Introduction to wireless sensor networks

Wireless sensor networks
Shigeng Zhang
sgzhang@csu.edu.cn

2
General Overview
 Introduction to Wireless Sensor Networks
 Data Dissemination and Routing Protocols
 Data Gathering
 Locationing and Coverage
 Testbeds/Applications
 Security in Wireless Sensor Networks
 Summary & Discussion

3
Motivation
 GOAL: Deeply Networked Systems or Pervasive
Networking
 98% of all processors are not in traditional desktop
computer systems, but in house-hold appliances,
vehicles, and machines on factory floors
 Add reliable wireless communications and sensing
functions to the billions of physically embedded
computing devices to support ubiquitous networked
computing
 Distributed Wireless Sensor Networks is a collection
of embedded sensor devices with networking
capabilities

5
Background , contd.
 Sensors
 Enabled by recent advances
in MEMS technology
 Integrated Wireless
Transceiver
 Limited in
 Energy
 Computation
 Storage
 Transmission range
 Bandwidth
Battery
Memory
CPU
Sensing Hardware
Wireless
Transceiver

8
Sensors (contd.)
 The overall architecture of a sensor node
consists of:
 The sensor node processing
subsystem running on sensor node
main CPU
 The sensor subsystem and
 The communication subsystem
 The processor and radio board includes:
 TI MSP430 microcontroller with
10kB RAM
 16-bit RISC with 48K Program Flash
 IEEE 802.15.4 compliant radio at 250
kbps
 1MB external data flash
 Runs TinyOS 1.1.10 or higher
 Two AA batteries or USB
 1.8 mA (active); 5.1uA (sleep)
Crossbow Mote
TPR2400CA-TelosB

9
Overall Architecture of a sensor node
Appl i cat i on Layer
Net work Layer
M
AC Layer
Physi cal Layer
Com
m
uni cat i on
SubSyst em
W
i rel ess Channel
Sl ow Seri al Li nk
Sensor
Sensor Node CPU
Radi o Board
Forward Packet Pat h

10
Wireless Sensor Networks (WSN)
 Distributed collection of networked sensors

11
Networked vs. individual sensors
 Extended range of sensing:
 Cover a wider area of operation
 Redundancy:
 Multiple nodes close to each other increase fault tolerance
 Improved accuracy:
 Sensor nodes collaborate and combine their data to
increase the accuracy of sensed data
 Extended functionality:
 Sensor nodes can not only perform sensing functionality,
but also provide forwarding service.

12
Applications of sensor networks
 Physical security for military operations
 Indoor/Outdoor Environmental monitoring
 Seismic and structural monitoring
 Industrial automation
 Bio-medical applications
 Health and Wellness Monitoring
 Inventory Location Awareness
 Future consumer applications, including smart
homes.

13
Applications, contd.
ALERT
Beam Formation
cooperative
ALERT
COM
MAND LEVEL
SENS
ING
COM
MUNICATIO
N
THREAT
processing
M
U
L
T
I
-H
O
P
cooperative
signalling
THREAT

15
Characteristics and challenges
 Deeply distributed architecture: localized coordination to reach
entire system goals, no infrastructure with no central control
support
 Autonomous operation: self-organization, self-configuration,
adaptation, exception-free
 TCP/IP is open, widely implemented, supports multiple
physical network, relatively efficient and light weight, but
requires manual intervention to configure and to use.
 Energy conservation: physical, MAC, link, route, application
 Scalability: scale with node density, number and kinds of
networks
 Data centric network: address free route, named data,
reinforcement-based adaptation, in-network data aggregation

16
Challenges, contd.
 Challenges
 Limited battery power
 Limited storage and computation
 Lower bandwidth and high error rates
 Scalability to 1000s of nodes
 Network Protocol Design Goals
 Operate in self-configured mode (no infrastructure
network support)
 Limit memory footprint of protocols
 Limit computation needs of protocols -> simple, yet
efficient protocols
 Conserve battery power in all ways possible

17
WSN vs. MANET
 Wireless sensor networks may be considered a subset
of Mobile Ad-hoc NETworks (MANET).
 WSN nodes have less power, computation and
communication compared to MANET nodes.
 MANETs have high degree of mobility, while sensor
networks are mostly stationary.
 Freq. node failures in WSN -> topology changes
 Routing protocols tend to be complex in MANET, but
need to be simple in sensor networks.
 Low-power operation is even more critical in WSN.
 MANET is address centric, WSN is data centric.

18
Why not port Ad Hoc Protocols?
 Ad Hoc networks require significant amount of
routing data storage and computation
 Sensor nodes are limited in memory and CPU
 Topology changes due to node mobility are infrequent
as in most applications sensor nodes are stationary
 Topology changes when nodes die in the network due to
energy dissipation
 Scalability with several hundred to a few thousand
nodes not well established
 GOAL: Simple, scalable, energy-efficient protocols

19
Focus: Radio Transceiver Usage
 The wireless radio transceiver is typically in three modes:
 Transmit – Maximum power consumption
 Receive
 Idle
 Turned off – Least power consumption
 Sensor node exists in three modes: Active, standby, and battery
dead
 Turnaround time: Time to change from one mode to another
(esp. important is time from sleep to wakeup and vice-versa)
 Protocol design attempts to place node in these different modes
depending upon several factors
 Sample power consumption from 2 sensor nodes shown next

20
Rockwell Node (SA-1100 proc)
MCU Mode Sensor Mode Radio Mode Power(mW)
Active On Tx(36.3mW) 1080.5
Tx(13.8mW) 942.6
Tx(0.30mW) 773.9
Active On Rx 751.6
Active On Idle 727.5
Active On Sleep 416.3
Active On Removed 383.3
Active Removed Removed 360.0
Sleep On Removed 64.0

21
UCLA Medusa node (ATMEL CPU)
MCU Mode Sensor Radio(mW) Data rate Power(mW)
Active On Tx(0.74,OOK) 2.4Kbps 24.58
Tx(0.74,OOK) 19.2Kbps 25.37
Tx(0.10,OOK) 2.4Kbps 19.24
Tx(0.74,OOK) 19.2Kbps 20.05
Tx(0.74,ASK) 19.2Kbps 27.46
Tx(0.10,ASK) 2.4Kbps 21.26
Active On Rx - 22.20
Active On Idle - 22.06
Active On Off - 9.72
Idle On Off - 5.92
Sleep Off Off - 0.02

22
Energy conservation
Physical layer
• Low power circuit(CMOS, ASIC) design
• Optimum hardware/software function division
• Energy effective waveform/code design
• Adaptive RF power control
MAC sub-layer • Energy effective MAC protocol
• Collision free, reduce retransmission and transceiver on-times
• Intermittent, synchronized operation
• Rendezvous protocols
Link layer
Network layer
Application layer
• FEC versus ARQ schemes; Link packet length adapt.
• Multi-hop route determination
• Energy aware route algorithm
• Route cache, directed diffusion
• Video applications: compression and frame-dropping
• In-network data aggregation and fusion
See Jones, Sivalingam, Agrawal, and Chen survey article in ACM WINET, July 2001;
See Lindsey, Sivalingam, and Raghavendra book chapter in Wiley Handbook of Mobile Computing,
Ivan Stojmenovic, Editor, 2002.

24
Network Architectures
Layer 1
Layer 2
Layer 3
Layered
Architecture
Base
Statio
n
Clustered
Architecture
Base
Statio
n
Larger Nodes denote Cluster Heads

25
Clustered network architecture
Ti er 1
Ti er 0
Ti er 2
Ti er 1
Ti er 0
 Sensor nodes autonomously form a group called clusters.
 The clustering process is applied recursively to form a hierarchy of clusters.

26
Cluster architecture (contd.)
( ( ) )
( ( ) )
( ( ) )
( ( ) )
( ( ) ) ( ( ) )
( ( ) )
( ( ) )
( ( ) )
( ( ) )
Base St ati on
Cl ust er- head
Cl ust er-head
Cl uster-head
Sensor
Cl uster
Cl ust er
Cl ust er
 Example - LEACH protocol
 It uses two-tier hierarchy
clustering architecture.
 It uses distributed
algorithm to organize the
sensor nodes into clusters.
 The cluster-head nodes
create TDMA schedules.
 Nodes transmit data during
their assigned slots.
 The energy efficiency of
the LEACH is mainly due
to data fusion.

27
Layered Network Architecture
 A few hundred sensor nodes
(half/full duplex)
 A single powerful base-station
 Network nodes are organized into
concentric Layers
 Layer: Set of nodes that have the
same hop-count to the base-
station
 Additional Mobile Nodes
traversing the network
 Wireless Multi-Hop
Infrastructure Network
Architecture (MINA)
A 10 node sensor network depicting cluster of node 3;
there are 2 mobile nodes

28
MINA, contd.
 Set of wireless sensor nodes create an infrastructure –
provide sensing and data forwarding functionality
 Mobile soldiers with hand-held units access the sensors
for data and also to communicate with a remote BS
 BS is data gathering, processing entity and
communication link to larger network
 Shorter-range, low-power transmissions preferred for
covert operations and to conserve power

29
Data Dissemination Architectures and
Protocols

30
Data Dissemination
 In ad hoc networks, traffic is peer-to-peer
 Multi-hop routing is used to communicate data
 In WSN, other traffic models are possible:
 Data Collection Model
 Data Diffusion Model
 Data Collection Model: Source sends data to a collection
entity (e.g. gateway): periodically or on-demand
 Data Diffusion Model:
 Source: A sensor node that generates data, based on its
sensing mechanisms’ observations
 Event: Something that needs to be reported, e.g. in
target detection; some abnormal activity
 Sink: A node, randomly located in the field, that is
interested in events and seeks such information

32
Data Diffusion: Concept
Sources
Sink 1
Sink 2

33
Diffusion: Basics
 Data-centric vs. address centric architecture
 Individual network address is not critical; Data is important
and is accessed as needed
 User can pose a specific task, that could be executed by sensor
nodes
 Concept of Named Data: (Attribute, Value) Pair
 Sink node requests data by sending “interests” for data
 Interests are propagated through the network, setting up
gradients in the network, designed to “draw” data
 Data matching the interest is then transmitted towards the sink,
over multiple paths (obtained by the gradients
 The sink can then reinforce some of these paths to optimize

34
Diffusion Basics, contd.
 Design Issues:
 How does a sink express its interest in one or more
events?
 How do sensor nodes keep track of existing interests
from multiple sinks?
 When an event occurs, how does data get propagated
from source(s) to sink(s)?
 Can in-network data processing (e.g. data fusion), data
aggregation and data caching help improve
performance?

35
Diffusion Basics, contd
 Example Task
 {Type = Animal; Interval = 20ms; Time = 10s;
 Region = [-100, 100, 200, 400] }
 The above task instructs a sensor node in the
specified region to track for animals; If animal is
tracked/detected, then send observations every 20
ms for 10s
 The above task is sent via interest messages and
all sensor nodes register this task.
 When a node detects an event, it then constructs a
Data Event message

36
Diffusion: Basics, contd
 Data Event Example:
 {Type = Animal; Instance = Tiger;
 Location = [101, 201]; Intensity = 0.4;
 Confidence = 0.8; Timestamp = 2:51:00}
 Interests and Gradients:
 For each active task that a sink is interested in:
 Sink broadcasts interest to its neighbors
 Initially, to explore, it could set large interval (e.g 1s)
 Sink refreshes each interest, using timestamps
 Each sensor node maintains an interest cache
 Interest aggregation is possible

37
Diffusion: Interests
 When a node receives an interest, it:
 Checks cache to see if an entry is present.
 If no entry, creates an entry with a single gradient to
neighbor who sent this interest
 Gradient specifies the direction and data rate.
 Resend interest to a subset of its neighbors
 This is essentially flooding-based approach
 Other probabilistic, location-based and other intelligent
forwarding approaches possible
 Similar to multicast tree formation, at sink instead
of at source

38
Diffusion: Interest Propagation
Sources
Sink 1
Sink 2

39
Diffusion: Data Propagation
 When a sensor node detects a target, it:
 Searches interest cache for matching entry
 If found, computes highest requested event rate among
its gradients
 Instructs sensor sub-system to generate data at this rate
 Sends data to neighbors on its gradient list
 Intermediate nodes maintain a data cache
 Caches recently received events
 Forwards event data to neighbors on its gradient list, at
original rate or reduced rate (intelligently)

40
Diffusion: Reinforcement
 When sink gets an event notification, it:
 Picks a suitable set of neighbor(s) (best link, low delay,
etc.) and sends a refresh interest message, with higher
notification rate (e.g. every 10 ms instead of every 1s)
• This will prune some of its neighbors (since interests in a
node’s cache will expire)
 Each selected neighbor forwards this new interest to a
subset of its neighbors; selecting a smaller set of paths
 Negative reinforcement also necessary to de-select
weaker paths if a better path found.

42
Problem Definition
 Objective: Transmit sensed data from each sensor node to a base station
 One round = BS collecting data from all nodes
 Goal is to maximize the number of rounds of communication before nodes die
and network is inoperable
 Minimize energy AND reduce delay
 Conflicting requirements
Sensor Nodes
Base station

43
Energy*Delay metric
 Why energy * delay metric?
 Find optimal balance to gather data quickly but in an
energy efficient manner
 Energy = Energy consumed per round
 Delay = Delay per round (I.e. for all nodes to send
packet to BS)
 Why is this metric important?
 Time critical applications

44
Direct Transmission
 Direct Transmission
 All nodes transmit to the base station (BS)
 Very expensive since BS may be located very far away and
nodes need more energy to transmit over longer distances
• Farther the distance, greater the propagation losses, and hence higher the
transmission power
 All nodes must take turns transmitting to the BS so delay is high
(N units for a N-node network)
 Better scheme is to have fewer nodes transmit this far distance
to lower energy costs and more simultaneous transmissions to
lower delay

45
LEACH
 Low Energy Adaptive Clustering Hierarchy
 Two-level hierarchy
Base
Station
Larger Nodes denote Cluster Heads

46
Scheme #1: PEGASIS
 Goals of PEGASIS (Power-Efficient GAthering
for Sensor Information Systems)
 Minimize distance nodes must transmit
 Minimize number of leaders that transmit to BS
 Minimize broadcasting overhead
 Minimize number or messages leader needs to receive
 Distribute work more equally among all nodes

47
PEGASIS
 Greedy Chain Algorithm
 Start with node furthest away from BS
 Add to chain closest neighbor to this node that has not
been visited
 Repeat until all nodes have been added to chain
 Constructed before 1st round of communication and
then reconstructed when nodes di
 Data fusion at each node (except end nodes)
 Only one message is passed at every node
 Delay calculation: N units for an N-node network
 Sequential transmission is assumed

50
Scheme #2: Binary Scheme
 Chain-based as described in PEGASIS
 At each level node only transmits to another node
 All nodes receiving at any level rise to the next
level
 Delay: O(log2 N)
Step 4: c3  BS
Step 3: c3 c7
Step 2: c1 c3 c5 c7
Step 1: c0c1 c2c3 c4c5 c6c7

 51
 Scheme # 3:Chain-based 3 level
 For non-CDMA sensor nodes, binary scheme is not
logical
 Construct chain as described in PEGASIS
 Divide chain into 10 groups (for the 100-node)
 To space out simultaneous transmissions to minimize
interference
 In each group, nodes will transmit one at a time
 Finally, one node out of each group at each level will
contain all the data and will rise to the next level until
finally the leader will transmit to the BS
 Total delay = 15 units (9+4+1+1) for 100-nodes

52
Chain-based 3 level scheme
 Third Level
 Two nodes rise to top and non-leader transmits to leader
 Leader transmits to BS
c18  BS
c18c68
c8 c18c28c38c48 c58 c68 c78 c88c98
c1c2…c7c8c9 c10c11…c18c19 …c90 c91…c98 c99

53
Localization (Location Discovery)
Algorithms

54
Location Information
 It is essential, in some applications, for
each node to know its location
 Sensed data coupled with loc. data and sent
 We need a cheap, low-power, low-weight,
low form-factor, and reasonably accurate
mechanism
 Global Positioning Sys (GPS) is not always
feasible
 GPS cannot work indoors, in dense foliage, etc.
 GPS power consumption is very high
 Size of GPS receiver and antenna will increase
node form factor

55
Indoor Localization
 Use a fixed infrastructure
 Beacon nodes are strategically placed
 Nodes receive beacon signals and measure:
 Signal Strength
 Signal Pattern
 Time of arrival; Time difference of arrival
 Angle of arrival
 Nodes use measurements from multiple beacons
and use different multi-lateration techniques to
estimate locations
 Accuracy of estimate depends on correlation
between measured entity and distance

56
Indoor Localization
 Examples of Indoor Loc. Systems
 RADAR (MSR), Cricket (MIT), BAT (AT&T), etc.
 Some approaches require a priori signal
measurement and characterization and
database creation
 Node obtains distance estimate by using
database
 Not always practical to have database loaded in
the individual node; only some nodes (e.g.
gateway) might carry it.

57
Sensor Net. Localization
 No fixed infrastructure available
 Prior measurements are not always possible
 Basic idea:
 Have a few sensor nodes who have known location
information
 These nodes sent periodic beacon signals
 Other nodes use beacon measurements and
triangulation, multi-lateration, etc. to estimate
distance
 Following mechanisms presented in Savvides
et. al. in ACM MobiCom 2001

58
Sensor Net. Localization, contd.
 Receiver Signal Strength Indicator (RSSI) was
used to determine correlation to distance
 Suitable for RF signals only
 Very sensitive to obstacles, multi-path fading,
environment factors (rain, etc.)
 Was not found to have good experimental
correlation
 RF signal had good range, few 10metres
 RF and Ultrasound signals
 The beacon node transmits an RF and an
ultrasound signal to receiver
 The time difference of arrival between 2
signals is used to measure distance
 Range of up to 3 m, with 2cm accuracy

59
Localization algorithms
 Based on the time diff. of arrival
 Atomic Multi-lateration:
 If a node receives 3 becaons, it can determine its
location (similar to GPS)
 Iterative ML:
 Some nodes not in direct range of beacons
 Once an unknown node estimates its location, will
send out a beacon
 Multi-hop approach; Errors propagated
 Collaborative ML:
 When 2+ nodes cannot receive 3 beacons (but can
receive say 2), they collaborate

60
Multi-lateration examples
Beacon Nodes
Unknown Nodes
Beacon Nodes
Unknown Nodes

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