28

CHAPTER 2

LITERATURE SURVEY

2.1 INTRODUCTION

Wireless sensor network is a disseminated independent sensor to observe physical

or ecological condition, such as level of temperature, level of noise, pressure depending

on environmental condition, etc. The sensor nodes are designed to send their data over all

networks to a central point of location. All sensor node activities are managed by a

central node in the up- to-date wireless sensor network. Each sensor node is associated to

all adjacent nodes to communicate with each other.

Data aggregation is a current techniques mostly used in wireless sensor networks.

Wireless Sensor Networks (WSNs) are compilation of sensor nodes that can monitor or

control physical or environmental conditions cooperatively. Data aggregation is a specific

process of aggregating data from many sensors to eradicate unnecessary broadcasting and

offers fused information to base station or sink node. Data aggregation usually employs

the fusion of data from multiple sensors at mediator nodes and transmits aggregated data

to sink.

Ant Colony Optimization with State Transition Ant Rule (ACO-STAR) is

introduced for achieving better reliable data aggregation computation capability.

Primarily, ACO is a significant factor to cluster the foraging movement of ants (i.e.) data

29

in wireless sensor network. The state transition rule is used to deeply analyze the foraging

movement of ants. State transition rule supports with direct way to balance between older

ACO - based data aggregation sensor system to new STAR - based effectual data

aggregation.

Fuzzy Ant Colony Optimized Clustering (FACOC) is introduced to achieve better

energy efficiency with optimization result. FACOC based on Node Degree Centrality is

used for effective dynamic clustering with cluster head. Fuzzy ant colony clustering

makes possible the same sensor node in more than one cluster with different degree of

membership functions, which inherently support the overlapping operation. FACOC

mechanism achieves computation from simple marginal degree to distances along

Euclidean center axes for energy efficient data aggregation.

Additionally, Hybrid Meta-heuristic Genetic method (HMG) is introduced for

multi - sink aggregated data transmission in wireless sensor network. Ant-fuzzy Meta

heuristic Genetic method carries out classification process on aggregated data. The

classification based on genetic method uses the Tabu search - based mathematical

operation to achieve the sufficient solution on multiple sinks. The classified records

perform the Tabu search operation to transmit the aggregated data to the multiple sink

nodes.

30

2.2 EFFECTIVE DATA AGGREGATION IN SENSOR NETWORK USING ANT

COLONY OPTIMIZATION WITH STATE TRANSITION ANT RULE

Ant Colony Optimization with State Transition Ant Rule (ACO-STAR) is

introduced for achieving reliable data aggregation computation capability in wireless

sensor network. Primarily, ACO is a significant factor to cluster the foraging movement

of ants (i.e.) data in wireless sensor network. The state transition rule is used to deeply

analyze the foraging movement of ants. State transition rule supports with direct way to

balance between older ACO based data aggregation sensor system to new STAR - based

effectual data aggregation. Ant colony system with STAR algorithm provides natural and

intrinsic way of exploring the search space for determining optimal data aggregation in

wireless sensor network. The solution of ACO with STAR progressively achieves the

global optimal solution throughout efficient forwarding of packets in terms of regulating

the clustering result based on quantities of foraging movement of ants in sensor network.

2.2.1 Energy efficiency with effective data aggregation in wireless sensor network

Yue Hsun Lin et al. [1] introduced a new concealed data aggregation scheme

(CDAMA) for homomorphism public encryption system. The data aggregation technique

is used to minimize the large amount of data broadcasting in wireless sensor network.

The Concealed Data Aggregation Scheme used for Multiple Applications depends on

many conditions. First one, the authors used multi application environment and accurate

31

data from aggregated cipher texts. After that, the author moderates the issues of

compromising attacks within the single application environments. Finally, it degrades

damage from illegal aggregations. As a result , CDAMA attains better performance in

terms of robustness and effectiveness.

Chi Lin et al. [2] suggest that data aggregation and ant colony algorithm involve

three stages such as initialization, packet transmission and operation on phenomenon.

The main issues in wireless sensor network are energy efficiency in data communication

between nodes. Most of the researchers applied energy efficient techniques to achieve

better energy efficiency for extending the lifetime of the network. The data transmission

from one node to another from aggregated cipher text depends the remaining power of

the node and the amount of pheromone of neighbouring node Consequent on certain

rounds of transmissions, pheromone alterations are performed, which takes the

compensation of both total and local merits for disappearing or depositing pheromones.

Suat Ozdemir and Hasan Cam [3] introduced data aggregation and authentication

protocol (DAA) which is used to forecast false data detection with data aggregation and

privacy. Most of the sensor nodes cooperate with each other node to insert the false data

during data aggregation and data broadcasting. This protocol maintains data aggregation

with false data recognition, observing nodes of every data aggregator to achieve data

aggregation and compute corresponding short size message verification codes for data

32

authentication. The authors used to maintain confidential data transmission and sensor

nodes among successive data aggregator confirm data integrity level on encrypted data.

Liu Xiang et al. [4] considered on wireless sensor network that achieves the data

gathering with the objective of acquiring the entire data set at base station. They

introduced innovative data aggregation scheme that utilizes compressed sensing to attain

recovery fidelity and power competence in wireless sensor network with arbitrary

topology. This scheme uses the diffusion wavelets to discover a sparse origin that

distinguishes the spatial (and temporal) correlations well on random wireless sensor

networks. It allows straightforward compressed sensing - based data aggregation in

addition to elevated fidelity data recovery at sink node. The authors proved NP-

Completeness by using mixed integer programming formulation beside greedy heuristic.

The compressed sensing is an effective data aggregation technique which is able to

deliver the data to sink node with elevated fidelity as well as achieving better energy

reduction.

Hsueyin et al. [5] initiated Localized Power-Efficient Data Aggregation Protocols

(L-PEDAPs) to achieve energy competent data aggregation tree methods with localized

self -managing, robust systems. LPEDAP protocol depends upon topology such as LMST

and RNG that expected minimum spanning tree and efficiently calculated using distance

information of one hop neighbor. The original routing is created based on topology

setting. They also regard different selection levels as constructing routing in tree.

33

LPEDAP protocol also comprises routing repairs that may be achieved when a sensor

node does not make it or new node is added to sensor network. LPEDAP protocol is used

to reduce the energy level and also to improve the lifetime of the network.

Liang et al. [6] prepared a traveling salesman problem with neighborhoods

(TSPN) and due to NP-hardness problem. The mobile elements created a novel

dimension to minimize and balance energy consumption in WSN. Nevertheless, data

gathering latency may become high due to reasonably less travel speed of mobile

elements. They introduced a combine-skip-substitute (CSS) scheme to achieve better

solutions within a small range of lesser limit of optimal solution. They also initiated a

multi-rate combine-skip-substitute (MR-CSS) for minimizing latency in data gathering.

Leandro et al. [7] introduced a new Data Routing for In-Network Aggregation

called DRINA. The energy consumption is a main problem in wireless sensor network.

The data fusion and aggregation must be depressed with save energy level. The redundant

data is to cooperate at intermediary nodes which minimize the size and number of

exchanged messages to provide low level of transmission costs and energy utilization.

The DRINA is used to reduce the number of messages for construction of routing tree,

creating the most number of overlapping paths, elevating data aggregation, and

dependable transmission in data aggregation.

34

Fong Pong and Nian-Feng Tzeng [8] introduced an innovative storage design for

IP routing table generation .The IP routing table generation is established based on a

single set associative hash table to support longest prefix matching. The proposed method

comprises two main techniques to lessen table storage needs radically. First one is to

store storing transformed prefix representations and to accommodate multiple prefixes

per table entry using prefix aggregation, to achieve superior storage-efficiency (SUSE).

There are four main parameters used in search for an LPM solution, incorporating small

table storage, less lookup latency, trouble -free route updates and less energy dissipation.

2.2.2. Effective data aggregation in sensor network using ant colony optimization

Cunqing Hua and Tak-Shing Peter Yum [9] established an optimal routing and

data aggregation scheme in wireless sensor network. The optimal routing and data

aggregation is used to extend the lifetime of network by equally optimizing the data

aggregation and routing techniques. The proposed model is to include data aggregation

and routing technique to present smoothing approximation function for optimization

problem. By using the distributed gradient algorithm data optimality is achieved. In

addition it is used to minimize the the data traffic and expand the lifetime of network.

In certain applications, the position of events reported by sensor network requires

to remain anonymous. Specifically, unauthorized observers should not be capable of

sensing the origin of such events by analyzing the traffic of network. Basel et al. [10]

35

introduced two main functions such as interval indistinguishability and offered

quantitative assess to model anonymity in wireless sensor networks. Second one, it maps

source anonymity to the arithmetical issue of binary hypothesis testing with nuisance

parameters. This technique is used to improve the anonymity level in wireless sensor

network.

The sensed data is collected and data gathering tree is frequently created as an

associate network in wireless sensor network. Energy saving is essential in such networks

using periodic sleep-wake cycles. Periodic sleep-wake cycles are used to achieve better

energy saving at each sensor node. Ungjin et al. [11] considered the trouble of scheduling

sleep-wake cycles of nodes in data gathering tree under deadline constraint. The optimal

wake-up frequency assignment (OWFA) algorithm is used to minimize the delay and data

rate at sensor nodes. Optimal wake-up frequency assignment is used to achieve better

results in terms of average energy utilization and lifetime of network with individual data

rates.

The major problem of wireless sensor network is privacy-preserving access

control for users and data owners. Rui et al. [12] used Distributed Privacy-Preserving

Access Control to provide the privacy preservation in wireless sensor network. Users in

Distributed Privacy-Preserving Access Control buy tokens from network owner, and

sensor nodes respond after complete validating of the tokens. This scheme introduced

distributed token reuse detection (DTRD) to avoid malicious users from reusing tokens

36

without involving the base station. STRD method is used to achieve better performance

in terms of token reuse detection capability, communication overhead, storage overhead

and attack resilience.

Chien et al. [13] suggest many data aggregation methods based on homomorphism

encryption with privacy are used in wireless sensor networks. This data aggregation

method is used to achieve improved security as cluster head aggregates cipher text

without decryption, in addition to reducing overhead of transmission. The sink node only

gets better aggregates result, not individual data. Base station cannot improve maximum

value of all sensing data. Summation of data sensing is to achieve better aggregated

result. Base station does not authenticate data integrity and accuracy via attaching

message digests or signatures to all sensing samples.

Hamid Al-Hamadi and Ing – Ray Chen[14] established redundancy management

of heterogeneous wireless sensor network and used multipath routing for user queries

with occasion of untrustworthy and malicious nodes. This method prepared tradeoff

optimization trouble for energetically deciding best redundancy level applied to multipath

routing for intrusion tolerance. It increases the query response success probability to

improve the lifetime of the network. The voting-based distributed intrusion detection

algorithm is used to notice malicious nodes in heterogeneous wireless sensor network.

37

Jiguo et al. [15] introduced cluster - based routing protocol for wireless sensor

network with non - uniform node [15] distribution technique. It integrates energy - aware

clustering algorithm and cluster - based routing algorithm. An energy - aware clustering

algorithm is used to achieve efficient level to generate clusters of even sizes. At the same

time, cluster - based routing algorithm is used to achieve better improve forwarding tasks

of nodes in scarcely covered areas by energy cluster heads. The cluster head is to make a

decision of nodes with higher energy and fewer member nodes as their next hops. Finally,

it is used to load balancing within the cluster head. As a result, cluster -based energy -

aware routing algorithms are used to achieve lifetime of network by minimizing energy

level.

The wireless sensor networks holding more number of nodes with controlled

energy power are deployed to collect helpful data from the fields. In wireless sensor

network, it is a vital issue to gather data in energy effective method. Most of the research

work is used as swarm intelligence - based optimization technique in wireless sensor

network. Selcuk Okdem., and Dervis Karaboga [16] introduced an innovative scheme

using Ant colony optimization algorithm for WSN with steady nodes. It implemented a

minute sized hardware component as a router chip. Ant colony optimization algorithm

offered capable solutions allowing node designers to efficiently operate routing tasks.

38

2.2.3 Clustering based data aggregation with using Ant colony algorithm in WSN

In wireless sensor network, intrusion detection is important in more applications in

finding malicious or unexpected intruders. Yun et al. [17] introduced Gaussian-

distributed wireless sensor network by distinguishing the detection likelihood. With

respect to application requirements and network parameters cooperation is needed for

single sensing detection and multiple sensing detection environments. In addition,

performance of Gaussian-distributed wireless senor network is compared with uniformly

distributed wireless senor network.

Ozlem et al. [18] deliberate on searching and approximating the number of various

methods using reasonable simulation representation below many-to-one communication

pattern known as unit cast. This scheme is set up on single frequency channel with the

need to reduce number of time slots essential with whole converge cast. Next, it

combines scheduling with broadcast power control which is used to self-effacing effects

of interference, and energy control with support of sinking schedule length. It is fewer

than single frequency; scheduling of transmission using multiple frequencies is more

capable.

Remya .K, and D .Keerna [19] introduced integration of Energy-efficient Trust -

based data aggregation (ETA) and routing protocol which depend on ant colony

39

optimization in WSN. These techniques are used to attain dependable and energy

effective data aggregation along with power prediction to evade route concentration.

Energy-efficient Trust - based data aggregation is used for functional reputation and trust

management to reach better reliability. Functional reputation is used to choose the nodes

for better assurance condition to be an aggregator on the origin of node quality. Ant

colony algorithm is used to search and select the optimal path based on the ants with the

cluster head and to send the data packet to base station by using multi hop transmission.

Ant colony algorithm is used to achieve better reliability, energy level and increases the

lifetime of the network.

Chia-Feng Juang, and Po-Han Chang [20] introduced the fuzzy-rule-

based systems using continuous ant-colony optimization (FRCACO) technique in

wireless sensor network. Fuzzy-rule-based systems using continuous ant-

colony optimization decide the number of fuzzy based rules and all the parameters to

optimize in each rule of fuzzy. This technique used online rule generation scheme to

decide the number of rules and to find appropriate parameter for fuzzy rule and optimize

the parameters by using ACO. This technique attains better learning accuracy. In fuzzy-

rule-based systems using continuous ant-colony optimization (FRCACO), the path of an

ant is considered as an integration of antecedent and resulting parameters from all fuzzy

rules.

40

In wireless sensor network, the sensor nodes are prearranged randomly. The

routing is demanding assignment in wireless sensor network. Saira Banu and Dhana

Sekaran [21] introduced a New Multipath Routing Approach (NMRA) for attaining better

energy consumption in wireless sensor network. Three phases are used to get better

energy consumption such as multipath routing, optimal energy path and energy

consumption model. The multipath routing is used to construct the routing based on

multipath selection. After that the optimal path is established based on energy

consumption. The energy consumption model is used to get better energy level in

wireless senor network. The NMAR is used to attain better performance in terms of

delivery ratio, network lifetime and energy consumption.

The main problem in wireless sensor network is energy efficiency. There are more

number of routing protocols used for related issues in wireless sensor network.

Hierarchical cluster-based routing is an effective method to route the sensed data and

send the data to base station node. Sohini Roy and Ayan Kumar Das [22] introduced

Energy Efficient Clustering Algorithm for data aggregation and Multipath Routing

Protocol Based on Clustering and Ant Colony Optimization (MRP) in wireless sensor

network. This protocol is used to concentrate on various event - based cluster formation

and cluster head selection. The aggregated data is sent to the base station by using the

cluster head in wireless sensor network.

41

In wireless sensor network, more number of routing protocols are used by many

researchers. Parul Saini and Ajay K Sharma [23] introduced an energy - efficient cluster

method for heterogeneous wireless sensor network. An energy - efficient cluster method

is used to alter the threshold values of node to select the cluster head with the cluster

members. This method is called Threshold Distributed Energy Efficient Clustering in

wireless sensor network. This method achieves better performance in terms of energy

efficiency, lifetime of network, fault tolerance and reliability.

Wireless AdHoc and Sensor Networks (WASNs) provide effortless, effective and

cheaper resolution for real life multidisciplinary troubles as in armed forces robotics,

climate forecasting and medicinal sciences. The energy control and security issues come

directly to mind as conservation of WASN. As the areas of WASNs are growing,

securities and power supplies are to be considered with special concentration. Jyoti

Kaurav and Kaushik Ghosh [24] used three major areas of WASN based on energy

efficiency such as battery, circuitry and topology -based routing protocols. The technique

is used to reduce the energy efficiency by lessening the number of transmissions and data

aggregation is a broadly used technique.

Energy efficiency is controlled to improve the network lifetime in wireless sensor

network. Cluster - based routing protocols are used to attain better energy as well as

expand the network lifetime. An intra - clustering communication is a main method used

for better energy consumption based on clustering protocols. Intra - cluster energy

42

efficiency is based on the location of cluster head in the cluster members. Vipin Paul [25]

introduced Smart Cluster Head Selection for trouble-free and competent cluster head

selection in wireless sensor network implementing distributed clustering techniques.

There are two areas separated in SCHS such as border and inner areas. Inner area is

responsible for cluster head. SCHS minimizes the intra- cluster communication distance

with LEACH protocol. SCHS is used to achieve better performance in terms of lifetime

of network and energy effectiveness.

The wireless sensor network provides energy efficient data aggregation methods to

develop the essential communication between nodes. The data aggregation is used to

many protocols to achieve energy efficiency and lifetime to attain the reliability in

wireless sensor network. Schemes like concealed data aggregation scheme,

Localized Power-Efficient Data Aggregation Protocols, optimal wake-up frequency

assignment,fuzzy-rule-based systems using continuous ant-colony optimization,

Multipath Routing Protocol Based on Clustering and Ant Colony Optimization and Smart

Cluster Head Selection are used to enhance the energy efficiency of data communication

in WSN.

43

2.3 FUZZY ANT COLONY OPTIMIZED CLUSTERING BASED ON DEGREE

CENTRALITY IN WIRELESS SENSOR NETWORK FOR ENERGY

EFFECTIVE DATA AGGREGATION

The Fuzzy Ant Colony Optimized Clustering (FACOC) is used to achieve

better energy efficiency with optimization result. The node degree centrality is used here

to select the cluster head .The same sensor node is used in more than one cluster with

different degree membership function, which inherently supports overlapping operation.

This overlapping operation enhances the flexibility during sensor node failure. The

FACOC mechanism achieves computation from simple marginal degree to distances

along Euclidean center axes for energy efficient data aggregation.

2.3.1 Energy efficiency - based on clustering with using Fuzzy ant colony algorithm

Frequently, road networks are distinguished by their huge dynamics comprising

different entities in interactions. This leads to more difficulties in road traffic

management. Habib et al. [26] introduced adaptive multi- agent system depending upon

ant colony behavior and hierarchical fuzzy model in wireless sensor network. This

method facilitates effectively the road traffic according to the genuine time modification

in road networks by the incorporation of an adaptive vehicle route control system.

Adaptive multi- agent system depends upon ant colony behavior, and hierarchical fuzzy

model is implemented in multi-agent environments to improve the total road traffic

quality in terms of time, flexibility and adaptivity.

44

Coverage conservation is a main function of QoS requirements in wireless sensor

networks; however this issue has not been adequately explored in the context of cluster -

based wireless sensor networks. Particularly, it is not recognized how to choose the best

candidates for cluster head in applications involving complete coverage of monitored

coverage area over extensive periods of time. Stanislava Soro and Wendi B. Heinzelman

[27] concentrated on the cluster head selection issue, exclusively concentrating on

applications where the upholding of entire network area coverage is the main constraint.

The cluster - based network organization is depending on sets of coverage aware of cost

metrics that support nodes deployed in closely occupied network areas as better

candidates for cluster head nodes, lively sensor nodes and routers.

Wireless Sensor Networks turn active rapidly, when an event happens in order to

act in response to an event. Samimi et al. [28] introduced a new fuzzy congestion

controller in wireless sensor network. The fuzzy congestion controller is used to identify

and evade congestion by developing the ad hoc fuzzy rule base in addition to membership

functions. There are used two types of parameter such as channel load and queue size

within intermediary nodes. These parameters comprise the input to Fuzzy congestion

controller. The output of Fuzzy congestion controller is obtained in conjunction with

Fuzzy Rules Base and Fuzzy Inference Engine. The congestion is controlled by using

sending rate. As a result the FCC is used to attain better performance in terms of packet

loss rate, throughput as well as energy saving.

45

Thrasyvoulos et al. [29] concentrated on, on end – to – end path establishment in

case of routing method failure before any data is sent. Most of the researchers used in

flooding – based routing techniques. Flooding – based routing techniques have an

elevated probability of delivery, waste of more energy and it suffers from severe

contention which can considerably corrupt their performance. Also, the intended efforts

to minimize the overhead of flooding technique have frequently been plagued by huge

delays. This scheme introduced a new routing technique “Spray” which is copy used to

some message into network, and then route each copy separately towards destination.

Sudakshina Dasgupta and Paramartha Dutta [30] introduced Game theoretic

approach to select a cluster head for each cluster in wireless sensor network. The game

theoretic holds the single round or repetitive information. Nevertheless, the clustering

issue in wireless sensor network is interrelated to self-organization of nodes into huge

groups and selections of Cluster Head. This game theoretic is based on the clusters

election for wireless sensor network. A game of scheduling of nodes responsible for

cluster head is an interactive decision making progression among a set of self-centered

nodes.

Ha Dang, and Hongyi Wu [31] introduced cluster - based routing protocol for

delay - tolerant mobile networks. The fundamental idea is to disseminate group of mobile

nodes with related mobility pattern into the cluster. This network shares the resource used

46

for reducing overhead and load balancing as well as attaining effective and scalable

routing in delay tolerant mobile networks. The authors introduced exponentially weighted

moving average (EWMA) method for on-line modernization of nodal contact probability

with its mean confirmed to converge to true contact probability. The exponentially

weighted moving average is used to achieve better efficient of data aggregation based on

clustering method. As a result the intended system is used to achieve better performance

in terms of elevated delivery ratio, less overhead and end - to - end delay.

Wireless sensor network is used to control and measure physical characteristics

from remote and occasionally hostile environments. In these conditions the sensing data

accurateness is an essential attribute for these applications such as , complete objectives,

requiring competent solutions to find out sensor anomalies. Daniel-Ioan Curiac and

Constantin Volosencu [32] introduced correct operation of sensors for sensing anomaly

discovery and also five various dynamical models are used to offered efficient solution.

An ensemble provides dependable estimation to modify the invalid measurement

provided by the sensor.

Data aggregation is an important method in wireless sensor network. It is the

process of aggregation of data from multiple sensors to eliminate unnecessary data

broadcasting and provided data fusion information to sink node. The data aggregation

algorithm is used to attain enhanced data aggregation with improve network lifetime by

reducing energy utilization. The cluster and hierarchical network is important for data

47

aggregation and sensor nodes are divided in top various groups and function as different

roles in wireless senor network. Muhammad Umar Farooq [33] concentrated on

Computational Intelligence which combines elements of learning, adaptation, evolution

and fuzzy logic to resolve complicated problems. The author introduced computational

intelligence, which comprises reinforcement learning, evolutionary computing and fuzzy

computing, techniques that use swarm intelligence and artificial immune systems.

2.3.2 Clustering tree based data aggregation with fuzzy ACO using optimization

technique

In wireless sensor network, event detection is the essential part of wireless related

applications. Most of the present event - based description and detection rely on using

exact values to identify event thresholds. Krasimira et al. [34] used fuzzy values as an

alternative of crisp ones to get better accurateness of event detection in WSN. Fuzzy

logic offers high level of accuracy in event detection with accurate classification

algorithms. Fuzzy logic is an exponentially increasing size of fuzzy logic rule-base. The

main challenges in wireless sensor network are limited memory and storing large rule

base.

Xiaonan Wang and Huanyan Qian [35] introduced constructing an Internet

Protocol (IP) version 6 over low-power wireless personal area networks (6LoWPAN) in

Wireless sensor network. The cluster generation algorithms are used to separate node

with maximum number of neighbor separated node that initiates cluster generation

48

process. So , the total number of cluster head is reduced. Cluster tree architecture with

cluster construction algorithm is used to reduce the nodes within the cluster tree and this

reduced the cost. Cluster tree repair algorithm is used and if any cluster head node fails or

shifts, a new cluster head is selected by the member nodes to maintain the clustering

topology.

Jose et al. [36] introduced a new method Fuzzy Inference Systems and ant colony

Optimization for multi - path routing protocols to decide the best route. Fuzzy system is

used to compute degree of route quality depending upon number of hops and less energy

level between nodes that structure the route. Ant colony optimization algorithm is used to

modify the rule base fuzzy system to enhance the level of classification in route and

moreover to maximize the energy efficiency level. As a result, Fuzzy Inference Systems

and ant colony Optimization are used to achieve energy, number of received message and

cost of received message.

Energy efficiency is one of the main problem factors in wireless sensor network.

Grouping affords an effectual way for encircling the network lifetime and S.Balaji and

V.Saranraj [37] introduced double cluster-heading clustering algorithm using particle

swarm optimization. Cluster-heading clustering algorithm is used to generate two cluster

skulls. The leading cluster is decided and the immorality cluster-head needs the current

state information, with location and energy reservation about nodes and adjacent nodes,

as each node encompasses the list of information about adjacent node and position using

connected dominant set. The dominant cluster head (DCH) obtains masses of data to

49

forecast directly. As a result, the system is used to achieve better energy consumption and

it increases the lifetime of network.

The main problem in wireless sensor network is network lifetime. Generally ,

network lifetime gets reduced due to redundant data processing by all sensor nodes and

sink node. Energy consumption is the main problem due to the lifetime of the network in

wireless sensor network. Tripti et al. [38] used more number of algorithms like Low-

Energy Adaptive Clustering Hierarchy (LEACH), Stable Election Protocol (SEP),

Distributed Energy-Efficient Clustering (DEEC) and so on. This scheme introduced

Fuzzy - based Redundancy Avoidance protocol to select the cluster head based on fuzzy

logic. It is used to eradicate the redundant data.

Dr.laxman et al. [39] concentrated to find out the different types of attack as well

as to repair the attacks in wireless sensor network. Normal outlier detection systems are

not directly relevant to wireless sensor network due to the nature of sensed data, definite

requirements and restrictions of wireless sensor networks. It introduced the supervised

learning and classification - based data mining technique based on attack detection,

recognized by the affected sensor nodes in a mostly organized cluster - based wireless

sensor network beneath common outlier detection framework.

Management of trust and reputation representation over disseminated systems

have been intended as new and exact way of dealing with various security insufficiencies

which are inherent to distributed environments. Firas Ali Al-Juboori and Sura F. Ismail

[40] proposed Linguistic Fuzzy Trust Model (LFTM) to improve the interpretability of

50

Bio-inspired Trust and Reputation Model (BTRM) in wireless sensor network. Linguistic

Fuzzy Trust Model constructs a closer decision to final user with moderately improving

accurateness rate. The inference power of fuzzy logic depends only between the

originally requested services and essentially received one in wireless sensor network.

Network lifetime is a significant issue for utilizing wireless sensor networks in

space and excessive environments. This is due to the actuality that sensing node power is

mostly consumed by broadcasting. Taheri et al. [41] introduced a multi-hop clustering

algorithm using the fuzzy logic improvement methods to increase the lifetime of network.

Cluster head selection process is based on residual energy, node proximity to its neighbor

distance to base station and node concentration. Multi hop communication - based cluster

nodes among cluster head minimize the energy level in network. Multi-hop clustering

algorithm is used to compare with LEACH, TLCP and EHEED algorithms in MATLAB

environment. The proposed method achieves better performance based on FND, HND

and LNA with increasing the lifetime of network

2.3.3 Balancing Energy Consumption to Maximize Network Lifetime in Data-

Gathering Sensor Networks

An energy control is an important issue in wireless sensor networks. Xue et al.

[42] introduced distributed energy optimization technique used for target tracking

applications. The entire sensor nodes are crowded jointly by maximum entropy

clustering. The parallel sensor deployment optimization is used to isolate many sensing

fields. Dijkstra and grid exclusion algorithm are used to calculate the area coverage and

51

energy consumption in each sensor node. Cluster heads attained parallel particle swarm

optimization to utilize with coverage area and minimize the energy efficiency level.

Dynamic energy management scheme is used in dynamic awakening and optimal sensing

methods. The alternative of sensor node is optimized facilitating sensing accuracy and

energy consumption.

Battery power and energy efficiency is a major problem in wireless sensor

network. The clustering method offered a well-organized scheme for increasing of the

life time of network. Ruihua et al. [43] introduced maximum votes and load-balance

clustering algorithm used to nodes are collected the vote and then to compute the whole

number of votes to be expected the sensor nodes share with each other based on

overall votes each one has received. The algorithm is totally distributed, locating

uninformed and autonomous network ranges and topology.

The data collection is an important role of wireless sensor network and data

gathering trees are used to generate aggregation operation. This technique is called

as Data Aggregation Trees. He et al.., [44] focused on constructing a Load Balanced Data

Aggregation Tree in Probabilistic Network Model. The author concentrated on three

problems such as Load-Balanced Maximal Independent Set (LBMIS) problem, the

Connected Maximal Independent Set (CMIS) problem, and LBDAT construction

problem. Connected Maximal Independent Set and Load-Balanced Maximal Independent

Set are illustrious as NP hard problems.

52

Energy efficiency and network lifetime is an increasing problem in wireless sensor

network. Feng et al. [45] used energy efficient routing protocol to reduce energy

consumption and sleep scheduling methods to minimize the cost in addition to increases

the lifetime of the network. The problem is altered into an equal Signomial Program (SP)

throughout relaxing flow preservation constraints. The Signomial program is determined

by iterative Geometric Programming. The optimal routing and sleep scheduling methods

are used to enlarge the network lifetime. The near optimal resolution provided by this

effort opens up novel potential for scheming sensible and heuristic schemes.

Mottola and Picco [46] introduced Adaptive Energy-Aware Multisink Routing in

Wireless Sensor Networks. MUSTER routing protocol particularly developed for multi -

hop communication is originally used to develop analytic model to compute in a

centralized method. The optimal solutions are provided by the routing system in multiple

sources and multiple sinks. The MUSTER protocol is used to extend lifetime of network,

in addition reducing the number of nodes occupied in many - to - many routing and o

stabilize their broadcasting load. MUSTER protocol is used to minimize the energy

consumption level and enlarge the lifetime of the network.

Yaxiong et al. [47] introduced new sleep scheduling algorithm for wireless sensor

network. This sleep scheduling algorithm is also called as virtual backbone scheduling.

Virtual backbone scheduling is used to minimize energy and enlarge network lifetime.

53

The wireless sensor network applications desired redundant sensor node to achieve fault

tolerance and quality of service. Nevertheless, the related redundancy may not be

necessary for multi - hop communication due to light traffic consignment

and stable wireless links. Virtual backbone scheduling structures are to be associated with

enhancing the lifetime of the network. In Virtual backbone scheduling, is easily

forwarded by backbone nodes and relaxes of sensor nodes to find a way out to save

energy level.

Energy consumption is a main issue with raising an energy resourceful clustering

protocol. Hierarchical clustering algorithms are used to enhance the lifetime of the

network. Cluster algorithm is based on two phases like setup and steady level. Dilip et al.

[48] introduced a new algorithm used to choose the cluster head , and each sensor node is

clustered hierarchically. If number of nodes increases in wireless sensor network, the

system needs more energy for data transmission. So the sensor nodes are rapidly

distributed and connected with base station node. This algorithm established energy -

efficient heterogeneous cluster - based scheme for wireless sensor network.

HaiboZhang and HongShen [49] determined on the issue of enlarged lifetime of

network throughput to balance energy utilization for constantly deployed data gathering

sensor networks. This technique introduced energy-balanced data gathering to attain the

efficient energy usage and increases the lifetime of the network. Localized zone-based

routing technique is used to provide guarantee to balance the energy consumption

54

between nodes inside every corona. It initiated a centralized algorithm based on time

complexity to solve broadcasting and data distribution problem aimed to balance the

power usage between nodes in dissimilar coronas. As a result , the centralized algorithm

is used to achieve reduced energy efficiency and also increases the lifetime of the

network.

Abdel Salam and Olariu [50] considered geographic area inhabited by small

sensors, each and every conceivably no superior than a dime. Sensor node is used energy

to expend the most of the network lifetime in sleep and wake up and do various behaviors

in wireless sensor network. The main involvement of this exertion is offered to

mathematical analysis of ESD from perception of observed events. It is provided to the

design level that probabilistically remains ESD at each stage desired by quality of service

requirements and also used the fully distributed sleep schedule with rapid control the duty

cycle of sensor nodes within the sensing coverage area based on the adjacent neighbor.

As a result this technique is used to provide to balance energy consumption and increase

the lifespan in network.

Finally, many protocols are used to provide energy efficient and data aggregation

based on cluster tree using ant colony optimization. The data aggregation is used in many

protocols to achieve energy efficiency and lifetime in addition to achieve the

responsibility. Using various energy efficiency - based schemes like MUSTER,

6LoWPAN, Fuzzy congestion controller, Fuzzy Inference Systems and ant colony

55

Optimization improves energy efficiency in data communication in wireless sensor

network.

2.4. ANT-FUZZY META HEURISTIC GENETIC SENSOR NETWORK SYSTEM

FOR MULTI SINK AGGREGATED DATA TRANSMISSION

The Hybrid Meta-heuristic Genetic method (HMG) used for multi sink aggregated

data transmission in wireless sensor network is introduced. This technique carries out the

classification process on aggregated data. It is based on genetic method and uses the Tabu

search based mathematical operation to achieve the sufficient solution on multiple sinks.

Initially, It classifies the data record based on the weighted meta-heuristic distance.

Genetic method combines the ant and fuzzy rule to optimize the classification capability

with the weighted meta-heuristic distance. The classified records perform the Tabu search

operation to transmit the aggregated data to the multiple sink nodes.

2.4.1 Multi sink aggregated data transmission in wireless sensor network

Effective management of business progression is a major component of enterprise

information system for association in competitive business environment. Hyerim et

al.[51] introduced mixed integer programming to improve the efficiency of business

process management with considered as a correct execution process. The optimized

manufacturing process is not applied unswervingly to business processes, because of

differences among business and manufacturing processes. Mixed integer programming is

56

used for process of business execution plan and Meta heuristic algorithm is used to get

better resolution for multi activity process.

End - to - end data aggregation without degrading sensing exactness, is an

important problem in wireless sensor network to avoid network obstruction occurrence.

In addition, privacy management involves anonymity, and data integrity is conserved in

such networks. Sabrina et al. [52] introduced dynamic secure end-to-end data aggregation

with privacy called as DyDAP. The DyDAP model initiated with unified modeling

language that includes significant building block in wireless sensor network with privacy

aware system includes policy of aggregation. It is introduced by real data aggregation

algorithm using discrete-time control loop. Discrete time control loop is capable to

animatedly hold in-network data fusion to minimize the communication load.

Sensible pair - wise key distribution technique is an essential for wireless sensor

networks as sensor nodes are vulnerable to physical capture and inhibited in their

resources. Taekyoung et al. [53] introduced location - based pair - wise key pre

distribution used to attain elevated connectivity and perfect flexibility with reducing

energy consumption of resource. The entire group hierarchies and sub areas

accommodate deployment errors on group-based deployment representation. On the

whole, sensing field is separated geographically into regular square zones as various

polygons also are measured via discrete deployment strategies.

57

Joohwan et al. [54] concentrated on reducing the delay and expanding the network

lifetime in wireless sensor network. Energy consumption and sleep scheduling are exact

methods for improving the network lifetime in wireless sensor network. This scheme

used sleep-wake scheduling protocol and any cast packet-forwarding protocol is used to

expand the network lifetime and achieve packet delivery delay.

Beaconless georouting algorithms are entirely reactive and without previous

knowledge of neighbor nodes. But the existing method does not provide data delivery

information based on neighborhood information. Stefan Rührup and Hanna Kalosha [55]

illustrate two common methods for entirely reactive routing with guaranteed delivery.

This technique introduced the beaconless forwarder planarization (BFP) to decide the

exact edge of the local planner sub graph exclusive of hearing from all adjacent nodes.

Angular relaying decides unswervingly the next hop of face traversal.

Jonathan et al. [56] concentrated on the problem of positioning or repairing sensor

network to assure precise level of multipath connectivity (k-connectivity) among sensor

nodes. Guarantee at the same time offers fault tolerance besides node failures and

elevated the whole network capacity. This algorithm places an almost-minimum number

of added sensors to expand an existing network into a k -connected network for any

desired parameter k. This greedy and distributed version algorithm is used to achieve high

quality placement.

58

The traffic monitoring is an efficient promotes urban planning method and it

encourages improved use of public transport. Competent information gathering is

important in traffic monitoring method. Jin et al. [57] provided flexible structure for local

traffic information gathering in accordance with the user request. This technique used

two layer network structures for information acquirement in the context of wireless

sensor network environment. It introduced user customizable data centric routing used to

achieve exact traffic delivery information with many different user requirements.

2.4.2 Efficient Multi sink data aggregation with ant colony optimization based data

distribution in WSN

Gagan Raj Gupta and Ness B. Shroff [58] considered as class of WSN with

common intervention constraints on set of links that are served at the same time at any

given time. The technique controlled the traffic to single hop but allowed concurrent

transmissions for providing satisfy the original interference constraints. The maximum

weighted matching (MWM) is used to achieve expected delay in wireless sensor network

which increases the upper and the lower bound analysis. Maximum weighted matching is

frequently related to lower bound to achieve better delay performance

The major problems in Medium Access Control (MAC) of Wireless Sensor

Networks are sleep or wake-up scheduling, network overhead, inactive listening,

collision and power used for retransmission of collided packets. Gholamhossein et al. [

59

59] introduced adaptive quorum-based MAC protocol known as Queen-MAC. This

protocol separately and adaptively plan nodes wake-up times, diminish inactive listening

and collisions, increase network throughput, and enlarge network lifetime. Queen-MAC

is particularly appropriate for data gathering based on applications. A novel quorum

system, dygrid is used to provide low duty cycle for control wake-up times of sensor

nodes. It also established the lightweight channel assignment technique to diminish

collision level and generate concurrent transmission possible.

Nathalie et al. [60] concentrated on optimization and solution algorithms for

tragedy response development in electric distribution systems. It provides the complete

survey of optimization and solution methodologies for emergency planning problems

interrelated to electric distribution operations. These issues incorporate service

restoration, switching operation of sequencing and repair of vehicle routing

Chan Chen and Michael A. Jensen [61] concentrated on establishing secret keys

using general wireless channel, with exact importance on spatial and temporal correlation

of channel coefficients. Particularly, it considered influence of channel correlation on the

limit of key size constructed from general channel using simple single-input and single-

output channel models. This technique verifies the reality of sampling method to produce

key using the minimum possible sampling window. It considered the decorrelation

of channel coefficients in multiple-input and multiple-output channels, and the statistical

60

independence test to exhibit this process cannot be divided into spatial and temporal

decorrelation processes.

Ehsan et al.[62] introduced adaptive beamforming-based multicast system in

wireless sensor network. ADAM used to achieve joint problem of adaptive beamformer

intended at physical layer and scheduling on client at media access control layer.

Adaptive beamforming-based multicast is used to implement field programmable gate

array environment. Performance evaluation is compared with omnidirectional and

switched beamforming - based multicast.

Undersea mobile sensor networks have newly introduced as a way to discover and

monitor the ocean, offering 4D monitoring of underwater environments. Youngtae et al.

[63] considered specific geographic routing issues called as pressure routing. The main

confront of pressure routing in sparse underwater networks encloses efficient handling of

3Dvoids. This scheme introduced the Void-Aware Pressure Routing (VAPR) used for

sequence number and hop count, depth information surrounded in periodic beacons to set

up next hop route and to construct directional track to neighboring sonobuoy.

Tao Shu and Marwan Krunz [64] considered as increases the coverage time for

clustered wireless sensor network by optimal balancing of energy utilization between

cluster heads. Clustering techniques are used to minimize the energy consumption with

each sensor node, to expand the communication load on cluster head. The scheme

61

considered both intra – and inter -cluster traffic. It introduced the coverage time optimal

joint clustering or routing algorithm in wireless sensor network. It considered cone-like

sensing region with regularly disseminated sensors and offered optimal power allocation

approach that assured an upper bound on end-to-end (inter-CH) path dependability. There

are two techniques used to achieve energy consumption like routing-aware

optimal cluster planning and clustering-aware optimal random relay. Routing-aware

optimal cluster planning is used to resolve the signomial optimization by using

generalized geometric programming. The clustering-aware optimal random relay is used

solve the time based on linear

Energy consumption is a main problem for complete deployment and utilization

of wireless sensor network (WSN). Ruqiang et al. [65] introduced energy aware sensor

node to generate energy effectiveness in wireless sensor network. Energy consumption is

decreased with each sensor node and network level. An energy aware technique reduces

the energy consumption of sensor nodes and distance among transmitter and receiver is

expected before accessible transmission. All sensor nodes are set as a sleep mode among

two successive measurements for power saving in usual operating conditions. Energy

consumption is achieved by estimating energy level with entire network based on various

network configurations.

62

2.4.3 Fuzzy Meta Heuristic Genetic Sensor Network System for Multi Sink

Aggregated Data Transmission

Zhen Yu and Yong Guan [66] introduced dynamic en-route filtering technique to

detect the false injection and Dos attacks in wireless sensor network. Each sensor node

holds the hash chain of authentication keys to aggregate reports. Each node has a hash

chain of authentication keys to aggregate reports; meanwhile, a valid report must be

validated by definite number of nodes. Initially, each sensor node distributes to key to

forwarding nodes after sending the key, nodes to release key level and confirms the

report on forwarding nodes. The well-known hill climbing key dissemination system to

tolerate nodes closer to data source holds stronger filtering capacity.

Different types of protocols used for sensor network security provide privacy for

substance of messages appropriate information frequently remains exposed. Such

contextual information is demoralized by challenger to obtain sensitive information such

as location of observed objects and data sinks in sensor field. Attacks on this element

significantly challenge of any type of network application. Rajieev Gupta and Krihi

Ramamritham [67] prescribed location - based privacy issues in wireless sensor network

under hard adversary model and compute lesser bound on overhead in communication.

Communication need for achieving privacy level is based on location. There are two

methods used in wireless sensor network such as source-location privacy and sink

location privacy. Source-location privacy is used to offer location privacy to observed

63

objects with periodic collection. Sink-location privacy is used to offer location privacy to

data

Mobile ad hoc network holds the set of transmitting nodes that generate random

network topology in wireless media. This data transmitting technique expressed

diversification in message technology important to resolve inflexible end-to-end

necessities of quality of service - based communication networks. Particularly, Larry et

al. [68] concentrated to transform a cluster-based quality of service routing technique for

MANET. The major objective is to offer that fault tolerance, which is an important

feature provided that quality of service in link fail environment of mobile networks.

Wireless sensor networks are used to monitor and control each node with

environmental monitoring and security level. The main issue is to improve fault tolerance

fraction of wireless sensor network and to provide an energy efficacy rapid data routing

service. Energy consumption is an essential element factor in wireless sensor network for

each sensor node has steady power supplier. Indrajit et al. [69] implied an energy

efficient multipath fault tolerant routing protocol in wireless sensor networks and this

protocol are known as MFTR. This protocol is used in fault tolerance and control traffic

using multiple data routing way. This protocol chooses direct path for data transmission

routing techniques in MFTR

64

ZigBee topologies and ZigBee cluster-tree are independently suitable for less

energy and low level cost in wireless sensor networks. However, the limited routing of

ZigBee cluster-tree network may not be accomplished to provide sufficient bandwidth for

amplified traffic load. So more information could not be delivered successfully. Yu-Kai

et al. [70] introduced adaptive parent- based framework for ZigBee cluster tree network

to enlarge bandwidth utilization exclusive of any additional message exchange.

Distributed algorithm is used to optimize throughput in structure.

Koushik et al. [71] established redundant radix - based number (RBN) used for

data distribution with format of encoding method. RBN is used to reduce the energy level

and costs by using with modulation techniques such as ASK, OOK and FSK. It is united

with quiet periods for distribution digit 0, these encoding techniques called

RBNSiZeComm. RBNSiZeComm offered and reduced energy efficiency in data

transmission and also called as energy saving protocol. RBNSiZeCommunication uses

FSK and ASK with detection - based mechanism. RBNSiZeCommunication protocol is

used to minimize the battery level energy efficiency to enlarge the network lifetime.

Singh and Zair Hussain [72] introduced new, multi-hop, secure routing, and top-

down hierarchical protocol used to detect the attacks in wireless sensor network. This

scheme introduced symmetric key techniques with appropriate random exploitation of

wireless sensor nodes. In this protocol, the sink node starts with mixture of protected

hierarchical topology using top down manner. The enquiry stage of protocol provided the

65

assurance for participation of all cluster heads in sheltered hierarchical topology

structure. This protocol provided better confidentiality, integrity, and authenticity of

monitoring application.

Data distribution is controlled by limited battery level of energy usage sensor

nodes in wireless sensor networks. Efficiently to arrange the sensor nodes will sense

unnecessary and consistent data which may cause more spending of energy in sensor

nodes. Vidya and Arun Anoop [73] determined to offer energy usage level to reduce and

enlarge network

lifetime. Clustering techniques are used to form cluster construction of wireless

network sensor nodes, the data aggregation and routing method are accomplished by the

cluster head. As a result, cluster - based routing method attained better energy harvesting

for enlarged lifetime of network, and energy level is based on the area coverage in sensor

nodes.

Chih-Kuang et al. [74] introduced distributed and scalable scheduling techniques

to reduce data loss in wireless senor network and supports the mobility in wireless sensor

network. This scheduling technique improves broadcast collisions by exploiting virtual

grids that implement Latin Squares characteristics to time slot distribution. This

algorithm obtains discrepancy - free time slot provision schedules exclusive of obtained

global overhead in scheduling. It verified the competence of distributed and scalable

66

protocol and supported sensor mobility with appropriate data loss, lesser packet delay,

and lesser overhead.

In wireless sensor network, all sensor nodes are deployed haphazardly exclusive of

any knowledge based on network environment and even their ID's at the starting stage of

their operations. Peng et al. [75] concentrated on clustering problems with a new

arrangement of multi hop wireless sensor network. Clustering algorithms are not

appropriate due to lack of MAC link connections nodes. The author introduced a well-

organized clustering algorithm based on random link demonstration.

Finally, there are many techniques like RBNSiZeCommunication protocol,

redundant radix based number scheme, Void-Aware Pressure Routing, ADAM, DyDAP

which are used to achieve better energy efficiency with optimization results

2.5 RESEARCH GAPS

Concealed Data Aggregation Scheme (CDAMA) is used for homomorphism

public encryption system. Concealed Data Aggregation Scheme is also used for Multiple

Applications depending on many conditions and sink node is accurate data from

aggregated cipher texts. CDAMA does not support the computation capability with

compromising secret keys

67

Data aggregation and authentication protocol (DAA) is used to predict false data

detection with data aggregation and privacy. This protocol maintains data aggregation

with false data recognition, observing nodes of every data aggregator also achieves data

aggregation and compute corresponding short size message verification codes for data

authentication. DAA sensor nodes are unable to improve the network security as well as

efficiency.

Localized Power-Efficient Data Aggregation Protocols (L-PEDAPs) are used to

achieve energy competent data aggregation tree methods with localized self managing,

robust systems. LPEDAP protocol is used to reduce the energy level and also improve the

lifetime of the network. LPEDAP protocol increases the energy efficiency level in data

aggregation process.

Optimal wake-up frequency assignment (OWFA) algorithm is used to minimize

the delay and data rate at sensor nodes. Optimal wake-up frequency assignment is used to

achieve better results in terms of average energy utilization, the lifetime of the network

with individual data rates. Sensor nodes take immediate action of node formation, due to

the topology changes. So OWFA algorithm increases the energy usage level in data

aggregation.

Fuzzy congestion controller is used to identify and evade congestion by

developing the ad hoc fuzzy rules base in addition to membership functions. FCC is used

68

to attain better performance in terms of packet loss rate, throughput as well as energy

saving. Fuzzy congestion controller does not provide optimal solution in wireless sensor

network.

Exponentially weighted moving average (EWMA) methods are used for on-line

modernizing of nodal contact probability with its mean confirmed to converge to true

contact probability. Exponentially weighted moving average method is used to achieve

better efficiency of data aggregation based on clustering, delivery ratio, less overhead and

end -to - end delay. If there is any failure in cluster head with cluster formation, cluster

member cannot acquire instant further selection in cluster head.

Constructing an Internet Protocol (IP) version 6 over low-power wireless personal

area networks (6LoWPAN) is used to form a clustering in Wireless sensor network. The

cluster generation algorithms are used to separate node with maximum number of

neighbors separated node that initiate cluster generation process. Cluster construction is

algorithm used to reduce the nodes within the cluster tree as well as reduce the cost.

Cluster tree repair algorithm is used, if any cluster head node fails or shift, a new cluster

head is elected by the member nodes to maintain the clustering topology. 6LoWPAN

does efficient dynamic clustering with cluster head.

Dynamic secure end-to-end data aggregation with privacy is called as DyDAP.

The DyDAP model initiated with unified modeling language that includes significant

building block in wireless sensor network with privacy aware system includes the policy

69

of aggregation. The real data aggregation algorithm is used to discrete-time control loop.

Discrete time control loop is capable of animatedly holding in-network data fusion to

minimize the communication load. DyDAP is not supported on Classification process in

data aggregation process.

Adaptive quorum-based MAC protocol is used to control the wake up times in

sensor nodes as known Queen-MAC. This protocol separately and adaptively plans

nodes’ wake-up times, diminishes inactive listening and collisions, increases network

throughput, and enlarges network lifetime. Lightweight channel assignment technique is

used to minimize collision level and generate concurrent transmission possible. Queen-

MAC is not possible at optimal result and energy efficiency

Maximum weighted matching (MWM) is used to achieve expected delay in

wireless sensor network with increases in the upper and lower bound analysis. Maximum

weighted matching is frequently related to lower bound to achieve better delay

performance. Maximum weighted matching does not maintain the optimization result.

Coverage time optimal joint clustering or routing algorithm is used to cone-like

sensing region with regularly disseminated sensors, and it offers optimal

power allocation. Routing-aware optimal cluster planning is used to solve the

signomial optimization by using generalized geometric programming. The clustering-

70

aware optimal random relay to solve the time based on linear. Coverage time optimal

joint clustering unable to balance the efficient energy usage.

2.6 CONTRIBUTION OF THE THESIS

The main contributions of thesis are as follows.

i. Ant Colony Optimization with State Transition Ant Rule (ACO-STAR) is used to

achieve reliable data aggregation with computation capability in WSN.

ii. ACO with STAR steadily achieves global optimal solution through effective

forwarding technique

iii. Fuzzy Ant Colony Optimized Clustering (FACOC) reduces energy efficiency with

provides optimization result

iv. FACOC based on Node Degree Centrality provides effective dynamic clustering

with cluster head.

v. FACOC mechanism achieves computation from simple marginal degree to

distances along Euclidean center axes for energy competent data aggregation.

71

vi. Hybrid Meta-heuristic Genetic method (HMG) is used to attain the Multisink -

based data aggregation in wireless sensor network

vii. Ant-fuzzy Meta heuristic Genetic method performed classification process on

aggregated data.

viii. Classification based on genetic method used Tabu search - based mathematical to

achieve sufficient solution on multiple sinks.