Wireless Sensor Networks
- 2. WIRELESS SENSOR NETWORKS(PE 831 EC)
COURSE OBJECTIVES:
Determine network architecture, node discovery and localization,
deployment strategies, fault tolerant and network security.
Build foundation for WSN by presenting challenges of wireless networking
at various protocol layers.
Determine suitable protocols and radio hardware.
Evaluate the performance of sensor network and identify bottlenecks.
Evaluate concepts of security in sensor networks.
COURSE OUTCOMES:
To understand network architecture, node discovery and localization,
deployment strategies, fault tolerant and network security.
To understand foundation for WSN by presenting challenges of
wireless networking at various protocol layers
Study suitable protocols and radio hardware.
To understand the performance of sensor network and identify
bottlenecks.
To understand concepts of security in sensor networks.
MATRUSRI
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- 3. INTRODUCTION:
Wireless sensor networks (WSNs) have been considered as one of the most
important technologies that are enabled by recent advances in –
Micro-electronic-mechanical-systems(MEMS)
Wireless Communication technologies.
UNIT-I: OVERVIEW OF WIRELESS SENSOR NETWORKS
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- 4. OUTCOMES:
To determine the network architecture, node discovery and localization,
deployment strategies, fault tolerant and network security.
To understand the gist of Wireless Sensor Networks.
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- 21. Module 5: Enabling Technologies for
Wireless Sensor Networks
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UNIT-II: ARCHITECTURES
INTRODUCTION
A Wireless Sensor Network is one kind of wireless network includes a
large number of circulating, self-directed, minute, low powered devices
named sensor nodes called motes. These networks certainly cover a huge
number of spatially distributed, little, battery-operated, embedded devices
that are networked to caringly collect, process, and transfer data to the
operators, and it has controlled the capabilities of computing & processing.
Nodes are the tiny computers, which work jointly to form the networks.
The sensor node is a multi-functional, energy efficient wireless device.
The applications of motes in industrial are widespread. A collection of
sensor nodes collects the data from the surroundings to achieve specific
application objectives. The communication between motes can be done
with each other using transceivers. In a wireless sensor network, the
number of motes can be in the order of hundreds/ even thousands. In
contrast with sensor n/ws, Ad Hoc networks will have fewer nodes without
any structure.
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OUTCOMES:
Build foundation for WSN by presenting challenges of
wireless networking at various protocol layers.
CONTENTS:
Single node architecture-hardware components
Energy consumption of sensor nodes
Operating system and execution environment
Network architecture- sensor network scenarios
Optimization goals and figure of merit
Gate- way concepts
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Module 2: Hardware Components
Power supply
Microcontrollers vs Microprocessors, FPGAs and ASIC
Memory
Communication devices
Sensors & Actuators
- Passive omni directional sensors
- Passive narrow- beam sensors
- Active sensors
- Actuators
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Power supply of sensor nodes:
Storing energy: Batteries
-Traditional batteries
- Capacity
- capacity under load
- self discharge
- Efficient recharging
- Relaxation
- Unconventional energy Sources
- DC-DC
- Energy Scavenging
- Photovolatic,Temparature gradients,
Vibrations, Pressure variations, flow and air/liquid
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Module 3: Energy consumption of sensor nodes
As the previous section has shown, energy supply for a
sensor node is at a premium: batteries have small capacity, and
recharging by energy scavenging is complicated and volatile. Hence,
the energy consumption of a sensor node must be tightly controlled.
The main consumers of energy are the controller, the radio front ends,
to some degree the memory, and depending on the type the sensors.
One important contribution to reduce power consumption of
these components comes from chip-level and lower technologies:
Designing low-power chips is the best starting point for an energy-
efficient sensor node. But this is only one half of the picture, as any
advantages gained by such designs can easily be squandered when
the components are improperly operated.
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Module 4:Operating systems and Execution Environments
1. Embedded operating systems: The traditional tasks of an operating
system are controlling and protecting the access to resources (including
support for input/output) and managing their allocation to different users as
well as the support for concurrent execution of several processes and
communication between these processes.
2.Programming paradigms and
application programming
interfaces (concurrent
programming):
- Process-based concurrency
- Event- based programming
- Interfaces to the operating
systems
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Module 5: NETWORK ARCHITECTURE-Sensor network scenarios
Types of Sources and sinks:
-Single hop versus Multi hop
Three types of sinks in a very simple single-hop sensor network
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Three types of mobility
Node mobility
Sink mobility
Event mobility
A mobile sinks moves through a mobile sensor network as a information
being retrieves on its behalf
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Module 6: Optimization goals and figure of merit
1. Quality of service
- Event detection/reporting probability
- Event classification error
- Event detection delay
- Missing reports
- Approximation accuracy
- Tracking accuracy
2. Energy efficiency
- Energy/correctly received
- Energy/reported event
- Delay
- N/w Life time
3. Scalability
4. Robustness
For all these scenarios and application types, different forms of networking
solutions can be found. The challenging question is how to optimize a network, how to
compare these solutions, how to decide which approach better supports a given
application, and how to turn relatively imprecise optimizing goals into measurable
figures of merit? While a general answer appears impossible considering the large
variety of possible applications, a few aspects are fairly evident.
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1. Need for Gate ways
For practical deployment, a sensor network only concerned with itself is
insufficient. The network rather has to be able to interact with other information devices,
for example, a user equipped with a PDA moving in the coverage area of the network or
with a remote user, trying to interact with the sensor network via the Internet (the
standard example is to read the temperature sensors in one’s home while traveling and
accessing the Internet via a wireless connection). Figure shows this networking scenario.
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CONCLUSION
Realization of sensor networks needs to satisfy several constraints such as
scalability, cost, hardware, topology change, environment and power
consumption.
Since these constraints are highly tight and specific for sensor networks,
new wireless ad hoc networking protocols are required.
To meet the requirements, many researchers are engaged in developing
the technologies needed for different layers of the sensor networks
protocol stack.
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UNIT-III: NETWORKING SENSORS
INTRODUCTION
The physical layer is mostly concerned with modulation and demodulation of
digital data; this task is carried out by so-called transceivers. In sensor
networks, the challenge is to find modulation schemes and transceiver
architectures that are simple, low cost, but still robust enough to provide the
desired service.
1. Wireless channels are therefore an unguided medium, meaning that signal
propagation is not restricted to well-defined locations, as is the case in wired
transmission with proper shielding. For a practical wireless, RF-based
system, the carrier frequency has to be carefully chosen.
2. In the process of modulation, (groups of) symbols from the channel
alphabet are mapped to one of a finite number of waveforms of the same
finite length; this length is called the symbol duration. The mapping from a
received waveform to symbols is called demodulation. Wave propagation
effects and noise results in bit errors.
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OUTCOMES
To determine the suitable protocols and radio hardware.
CONTENTS:
Physical layer and Transceiver Design considerations
MAC protocols for wireless sensors networks
Low Duty cycle and wakeup concepts
- STEM
- S-MAC
- The mediation device protocol
- wakeup radio protocols
Address and Name management
Assignment of MAC Addresses
Routing Protocols
- Energy efficient Routing
- Geographic Routing
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Module 1: Physical Layer and Transceiver Design
Considerations
The physical layer in wireless networked sensors has to be
designed with sensor networking requirements in mind. In particular
The Communication device must be containable in a small size, since
the sensor nodes are small. So cheaper, slightly larger antennas may be
acceptable in those cases.
The Communication devices must be cheap, since the sensors will be
used in large numbers in redundant fashion.
The radio technology must work with higher layers in the protocol
stack to consume very low power levels.
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Physical layer Evaluation of Technologies:
We consider 3 main classes of physical layer technologies for use in
wireless sensor networks, based on bandwidth considerations:
Narrowband technologies.
Spread spectrum technologies
Ultra-Wideband (UWB) technologies.
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Module 2: MAC protocols for wireless sensors networks
Medium Access Control (MAC) protocols solve a
seemingly simple task: they coordinate the times where a
number of nodes access a shared communication medium.
An “un over seeable” number of protocols have emerged
in more than thirty years of research in this area. They differ,
among others, in the types of media they use and in the
performance requirements for which they are optimized.
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Fundamentals of (wireless) MAC protocols:
Requirements and design constraints for wireless MAC protocols:
Throughput, efficiency, stability, fairness, low access delay, low
transmission delay
Hidden Terminal Problem
Exposed terminal scenario
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Important classes of MAC protocols
Fixed assignment protocols
-TDMA, FDMA, CDMA, and SDMA.
Demand assignment protocols
- HIPERLAN/2 protocol
- DQRUMA
- MASCARA protocol
- polling schemes
Random access protocols
- CSMA protocols
- Non-persistent CSMA
- Persistent CSMA
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MAC protocols for wireless sensor networks
Balance of requirements
Energy problems on the MAC layer
- Collisions
- Overhearing
- Protocol overhead
- Idle listening
Structure
- Contention-based
- Schedule-based protocols
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Module 3: Low duty cycle protocols and wakeup concepts
Low duty cycle protocols try to avoid spending (much)
time in the idle state and to reduce the communication
activities of a sensor node to a minimum.
Periodic wake up scheme
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Sparse topology and energy management (STEM)
The Sparse Topology and Energy Management
(STEM) protocol does not cover all aspects of a MAC
protocol but provides a solution for the idle listening
problem
STEM duty cycle for a single node
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The S-MAC (Sensor-MAC) protocol provides mechanisms
to circumvent idle listening, collisions, and overhearing. As opposed
to STEM, it does not require two different channels.
S-MAC
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S-MAC adopts a periodic wakeup scheme, that is, each node
alternates between a fixed-length listen period and a fixed-
length sleep period according to its schedule, as opposed to
STEM, the listen period of S-MAC can be used to receive and
transmit packets.
S-MAC attempts to coordinate the schedules of neighboring
nodes such that their listen periods
Start at the same time. A node x’s listen period is subdivided
into three different phases:
• In the first phase (SYNCH phase),
• In the second phase (RTS phase),
• In the third phase (CTS phase),
S-MAC Principle
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The Mediation device protocol
The mediation device protocol is compatible with the peer-to-
peer communication mode of the IEEE 802.15.4 low-rate WPAN
standard. It allows each node in a WSN to go into sleep mode
periodically and to wake up only for short times to receive packets
from neighbor nodes. There is no global time reference, each node has
its own sleeping schedule, and does not take care of its neighbors sleep
schedules.
Upon each periodic wakeup, a node transmits a short query
beacon, indicating its node address and its willingness to accept
packets from other nodes. The node stays awake for some short time
following the query beacon, to open up a window for incoming
packets. If no packet is received during this window, the node goes
back into sleep mode.
Dynamic synchronization
Mediation device (MD)
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Wakeup radio concepts
The ideal situation would be if a node were always in
the receiving state when a packet is transmitted to it, in the
transmitting state when it transmits a packet, and in the sleep
state at all other times; the idle state should be avoided. The
wakeup radio concept strives to achieve this goal by a
simple, “powerless” receiver that can trigger a main receiver
if necessary.
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The IEEE 802.15.4 MAC protocol
Wireless Personal Area Network (WPAN)
The standard distinguishes on the MAC layer two types of
nodes:
A Full Function Device (FFD) can operate in three
different roles: it can be a PAN coordinator (PAN =
Personal Area Network), a simple coordinator or a device.
A Reduced Function Device (RFD) can operate only as
a device.
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UNIT- IV: INFRASTRUCTURE ESTABLISHMENT
INTRODUCTION
In a densely deployed wireless network, a single node has many neighboring
nodes with which direct communication would be possible when using
sufficiently large transmission power.
This is, however, not necessarily beneficial: high transmission power
requires lots of energy, many neighbors are a burden for a MAC protocol, and
routing protocols suffer from volatility in the network when nodes move around
and frequently form or sever many links.
To overcome these problems, topology control can be applied.
The idea is to deliberately restrict the set of nodes that are considered
neighbors of a given node. This can be done by controlling transmission power,
by introducing hierarchies in the network and signaling out some nodes to take
over certain coordination tasks, or by simply turning off some nodes for a
certain time.
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Module 1: Motivation - Dense networks
In a very dense networks, too many nodes might be in range for an
efficient operation
• Too many collisions/too complex operation for a MAC
protocol, too many paths to choose from for a routing protocol.
Idea: Make topology less complex
• Topology: Which node is able/allowed to communicate with
which other nodes
• Topology control needs to maintain invariants, e.g.,
connectivity
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Flat networks
Main option: Control transmission power
• Do not always use maximum power
• Selectively for some links or for a node as a whole
• Topology looks “thinner”
• Less interference.
Alternative: Selectively discard some links
• Usually done by introducing hierarchies
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Hierarchical networks – Backbone
Construct a backbone network
• Some nodes “control” their neighbors –
they form a (minimal) dominating set
• Each node should have a controlling
neighbor
• Controlling nodes have to be connected
(backbone)
• Only links within backbone and from
backbone to controlled neighbors are used.
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Hierarchical network – clustering
Construct clusters
Partition nodes into groups (“clusters”)
Each node in exactly one group
• Except for nodes “bridging” between two or more groups
Groups can have cluster heads
Typically: all nodes in a cluster are direct neighbors of their cluster head
Cluster heads are also a dominating set, but should be separated from each
other – they form an independent set
Formally: Given graph G=(V,E), construct C ½ V such that
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Aspects of topology-control algorithms
Connectivity – If two nodes connected in G, they have to
be connected in G0 resulting from topology
control
Stretch factor – should be small
Hop stretch factor: how much longer are paths in G0
than in G?
Energy stretch factor: how much more energy does the
most energy-efficient path need?
Throughput – removing nodes/links can reduce
throughput, by how much?
Robustness to mobility
Algorithm overhead
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Example: Price for maintaining connectivity
Maintaining connectivity can be very “costly” for a power control approach
Compare power required for connectivity compared to power required to reach a
very big maximum component
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Controlling transmission range
Assume all nodes have identical transmission range r=r(|V|),
network covers area A, V nodes, uniformly distr.
Fact: Probability of connectivity goes to zero if:
Fact: Probability of connectivity goes to 1 for
if and only if |V| ! 1 with |V|
Fact (uniform node distribution, density ):
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Controlling number of neighbors
Knowledge about range also tells about number of neighbors
• Assuming node distribution (and density) is known, e.g.,
uniform
Alternative: directly analyze number of neighbors
• Assumption: Nodes randomly, uniformly placed, only
transmission range is controlled, identical for all nodes, only
symmetric links are considered
Result: For connected network, required number of neighbors per
node is (log |V|)
• It is not a constant, but depends on the number of nodes!
• For a larger network, nodes need to have more neighbors &
larger transmission range! – Rather inconvenient
• Constants can be bounded
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Example 1: Relative Neighborhood Graph (RNG)
Edge between nodes u and v if and only if there is no other node w that is
closer to either u or v
Formally:
RNG maintains connectivity of the original graph
Easy to compute locally
But: Worst-case spanning ratio is (|V|)
Average degree is 2.6
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Example 2: Gabriel graph
Gabriel graph (GG) similar to RNG
Difference: Smallest circle with nodes u and v on its circumference must only
contain node u and v for u and v to be connected
Formally:
Properties: Maintains connectivity, Worst-case spanning ratio (|V|1/2), energy
stretch O(1) (depending on consumption model!), worst-case degree (|V|)
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Example 3: Delaunay triangulation
Assign, to each node, all points in the
plane for which it is the closest node
! Voronoi diagram
• Constructed in O(|V| log |V|) time
Connect any two nodes for which the
Voronoi regions touch
! Delaunay triangulation
Problem: Might produce very long
links; not well suited for power control
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Example: Cone-based topology control
Assumption: Distance and angle information between nodes is available
Two-phase algorithm
Phase 1
Every node starts with a small transmission power
Increase it until a node has sufficiently many neighbors
What is “sufficient”? – When there is at least one neighbor in each
cone of angle
= 5/6 is necessary and sufficient condition for connectivity!
Phase 2
Remove redundant edges: Drop a neighbor w of u if there is a node
v of w and u such that sending from u to w directly is less efficient
than sending from u via v to w
Essentially, a local Gabriel graph construction
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Centralized power control algorithm
Goal: Find topology control algorithm minimizing the maximum
power used by any node
Ensuring simple or bi-connectivity
Assumptions: Locations of all nodes and path loss
between all node pairs are known; each node uses an
individually set power level to communicate with all its
neighbors
Idea: Use a centralized, greedy algorithm
Initially, all nodes have transmission power 0
Connect those two components with the shortest distance
between them (raise transmission power accordingly)
Second phase: Remove links (=reduce transmission power) not
needed for connectivity
Exercise: Relation to Kruskal’s MST algorithm?
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Centralized power control algorithm
1 1
2
3
4 4
A B
C D
E F
D
Topology
1 1
A B
C D
E F
1) Connect A-C and B-D
1 1
2
A B
C D
E F
2) Connect A-B
1 1
2
3
A B
C D
E F
3) Connect C-D
1 1
2
3
4 4
A B
C
E F
4) Connect C-E and D-F
1 1
3
4 4
A B
C D
E F
5) Remove edge A-B
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Hierarchical networks – backbones
Idea: Select some nodes from the network/graph to
form a backbone
A connected, minimal, dominating set (MDS
or MCDS)
Dominating nodes control their neighbors
Protocols like routing are confronted with a
simple topology – from a simple node, route to
the backbone, routing in backbone is simple
(few nodes)
Problem: MDS is an NP-hard problem
Hard to approximate, and even approximations
need quite a few messages
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Performance of tree growing with look ahead
Dominating set obtained by growing a tree with the
look ahead heuristic is at most a factor 2(1+ H()) larger
than MDS
H(¢) harmonic function, H(k) = i=1
k 1/i <= ln k + 1
is maximum degree of the graph
It is automatically connected
Can be implemented in a distributed fashion as well
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Start big, make lean
Idea: start with some, possibly large, connected dominating set,
reduce it by removing unnecessary nodes
Initial construction for dominating set
All nodes are initially white
Mark any node black that has two neighbors that are not
neighbors of each other (they might need to be dominated)
Black nodes form a connected dominating set (proof by
contradiction); shortest path between ANY two nodes only
contains black nodes
Needed: Pruning heuristics
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Pruning heuristics
Heuristic 1: Unmark node v if
Node v and its neighborhood are included in the neighborhood
of some node marked node u (then u will do the domination for v
as well)
Node v has a smaller unique identifier than u (to break ties)
Heuristic 2: Unmark node v if
Node v’s neighborhood is included in the neighborhood of two
marked neighbors u and w
Node v has the smallest
identifier of the tree nodes
Nice and easy, but only linear approximation
factor
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One more distributed backbone heuristic: Span
Construct backbone, but take into account need to carry traffic –
preserve capacity
Means: If two paths could operate without interference in the
original graph, they should be present in the reduced graph as
well
Idea: If the stretch factor (induced by the backbone) becomes
too large, more nodes are needed in the backbone
Rule: Each node observes traffic around itself
If node detects two neighbors that need three hops to
communicate with each other,
node joins the backbone, shortening the path
Contention among potential
new backbone nodes handled
using random backoff
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Module 2: Clustering
Partition nodes into groups of nodes – clusters
Many options for details
Are there cluster heads? – One controller/representative node per cluster
May cluster heads be neighbors? If no: cluster heads form an
independent set C:
Typically: cluster heads form a maximum independent set
May clusters overlap? Do they have nodes in common?
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Clustering
Further options
How do clusters communicate? Some nodes need to act as
gateways between clusters
If clusters may not overlap, two nodes need to jointly act as a
distributed gateway
How many gateways exist between clusters? Are all active, or
some standby?
What is the maximal diameter of a cluster? If more than 2, then
cluster heads are not necessarily a maximum independent set
Is there a hierarchy of clusters?
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Maximum independent set
Computing a maximum independent set is NP-complete
Can be approximate within ( +3)/5 for small , within
O( log log / log ) else; bounded degree
Show: A maximum independent set is also a dominating set
Maximum independent set not necessarily intuitively desired
solution
Example: Radial graph, with only (v0,vi) 2 E
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Determining gateways to connect clusters
Suppose: Cluster heads have been found
How to connect the clusters, how to select gateways?
It suffices for each cluster head to connect to all other cluster heads
that are at most three hops
Resulting backbone (!) is connected
Formally: Steiner tree problem
Given: Graph G=(V,E), a subset C ½ V
Required: Find another subset T ½ V such that S [T] is
connected and S [T] is a cheapest such set
Cost metric: number of nodes in T, link cost
Here: special case since C are an independent set
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Rotating cluster heads
Serving as a cluster head can put additional burdens on a node
For MAC coordination, routing, …
Let this duty rotate among various members
Periodically reelect – useful when energy reserves are used as
discriminating attribute
LEACH – determine an optimal percentage P of nodes to become
cluster heads in a network
• Use 1/P rounds to form a period
• In each round, nP nodes are elected as cluster heads
• At beginning of round r, node that has not served as cluster head in
this period becomes cluster head with probability P/(1-p(r mod 1/P))
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Multi-hop clusters
Clusters with diameters larger than 2 can be useful, e.g., when
used for routing protocol support
Formally: Extend “domination” definition to also dominate nodes
that are at most d hops away
Goal: Find a smallest set D of dominating nodes with this
extended definition of dominance
Only somewhat complicated heuristics exist
Different tilt: Fix the size (not the diameter) of clusters
Idea: Use growth budgets – amount of nodes that can still be
adopted into a cluster, pass this number along with broadcast
adoption messages, reduce budget as new nodes are found
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Passive clustering
Constructing a clustering structure brings overheads
Not clear whether they can be amortized via improved efficiency
Question: Eat cake and have it?
Have a clustering structure without any overhead?
Maybe not the best structure, and maybe not immediately, but
benefits at zero cost are no bad deal…
Passive clustering
Whenever a broadcast message travels the network, use it to
construct
clusters on the fly
Node to start a broadcast: Initial node
Nodes to forward this first packet: Cluster head
Nodes forwarding packets from cluster heads: ordinary/gateway
nodes
And so on… ! Clusters will emerge at low overhead
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Adaptive node activity
Remaining option: Turn some nodes off
deliberately
Only possible if other nodes remain on that
can take over their duties
Example duty: Packet forwarding
Approach: Geographic Adaptive Fidelity (GAF)
Observation: Any two nodes within a
square of length r < R/51/2 can
replace each other with respect to
forwarding
R radio range
Keep only one such node active, let
the other sleep
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Module 3: SENSOR TASKING and CONTROL
To efficiently and optimally utilize scarce resources in a sensor
network, such as limited on-board battery power supply and limited
communication bandwidth, nodes in a sensor network must be carefully
tasked and controlled to carry out the required set of tasks while
consuming only a modest amount of resources.
For example :a camera sensor may be tasked to look for animals of a
particular size and color, or an acoustic sensor may be tasked to detect
the presence of a particular type of vehicle.
To detect and track a moving vehicle, a pan-and-tilt camera may be
tasked to anticipate and follow the vehicle object. It should be noted that
to achieve scalability and autonomy, sensor tasking and control have to
be carried out in a distributed fashion, largely using only local
information available to each sensor.
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TASK DRIVEN SENSING
However, this classical algorithm/complexity view needs to be
modified in the sensor network context because
The values of the relevant manifest variables are not known, but
have to be sensed.
The cost of sensing different variables or relations of the same type
can be vastly different—depending on the relative locations of targets
and sensors, the sensing modalities available, the environmental
conditions, and the communication costs.
Frequently the value of a variable, or a relationship between
variables, may be impossible to determine using the resources
available in the sensor network; however, alternate variable values or
relations may serve our purposes equally well.
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TASK DRIVEN SENSING
To design an overall strategy, several key questions need to be
addressed:
What are the important objects in the environment to be sensed?
What parameters of these objects are most relevant?
What relations among these objects are critical to whatever high
level information we need to know?
Which is the best sensor to acquire a particular parameter?
How many sensing and communication operations will be needed
to accomplish the task?
How coordinated do the world models of the different sensors
need to be?
At what level do we communicate information, in the spectrum
from signal to symbol?
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Roles of Sensor Nodes and Utilities
Sensors in a network may take on different roles.
Consider the following example: of monitoring toxicity levels in
an area around a chemical plant that generates hazardous waste
during processing.
A number of wireless sensors are initially deployed in the ,
Due to the nature of the environment and the cost of deployment,
further human intervention or node replacement is not feasible.
The sensors form a mesh network, and data collected by a subset
of nodes is transmitted, through the multi-hop network.
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JOINT ROUTING and INFORMATION AGGREGATION
Moving center of Aggregation
Locally optimization
Simulation Experiments
Multi step information-Directed Routing
Sensor Group management
Distributed group management
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UNIT V : SURVEY OF SECURITY PROTOCOLS
INTRODUCTION
Advancements in wireless communications, low-power electronics,
battery technology, and power harvesting capabilities have enabled the
development of low-cost WSNs. WSNs are characterized by limited power,
unreliable communication, need for self-configuration and scalability, harsh
environmental conditions, small size, cooperative network behavior, data
centricity (as opposed to address centricity), very small packet size,
unattended operation, and random deployment. Given those characteristics,
the most common WSN applications are environmental monitoring, health
monitoring, terror threat detection, terrestrial and underwater habitat
monitoring, military surveillance, seismic oil and gas explorations, inventory
tracking, process monitoring, acoustic detections, object localization and
tracking, homeland security protection, disaster prevention and disaster
recovery, and pipelines corrosion detection. Figure 1. shows an example of
WSN architecture. Each node consists of a sensing unit, a processing unit, a
communication unit, a battery, and a power harvester
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Problems Applying Traditional Network Security Techniques
Sensor devices are limited in their energy, computation, and
communication capabilities
Sensor nodes are often deployed in open areas, thus allowing physical
attack
Sensor networks closely interact with their physical environments and
with people , posing new security problems
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Key Establishment and Trust
Sensor devices have limited computational power,
making public-key cryptographic primitives too
expensive in terms of system overhead
Simplest solution is a network-wide shared key
Problem: if even a single node were compromised,
the secret key would be revealed, and decryption of
all network traffic would be possible
Slightly better solution:
Use a single shared key to establish a set of link
keys, one per pair of communicating nodes, then
erase the network-wide key
Problem: does not allow addition of new nodes
after initial deployment
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Random-key pre-distribution protocols
Large pool of symmetric keys is chosen
Random subset of the pool is distributed to each sensor node
To communicate, two nodes search their pools for a common key
If they find one, they use it to establish a session key
Not every pair of nodes shares a common key, but if the key-
establishment probability is sufficiently high, nodes can securely
communicate with sufficiently many nodes to obtain a connected
network
No need to include a central trusted base station
Disadvantage: Attackers who compromised sufficiently many
nodes could also reconstruct the complete key pool and break the
scheme
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Secrecy and Authentication
We need cryptography as protection against eavesdropping,
injection, and modification of packets
Trade-offs when incorporating cryptography into sensor
networks:
End-to-end cryptography achieves a high level of security
but requires that keys be set up among all end points and be
incompatible with passive participation and local broadcast
Link-layer cryptography with a network-wide shared key
simplifies key setup and supports passive participation and
local broadcast, but intermediate nodes might eavesdrop or
alter messages
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Hardware vs. Software Cryptography
Hardware solutions are generally more efficient, but also more
costly ($)
University of California, Berkeley, implementation of Tiny Sec
incurs only an additional 5%–10% performance overhead using
software-only methods
Most of the overhead is due to increases in packet size
Cryptographic calculations have little effect on latency or
throughput, since they can overlap with data transfer
Hardware reduces only the computational costs, not packet size
Thus, software-only techniques are sufficient (or reasonable to be
more careful)
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Privacy
Issues
Employers might spy on their employees
Shop owners might spy on customers
Neighbours might spy on each other
Law enforcement agencies might spy on public places
Technological improvements will only worsen the problem
Devices will get smaller and easier to conceal
Devices will get cheaper, thus surveillance will be more
affordable
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Sensor networks raise new threats that are qualitatively different
from what private citizens worldwide faced before
Sensor networks allow data collection, coordinated analysis,
and automated event correlation
Networked systems of sensors can enable routine tracking of
people and vehicles over long periods of time
EZ Pass + On Star == Big Brother?
Suggested ways of approaching solution include a mix of:
Societal norms
New laws
Technological responses
Privacy(Contd)
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Network Security Services
So far, we’ve explored low-level security primitives
for securing sensor networks.
Now, we consider high-level security mechanisms.
Secure group management
Intrusion detection
Secure data aggregation
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Secure Group Management
Protocols for group management are required to
Securely admit new group members
Support secure group communication
Outcome of group computation must be authenticated to ensure
it comes from a valid group
Any solution must also be efficient in terms of time and energy
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Intrusion detection
In wired networks, traffic and computation are typically
monitored and analyzed for anomalies at various concentration
points
Expensive in terms of the network’s memory and energy
consumption
Hurts bandwidth constraints
Wireless sensor networks require a solution that is fully
distributed and inexpensive in terms of communication, energy, and
memory requirements
In order to look for anomalies, applications and typical threat
models must be understood
It is particularly important for researchers and practitioners to
understand how cooperating adversaries might attack the system
The use of secure groups may be a promising approach for
decentralized intrusion detection
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Secure Data Aggregation
One benefit of a wireless sensor network is the fine-grain
sensing that large and dense sets of nodes can provide
The sensed values must be aggregated to avoid overwhelming
amounts of traffic back to the base station
Depending on the architecture of the network, aggregation may
take place in many places
All aggregation locations must be secured
If the application tolerates approximate answers, powerful
techniques are available
Randomly sampling a small fraction of nodes and checking
that they have behaved properly supports detection of many
different types of attacks
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Conclusions
Constraints and open environments of wireless sensor networks
make security for these systems challenging.
Several properties of sensor networks may provide solutions.
Architect security into these systems from the outset (they
are still in their early design stages)
Exploit redundancy, scale, and the physical characteristics of
the environment in the solutions
Build sensor networks so that they can detect and work
around some fraction of their nodes which are compromised