CHAPTER-2 LITERATURE REVIEW - Shodhgangashodhganga.inflibnet.ac.in/.../10604/10/10_chapter2.pdfChapter 2: Literature Review 17 A typical hardware structure of a sensor node is shown

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CHAPTER-2

LITERATURE REVIEW

In the past decade, WSNs have received tremendous attention from both academia and

industry all over the world. A large amount of research activities have bee n carried out to

explore and solve various design and application i ssues and significant advances have been

made in the development and deployment of WSNs. The development of wireless sensor

networks was originally motivated by military applications such as battlefield surveillance.

WSNs are now used for many application s, including animal tracking [ 60][61][62][63],

structural monitoring [ 64][65][66], environmental and habitat monitoring [ 67][68][69] ,

health care applications, traffic control and resource monitoring in offices , agriculture

[12][36][14] and homes [ 70][71]. In many applications like battlefield surveillance and

forest fire detection, reliability is the primary concern and can not be compromised as some

important decisions depend on the information received from WSN.

This chapter has been organized in f ive sections. Section 2.1 presents the terminology,

architecture, advantages and applications of WSNs. Section 2.2 provides background for

the subject of neural networks followed by the description of the neural network models

used in this work. Section 2.3 des cribes Markov Chain and its terminology relevant to the

reliability in WSNs. Section 2.4 presents the literature review for reliability and fault

tolerance in WSNs. It also introduces the relevance and terminology of fault tolerance in

the domain of WSNs. Finally, chapter has been summarized in Section 2.5.

2.1 WIRELESS SENSOR NETWORKS

Historically, WSNs have been characterized as wireless networks consisting of numerous

small, energy constrained, low cost and autonomous nodes that are distributed over an area

for the purpose of monitoring or sensing the physical entities such as temperature, light,

motion, metallic objects and humidity [26] [72][73][74]. Communication or relaying of

data typically occurs via wireless multi -hop routing. The majority of WSNs des cribed in

the literature exhibit a (source, sink) architecture, which may include any number of:

1. Source nodes, which generate data, usually by using sensors to measure

environmental factors such as temperature, humidity or radiation,

2. Sink nodes, which collect the data gathered by source nodes and

Chapter 2: Literature Review

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A typical hardware structure of a sensor node is shown in Figure 2.1. It mainly consists of

the sensing unit, the wireless communication unit, the processing unit and the power unit.

The basic work ing of a wireless sensor node is: Firstly, the sensor node obtains some

analog signal from the environment by the sensor. The signal got from environment may be

light, heat and movement, etc. Then the analog signal is converted into dig ital signal by

A/D converter. Processing unit receives the digital signal from A/D converter and deal with

it locally, usually cooperating with a small -scale storage unit. Finally, the transceiver is

responsible for transferring the data required to some c ontrol center through the wireless

network, at the same time, receiving commands and data from it. The power unit is one of

the most important component of WSNs. Compare with other communication networks,

the WSN pays more attention to the power unit since it is always limited and hard to

replace, which usually makes the node unavailable directly. The power unit offers the

energy to other parts of the node usually by batteries. However, the power of the batteries

is always exhausted rapidly. So , it is meani ngful to make use of energy from the

environment directly if possible, such as light energy, wave energy, etc.

2.1.4 Architecture of WSN

The typical architecture of WSN can be seen in Figure 2. 2. Generally, a classic WSN

contains: sensor nodes, sink nodes, manager nodes, etc. Firstly, a number of sensor nodes

are deployed randomly in a sensor field that observers are interested in. These nodes

communicate with each other through wireless channels to exchange information.

Secondly, a sink node is arranged in or near the sensor field to collect the data generated

from the sensor nodes. Finally, the sink sends the data collected to the task manager node

through the Internet.

A WSN consists of hundreds or thousands of low cost nodes which could either have a

fixed location or randomly deployed to monitor the environment . The flow of data ends at

special nodes called base stations (sinks). A base station links the sensor network to

another network (like a gateway) to disseminate the data sensed for further process ing.

Base stations have enhanced capabilities over simple sensor nodes since they must do

complex data processing. This justifies the fact that base stations have workstation/laptop

class processors and of course enough memory, energy and computational pow er to

perform their tasks well.

The term WSN describes an association among miniaturized embedded communication

devices that monitor and analyze their surrounding environment. The network is composed


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of many tiny nodes sometimes referred to as motes [11]. Wireless sensor nodes range in

size from a few millimeters to the size of a handheld computer. Regardless of size, sensor

nodes share common constraints.

Figure 2.2 Architecture of WSN

Power is an especially critical resource as it is difficult to rech arge or replace failed nodes

[11][45] after deployment and when the node power is depleted, the node is no longer able

to transmit or receive communications. The communication component s consume the most

power in the sensor node making efficient communicat ions paramount to sustain viable

operations. In wireless communications, power is proportional to the range of

communications by a factor of r n, where r is the radius of communications between the

transmitter and receiver and n is the pathloss exponent in the range of 2 and 5. From this, it

is expected that multihop routing is more efficient than direct transmission from nodes to

the sink [26]. It has also been proposed that to conserve power, the WSN should establish a

“communication backbone” to minimize the number of routes.

To send data to the sink, each sensor node can use single - hop long distance transmission,

which leads to the single - hop network architecture as shown in Figure 2.3. However, long


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distance transmission is costly in terms of energy consumption. In sensor networks, the

energy consumed for communication is much higher than that for sensing and computation.

For example, the energy consumed for transferring one bit of data to a receiver at 100 m trs

away is equal to that needed to execute 3,000 instructions [ 120]. The ratio of energy

consumption for communicating 1 bit over the wireless medium to that for processing the

same bit could be in the range of 1,000 – 10,000 [ 121][19] . Furthermore, the energy

consumed for transmission dominates the total energy consumed for communication and

the required transmission power grows exponentially with the increase of transmission

distance. Therefore, it is desired to reduce the amount of traffic and transmission distance

in order to increase energy savings and prolong network lifetime.

Figure 2.3 Single - Hop Network Architecture

For this purpose, multihop short distance communication is highly preferred. In most

sensor networks, sensor nodes are densely deployed and neighbor nodes are close to each

other, which makes it feasible to use short distance communication. In multihop

communication, a sensor node transmits its sensed data toward the sink via one or more

intermediate nodes, which can reduce the energy consumption for communication. The


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architecture of a multihop network can be organized into two types: flat and hierarchical

[27].

2.1.4.1 Flat Architecture

In a flat network, each node plays the same role in performing a sensing task and all sensor

nodes are peers. Due to the large number of s ensor nodes, it is not feasible to assign a

global identifier to each node in a sensor network. For this reason, data gathering is usually

accomplished by using data - centric routing, where the sink transmits a query to all nodes

in the sensing region via flooding and only the sensor nodes that have the data matching the

query will respond to the sink. Each sensor node communicates with the sink via a

multihop path and uses its peer nodes as relays. Figure 2. 4 illustrates the typical

architecture of a flat network.

Figure 2.4 Flat Network Architecture

2.1.4.2 Hierarchical Architecture

In a hierarchical network, sensor nodes are organized into clusters, where the cluster

members send their data to the cluster heads while the cluster heads serve as relays for

transmitting the data to the sink. A node with lower energy can be used to perform the


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sensing task and send the sensed data to its cluster head at short distance while a node with

higher energy can be selected as a cluster head to process the data fro m its cluster members

and transmit the processed data to the sink.

This process can not only reduce the energy consumption for communication but also

balance traffic load and improve scalability when the network size grows. Since all sensor

nodes have the same transmission capability, clustering must be periodically performed in

order to balance the traffic load among all sensor nodes. Moreover, data aggregation can be

performed at cluster heads to reduce the amount of data transmitted to the sink and improve

the energy efficiency of the network [ 38]. The major problem with clustering is how to

select the cluster heads and how to organize the clusters [ 39]. In this context, there are

many clustering strategies.

Figure 2.5 Single - Hop Clustering Architecture

According to the distance between the cluster members and their cluster heads, a sensor

network can be organized into a single - hop clustering architecture or a multihop

clustering architecture as shown in Fig ures 2.5 and 2.6 respectively [52]. According to the

number of tiers in the clustering hierarchy, a sensor network can be organized into a single

- tier clustering architecture or a multitier clustering architecture.


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Figure 2.6 Multihop Clustering Architecture

Figure 2.7 Multitier Clustering Architecture


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as neural network or simply a neural net (NN). Neural network is an interconnected group

of artificial neurons that uses a mathematical model for information processing based on a

connectionist approach to computation. Neural network is an adaptive system that changes

its structure based on external or internal information which flows through the network. In

general, an artificial neural network is a system that receives an input, processes the data

and provides an output. Commonly, the input is in the form of a data array which can be

anything such as data from an image file, a sound or any kind of data that can be

represented in an array. Once an input is presented to the neural network and a

corresponding desired or target response is set at the output, an error is compo sed from the

difference of the desired response and the real system output. The error information is fed

back to the system which makes all adjustments to their parameters in a systematic fashion

(commonly known as the learning rule). This process is repea ted until the desired output is

acceptable. In neural network design, the engineer or designer chooses the network

topology, the trigger function or performance function, learning rule and the criteria for

stopping the training phase. So, it is pretty diff icult to determine the size and parameters of

the network as there is no rule or formula to do it. The best we can do for having success

with our design is playing with it. The problem with this method is when the system does

not work properly it is hard to refine the solution. Despite this issue, neural networks based

solution is very efficient in terms of development, time and resources. Neural networks

provide real solutions that are difficult to match with other technologies.

Another aspect of the arti ficial neural networks is that there are different architectures,

which consequently requires different types of algorithms, but despite to be an apparently

complex system, a neural network is relatively simple. Artificial neural networks are

among the new est signal processing technologies nowadays. In the world of engineering,

neural networks have two main functions: Pattern classifiers and as non linear adaptive

filters. An artificial neural network is developed with a systematic step -by-step procedure

which optimizes a criterion commonly known as the learning rule. The input/output

training data is fundamental for these networks as it conveys the information which is

necessary to discover the optimal operating point. In addition, a non linear nature makes

neural network processing elements a very flexible system.

2.2.1 Characteristics of Neural Networks

Neural Networks exhibit some brain -like behaviors that are difficult to program directly

like:


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Table 2.1 Applications of Neural Network

Major Area ApplicationsAerospace High performance aircraft autopilots, flight path simulations, aircraft

control systems, autopilot enfacements, aircraft component simulations,

aircraft component fault detectors

Automotive Automobile automatic guidance systems, warranty activity analyzers

Banking Document readers, credit application evaluators.

Defense Weapon steering, target tracking, object discrimination, facial

recognition, sonar, radar and image signal/image identification

Telecom Image and data compression, automated information services, real time

translation of spoken language, customer payment processing systems.

Transportation Truck break diagnosis systems, vehicle scheduling, routing systems

Security Market analysis, automatic bond rating, stock trading advisory systems

Speech Speech recognition, speech compression, text to speech synthesis

Robotics Trajectory control, forklift robot, manipulator controller, vision systems.

Medical Breast cancer cell analysis, ECG and EEG analysis, prosthesis design,

optimization of transplant times, hospital expense reduction, hospital

quality improvement and emergency room test advisement.

Manufacturing Manufacturing process control, product design and analysis, process and

machine diagnosis, real -time particle identification, visual quality

inspection system beer testing, welding quality analysis , paper quality

prediction, computer chip quality analysis, analysis of grinding

operations, chemical product design analysis, machine maintenance

analysis, project bidding, planning and management and dynamic

modeling of chemical process systems

Financial Real estate appraisal, loan advisor, mortgage screening, corporate bond

rating, credit line use analysis, portfolio trading program, corporate

financial analysis and currency price prediction

Electronics Code sequence prediction, integrated circuit chip layout, process

control, chip failure analysis, machine vision and voice synthesis.


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If activation vectors a and b have reached equilibrium, then stop; otherwise continue

2.2.10 Hopfield Network

Since the introduction of Hopfield [ 87][88] of a neural model as a content - addressable

memory, many research efforts have been directed toward the analysis and

implementations of neural network associative memories. Many algorithms have been

proposed to improve the performance of the Hopfield model: (i) by modifying the thresh -

holding condition, (ii) By restoring the diagonal terms in the memory matrix, (iii ) By the

use of a controllable non -linearity in the correlation domain and (iv) By employing higher

order association. Different learning schemes have also been reported like (i) the use of

generalized inverse matrix, (ii) The use of a projection matrix or (iii) Using some

orthogonalization techniques instead of the outer product in computing the memory matrix.

2.2.10.1 Architecture of Hopfield Neural Network

Hopfield introduced a powerful new kind of associative or content -addressable memory

which allows information to be store distributively in the structure of the neural network in

such a way that it may be retrieved from partial or noisy information [87][88].

In Hopfield network, every neuron is connected to all other neurons except to themselves,

although the value of wij varies, it may also be zero to indicate no unit interconnection.

Figure 2.10 Layout of Hopfield Network

Figure 2.10 shows a multiple loop feedback Hopfield type neural network computation

system. The connection w ij from the output neuron i to the input neuron j has the same

W24

W13W42

W32 W23W14W41

W12

W21

W31

W34

W43

1

3

2

4


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The matrix R (I – Q)-1 has the following meaning. The column i of R (I – Q)-1 gives the

probabilities of ending up in each of the absorbing states, given that the process started in

the ith transient states.

If the chain starts in states S i then expected number of steps before the chain is absorbed is

called time of absorption. Let t i be the expected number of steps before the chain is

absorbed, given that the chain start in state S i, and let t be the column vector whose ith entry

is ti .then T=Nc, Where c is a column vector all of whose entries are 1.

2.3.2 Markov Modeling Process

Figure 2.11 presents a flow chart that illustrates the procedure used to develop a Markov

model.

Figure 2.11 Markov Modeling Process


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2.3.3 Advantages of Markov Method over other Reliability Modeling Techniques

Markov methods are powerful tools in RMS engineering. Markov chains are commonly

used to study the dependability of complex systems. Markov analysis provides a means of

analyzing the RMS of systems whose components exhibit strong dependencies. Other

systems analysis methods (e.g., fault tree analysis) often a ssume total component

independence. Used alone, these methods may lead to optimistic predictions for the system

reliability and safety parameters. The exponential distribution , i.e., a constant failure rate is

a common assumption in system analysis based upon Markov methods.

Markov methods offer significant advantages over other reliability modeling techniques,

some of these advantages are:

1. Simplistic Modeling Approach : The models are simple to generate although they

do require a more complicated mathematical approach.

2. Redundancy Management Techniques : System reconfiguration required by

failures is easily incorporated in the model.

3. Coverage: Covered and uncovered failures of components are mutually exclusive

events. These are not easily modeled using classic al techniques, but are readily

handled by the Markov mathematics.

4. Complex Systems: Many simplifying techniques exist which allow the modeling of

complex systems.

5. Sequenced Events: Often the analyst is interested in computing the probability of

an event resulting from a sequence of sub-events. While these types of problems do

not lend themselves well to classical techniques, they are easily handled using

Markov modeling.

The advantage of the Markov process is that it neatly describes both the failure of an i tem

and its subsequent repair. It develops the probability of an item being in a given state, as a

function of the sequence through which the item has traveled. The Markov process can thus

easily describe degraded states of operation, where the item has ei ther partially failed or is

in a degraded state where some functions are performed while others are not.

2.4 REVIEW OF PREVIOUS WORK FOR RELIABILITY AND FAULT

TOLERANCE IN WSNs

The reliability of wireless sensor networks (WSN) is affected by faults that m ay occur due

to various reasons such as malfunctioning hardware, software glitches, dislocation or


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Avizienis et al. [ 117] proposes that dependability can be addressed through fault

prevention, fault-tolerance, fault removal and fault forecasting. Fault prevention is a priori

preclusion of the occurrence of faults in a system and is considered “almost impossible” to

achieve. Fault-tolerance accounts for the expectation of faults through the incorporation of

redundancy to mitigate the fa ults and prevent system failure . Fault removal is the process

of modifying the system to correct the fault and is considered maintenance . Fault

forecasting predicts the occurrence of faults and assesses the consequences but does not

mitigate faults and is also beyond the scope of this work. This yields fault -tolerance as the

logical pursuit in attempting to improve the reliability and hence dependability, of a WSN.

2.4.2 Fault Tolerant Schemes in WSNs

Sensor Nodes ( SNs) in WSNs a re prone to failure due to energy depletion, hardware

failure, communication link errors, malicious attack and so on. Unlike the cellular and ad

hoc networks where energy has no limits in Base Station (BS) or batteries can be replaced

as needed, SNs in WSN s have very limited energy and their batteries cannot usually be

recharged or replaced due to hostile or hazardous environments. So, one important

characteristic of WSNs is the stringent power management of the SNs. Two components of

a SN, viz., sensing un it and wireless transceiver, usually directly interact with the

environment which is subject to variety of physical, chemical and biological factors. It

results in low reliability of performance of the SNs. Even if condition of the hardware is

good, the co mmunication between the SNs are affected by many factors, such as signal

strength, antenna angle, obstacles, weather conditions, interference.

The services provided by a WSN to a large extent depend on fault tolerance, because it

cannot be assumed that all sources of error can be eliminated, even through careful

engineering. By considering service availability, we understand the probability with which

a request will lead to a valid and useful response. The availability of WSN is defined as

MTTF / (MTTF + MT TR); where MTTF stands for Mean Time to Failure and MTTR

stands for Mean Time to Repair [111].

By analyzing this equation we can conclude that systems that constantly fail and require

long repair time will result in systems with very low availability. Howe ver, systems that

have a high MTTF and can be quickly repaired are considered highly available systems.

Attacks on the availability of SNs are analyzed in [112]. These attacks directly influence

the availability of services. Fault tolerant systems can over come faults and system failures,

therefore increasing the MTTF and system availability. Fault tolerance techniques


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especially crafted for WSNs have not been extensively studied so far. The most important

one is replication, which is well suited for WSNs du e to their inherent SN redundancy.

Fault Tolerance has been discussed in detail in the literature on distributed systems [113].

Although WSNs have special characteristics that distinguish them from traditional

distributed systems, many of the existing tech niques still apply. An introduction to the

fundamental mechanisms for implementing replicated services in distributed systems is

discussed in [114].

2.4.2.1 Fault Tolerance in Different Layers of the Network

Fault tolerance in the wireless sensor network m ay exist at the different layers [119]. Fault

tolerance can be classified into hardware layer, software layer, network communication

layer and application layer.

1. Hardware Layer : In this layer Faults can occur due to malfunctioning of any

hardware component of a sensor node, such as memory, battery, microprocessor,

sensing unit and network interface. Reasons behind the failure of hardware of a

sensor node may be: (i) sensor node may not always design with the high quality

hardware as sensor nodes are cost se nsitive, (ii) strict energy constraints which

restrict long and reliable performance of sensor nodes (iii) sensor network deployed

in the harsh and hazardous environments which affect normal operation of sensor

nodes.

2. Software Layer: System software and mi ddleware are the two components of the

software of a sensor node. An important function of system software is to support

distributed and simultaneous execution of localized algorithms. Software bugs are a

common source of errors in WSNs. One promising meth od is through software

diversity where each program is implemented in several different versions. Since it

is difficult to provide fault tolerance in an economic way at hardware level of a

sensor node, numerous fault -tolerant approaches are expected at the middleware

level. To adopt the real life application, there is need to develop more complex

middleware for WSNs.

3. Network Communication Layer : Faults at network communication layer are the

faults on wireless communication links. Assuming that there is no error on

hardware, link faults in WSNs are usually related to surrounding environments. In

addition, link faults can also be caused by radio interference of sensor nodes. The


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1) Node Selecti on: After it has been established that certain functionality is not

available any longer due to a failure in the primary replica, a new service provider

must be selected. After this selection phase, one or several nodes become service

providers. Several ap proaches to how the selection is performed have been

proposed.

Self-organization techniques have proven to increase the reliability and fault -

tolerance of distributed systems [46]. In the extreme case, each node makes an

individual decision (possibly takin g information from its neighbors into account).

Or, local groups of nodes work together, coordinating their actions. On the other

end, hierarchical systems assign tasks in a top -down manner. We discuss these

options in turn.

a) Self Election: In LEACH [15] , nodes periodically execute a probabilistic

algorithm to establish whether they should serve as cluster head to their neighbors.

In this probabilistic rotation system, nodes keep changing their role in the network.

When a cluster head node fails, it will take only one rotation period until another

node starts providing the functionality of the failed or absent node. Role assignment

algorithms determine which of a certain role, such as coverage, clustering and in-

network aggregation, should be assumed by a node. In [9], a deterministic

algorithm for autonomous role assignment is proposed that takes into account

properties of the node such as battery status and location, but also its neighborhood

and the roles chosen by neighboring nodes.

b) Group election: In [12], a reallocation of nodes that were part of a cluster that

suffered a cluster head failure is proposed. The cluster head , called gateway, is

considered to be a resourceful node. The solution presented considers that all the

gateways in the network ma intain a list of the nodes that are currently in their

cluster and another backup list of nodes that could become part of their cluster.

When a gateway fails, the nodes from its cluster are reallocated to the other

gateways that have the nodes in their bac kup lists. If more than one gateway has a

specific node in its backup list , the node is assigned to the cluster head that has the

smallest communication cost.

c) Hierarchical election : In a hierarchical election, a coordinator selects the new

primary node. This applies to the rebuilding of routing paths [37] as well as the

selection of a new cluster head [13]. The former describes an algorithm to select the


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node that is closest to the base station. The latter approach applies fuzzy logic in the

base station to select which node will become a cluster head. This algorithm makes

use of a fuzzy descriptor, the node concentration, energy level in each node and its

centrality with respect to the entire cluster. Although these centralized algorithms

could perform a better selection of the nodes than a local algorithm due to its global

view of the network, such approaches require that nodes send periodically messages

to the base station.

2) Service Distribution : During this phase, nodes elected to become service

providers must activate the service. In some cases the service is already available on

the nodes and a simple configuration change to inform the node that this service

should be activated is required. However in some cases, for instance when nodes do

not have enough memory to store the code of all potential services, it is necessary to

inject code into the node through some technique. There are different techniques

that can be used for service distribution: completely reprogramming the node,

sending entire blocks of executable code or sending small pieces of code such as

scripts.

a) Pre-Copy: Pre-copying as described in [9], consists in making the code of all

services available on all nodes before deployment. This allows nodes to change

their behavior according to the role that they are assigned to.

b) Code Distribution: Several approaches have been proposed for disseminating

code throughout the network. Maté [21] is an example for a bytecode interpreter

for TinyOS where code is broken into capsules of 24 instructi ons. These capsules

can be distributed through the network and installed on nodes, which start to

execute the new code. Agilla [8] is a Maté -based mobile agent middleware for

programming wireless sensor networks. These mobile agents can be programmed to

move through the network or replicate themselves to other nodes according to

changes in the environment. Impala [23] is a middleware for sensor networks that

supports software updates and on -the-fly application adaptation. Unlike Maté, the

focus of Impala is networks that have a high degree of mobility, which can lead to

long delays until an update is finished. While Maté stops the execution of an

application until the update is finished, Impala processes ongoing software updates

in parallel.


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c) Remote Execution: On the one hand code migration is an approach that reduces

the amount of memory required in the entire network since not all nodes need to

have the application pre -installed, on the other hand it consumes energy on the

nodes exchanging the code and is susceptible to link failures, which could cause

long delays until the code update is completed. Remote Execution [34] [32] is an

alternative approach where low power devices transfer tasks to more powerful

devices without transferring the entire application code. Instead, only the required

state information is transmitted. Such an approach is especially suited for

heterogeneous sensor networks with at least some resourceful nodes.

2.5 SUMMARY

In this chapter, we have pr esented a state of art literature review on reliability and fault

tolerance in WSNs. We have also provided the background and terminology for the

subjects of WSN, neural networks and Markov chains. In the next chapter, Markov model

for a redundant system h as been presented to analyze the reliability in terms of MTTF

(Mean-Time-To-Failure).

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CHAPTER-2 LITERATURE REVIEW - Shodhgangashodhganga.inflibnet.ac.in/.../10604/10/10_chapter2.pdfChapter 2: Literature Review 17 A typical hardware structure of a sensor node is shown