Wireless Sensor Networks

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DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
SUBJECT NAME: WIRELESS SENSOR NETWORKS(PE 831 EC)
FACULTY NAME: Mrs. P.SRAVANI
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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,
 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.
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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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OUTCOMES:
To determine the network architecture, node discovery and localization,
To understand the gist of Wireless Sensor Networks.
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Module 1: Challenges for Wireless Sensor Networks
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Module 2: Characteristics requirements-required mechanisms
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Module 3: Difference between mobile ad-hoc and sensor
networks
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Module 4: Applications of sensor networks
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Applications 1

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Applications 2

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Applications 3

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Applications 4

Module 5: Enabling Technologies for
Wireless Sensor Networks
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Conclusion

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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 1: Single node architecture-hardware components

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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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Transceiver (Front end)

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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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MEMS device for converting vibrations to electrical energy
(Based on a variable Capacitor)

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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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Energy consumption of sensor nodes:
(Energy savings and overhead of sleeping nodes)

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 Microcontroller energy consumption
 Memory (Intel Strong ARM SA-1100)
 Radio transceivers
 Power consumption of sensor and actuators

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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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Event based programming model

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Timer component using Interfaces

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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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Multi-hop network

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Multiple sources and/or multiple sinks

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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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Area of sensor nodes detecting an event-an elephant-that moves through
the network along with the event source

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Module 7: Gateway Concepts
 The need for gate ways
 WSN to Internet Communication
 Internet to WSN communication
 WSN tunneling

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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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2. WSN to Internet Communication

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3. Internet to WSN communication

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4. WSN tunneling

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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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RTS/CTS handshake in IEEE 802.11

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Two problems in RTS/CTS Handshake

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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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S-MAC Fragmentation and NAV settings

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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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The Mediation device protocol
Mediation device protocol with unconstrained protocol

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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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ROUTING PROTOCOLS
 Energy Efficient Routing
 Geographic Routing

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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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OUTCOMES
 To evaluate the performance of sensor network and identify
bottlenecks.
CONTENTS
 Topology control
 Clustering
 Time synchronization
 Localization and positioning
 Sensor Tasking and control

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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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Options for Topology control

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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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ENGINEERING COLLEGE
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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INFORMATION BASED SENSOR TASKING
Sensor selection
Information driven sensor query (IDSN)
Cluster-leader based Protocol
Leader election protocol
Sensor tasking in tasking relations

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ENGINEERING COLLEGE
JOINT ROUTING and INFORMATION AGGREGEATION

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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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CONTENTS
 Security Architectures
 Survey of Security protocols for Wireless Sensor
Networks
 Comparisons
OUTCOMES
 Evaluate concepts of security in sensor networks

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ENGINEERING COLLEGE
A typical sensor network and components of a sensor node

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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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ENGINEERING COLLEGE
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

Wireless Sensor Networks

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