Unit VI
Infrastructure Establishment for WSN
• Localization and Positioning, tracking: Properties of positioning,
Possible approaches, Task driven Sensing, Rolls of Sensor nodes and
utilities, Information based sensor tracking, joint routing and
information aggregation, Sensor Network Databases-BIGDATA,
Sensor network platforms and tools, Single-hop localization,
Positioning in multi-hop environments, Impact of anchor placement,
• Operating Systems for WSN:
OS Design Issues, Examples of OS(Architecture, Design Issues,
Functions): Tiny OS, Mate, Magnet OS, MANTIS, Nano-RK OS
Architecture Block Diagram, LiteOS Architectural Block Diagram,
LiteFS Architectural Block Diagram, Content delivery networks.
Introduction to Internet of Things(IoT).
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Infrastructure Establishment for WSN
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Architecture
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Infrastructure Establishment for WSN
Definition:
The task of initiating collaborative environment for sensor
network when that network is activated is called
infrastructure establishment.
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Infrastructure Establishment for WSN
When sensor network is activated various task must be
performed to establish necessary infrastructure that will allow
useful collaborative work to be performed:
1) Discovering other nodes
2) Radio power adjustment to ensure adequate connectivity.
3) Cluster formation.
4) Node placement in a common temporal and spacial
framework.
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Infrastructure Establishment for WSN
Some common techniques used to establish
the network are:
1) Topology control
2) Clustering
3) Time synchronization
4) Localization
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1) Topology control
: idle node
: Active node
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1) Topology control
• A sensor node that wakes up execute a protocol to discover which
other nodes it can communicate with. (bidirectional)
• At initial state each node try to connect with neighbors according to
the radio link capacity of its own.
• The neighbor is determined by the radio power of the node as well as
local topology and other conditions that may degrade performance of
the radio link.
• Sensor node are capable of broadcasting less that their maximum
possible radio power. (for energy saving and network lifetime)
• Example : Homogeneous topology : all nodes with same transmission
range.
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Critical Transmission Range Problem
Computing minimum common transmitting range “ r ”
such that the network is connected.
Solution :
1) Depends on physical placement of the node.
2) If node location is known CTR problem has a simple
solution.
CTR is defined as longest edge of minimum spanning tree
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Solution to CTR problem
Example :
GRG (Geometric Random Graph):
N points are distributed into a region according to some
distribution and then some aspect of the node placement
is investigated with high probability
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2) Clustering
Clustered
Architecture
Layered
Architecture
Base
Station
Base
Station
Layer 1
Layer 2
Layer 3
Larger Nodes denote Cluster Heads
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2) Clustering
Hierarchical architecture enables more efficient use of
sensor resources such as:
• Frequency spectrum
• Bandwidth
• Power
Advantages:
1) Health monitoring of network is easy.
2) Identifying misbehaving node is easy.
3) Some nodes can act as watchdogs for other nodes.
4) Maintenance of network is easy.
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2) Clustering
Cluster formation:
1) Initially unique ids (UIDs) are assigned to each node
2) Node with higher ID than its uncovered neighbors declares
itself as cluster head.
3) Cluster head nominated nodes then communicate with each
other.
4) Node that can communicate with two or more cluster heads
may become gateway.
Gateway : node that aid in passing traffic from one cluster to
other.
Uncovered neighbors : node that have not been already
claimed by another cluster head.
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3) Time Synchronization
• Every node is operating independently so their clocks
may not be synchronized with each other.
• It is important to run network efficiently
• to detect events
• for localization
• estimating internodes distances.
• to arrange TDMA schedule
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3) Time Synchronization
• In wired network NTP is used to achieve coordinated
universal time (UTC).
• In NTP highly accurate clock is mounted on one of the
machine of the network. This is not applicable for WSN :
• No master clocks are available.
• Inconsistent common delay.
• Connections are variable/dynamic and unpredictable.
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3) Time Synchronization
• Time difference caused by the lack of common time
origin is called as clock phase difference or clock bias.
• Methods for clock synchronization in WSN :
1) Clock phase diff estimation using three msg
exchanges.
2) Interval method.
3) Reference broadcast.
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4 ) Localization and Localization Services
• What?
– To determine the physical coordinates of a group of sensor
nodes in a wireless sensor network (WSN)
– Due to application context, use of GPS is unrealistic, therefore,
sensors need to self-organize a coordinate system
• Why?
– To report data that is geographically meaningful
– Services such as routing rely on location information;
geographic routing protocols; context-based routing protocols,
location-aware services
Localization in Wireless Sensor Networks
In general, almost all the sensor network
localization algorithms share three main phases
• DISTANCE ESTIMATION
• POSITION COMPUTATION
• LOCALIZATION ALGHORITHM
Localization in Wireless Sensor Networks
• The distance estimation phase involves measurement
techniques to estimate the relative distance between the
nodes.
• The Position computation consists of algorithms to calculate
the coordinates of the unknown node with respect to the
known anchor nodes or other neighboring nodes.
• The localization algorithm, in general, determines how the
information concerning distances and positions, is
manipulated in order to allow most or all of the nodes of a
WSN to estimate their position. Optimally the localization
algorithm may involve algorithms to reduce the errors and
refine the node positions.
Distance Estimation
There are four common methods for measuring in distance
estimation technique:
• ANGLE OF ARRIVAL (AOA)
• TIME OF ARRIVAL (TOA)
• TIME DIFFERENT OF ARRIVAL (TDOA)
• THE RECEIVED SIGNAL STRENGH INDICATOR (RSSI)
Distance Estimation
• ANGLE OF ARRIVAL method allows each sensor to
evaluate the relative angles between received radio signals
• TIME OF ARRIVAL method tries to estimate distances
between two nodes using time based measures
• TIME DIFFERENT OF ARRIVAL is a method for
determining the distance between a mobile station and
nearby synchronized base station
• THE RECEIVED SIGNAL STRENGTH INDICATOR
techniques are used to translate signal strength into distance.
Position Computation
The common methods for position computation
techniques are:
• LATERATION
• ANGULATION
Position Computation
• LATERATION techniques based on the precise
measurements to three non collinear anchors.
Lateration with more than three anchors called
multilateration.
• ANGULATION or triangulation is based on
information about angles instead of distance.
Classifications of Localization Methods
•
•
•
•
According to the ways of Sensors implementation, we
classify the current wireless sensor network localization
algorithms into several categories such as:
Centralized vs Distributed
Anchor-free vs Anchor-based
Range-free vs Range-based
Mobile vs Stationary
Sensor Tasking and Control
• Because of Limited battery power and Limited
bandwidth careful tasking and the control id needed.
• Information collected from the sensors.
– All information aggregation is needed.
– Selective information aggregation is needed.
• Which sensor nodes to activate and what information to
transmit is a critical issue.
• Classical algorithms are not suitable for WSN :
– Sense values are not known.
– Cost of sensing may vary with the data.
Designing strategy for Sensor Tasking and Ctrl:
1) What are the important object in the environment to be
sensed ?
2) What parameters of these object are relevant?
3) What relations among these objects are critical to
whatever high level information we need to know?
4) Which is the best sensor to acquire a particular
parameter?
5) How many sensing and the communication operations
will be needed to accomplish the task?
6) How coordinated do the world models of the different
sensor need to be ?
7) At what level do we communicate information in a
spectrum from a signal to symbol?
Roles of Sensor nodes and utilities
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Roles of Sensor nodes
Sensors
R
SR
S
SR
I
S
S
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Information Driven Sensor Querying
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Sensor Network Databases
• Sensor Network as Database: Think of a sensor
network as a distributed database that store data
within the network and allow queries to be injected
anywhere in the network.
• Research issues
– how is data stored and organized after sensing
– what’s the user interface to the sensor database
– How does an external query find and process the
data in an efficient manner?
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Challenges
– The system is highly volatile.
– Relational tables are not static.
• New data is continuously sensed.
– High communication cost.
• In-net processing during query execution.
– Arbitrarily long delay and rate of data arrival is variable
– Limited storage
• Older data has to be discarded
• Keep statistics
– Long-running, continuous queries.
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Querying the physical world
– Common user operations in sensor network:
• Queries
• Actuate & control
– Query: user specify the data they want
• Simple, SQL-like queries
• Using predicates, not specific addresses
– Challenge is to provide:
• Expressive and easy-to-use interface
• High-level operators
• Power efficient execution framework
– Do sensor networks change query processing?
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Type of queries
– Historical
• What is the average rainfall over past 2 days?
– Current
• What is the current temperature in Lab_no 3 at
building no 5 ?
– Long running, continuous
• Obtain an ID whenever sensors in region R detects
person.
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Example Query
– High-level query interface such as SQL
 For next 3 hours, retrieve every 10 minutes the max
rainfall level in each county in California, if it is
greater than 3.0 inch.
 SELECT max (rainfal_Level) ,county
FROM sensors
Where STATE = California
GROUP BY country
HAVING max (rainfal_Level) > 3.0in
DURATION [ now, now+180min]
SAMPLING PERIOD 10 min
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Sensor Database Properties
1. Persistence: Data stored in the system must remain
available to queries, despite sensor node failures and
changes in the network topology.
2. Consistency: A query must be routed correctly to a node
where the data are currently stored. If this node
changes, queries and stored data must choose a new
node consistently.
3. Controlled access to data: Different update operations
must not undo one another’s work, and queries must
always see a valid state of the database.
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Sensor Database Properties…
4. Scalability in network size: As the number of nodes
increases, the system’s total storage capacity should
increase, and the communication cost of the system
should not grow unduly.
5. Load balancing: Storage should not unduly burden any
one node. Nor should any node become a concentration
point of communication.
6. Topological generality: The database architecture
should work well on a broad range of network
topologies.
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Issues
– How to identify relevant sensors?
– Computation vs. communication tradoff
Where to process query?
• Inside the sensor network (route query)
• At centralized location (route data)
–Large amount of data transfer  not efficiency
• How to process?
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Cougar Sensor Network Database
• SQL type query interface.
• Distributed query execution.
• Represents each sensor as ADT (applies
encapsulation as OOP).
• Each measurement is associate with a time
stamp.
• Whenever a signal processing function returns
value a record is inserted into virtual relations
(never updated or deleted).
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Probabilistic queries
• Uncertainty in reading due to noise environmental
disturbances.
• Gaussian ADT (GADT) which models probability
distribution function over possible measurement
values.
• Example
SELECT *
From Sensors
WHERE Sensor.Temp.Prob([67.5,68.5]>=0.6)
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TinyDB Query Processing
• System designed to support in-network aggregate
query processing.
• SQL type query interface.
• Support functions like:
min,max,count,sum,average
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Data Indices and Range Queries
– Key idea: pre-storing the answers to certain
special queries
– One-dimensional indices
• Canonical subset: the subset of data forming
pre-stored answers.
– Example:
• Counting cars passing in each sensor
• Query for counts over various contiguous
segments of the road.
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One-Dimensional Indices
u4=s0S1s2 s3 s4 s5 s6 s7
…
– Build a binary tree:
• Map ui to si-1
• An internal node aggregates
counts from all its
descendant sensors in the
tree
• If query for segment between
s0 an s4, can get answer
from u2( stored in s1)
• Any segment can be
answered by combining
subtrees
– Partial data aggregation: key
for indexing range queries
u1=s0S1
u3=s2 s3
u4
u2
u1
s0
u7
u3 u5
s1 s2 s3 s4 s5 s6
u6
s7
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Multi-dimensional Indices
Select *
From event s
Where Temp >=50 and Temp<=60 and
Light >=5 and Light <10
Light_reading
40
30
20
10
0
10
20
30 40
50 60 70
Temperature
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Sensor Network Platforms and tools
• Commercially available sensor nodes :
1. Specialized sensing platform such as Spec node
designed at University of California-Berkeley.
2. Generic Sensor platform – Berkeley Mote.
3. High bandwidth sensing platform such as iMote.
4. Gateway Platform such as Stargate. (sink node).
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Mica2 Wireless Sensors
MTS310 Sensor Boards
• Acceleration,
• Magnetic,
• Light,
• Temperature,
• Acoustic,
• Sounder
New MicaZ follows IEEE 802.15.4
Zigbee standard with direct sequence
spred spectrum radio and 256kbps data
rate
OS Design issues
•
•
•
•
Should be compact and small in size.
Should provide real time support.
Should provide efficient resource management.
Should provide reliable and efficient code
distribution.
• Should support Power Management.
• Should provide generic programming interface.
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TinyOS
• Started out as a research project at Berkeley
 goal: conserving resources
• No file system.
• No dynamic memory allocation.
• No memory protection.
• Very simple task model.
• Minimal device and networking abstractions.
• Application and OS are coupled—composed into one
image.
• Supports event based model.
• Sleep mode facility is provided.
• Commands are non blocking requests with return status.
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TinyOS components
• Components: reusable building blocks
• Each component is specified by a set of interfaces
– Provide “hooks” for wiring components together
• A component C can provide an interface I
– C must implement all commands available through I
– Commands are methods exposed to an upper layer
• An upper layer can call a command
• A component C can use an interface J
– C must implement all events that can be signaled by J
– These are methods available to a lower layer
• By signaling an event, the lower layer calls the
appropriate handler
• Components are then wired together into an application
I
C
J
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Mate
• Is designed to work on the top of the TinyOS
as one of its components.
• It is a byte code interface aims to make
TinyOS accessible to non expert programmers.
• Also provides execution environment.
• Program code is made up of capsules.(each
capsule contains 24 instructions- 1 byte each)
• Capsules types : msg send , msg rec , timer ,
subroutine.
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MagnetOS
• Distributed adaptive Operating System for
application adaption and Energy conservation.
• Goals :
1) Adapt to underlying resource and its changes
in stable manner.
2) Efficient energy conservation.
3) Provide general abstraction for applications.
4) To be scalable for large networks.
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MagnetOS
• Is a Single System Image (SSI) or Single unified Java
Virtual machine with static and dynamic components.
• Dynamic Components are used for : monitoring , object
creation, invocation, migration.
• provide two online power aware algorithms : NetPull
and NetCenter
• NetPull and NetCenter use in moving components
within the network to reduce energy consumption and
extend network Lifetime.
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MANTIS
• The MultimodAl system for NeTworks of
In-situ wireless Sensors (MANTIS) provides a
new multithreaded operating system for
WSNs.
• MANTIS is a lightweight and energy efficient
operating system.
• Includes kernel, scheduler, and network stack.
• It is portable across multiple platforms, i.e., we
can test MOS applications on a PDA or a PC
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MANTIS
• MOS also supports remote management of sensor nodes
through dynamic programming.
• MOS is written in C and it supports application dev. in C.
• MOS uses preemptive priority-based scheduling.
• MOS uses a UNIX-like scheduler.
• The length of time slice is configurable, by default it is set
to 10 milliseconds (ms).
• Context switches are also triggered by system calls and
semaphore operations.
• Energy efficiency is achieved by the MOS scheduler by
switching the microcontroller to sleep mode when
application threads are idle.
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Nano-RK Architeture
• Nano-RK is a fixed, preemptive multitasking real-time OS.
• The design goals for Nano-RK are:
1) Multitasking,
2) Support for multi-hop networking,
3) Support for priority-based scheduling,
4) Timeliness and schedulability,
5) Extended WSN lifetime,
6) Application resource usage limits, and small footprint.
• It supports hard and soft real-time applications by the means
of different real-time scheduling algorithms,
• socket-like abstraction for N/W and supports FireFly and
MicaZ sensing platforms.
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Nano-RK Architeture
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Lite OS
• LiteOS is a Unix-like operating system designed for WSNs
at the University of Illinois at Urbana-Champaign.
• Unix-like OS for WSN,
• Provide system programmers with a familiar programming
paradigm .
• A hierarchical file system, support for object-oriented
programming in the form of LiteC++, and a Unix-like shell.
• The footprint of LiteOS is small enough to run on MicaZ
nodes.
• LiteOS is primarily composed of three components:
LiteShell, LiteFS, and the Kernel.
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Lite OS
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