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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
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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.
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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
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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
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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.
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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.
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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]
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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
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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.
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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.
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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
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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.
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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.
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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
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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.
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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.
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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.
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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
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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
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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
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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
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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
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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
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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.
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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.
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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
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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
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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
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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.
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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.
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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. [
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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
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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
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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.
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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
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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
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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
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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
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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
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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
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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
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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-
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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.
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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.