the set of regions that cover the
  entire area
• Sum the counts
• The population is no more than this
• Get the minimal of these total counts
• Equivalent to the
• Minimal coverage set problem in graph algorithm

59
Estimated Population
60
Estimation Range

• Upper bound remains on 7080, and lower bound
  remains on 30

61
Discussions

• The lower and upper bounds include all possible
  population, not a statistical result
• Because the estimation range is steady, possibly
  we could infer the exact population by lower and
  upper bound.
• The estimation range would strongly depend on the
  deployment of sensors

62
Summary

• What more convenience can technologies bring?
• Context-aware and intelligent services anywhere
  and anytime
• We need small devices with processing, sensing,
  and communication capabilities (SOCs)
• See communication?
• We need to design all over again how to network
  these SOCs

63
Questions?

• Polly Huang
• iSpace Labs
• EE GINM, NTU
• http//cc.ee.ntu.edu.tw/phuang