Introduction to Sensor Networks
Rabie A. Ramadan, PhD
Cairo University
http://rabieramadan.org
rabie@rabieramadan.org
2
Do not think how hard the
problem you are solving
Just,
“keep your eyes on the
prize”
2
Hardware Platforms

Augmented General Purpose PCs
• Embedded PCs (PC104), PDAs, etc..
• Usually have O.S like Linux and wireless device
such as Bluetooth.

Dedicated Sensor Nodes
• Commercially off the shelf components (e.g.
Berkeley Motes)

System-on-chip Sensor
• Platform like Smart dust, PicoNode
3
Software Platforms






Operating Systems and Language Platforms
Typical Platforms are:
• TinyOS, nesC, TinyGALS, and Mote’
TinyOS
• Event Driven O.S.
• Requires 178 bytes of memory
• Supports Multitasking and code Modularity
• Has no file system – only static memory allocation
• Simple task scheduler
nesC – extension of C language for TinyOS- set of language constructs
TinyGALS - language for TinyOS for event triggered concurrent execution .
Mote’ - Virtual machine for Berkeley Mote
4
Wireless Sensor Network
Standards

IEEE 802.15.4 Standard
• Specifies the physical and MAC Layers for low-rate
WPANs
• Data rates of 250 kbps, 40 kbps, and 20 kbps.
• Two addressing modes: 16 - bit short and 64 - bit IEEE
addressing.
• Support for critical latency devices, for example, joysticks.
• The CSMA - CA channel access.
• Fully handshaking protocol for transfer reliability.
• Power management to ensure low - power consumption.
5
CSMA-CA Protocol
How it works?
6
Wireless Sensor Network
Standards

IEEE 802.15.4 Standard
• The physical layer is compatible with current
•
wireless standards such as Bluetooth
MAC layer implements synchronization , time slot
management , and basic security mechanisms.
7
Wireless Sensor Network Standards
IEEE 802.15.4 & ZigBee In Context
Customer
Application
API
– “the software”
Security
32- / 64- / 128-bit encryption
Network
ZigBee
Alliance
– Brand management
Star / Mesh / Cluster-Tree
IEEE 802.15.4
MAC
IEEE
802.15.4
PHY
868MHz / 915MHz / 2.4GHz
Silicon
Stack
– Network, Security &
Application layers
– “the hardware”
– Physical & Media Access
Control layers
App
8
ZigBee Utilization
security
HVAC
lighting control
access control
BUILDING
AUTOMATION
patient
monitoring
fitness
monitoring
CONSUMER
ELECTRONICS
TV
VCR
DVD/CD
remote
ZigBee
PERSONAL
HEALTH CARE
asset mgt
process control
environmental
energy mgt
Wireless Control that
Simply Works
INDUSTRIAL
CONTROL
RESIDENTIAL/
LIGHT
COMMERCIAL
CONTROL
mouse
keyboard
joystick
PC &
PERIPHERALS
security
HVAC
lighting control
access control
lawn & garden irrigation
9
Applications Example
10
Project ExScal: Concept of
operation
Put tripwires anywhere—in deserts, other areas where physical
terrain does not constrain troop or vehicle movement—to
detect, classify & track intruders [Computer Networks 2004,
ALineInTheSand webpage, ExScal webpage]
11
ExScal scenarios
Border Monitoring:

Detect movement where none
should exist ,

Decide target classes, e.g., foot
traffic to tanks

Ideal when combined with
towers, tethered balloons, or
UAVs
12
WSN Research Fields












Sensors HW and Software
Deployment
Physical , MAC, Routing, Applications
Data Aggregation and Data Mining
Artificial Intelligence and data handling
Self Healing
Web Integration
Heterogeneity
Security
Software Engineering (Simulators )
Cloud Computing and Sensor Networks
Mobility Issues and Localization
13
Assignment 1
 Report
the main security
considerations of IEEE 802.15.4 ?
14
Deployment, Clustering , and
and Routing in WSN
15
Deployment Constraints

Sensor characteristics

Monitored field characteristics

Monitored/probed object
16
Deployment Parameters
17
Deployment Parameters
Diffraction: passing the signal through small opening and
spreading it after passing the opening
Scattering: scatter the coming signal
Reflection : send the signal back towards the sender
18
Deployment Parameters
19
Deployment Parameters
20
Deployment Problems and
Solutions




Random Deployment
•
Virtual force Algorithm
Deterministic Deployment
•
•
•
Circle Packing
Energy Mapping
Movement-Assisted Sensor Deployment
Sink Placement Problem
•
•
Single node
Multiple sink deployment
Relay Node Placement in WSN
21
Random Deployment
Virtual Force Algorithm
22
Virtual Force Algorithm

Sensors are initially deployed randomly
Objective:

Assumptions:

• To maximize the Coverage
• Assume no prior knowledge about the monitored field
• All nodes are mobile
• Energy and obstacles might present in the field
23
Virtual Force Algorithm (Cont.)

Attractive and Repulsive forces

Sensors do not physically move

A sequence of virtual motion paths is determined for the randomly
placed sensors.

Once the effective sensor positions are identified, a one-time
movement is carried out to redeploy the sensors at these positions.
24
Virtual Force Algorithm
(Semi Distributed.)

Assumptions:
• Clustered network
• All clustered heads are able to communicate with the sink
node
• The cluster head is responsible for executing the VFA and
managing the one-time movement of sensors to the desired
locations.
25
Virtual Force Algorithm (Cont.)

Each sensor behaves as a “Source of force” for all other
sensors.

This force can be either positive (Attractive) or negative
(Repulsive).

The closeness and wide distance between two sensors are
measured using a predefined threshold.
26
Virtual Force Algorithm (Cont.)

Sensor Binary Model
• Consider an n by m sensor field grid and assume that there are k
sensors deployed in the random deployment stage.
• Each sensor has a detection range r. Assume sensor si is deployed at
point (xi , yi ).
• For any point P at (x, y), we denote the Euclidean distance between
si and P as d(si , P),
•
The coverage of a Grid Point P can be expressed by:
27
Virtual Force Algorithm (Cont.)

Virtual Forces
• Attraction force  F12
• Repulsive force  F13
• Zero Force  F14
• Obstacle Force 
• preferential coverage
Force

Total Force on node i =
28
Virtual Force Algorithm (Cont.)

Using such forces , the cluster head runs the VFA
After stability occurs , Sensors are ordered to
move to the new positions

Energy and Obstacles might be problems

• Any sensor will not be able to move the required
distance , the moving order is discarded
• Obstacles need an obstacle avoidance algorithm
29
Think…..

If some sensors are stationary, does this affect
the virtual force algorithm?

What other problems you see in the algorithm?
• Coverage might not be satisfied due to the limitation
•
in the energy since some nodes might not be able to
move to the specified place.
Mobility assumption might not be the case for all
WSNs
30
SENSOR REPLACEMENT BASED
ENERGY MAPPING
31
The problem






A set of sensors S is deployed in a monitored field F(A)for
a period of time T.
The field is divided into a grid of cells A.
Each cell is assigned a weight where represents the
importance of the cell i.
The location of each sensor is assumed known;
More than one sensor could be deployed in one cell.
Sensors are assumed heterogeneous in terms of their
energy and mobility.
32
Assumptions


A sensor could be in different states;
it could have its sensing off or on based on
the field monitoring requirements.
•
Sensing off, radio off
•
Sensing off, radio receiving
– (Receiving mode)
Sensing off, radio transmitting – (Routing mode)
Sensing on, radio receiving
– (Sensing and Receiving mode)
Sensing on, radio transmitting – (Sensing and Transmitting
mode)
Sensing on, radio off
- (Sensing mode)
•
•
•
•
– (sleep mode)
33
The main idea

Knowing the energy map
of the network :
•
•
•
•
•
May lead to early detection to the
uncovered areas.
Redeploy new sensors
Turn off some of the sensors due
to their coverage redundancy
Wake up some of the nodes
when needed
Move one or mobile nodes to
cover the required uncovered
spots
34
Redeployment based Energy
map

Step 1: Energy dissipation rate prediction
• Each sensor predicts its own energy rate based on its
history (e.g. Markov Chain ..)

Step 2: sensors send their initial energy and the
location, predicted energy dissipation rate to the sink
node through a cluster head.
•
Sensors update their energy dissipation rate based on a specific
threshold (if the new dissipation rate increased more than the
given threshold , the node sends the new dissipation rate)
35
Redeployment based Energy
map

Step 3: the sink node constructs the energy map based on
the received dissipated energy rate from the sensors.

The sink may move one of the mobile sensors to the
uncovered spot or wake up one of the sleeping sensors
36
Think …….

What are the disadvantages of energy
mapping algorithm ?
 Sensor network is an event based network . Therefore ,
events are not frequently or based on specific pattern.
Thus, the amount of messages to be transmitted to report
the energy mapping will not be expected and might play a
role in sensors energy dissipation.

Centralized algorithm
37
Movement-Assisted Sensor
Deployment
38
The problem of sensor
deployment

Given the target area, how to maximize the
sensor coverage with less time, movement
distance and message complexity

The importance of the problem
• Distributed instead of centralized
39
Voronoi Diagram

Definition:
• Every point in a
given polygon is
closer to the node in
this polygon than to
any other node.
40
Overview of the proposed
algorithm



Sensors broadcast their locations
construct local Voronoi polygons
Find the coverage holes by examining
Voronoi polygons
If holes exist, reduce coverage hole by
moving
and
• VOR : VORonoi-based
• Pull sensors to the sparsely covered area
41
Part of Assignment 1 (on CD
and a printed report)

Implement both Virtual Force algorithm and Voronoi based algorithm ? Report
your experience and algorithms efficiency?
 Given a set of sensors with limited amount of energy. Some of these sensors are

assumed mobile and others are assumed stationary. Assume similar sensing and
communication ranges for all sensors. Sensors are allowed to move from one
place to another iff they have enough energy to move to the required destination.
In addition , the borders of the monitored area is assumed known in terms of 2D
coordinates. Borders may be found in the monitored area. Advice a suitable
deterministic deployment algorithm for efficient deployment to the sensors given
that the deployed sensors have to be connected and important areas in the field are
covered. In addition , your algorithm must guarantee the coverage of the
monitored field for certain period of time.
You may look for an already given solution or come up with a convincing one
.
42
Deterministic Deployment
Deployment Using Circle Packing
43
Deployment Using Circle Packing

Deployment of homogenous sensors

Full Coverage Deployment

Deployment of connected
heterogeneous sensors
44
Deployment of homogenous
sensors
These results
were based on the
information presented
at “introduction to
circle packing” book
s
Sensing range
Density
1
0.500000000000
0.785398163397
2
0.292893218813
0.539012084453
3
0.254333095030
0.609644808741
4
0.250000000000
0.785398163397
5
0.207106781187
0.673765105566
6
0.187680601147
0.663956909464
7
0.174457630187
0.669310826841
8
0.170540688701
0.730963825254
9
0.166666666667
0.785398163397
14
0.129331793710
0.735679255543
16
0.125000000000
0.785398163397
25
0.100000000000
0.785398163397
36
0.083333333333
0.785398163397
45
Full Coverage Deployment
s
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Sensor’s sensing range (r)
0.70710678118654752440
0.55901699437494742410
0.55901699437494742410
0.35355339059327376220
0.32616058400398728086
0.29872706223691915876
0.27429188517743176508
0.26030010588652494367
0.23063692781954790734
0.21823351279308384300
0.21251601649318384587
0.20227588920818008037
0.19431237143171902878
0.18551054726041864107
0.17966175993333219846
s
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Sensor’s sensing range (r)
0.16942705159811602395
0.16568092957077472538
0.16063966359715453523
0.15784198174667375675
0.15224681123338031005
0.14895378955109932188
0.14369317712168800049
0.14124482238793135951
0.13830288328269767697
0.13354870656077049693
0.13176487561482596463
0.12863353450309966807
0.12731755346561372147
0.12555350796411353317
0.12203686881944873607
46
Sequential Packing-based
Deployment Algorithm (SPDA)

Given

Objective
• Sensors Sensing Ranges
• Sensors Communication Ranges
• Bounded Monitored Field
• Best Connected Deployment Scheme
• Max. Coverage.
• Min. Overlapped Areas
• Benefit from the properties learned from the optimal
deployment using circle packing
47
Sequential Packing-based
Deployment Algorithm
48
Sequential Packing-based
Deployment Algorithm
49
Potential Points
50
Think …..
How do you guarantee connectivity ?
51
Correctness of the Algorithm
52
Sink Re-Placement Problem
53
Potential benefits of sink
relocation
Increased network longevity: shortened data paths can safe the
total energy consumed to data collection and extend the life of
relaying nodes.
Improved timeliness: involves fewer relays leading to avoidance
of large packet backlogs
Enhanced safety: moves the sink away from harmful events
without damaging network performance
54
Energy-Based Relocation -Motivation
Normal Operational Mode:
Can repositioning
 Sensors pursue multi-hop paths to
the sink node
communicate with the sink node
help?
Issues:
 When the sink is stationary, nearby sensors To where ?
get involved in heavy packet forwarding and
die quickly
•
Sink node
Inactive Sensor
Active Sensor
One hop Sensor
Dead Sensor
Nodes further away are picked as substitute
relays
Consequence:
•
•
Increase in total transmission power  rapid
energy depletion
Effect grows spirally outward
55
Moving the Sink

Where to go
Towards the region,
whose sensors generate
the most number of
packets
Centroid of the lasthop nodes that route the
largest traffic (use a
distance * traffic
metric)
A
Sink is placed on
the dotted arrow
30
S
B
The Sink nod direction
is set to balance nodes
A and B's interest
18
6
C
56
Think….

What about putting the sink node initially
in the center of all nodes? Will this be the
best position for the sink node?

No , because sensor networks again are
event based networks
57
Part of your assignment

Device an algorithm for Multiple Sink Network Design
Problem in Large Scale Wireless Sensor Networks?

You may look at :
•
E. Ilker Oyman and Cem Ersoy, Multiple Sink Network Design Problem
in Large Scale Wireless Sensor Networks,, IEEE International
Conference on Communications, 2004
58
Relay Node Placement in WSN
Clustering Algorithms
59
Clustering Facts

Clustering plays a dominant role in delaying the first
node death, while aggregation plays a dominant role in
delaying the last node death

In each cluster one node acts as a cluster head which is
in charge of coordinating with other cluster heads
60
LEACH Algorithm

The LEACH Network is made up of nodes, some of
which are called cluster-heads
• The job of the cluster-head is to collect data from their
surrounding nodes and pass it on to the base station
• LEACH is dynamic because the job of cluster-head rotates

LEACH is considered as clustering and routing
protocol
61
The Amount of Energy Depletion

This is the formula for the amount of energy
depletion by data transfer:
62
LEACH’s Two Phases

The LEACH network has
two phases: the set-up phase
and the steady-state
• The Set-Up Phase
• Where cluster-heads are chosen
• The Steady-State
• The cluster-head is maintained
• Data is transmitted between
nodes
63
Stochastic Threshold Algorithm

Cluster-heads can be chosen stochastically
(randomly based) on this algorithm:

If n < T(n), then that node becomes a cluster-head
The algorithm is designed so that each node becomes
a cluster-head at least once.

64
Deterministic Threshold
Algorithm


A modified version of this protocol is known as
LEACH-C (or LEACH Centralized)
This version has a deterministic threshold
algorithm, which takes into account the amount
of energy in the node…
65
Think more …..

How to modify LEACH to include more
parameters such as node degree?

66
HEED: Hybrid Energy Efficient
Distributed Clustering
67/103
HEED: Hybrid Energy Efficient
Distributed Clustering

HEED was designed to select different cluster heads in a field
according to the amount of energy that is distributed in relation to
a neighboring node.

Four primary goals:
• prolonging network life-time by distributing energy
consumption
• terminating the clustering process within a constant number of
iterations/steps
• minimizing control overhead
• producing well-distributed cluster heads and compact clusters.
68
Heed Algorithm





Each node performs neighbor discovery, and broadcasts its cost to the detected
neighbors.
Each node sets its probability of becoming a cluster head, Chprob , as follows:
Where, Cprob is the initial percentage of cluster heads among n nodes (it was
set to 0.05),
Eresidual and Emax are the residual and the maximum energy of a node
(corresponding to the fully charged battery), respectively.
The value of CHprob is not allowed to fall below the threshold pmin .
69
Disadvantage (LEACH and
HEED) – think….

Nodes’ score is computed based on node identifiers , and each
node holds its message transmission until all its neighbors with
lower IDs have done so.

It is assumed that the network topology does not change during
the algorithm execution, and it is thus valid for each node to wait
until it overhears every higher-scored neighbor transmitting.
70
Think…

How to solve Heed’s problems?
71
HEED Assignment

Previous Algorithm is used with homogenous sensors (all have
the same characteristics ).

Device another clustering algorithm for heterogeneous WSN
(nodes with different capabilities) .

You may have a look at the following paper
•
Harneet Kour and Ajay K. Sharma, “Hybrid Energy Efficient Distributed
Protocol for Heterogeneous Wireless Sensor Network, ” International
Journal of Computer Applications (0975 – 8887) Volume 4 – No.6, July
2010
72
Mobility Resistant Clustering in
Multi-Hop Wireless Networks
--- Distributed Efficient Clustering Approach (DECA) ---
73
DECA

Each node periodically transmits a Hello message to identify itself, and based
on such Hello messages, each node maintains a neighbor list.

Define for each node the score function as:

Where E stands for the node residual energy, C stands for the node connectivity,
I stands for the node identifier, and the weights follow

The computed score is then used to compute the delay for this node to announce
itself as the cluster head. The higher the score, the sooner the node will transmit.
The computed delay is normalized between 0 and a certain upper bound Dmax

74
Think…

How mobility can affect DECA algorithm?

The connectivity parameter changes with
mobility and the node might be selected
as a cluster head multiple times

75
Multimodal Limited Similarity
Clustering (MFLC)
76
MFLC for single and
multimodal sensor networks

A single feature sensor network is a network with each sensor
node reports only one feature.

Multimodal sensor network is a network with nodes report more
than one feature.

MFLC adapts LEACH clustering technique to support the
multimodal sensor networks.

MFLC differs from the LEACH on the criteria used for a node to
decide to be a cluster head or not.
77
MFLC single and multimodal
sensor networks

Score Equation :
78
Data Similarity Clustering Based
Fuzzy Logic (DSBF)
79
DSBF

Phase One: Computing Node Degrees

Phase Two: Cluster Head Election

Phase Three: Data Reporting
80
Phase One: Computing Node
Degrees


The node degree based similarity feature is
computed
The node degree in this context means the
number of similar sensors around s  S
81
Phase Two: Cluster Head
Election

82
Fuzzy C-Means Clustering for
Efficient Operations in WSNs
83/103
Main idea
Instead of one cluster per node use multiple
clusters with different membership functions
84/103

Multilayer clustering example
85/103
Semi Distributed Clustering

Monitoring Nodes Clustering
86/103
Think

Can the percentage more than 100% ?
87/103
Routing in WSN
88

89
Flat Routing



Each node plays the same role
Data-centric routing
•
•
Due to not feasible to assign a global id to each node
Save energy through data negotiation and elimination of redundant data
Protocols
•
•
•
•
•
•
•
•
•
•
Sensor Protocols for Information via Negotiation (SPIN)
Directed diffusion (DD)
Rumor routing
Minimum Cost Forwarding Algorithm (MCFA)
Gradient-based routing (GBR)
Information-driven sensor querying/Constrained anisotropic diffusion routing
(IDSQ/CADR)
COUGAR
ACQUIRE
Energy-Aware Routing
Routing protocols with random walks
90
Sensor Protocols for
Information via Negotiation
(SPIN)
91/103
Sensor protocols for information
via negotiation (SPIN)


Features
• Negotiation
• to operate efficiently and to conserve energy
• using a meta-data
• Resource adaptation
• To extend the operating lifetime of the system
• monitoring their own energy resources
SPIN Message
• ADV – new data advertisement
• REQ – request for ADV data
• DATA – actual data message
• ADV, REQ messages contain only meta-data
92
Sensor protocols for information
via negotiation (SPIN)
•
Operation process
ADV
REQ
Step1
Step2
ADV
REQ
Step4
Step5
DATA
Step3
DATA
Step6
93
Sensor protocols for information
via negotiation (SPIN)



Resource adaptive algorithm
• When energy is plentiful
• Communicate using the 3-stage handshake protocol
• When energy is approaching a low-energy threshold
• If a node receives ADV, it does not send out REQ
• Energy is reserved to sensing the event
Advantage
• Simplicity
• Each node performs little decision making when it receives new
data
• Need not forwarding table
• Robust to topology change
Drawback
• Large overhead
• Data broadcasting
94
Think…. In SPIN

What about mobile nodes?

What about the multimodal Wireless
nodes?
95
Directed Diffusion (DD)
96/103
Directed Diffusion (DD)


Feature
• Data-centric routing protocol
• A path is established between sink node and source node
• Localized interactions
• The propagation and aggregation procedures are all based on local
information
Four elements
• Interest
• A task description which is named by a list of attribute-value pairs that
describe a task
• Gradient
• Path direction, data transmission rate
• Data message
• Reinforcement
• To select a single path from multiple paths
97
Interest Propagation



Flooding
Constrained or Directional flooding based on location.
Directional Propagation based on previously cached data.
Gradient
Source
Interest
Sink
Data Propagation

Reinforcement to single path delivery.

Multipath delivery with probabilistic forwarding.

Multipath delivery with selective quality along different paths.
Gradient
Source
Data
Sink
Directed Diffusion (DD)

Advantage
• Small delay
• Always transmit the data through shortest path
• Robust to failed path

Drawback
• Imbalance of node lifetime
• The energy of node on shortest path is drained faster than another
•
•
Time synchronization technique
• To implement data aggregation- paths change with interests
• Not easy to realize in a sensor network
The overhead involved in recording information
• Increasing the cost of a sensor node
100
Think…. In DD

What about mobile nodes?

What about the multimodal Wireless
nodes?
101
Comparison between SPIN, LEACH
& Directed Diffusion
Optimal
Route
Network
Lifetime
Resource
Awareness
Use of
meta-data
SPIN
LEACH
No
No
Directed
Diffusion
Yes
Good
Very good
Good
Yes
Yes
Yes
Yes
No
Yes
102
Minimum Cost Forwarding
Algorithm (MCFA)

Objective
• Establish the cost field
• Transmit the data through the minimum-cost path

Feature
• Optimality
• Minimum cost path criteria : hop count, energy consumption, delay
etc.
• Simplicity
• Need not to maintain forwarding table
• Need not to know an ID for a neighbor node
103
Minimum Cost Forwarding
Algorithm (MCFA)

Operation process
• Each node stores its cost to the sink
• The sink broadcasts an ADV message
• containing its own cost (0 initially)
• Each node receiving the message transmits neighbor node
• Add the cost in ADV message to its own cost
• The cost field is set up
• after the ADV message propagates through the network
• The source transmits an information through using the cost field
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Drawback
• Limited network size
• The time to set the cost field is directly proportional to the size of
the network
• Load is not balanced
104
Think….

What about mobile nodes?

What about the multimodal Wireless
nodes?
105
Geographic Adaptive Fidelity (GAF)





Forms a virtual grid of the covered area
Each node associates itself with a point in the grid
based on its location
Nodes associated with same point in grid are
considered equivalent
Some nodes in an area are kept sleeping to conserve
energy
Nodes change state from sleeping to active for load
balancing
106
Creating a Virtual Grid





Use location information (GPS) to
create a virtual grid
All nodes in a grid are equivalent
Only one node from a grid point is
active at a time
All nodes in a grid point is within the
radio range of nodes in adjacent grids
Virtual grid results in hierarchical
clusters of nodes
107
Think once more ….

What are the problems of GAF?

What about mobile nodes?

What about the multimodal Wireless
nodes?
108