8

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,

9

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

10

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

11

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.

12

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

13

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.

14

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.

15

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.

16

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

17

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.

18

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

19

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.

20

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

21

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.

22

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

23

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.

24

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

25

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

26

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

27

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.

28

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

29

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.