CHAPTER 2 BACKGROUND ON WIRELESS SENSOR NETWORKS...
Transcript of CHAPTER 2 BACKGROUND ON WIRELESS SENSOR NETWORKS...
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CHAPTER 2
BACKGROUND ON WIRELESS SENSOR NETWORKS
2.1 INTRODUCTION
Recent advances in sensor, computer hardware, and wireless
communication technologies have enabled the development of tiny sensor
nodes capable of sensing, processing and communicating wirelessly to the
central base station. These wireless sensor nodes can sense various physical
parameters like temperature, pressure, level, magnetic field and many more
(Akyildiz et al 2002 and Zhao and Gibas 2004). WSN consist of hundreds or
thousands of sensor nodes deployed in a field to collect the data, process the
sensed data and transmit it wirelessly to the base station for future analyzes
and take decisions based on the received information (Estrin et al 1999, Min
et al 2001 and Pottie and Kaiser 2000). Though these nodes can work
autonomously, they work in collaborative way to sense the physical
parameters of an environment. This chapter discusses some foundational
details to provide the background knowledge about WSN. It also focuses
more on the topics related to the main theme of this research work.
2.1.1 Limitations and Challenges
The unique characteristics of WSN include large scale of deployment,
ability to withstand harsh environmental conditions, unattended operation,
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limited battery power, ability to cope with node failures, dynamic network
topology, communication failures, mobility of the nodes, heterogeneity of
nodes, and so on. Each sensor node has limited processing power, very small
memory, limited communication range and low power. The design
challenges faced by the sensor networks are huge as compared to the
traditional ad hoc wireless network as given by Karl and Willig (2005).
The individual nodes have limited computational power and
storage capacity, they operate on non-renewable power
sources and employ a short-range transceiver to send and
receive messages. These create huge constrains in the routing
protocol design and development (Akkaya and Younis 2005).
Since the WSN nodes are having limited memory capacity,
the well known sophisticated table driven adhoc routing
protocols may not be suitable for WSN environment. The
WSN nodes have less mobility than wireless ad hoc networks
hence the WSN routing protocols are simpler than adhoc
routing protocols. The many-to-one data flow of WSN makes
the WSN routing different from adhoc routing. Also, the
effective utilization of energy is the main objective of WSN
routing in contrast to the shortest route in adhoc network.
Sensor nodes are generally densely deployed to provide
redundancy and fault tolerance. This leads to a situation of
same event being observed by many nodes simultaneously and
transmission overlap taking place. Algorithms are being
developed to utilize the close proximity and to make nodes
collaborate with each other.
Wireless sensor networks are prone to frequent topology
changes due to many reasons namely, sensor node hardware
failures, communication failure, attack from the adversaries,
battery life and so on. Consequently the designer has to device
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Processing UnitADC & Signal
Conditioning Unit
Sensor Unit
Radio Unit
Memory
GPS
ACTUATOR
Power Supply Power
a network with inherent fault tolerance and the ability to
reconfigure themselves.
The number of nodes in a wireless sensor network can be of
several orders of magnitude higher than that in an ad hoc
network. Hence scalability is an important design criterion for
sensor network applications. Moreover the addressing of
nodes needs different mechanism for individual identification.
2.1.2 Architecture of Sensor Node
The typical hardware of a wireless sensor node consists of sensing,
processing, memory, radio units and a power supply. The optional units that
are present in some nodes are location-finding unit, power scavenging unit
and actuators. Figure 2.1 shows the different functional blocks and their
interconnections of a sensor node.
Figure 2.1 Architecture of Sensor Node
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Sensing Unit: It consists of different sensors to sense the
environment parameters. Due to the bandwidth and power constraints of the
sensor nodes, only low power sensors are mostly used by WSN nodes. Multi-
modal sensing is an advanced feature, which includes several sensors on a
single board of sensor node. For example, the common sensors like
temperature sensor, light sensor and acoustic sensors may be present on the
same sensor board.
Processing Unit: Usually it is a low power embedded processor,
which is aimed to do limited processing on the sensed data. The most suitable
processing unit for sensor node is a microcontroller. It performs tasks,
processes the sensed data and controls the functionality of other components
in the node. The other alternatives that can be used as a processing unit are the
microprocessors of ordinary PC, Digital Signal processors (DSP) and
Application Specific Integrated Circuits(ASIC).
Memory Unit: Since the physical dimensions of the sensor node is
an important factor for many applications, it is necessary to keep the
components as tiny as possible. Getting a smaller physical size memory with
minimum cost is a challenging task in the design of sensor node. Limited
memory is offered with a sensor node to keep the size comfortable and to
make the sensor node inexpensive. Most of the sensor nodes are coming with
very little memory for processing, usually, a few kilobytes of RAM as
program memory and few more kilo bytes of flash as data memory for storing
the collected data.
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Radio Unit: The WSN nodes utilize the freely available ISM
(Industrial, Scientific and Medical) band for communication. It generally
operates in 2.4 GHz band and uses the low power MAC IEEE 802.15.4. The
radio unit operates in four different states namely transmit, receive, idle, and
sleep states. The power consumed in idle state is almost equal to that
consumed in the receive state and hence usually the radios will go to sleep
state if there is no data to transmit.
Power Supply Unit: The usual form of power source for sensor
node is battery, which provides energy for sensing, data processing and
communication. In many applications, the wireless sensor node has been
deployed in an unreachable terrain where replenishment of battery may be
limited or impossible altogether and hence the battery energy should be
effectively utilized. The lifetime of sensor node exhibits a strong dependency
on battery life. Commercial nodes use two AA alkaline batteries or one Li-
AA battery.
Location finding Unit: The sensor networks for outdoor
applications need to identify the location of each sensor node in the field and
hence some of them are equipped with location-finding unit. It can
communicate with the satellite to get its location information using Global
Positioning System (GPS). Owing to the cost constraints, only a fraction of
the nodes are equipped with GPS capability.
Actuators: Instead of simply sensing the unmanned areas, it is
always good to activate the control signals based on the monitored
information. For such scenarios, actuators are attached to the sensor nodes,
which can perform control actions based on the commands from the sink or
the control station. This is a new requirement from the perspective of sensor
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network. The energy requirement is high for the actuators and the protocol for
sending command from the sink (downstream data) may be different from
receiving data from the sensors to the sink (upstream data). Such
modifications shall be incorporated before implementing actuators in sensor
network.
2.1.3 Commercial Sensor Nodes
There are two kinds of sensor nodes used in the sensor networks, a
sensing node, which is normally deployed in the field to sense the phenomena
and the other node is a gateway, which is used to connect the sensor network
to external world. The following paragraphs give quick overview of available
sensor nodes from various vendors in the market.
Sun SPOT (Sun Small Programmable Object Technology) is a
WSN node developed by Sun Microsystems. Unlike other available WSN
systems, the Sun SPOT is built on the Squawk Java Virtual Machine. It is
based on the ARM920T processor with 512K RAM and 4 MB flash memory.
It has an IEEE 802.15.4 based radio operating at 2.4 GHz. It has sensor board
with three axis accelerometer, temperature and light sensors and six analog
inputs for external sensor interfacing.
Moteiv’s TmoteSky is an ultra low power wireless module from
Moteiv Corporation, USA. It can be used for monitoring applications and
rapid prototyping of any application. It has integrated temperature, humidity
and light sensors. It is based on the MSP430 microcontroller with 10K RAM
and 48K Flash. It has an IEEE 802.15.4 based radio operating at 2.4 GHz and
provides 256kbps over 50m indoor and 135m outdoor.
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BTNode is an autonomous wireless communication and computing
platform based on a Bluetooth radio and a microcontroller. It serves as a
demonstration platform for research in Mobile and Ad-hoc NETworks
(MANETs) and distributed sensor networks. The BTnode has been developed
at ETH Zurich. It is based on Atmega 128L with 128K flash ROM, 244K
RAM and 4K EEPROM. It uses low power radio CC1000 operating in the
ISM band 433- 915MHz. It supports C programming and TinyOS.
Some of the most popular WSN nodes used by the research
community are from Crossbow technologies, USA. It provides a variety of
nodes that suits for various applications. The following paragraphs give the
brief overview of some of the most popular nodes in the market.
MICAz uses Chipcon’s CC2420 radio, which is based on the IEEE
802.15.4 standard. It is one of the most commonly used WSN systems in the
research community. It is based on an AtMega128L processor with 128K
program flash memory, 512K serial flash memory and 4K EEPROM. It can
communicate over 100m radius in outdoor and 30m radius in indoor with the
data rate of 256kbps.
IRIS is built upon the IEEE 802.15.4 based radio chip RF230. It
uses ATMega1281 processor with 8K RAM, 128K program flash memory
and 512K serial flash. It supports a data rate of 256kbps over 300m outdoor.
TelosB is based on TI MSP430 microcontroller with 10K RAM,
250kbps data rate. This platform delivers low power consumption allowing
for long battery life as well as fast wakeup from sleep state. The coverage
area of the mote is similar to Micaz nodes.
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Imote2 (IPR2400) is an advanced wireless sensor node platform. It
is built around the low-power PXA271 XScale processor and integrates an
802.15.4 radio (CC2420) with a built-in 2.4GHz antenna. It has a 256K
SRAM, 32MB flash and 32MB SDRAM. It provides a camera chip interface.
2.1.4 Applications of WSN
The unique characteristics of WSN nodes such as small size, low
cost and ability to communicate wirelessly, can provide an important
advantages over other networks that the measurement of required parameters
can be taken very close to the phenomenon and this finds the potential
applications of WSN in various domains like military surveillance,
environmental monitoring, structural monitoring, industrial process
monitoring, health monitoring and many more (Xu et al 2002). The
applications of WSN in various fields are briefed in the following paragraphs.
Military Surveillance and Target Tracking: The emergence of
WSN originally started with military related research and now it is adopted in
numerous applications in military as well as other fields. It can be used for
detecting the enemy’s objects and their tracking, monitoring the friendly
forces and their movement, battle field surveillance and battle damage
assessment, reconnaissance of opposing forces and terrains, detecting the
Nuclear, Biological and Chemical (NBC) attacks (Arora et al 2004 and Huang
et al 2008).
Environmental Monitoring: WSN can be used for habitat
monitoring, precision farming, disaster management, home applications and
many more. The habitat of the small birds, insects and animals (Mainwaring
et al 2002) can be studied by deploying the WSN nodes at their habitation.
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This study helps the researchers to find their living conditions, tracking their
movements and understanding the most favorable condition for breeding.
Precision farming (Burrel et al 2004) is used to monitor the soil condition that
helps in increasing the yield of the crop. The sensor nodes are embedded in
the field at required places to give the complete analysis of the soil type, soil
condition, water level, amount of fertilizers to be used and required pesticides
level, etc to maximize the yield. By deploying proper sensor nodes inside the
sea, the seismic waves are determined and the people near to the shore can be
evacuated before the Tsunami waves hit the seashore.
Nuclear Power Plant Monitoring and Control: In the nuclear
reactor, critical parameters such as concentration of neutron flux, core
temperature, radiation level and other vital parameters should be monitored
and controlled in real time. WSN can be effectively deployed to monitor these
parameters, so that the equipments can be serviced before they fail completely
or the preventive action can be taken before a major accident takes place. In
addition to the reactor monitoring and controlling, it is mandatory to measure
the radiation levels at different points in and around the reactor complex to
ensure that the radiation levels are within the permissible limits both in
normal and emergency conditions( Barbaran et al 2007, Brennan et al 2005,
Ding et al 2009 and Yang et al 2008).
Structural Health Monitoring: Extreme events like earthquakes,
fire accidents may cause enormous damage to the health of civil structures
without producing any apparent visible damage. The structural monitoring of
civil structures reduces the loss of human lives by warning about hazardous
structures and impending collapses and also provides the required information
to the disaster management teams. In addition to extreme events, the civil
structures will undergo normal wear and tear and thus reducing the
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operational lifetime. This happens for buildings, road bridges, rail bridges
etc., and an early warning system will help in reducing the effects of the
damage to the civil structure. WSN nodes equipped with vibration,
acceleration, linear displacement, strain and angular displacement sensors can
help in finding the soundness of the structure and alert the concerned, if
required (Chebrolu et al 2008, Paek et al 2005 and Pakzad et al 2008). In
India, WSN is used for predicting landslides in the hilly regions of western
India (Sheth et al 2007).
Health Monitoring: Wearable Health Monitoring Systems allow
an individual to closely monitor changes in his or her vital signs to maintain
an optimal health status. If integrated into a networked system, it can even
alert medical personnel when life-threatening changes occur. For example, an
electrocardiogram sensor (ECG) can be used for monitoring heart activity, an
electroencephalogram sensor (EEG) for monitoring brain activity, a blood
pressure sensor for monitoring blood pressure, a breathing sensor for
monitoring respiration and so on. The data from all these sensor nodes is
transmitted wirelessly to the doctor for continuous monitoring and health care
(Chipara et al 2009 and Gao et al 2008).
Home Automation: Home appliance like T.V, Refrigerator,
washing machines, etc can be embedded with smart sensors and these sensors
can communicate with each other and also with an external network through
Internet (Callaway et al 2002). Hence it is possible to operate these devices
wirelessly from anywhere in the world through networks.
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2.2 DATA AGGREGATION TECHNIQUES
2.2.1 Need for Data Aggregation
Sensor nodes are battery driven and hence operate on an extremely
frugal energy budget. It is impracticable to replace the battery for the network
with thousands of physically embedded nodes. Raghunathan et al (2002)
suggested that the lifetime of a network can be maximized by incorporating
energy-awareness into every stage of WSN design and operation. In a sensor
node, the battery power is utilized by computing sub system, sensing
subsystem, and communication sub system. The microcontroller is
responsible for the controlling the sensors, executing the communication
protocols, and processing the gathered data. The sensor node radio is
responsible for wireless communication with neighboring nodes and the
outside world. Sensor transducers translate the physical phenomena into
electrical signals. There are several sources of power consumption in the
sensing unit such as sampling unit, signal conditioning unit and ADC unit.
From the data sheet of many commercially available nodes, it is understood
that the power used by the microcontroller and the sensing sub systems is less
compared to that used by communication unit. In order to increase the
lifetime of the network, it is good to design an algorithm that reduces the
number of transmissions.
The low processing and limited communication capabilities of the
sensor nodes demand the dense deployment of sensor nodes in the monitoring
terrain to cover the entire area and provide fault tolerance to node failure. The
densely deployed nodes will sense similar data and there is a high correlation
among these data. It is not worthy to transmit the similar information by
many nodes, since the communication cost is the dominant energy consumer
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in WSN (Feeney and Nilsson 2001). Many efforts are taken to reduce the
number of unwanted transmissions in sensor networks. The data aggregation
techniques gained more attention in achieving energy saving in WSN. The
data aggregation is a technique to combine data from various sensor nodes to
eliminate redundant information and provide a rich and multi- dimensional
view of the monitoring environment (Li et al 2006). The data aggregation
algorithm can reduce the number of transmission by allowing the aggregator
node to transmit only the required data, not the redundant information.
Rajagopalan and Varshney (2006) have given an elaborate literature survey
on data aggregation and the various issues involved in the design. The
architecture of the network plays an important role on the performance of data
aggregation. A brief survey on network topology and various protocols
proposed for each topology is discussed in the following paragraphs.
2.2.2 Network Topology
WSN networks are classified into flat and hierarchical networks
according to their architecture. The architecture of the network plays an
important role in designing the data aggregation algorithm.
Flat Networks: In flat networks, all the sensor nodes play the
same role, having similar capabilities and responsibilities. Data dissemination
algorithms that do an in-network data processing to move data from sources
to sinks are called diffusion algorithms. In the push diffusion scheme, the
sources are the active participants and initiate diffusion while the sinks
respond to the sources. The sources flood the data when they detect an event
while the sink subscribes to the sources through enforcements. The sensor
protocol for information via negotiation (SPIN) proposed by Kulik et al
(2002) can be classified as a push based diffusion protocol.
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Directed diffusion was proposed by Intanagonwiwat et al (2000),
which is a two-phase pull diffusion algorithm. Data is represented as named
attribute-value pairs. Sink uses a set of attributes to identify data and
broadcasts an interest message by flooding to establish gradients. Once the
source receives an interest, it starts sending an exploratory data. It will be
forwarded to all neighbors that have matching gradients. Once a sink receives
exploratory data, it reinforces a neighbor and this process repeats resulting in
a graph of reinforced gradients. The source will send the data through this
reinforced path.
Hierarchical Networks: Flat network architecture is not suitable
if the size of the network is large since the communication and the
computation cost of the nodes will be high. In hierarchical networks, data
fusion takes place at some special nodes and thus reduces the transmission
cost. The different types of hierarchical networks such as cluster based, chain
based and tree based networks are discussed here.
Cluster based Data Aggregation: Instead of transmitting the
data directly to the sink, in the cluster based networks, all
nodes transmit their data to the cluster head. The cluster heads
will aggregate the data coming from its member and forward
it to the sink. The most popular cluster based protocols are
Low Energy Adaptive Clustering Hierarchy (LEACH)
proposed by Heinzelman et al (2002), Hybrid Energy Efficient
Distributed clustering Approach (HEED) proposed by Younis
and Fahmy(2004), and Clustered Diffusion with Dynamic
Data Aggregation (CLUDDA) proposed by Chatterjea and
Havinga (2003). In these networks, if the cluster head is far
away from the sensor nodes, it requires more transmit power
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to reach the cluster head and hence more energy consumption.
This can be avoided in the chain-based networks.
Chain based Data Aggregation: The key idea behind chain
based data aggregation is that each sensor node transmits only
to its closest neighbor. A chain based data aggregation
protocol called Power Efficient Data Aggregation protocol for
Sensor Information Systems (PEGASIS) was proposed by
Lindsey et al (2002). In PEGASIS, nodes are organized into a
linear chain for data aggregation. The nodes can form a chain
by employing a greedy algorithm or the sink can determine
the chain in a centralized manner.
Tree Based Data Aggregation: In this method, the sensor
nodes are organized into a tree and the data aggregation takes
place at the intermediate root nodes along the tree. The
fundamental traffic pattern of WSN over tree based topology
is many-to-one, in which the data flow from many nodes to
single sink node and is called convergecast. In the tree
structure, the energy of the non-leaf nodes decreases faster
than that of the leaf nodes since they need to forward data
from their children. This leads to unbalanced energy
utilization in the network. If energy of a non-leaf node
decreases beyond some threshold, it cannot involve in the
communication and hence the tree may be partitioned into
several sub trees. Hence the energy aware tree construction is
a prime research area and many researchers proposed various
tree algorithms in literature (Al-Karaki et al 2004, Dasgupta et
al 2003, Ding et al 2003, Erramilli et al 2004, Harris et al
2007, Hartl and Li 2005 and Solis and Obraczka 2005). Figure
2.2 shows the sample DAT that exhibits the many-to-one flow
of convergecast tree.
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Figure 2.2 Data Aggregation Tree
In Figure 2.2, the nodes 4, 5 and 7 are the leaf nodes which send the
raw data to their parents 3 and 6 respectively. The nodes 6, 2 and 3 perform
aggregation on the received data and forward this aggregated information to
the nodes in the next level. Since the proposed framework is basically a tree
topology, the literature survey on this structure is discussed elaborately.
In a network graph G (V, E) where V is the set of nodes and E is
the set of edges that connect nodes which can communicate directly. Let S1,
S2..., Sk S be data sources and D be a sink node. For optimal aggregation, a
minimal spanning tree (MST) connecting nodes in S and node D with
minimal number of edges should be found. This is the Steiner Tree problem,
which is an NP-hard. Krishnamachari et al (2002) proposed suboptimal
aggregation protocols such as Center at Nearest Source (CNS), Shortest Paths
Tree (SPT) and Greedy Incremental Tree (GIT) for constructing MST. In
CNS, the source which is nearest to the sink acts as the aggregator node. All
other sources send their data directly to this aggregator node, which then
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sends the aggregated information to the sink. The SPT allows each source to
send its information to the sink along the shortest path and the overlapping
paths are combined to perform aggregation. The GIT builds the aggregation
tree by allowing the source which is nearest to the sink to send its data via
shortest path. Then the next source nearer to the sink is allowed to join the
tree and the entire tree is constructed.
The very first tree based data aggregation algorithm Tiny
Aggregation Service (TAG) was proposed by Madden et al (2002), which
saves energy by minimizing number of message transfers and allowing the
node to sleep while idle in each epoch. In Temporal coherency aware in-
Network Aggregation (TiNA) (Sharaf et al 2004), the node sends the
information only when there is a significant change in the sensed data.
Dynamic query-tree Energy Balancing Protocol DQEB (Yang et al 2004) is
an energy balanced protocol that dynamically modifies the tree structure
based on the energy left at nodes. Adaptive Application-Independent Data
Aggregation AIDA (He et al 2004) resides between the Routing and MAC
layers of the network stack and hence it doesn’t require any modification in
the existing network and medium access protocols. It adaptively adjusts its
aggregation strategies according to the traffic conditions and the sensor
network requirements. There are four aggregation strategies supported in this
framework. No Aggregation, where packets are not aggregated, Fixed
Aggregation in which fixed numbers of packets are aggregated, On-demand
scheme, in which the aggregation takes place until the channel is available for
transmission and Dynamic Feedback loop combines the fixed and the on-
demand scheme. In Load Balanced Tree Protocol LBTP (Chen et al 2006), the
non leaf nodes have similar amount of children and the tree structure changes
when the energy of the non leaf node is lower than the predefined threshold.
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Cheng et al (2006) suggested the three different tree construction algorithms
for real time data gathering in which the packet should be transmitted within a
specified time bound. They proposed three heuristics algorithms to build a
MST with hop and degree constraints Node-First Heuristic (NFH), Tree-First
Heuristic (TFH), and Hop-Bounded Heuristic (HBH).
2.2.3 Open Research Issues on Data Aggregation
Even though the data aggregation techniques aid the energy
conservation, it also has an impact on the other performance parameters such
as accuracy, delay, fault tolerant and security (Akkaya et al 2008). There are
many open research problems involved in performing data aggregation, which
has dual objectives such as minimizing energy consumption while reducing
the delay, maximizing the accuracy while minimizing energy and so on. Also,
the aggregated packets contain more information and hence should not be lost
or hacked. Therefore the reliability and security issues give more research
opportunities. These open research issues are discussed in detail.
Data Representation: The accuracy of the aggregated information
depends on the aggregation operator involved in the system and the amount of
data collected by the network. The data aggregation operator may be either
simple like SUM, AVERAGE, MAXIMUM, MINIMUM and COUNT
(Lindsey et al 2002 and Madden et al 2002) or more complicated like
MEDIAN(Shrivastava et al 2004), Wavelet Histogram(Hellerstein et al 2003).
The ability of the aggregation function is to represent the information with
more accuracy from the received data. The discrete source coding (DSC) is
one of the promising techniques to represent data effectively based on the
knowledge about the correlation among the sensed data (Akyildiz et al 2004,
Chou et al 2003, Cristescu et al 2004, Sartipi and Fekri 2004 and Xiong et al
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2004). Extensive research is going on in this area to represent the useful
information on resource constrained WSN nodes (Fasolo et al 2007). Also,
the choice of aggregator operator has great influence on the amount of energy
saving in the network. For example, if the MAX operator is used for
aggregation, then the resultant aggregated packet is a single packet of same
size as that of individual sensor readings. If the aggregation ratio is n: 1, then
the energy saving will be n-fold. Suppose, the CONCATENATE operator is
used for aggregation, the aggregator node appends the individual sensor
readings. The size of the packet will increase as it moves towards the sink and
the energy saving takes place only on medium access.
Increased Latency: Even though, aggregation reduces the energy
consumption, it increases the data delivery delay. This is due to the fact that
each aggregator has to wait for a predefined time interval to collect data from
its children. This leads to a delay in delivering the data to the sink and
therefore the sink may not get the fresh data. If a node waits for longer time, it
could receive more readings and therefore, the more accurate the information
it could send out. On the other hand, waiting too long may result in stale data.
Furthermore, if a node waits too long, it may interfere with the next “data
wave” (Akkaya et al 2008). More waiting time provides good energy saving
but the latency of the packet increases. Hence there is a tradeoff between the
energy and latency (Yu et al 2004). This is called E-L problem in data
aggregation. Also, more waiting time increases the accuracy of the
aggregation. Hence there is a tradeoff between latency and accuracy (Boulis
et al 2003). This is called L-A problem. Hence there is a wide scope of
research in reducing the delay while aggregating the data. This work
addresses these issues and proposes an optimum waiting time for the nodes so
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that it could deliver the data to the sink before the deadline in an energy
efficient way.
Security and Reliability issues: Normally, the wireless channels
are unreliable and prone to error due to its dynamic channel characteristics.
Therefore the packet should be delivered reliably over unreliable wireless
medium. The loss of aggregated packets in WSN causes more energy loss
since lot of resources is already invested to transmit the sensor readings from
various sensors and retransmission of the lost packets requires more energy.
Loss of messages without aggregation in the child – parent link will not create
much loss but the loss of aggregated message leads to lot of information as
well as energy loss (Karl et al 2004).
Also the aggregated packets should be transmitted to the sink in
secured manner, since they contain more information. The hackers should not
modify the content or they should not send the wrong information to the sink,
which will mislead the sink. The source node or the aggregator node may
become malicious and it can modify, forge or discard messages. If the source
node is compromised, it may send the wrong reading to the aggregator, which
results in corrupted aggregation at the aggregator. If the aggregator is
compromised, it can either send the wrong aggregated result to the sink or it
can use the wrong aggregator operator. Both of them make it difficult for the
sink to estimate the original readings from the altered aggregated readings
(Sang et al 2006).
2.3 PERFORMANCE METRICS
Before carrying out the evaluation of a proposed protocol either
through simulation or by experiment, it is useful to identify the basic
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parameters on which the evaluation should be done. Network lifetime, data
accuracy, and latency are some of the important performance metrics used for
the evaluation of data aggregation algorithms. The definitions of these
measures are highly dependent on the desired application.
Network lifetime: Network lifetime is defined as the number of
data aggregation rounds till the specified percentages of the total nodes dies
and the percentage depends on the application. In some applications,
simultaneous working of the all the sensor nodes is crucial and hence the
lifetime of the network is the number of rounds until the first node dies.
Latency: Latency is defined as the delay involved in data
transmission, routing and data aggregation. It can be measured as the time
delay between the data packets received at the sink and the data generated at
the source node. It is also called as data freshness.
Data accuracy: The definition of data accuracy depends on the
specific application for which the sensor network is designed. For instance, in
a target localization problem, the estimate of target location at the sink
determines the data accuracy. In general it is a measure of ratio of total
number of readings received at the sink to the total number of readings
generated.
Communication Overhead: It measures the communication
complexity of the data aggregation algorithms. The control packets are
transmitted between nodes to maintain the network. These packets will not
relay any useful information to the sink and hence these are considered as
overhead. An aggregation algorithm should use minimum amount of control
packets, since these control packets drains out the battery energy.
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2.4 DISCUSSION
WSNs are an important class of resource-constrained distributed
system for monitoring and controlling the required parameters of our interest.
Designing a data gathering framework for collecting data from the field for
real time applications is considered in this research. The real time applications
require the timely delivery of data to the sink. The necessary requirements of
this framework are prolonged lifetime of the network and timely delivery of
accurate information to the sink.
From this basic study, it is understood that the lifetime of a sensor
node is limited due its limited battery capacity. One approach to improve the
lifetime of the network is data aggregation, which reduces the number of
transmissions by exploiting the redundancy in the sensor readings. But data
aggregation suffers from E-L and L-A problem and hence adapting data
aggregation for real time WSN is a challenging task. In order to satisfy the
timing requirement of an application, a proper timing model should be
designed. The timing model specifies the waiting time for each aggregator
node in the network. The timing models address the E-L and L-A problems to
meet the application requirements. These timing models are discussed in
Chapter 5 and 6.
Another approach to increase the lifetime of the network is by
designing an energy efficient DAT, which can reduce the total transmission
cost of the network. The unreliable nature of wireless channels demands the
packet retransmissions, which leads to unnecessary energy expenditure. In
order to reduce unwanted transmission cost, the packets should be transmitted
through reliable wireless link. Also, to improve the lifetime of the network,
the packets should be routed through the nodes which are having more
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energy. Hence the DAT is constructed based on the residual energy of the
node and reliability of the link. Chapter 4 gives the detailed construction of
DAT and its performance over existing algorithms. The data aggregation
timing models operate on this DAT.
The WSN nodes from Crossbow Technologies are having Indian market and
their newest product is IRIS. IRIS nodes use RF230 radio which has better
coverage distance and consumes less energy for communication as compared
to its predecessor Micaz. By considering these factors, IRIS has been chosen
as our test platform to analyze the proposed framework.