2.  Large scale sensor networks are only recently emerging with a
large spectrum of applications .
 Distributed relaying will be shown to decrease the power
consumption per relaying sensor node.
Figure 12: Distributed relaying sensor network
for fire detection in forests.

3. © 2003 –2004 byYu Hen Hu 3
 Smart sensors
 Transducers
 Power
 On-board processor, storage
 Wireless transceivers
 Ad hoc network
 No predefined, fixed network
configuration
 Transmit, receive, and relay
information
 Wireless communication
 Radio, infrared, optical, and
other modalities
 Vision
 Smart environment:
▪ Monitoring
▪ Control, interaction
 Large number of low cost
sensor nodes deploy-n-play,
self-configuration to form
network, Collaborative in-situ
information processing
 Applications
 Environmental monitoring
 Civil structure/earth quake
monitoring
 Premises security
 Machine instrument diagnosis
 Health care

4.  wireless sensor prototype byWang et al.
(2005).

5. 5
Wireless Sensor Networks
Sensing and
Processing Unit
WirelessTransceiver
Ad Hoc Network
Topology
Battlefield surveillance, disaster relief, border
control, environment monitoring, … etc.

6. 6
1,1x
Manager
node
2,1x 1,2x 2,2x
1,3x 2,3x
• Data fusion
– Feature vectors from different node
measurements are combined
– Higher computational burden since
higher dimensional (vector) data is
jointly processed
– Higher communication burden
– Larger training data requirement
1,1x
Manager
node
2,1x 1,2x 2,2x
1,3x 2,3x
• Decision fusion
– Decisions (hard or soft) based on
node measurements are combined
– Lower computational burden since
lower dimensional data (scalar
decisions) is jointly processed
– Lower communication burden
– Lower training data requirement

7. Two Extremes
destination
Code and communicate all data to a
central point for processing and
analysis
Local processing and communication
between nodes; communicate result
to central point
data estimate
destination

8. Physical Limitations
Low density network
• low bandwidth/energy
consumption
• low spatial resolution
High density network
• high bandwidth/energy
consumption
• high spatial resolution

9. KeyQuestions
How dense should we sample ?
What are the transmission rate limitations
for a given network density ?
What are the energy/power requirements
for a given network density ?
What accuracy is achievable under bandwidth
and energy constraints ?

10. Signal + Noise Model
noiseless field noisy sensor
measurements
1. D(n): Achievable accuracy using n sensors ?
2. E(n): Energy required to transmit data or estimates ?
3. How do accuracy and energy scale with node density ?

11. MSE-EnergyAnalysis
higher density
higher resolution
more averaging
higher density
more data
more communication

12. Ex: Estimating a PiecewiseConstant Field

13. Estimation
n sensors
each makes a noisy
measurement of the
field at its location
(e.g., each contaminated
with Gaussian noise)

14. Energy and Communication
Goal: transmit a good
estimate of field to upper
left corner via multi-hop
communication

15. HierarchicalComm and Data Processing
• hierarchical pyramid structure for sensor network
comm and data handling (Ganesan, Estrin, Heidiman ’02
Madden et al ’02, Hellerstein et al ‘03)

16. HierarchicalCommunication

17. Hierarchical Data Fusion

18. Estimation in Action

19. FIGURE
Sensor network protocol stack. (Reprinted from Akyildiz, I.F. et al., Computer Networks,
Vol. 38, 393–422, 2002. With permission.)

20.  Sensor Nodes:sense target events, gather sensor readings,
manipulate informations, send them to gateway via radio
link
 Base station/sink: communicate with sensor nodes and
user/operator, (database-stores the data)
 Operator/user: task manager, send query

21. Task Management Plane
Mobility Management Plane
Power Management Plane
Application Layer: middleware, OS
Network Layer: Routing
Application Layer
Transport Layer
Network Layer
Data Link Layer
Physical Layer

22.  State of the art routing protocols are
distributed and reactive : the systems start
looking for a route only when they have
application data to transmit
 We study here Ad hoc On demand Distance
Vector (AODV) and Dynamic Source Routing
(DSR) for the sensor network

23.  Route Discovery
A node sends a Route Request message to all of its neighbours.
Any node receiving such a request, either answers to it or rebroadcasts it.
The procedure finishes either when the request sender has received the
route information, or when the request times out.
 With AODV, each node remembers the next hop information associated with
the destination.The route knowledge itself is distributed in the
network.
 With DSR, the complete route is sent to the route requester.
 Message transmission
 With AODV, the message is sent to the next hop as recorded in the routing
table, and this procedure is repeated at each hop.
 With DSR, the message is sent with its complete route as header.

24.  Rumor Routing
 "Rumor Routing Algorithm for Sensor Network"
by Braginsky and Estrin
 How to make information available in a sensor
network
 Assumption: sense particular eventt when
requested, don't know the existence or the
location of the event

25.  An event sends out agents which
travel the network from node to
node on a random path.Each visit
leaves information about the event
in the node's database.After a
predefinedTTL the agent stops
 A requester also sends out an agent.
After some time it will hopefully
come across the path of the
information agent by checking the
node's databases. It can then travel
the backward references the first
agents left in the nodes to reach the
event.

26.  Critical review
+ Only a small number of nodes have to adopt the
same information
+ Only a small number of nodes have to process the
request When or whether requested information
can be delivered is a random process.
-The failure of nodes can interrupt the path to the
event (depending on how broad it is).
-The actual behavior of a node is very different from
what is shown in the former slides

27.  Sensing: sensor --a
transducer that converts a
physical, chemical, or
biological parameter into
an electrical signal
 Processing:
microprocessor(CPU)
data storage(Mem)
AD converter
 Communicating: data
transceiver(Radio),
 Energy source: battery

28. 68HC11
Node
Specific
OS Modules
Middleware
Middleware
Sensor
Driver
Node
Specific
OS Modules
Hardware Sensor
Middleware
ARM
Temperature
Sensor
Sensor
Driver
Node
Specific OS
Modules
68HC11
Pressure
Sensor
Node
Specific
OS Modules
Sensor
Driver
Middleware
Sensor Node
Model
• 68HC11 µC
• No Sensor
• ARM Microcontroller
• Temperature Sensor
• 68HC11 Microcontroller
• Pressure Sensor

30. Picoradio
(UCB)
WINS
(UCLA)
Smart
Dust
(UCB)
Sensor,
Actuator
Battery
Processor HF

31. Characteristics of Sensor Nodes
 Limited capacity of
 Battery (Lifetime: day - 10 years)
 Processing capabilities (10MHz)
 Transmission range (5 - 20 meters)
 Data rates: Bit/s - KB/s
 Transmission methods:
 802.11 (WiFi)
 Bluetooth – short distance, other applications
 ZigBee – for sensor network
 Price: some cents

32.  Storage
 persistent storage for data
streams
Integrity Service/
Access Control
Query Manager
Storage
Sensor Manager

33.  Query Manager
 manages active queries
 query processing
 delivery of events and
query results to
registered, local or
remote consumers
Integrity Service/
Access Control
Query Manager
Storage
Sensor Manager

34. Integrity Service/
Access Control
Query Manager
Storage
Sensor Manager
 Top layer: access
control and integrity
service
 OS examples:
 TinyOS: when an event
occurs, it calls the
appropriate event
handler to handle the
event.
 Others: Contiki,
MANTIS, and SOS.

35.  Create Hardware-optimized software
components (driver, operating system )
 Create hardware- independent software
components (middleware, services)
 Combining of predefined components
 Source code generation
 Removing unused components
 Optimizaion of interface
 Optimizaion to node's hardware
 Distribution of nodes in different environments
 Monitoring the execution
 Creation of logfiles
 Evaluation of logfiles
Components
Design & Edit
Complie/Link
Distribute
Execute/Administrate
Evaluation
Resource
Hardware
driven
Monitoring
Optimization

36. www.themegallery.com
2:Security
application
4:Medical
Application
1: Military
applications
3:Environmenta
l application
5:Commercial application
Location of combatants,
vehicles and weapons
on the battlefield
It may be used to monitor
patients from a distance
•Report a possible
outbreak of fire
•Detect dry areas
Detect movements of the earth
to predict earthquake
Facilitate stock
management
16/24

37. 17/24

38. 38

39.  Based on the IEEE 802.15.4 Standard
 Popular for WSN devices.
 ZigBee adds:
 Network topologies
 Interoperability with other wireless products
39

40.  TinyOS is a free and open source operating system.
 TinyOS is an embedded operating system written in the
nesC.
40

41.  Given:
 Field A
 N sensors
How well can the field be observed ?
 Closest Sensor (minimum distance) only
 WorstCase Coverage: Maximal Breach Path
 Best Case Coverage: Maximal Support Path
 Multiple Sensors: speed and path considered
Minimal Exposure Path

43.  Applications of Wireless Sensor Networking:
In the present era there are lot of technologies which are
used for monitoring are completely based on the wireless
sensor networking. Some of important applications are
environmental monitoring, traffic control application,
weather checking, regularity checking of temperature etc.
Wireless sensor networks can also be used for detecting
the presence of vehicles such as motor cycles up to trains.
These are some important wireless sensor networking
based technologies which help us in our daily life. Some of
there daily life applications are: used in agriculture, water
level monitoring, green house monitoring, landfill
monitoring etc.

44.  In studying the performance or a wireless sensor
network, you must take into consideration the
deployment scenario which includes; topology,
radio ranges, trajectory of targets and event
traffic, and trajectories of user nodes and query
traffic. All of these affect design trade-offs, and
therefore any algorithm or protocol chosen
should be evaluated under diverse deployment
scenarios.

45. © 2003 –2004 byYu Hen Hu 45
 Sensor network is a new application area for
computer vision, graphics and image
processing
 It requires multi-modality, multimedia
processing under the constraint of
minimizing communication and energy
consumption.

46.  Sensor Network can be used in many
applications, such as Military, Environmental
and Health…etc.
 Its characteristics are tiny node, low power,
limited resources, dynamic network topology
and various scales of network deployment.
 Middleware is used to connect the network
hardware, operating systems, network stacks,
and applications in different approaches.
 For examples,Virtual Machine, Mobile Agent,
Database and Message Oriented.

47.  Security in Sensor Networks.
 Public/Private Key
▪ Key establishment beyond sensor network capabilities.
 Shared Key
▪ Simple solution, but single node may reveal the secret key.
▪ Scalability? → each node stores n-1 keys (n(n-1) keys need to be
established)
 Solution?
 Privacy Aspects in Sensor Networks.
 Sensor technology may be used for illegal surveillance.
 Providing awareness of the presence of sensor nodes?
 Solution?

48.  FIGURE
Sensor signal processing flow.

49. 49
 ISI team experimented with three iPAQ-based
video sender nodes and collected video
baseline of several vehicles.
 RTP packet dumps andVHS video tape.
 VT team supported BBN integrated
experiment with Sensoria 2.0 nodes.
 UCLA ran developmental
experiments on sensor field
coverage algorithms (under
Sensorware project).

50. 50

51. 51

52. 52