Computational Intelligence in WSN for Network Life Optimization

Rakesh Kumar Singh

, Ajay Kumar Bharti

1,2

Dept. of Computer Science, Maharishi University of Information Technology, Lucknow (UP),India

Keywords: Optimization techniques, Routing in WSN, Energy efficiency, Cluster head, Delay

Abstract: Computational Intelligence (CI) have been increasingly used by researchers in pat years to Solve difficult

problems. The sensor networks are controlled by battery and in this way they end up being dead after a specific

period. Thus, improving the information exchange in power effective way still stays challenge for expanding

the life expectancy of sensor gadgets. It has been demonstrated that the clustering technique could upgrade

the life expectancy of WSNs. In the clustering approach, the choice of right cluster head in each cluster has

been observed as the most appropriate technique for energy efficiency, which limits the transmission delay in

WSN. Much exploration has been done in the recent past to decide an ideal path among source and goal sensor

nodes, which will bring about improving the battery power dissipation of a system. The challenge is to design

a scheduling algorithm that thinks about the significant issues of limiting power consumption and boosting

system lifetime. Different ways of optimization are accessible to decide a proper routing method between a

source and sink node. This paper investigates various optimization tools for effective routing in WSN. This

article gives us a glimpse of the past investigations in WSN field during the period of 2010–2020. The

outcomes listed in this article will guide to research community for bridging the gap in the WSN field and to

discover new exploration in this area.

1 INTRODUCTION

WSN is playing a key job in remote or unattended kind

of infrastructure less networks for the numerous

applications, like checking the environment

conditions, traffic tracking, observation in war zone,

disaster event counteraction, health monitoring,

clinical observations, weather and climate observing,

Industrial monitoring and so on.(Akyildiz et al. 2002).

Collecting the information from the sensor field,

processing the data and communicating with other

SNs are major activities performed by sensor nodes.

WSN has constrained force with restricted limit with

regards to processing. In some application, the energy

can be renewed by external source, for example, solar

based cells (Want et al.2005). However it is not able

to make uninterrupted power supply due to weather

and energy dissipation are prime issues to be

addressed for the betterment of throughput in

different application areas. Clustering plans, which

partition the network with the aid of grouping the

nodes, plays an important job in keeping up the

network topology in successful way. It is inescapable

to create clustering algorithm, which is proficient in

preserving energy for hauling out the range of the

system. Information is imparted from SN (for example

it’s starting point) to the base station (BS) or sink by

single hop or multi hop communication. Trial results

display that communication is relatively costly than

computing which is less energy consuming.

(Raghunathan et al., 2002). Transmitters and receivers

consume much more power to communicate

information than the processing counterpart. The

energy dissipated by the sensors to sense information

from surrounding is very small as compared to

communication and computing activities. Power

preservation techniques focuses on two parts: activity

of sensor node and the communication protocol

employed. Amalgam of various procedures can be

applied for extensibility of the sensor system lifetime

(Anastasi et al., 2009). Routing is probably the most

difficult issue for which we can't utilize deterministic

algorithms. Along this line, optimization calculations

are utilized to introduce low cost routes among various

possible routes. By actualizing Swarm Intelligence

(SI) based calculations, different routing calculations

have been created. SI can be thought of as a similarity

between machine behaviour and nature driven conduct

of the swarm. These swarm based intelligent

calculations can possibly accomplish ideal solutions

224

Singh, R. and Bharti, A.

Computational Intelligence in WSN for Network Life Optimization.

DOI: 10.5220/0010567300003161

In Proceedings of the 3rd International Conference on Advanced Computing and Software Engineering (ICACSE 2021), pages 224-231

ISBN: 978-989-758-544-9

for real world tasks. Ant Colony Optimization (ACO),

Particle swarm Optimization (PSO), Firefly algorithm

(FA), Artificial Bee Colony (ABC), Fuzzy logic and

Bacterial Foraging Optimization (BFO) are few

examples of most famous routing methods. Here we

have presented a review with respect to these

strategies and look at them to figure out which

procedures are progressively suitable as far as power

utilization and system lifetime are concerned. In this

survey, we additionally talk about different difficulties

in routing methods of WSN and recognize approaches

to address these difficulties utilizing optimization

strategies. The primary goal is to examine the present

status of-the-art enhancement methods utilized in

routing information by means of WSN and identify

proficient methodologies for routing in a WSN. The

remainder of the article is sorted out as follows:

In Section II, a portion from the current related

work with respect to taken subject is talked about.

Section 3 explains various techniques to lead this

review, while Sect. 4 describes a comparison of these

optimization methods. Segment 5 is comprised of

discussions and future directions and at last, Section

6 concludes this work.

2 LITERATURE REVIEW

Zengin et al. directed an overview where various

routing strategies were examined to manage the

issues of energy, expendability, intricacy,

survivability and computational overhead. As

indicated by this review, ant based methodology is

viewed as a decent methodology and has pulled in

numerous scientists than some other algorithm.

Parwekar et al. gives investigation of

enhancement strategies to WSN. They recognized

few difficult issues as routing, node localization and

clustering. These issues can't be solved using

deterministic methodology. Hence optimization

algorithms are increasingly reasonable for them. They

give basic investigation of all optimization strategies

and utilize this for future research.

Ali et al. led a study on MANETs and WSNs

dependent on swarm knowledge. They recognized

that in loop free, power efficient and multi hop

routing the “Ant Colony Optimization (ACO)” and

“Particle Swarm Optimization (PSO)” give all the

more encouraging outcomes. This article incorporates

detailed examination of all methods for wired and

wireless network and states that PSO and ACO beat

the other routing protocols.

An overview on routing with a point of upgrading

energy utilization was introduced by Saleh et al. This

gives a complete overview of residual power centric

convention in WSN. It gives an outline of significant

sensor nodes’ attributes that are utilized in various

routing methods. Diverse routing conventions that

fall under ACO algorithm are discussed with their

pros and cons.

A review on Swarm intelligence based scheduling

convention in WSNs was led by Saleem et al., in

which design and implementation plans are talked

about. The point is to recognize viable optimization

methods to discuss the issues of scalability, fault

tolerant property and adaptability. They additionally

give a few insights about swarm intelligence and its

conceivable execution for communication in a WSN.

A rundown of basic highlights is recognized to bring

up the difficulties in evaluation procedure and

consider this for actualizing real-world

implementation.

SGui et al. led an overview on swarm based

routing convention. They first introduced the

properties of swarm intelligent methods, then after

they investigated routing protocols to have new

optimization method. They talk about the properties

of “ant colony and spider monkey optimization”.

They additionally discuss about the issues present in

this approach and show future bearings.

Zungeru et al. presented a survey by looking at

“Swarm based routing” conventions with traditional

routing protocols. Routing protocols are ordered as

information driven, level based, geographic location

based and Quality of service (QOS). Distinctive

routing protocols are re-evaluated utilizing

MATLAB based test system to see the outcomes and

give a benchmark to future work.

Guo et al. directed an overview of intelligent

routing conventions in a WSN with a point of

upgrading system lifetime. They talked about

intelligent algorithms, for example, “Fuzzy Logic

(FL), Reinforcement learning (RL), Neural Networks

(NNs), and Genetic Algorithm (GA)”, to examine

their behaviour regarding the system lifetime.

3 ROUTING IN WSN USING

DIFFERENT OPTIMIZATION

TECHNIQUES

3.1 Fuzzy Logic based Routing

Protocols

“Fuzzy Logic is a decision making control framework

approach that fits usage in frameworks extending

from straightforward, little, inserted smaller scale

Computational Intelligence in WSN for Network Life Optimization

225

controllers to huge, organized, multi-channel PC or

workstation-based information securing and control

frameworks”. It may be actuated in hardware,

software, or hybrid model. FL gives a straightforward

method to arrive to a distinct end result dependent on

dubious, questionable, uncertain, noisy, or missing

information. FL's way to deal with control issues

emulates how an individual would settle on choices a

lot quicker. FL joins a straightforward, rule-based IF

A AND B THEN C way to deal with a taking care of

control issue instead of setting a framework

mathematically. The FL model is observation based,

depending on user's experience as opposed to their

specialized technical knowledge of the framework.

For instance, as opposed to managing temperature

control in terms, for example, "BT =100F", "T

<500F", or "20C <TEMP <200C", terms like "IF

(process is too hot) AND (process is getting hotter)

THEN (add coolant to the process)" or "IF (process is

too cold) AND (process is cooling rapidly) THEN

(add heat to the process quickly)" are used. These

terms are loose but extremely descriptive of what

should really occur. Consider what you do in the

shower if the temperature is excessively cool, you

will make the water agreeable rapidly with little

difficulty. FL is fit for mirroring this kind of

behaviour at exceptionally high rate. FL is comprised

of two steps. A fuzzy membership- function is

designed to generate the membership for an input of

a linguistic variable. The membership-function can be

formulated in a precise manner to represent the

needed output pattern of an objective-function. FL

also offers a “fuzzy aggregation operator, Ordered

Weighted Averaging (OWA)”, to design a multi-

objective cost function as an alternative. Normally,

the “And-like” and “Or-like” OWA operators are

used in FL. Control systems such as automobile

systems, energy systems, image processing, pattern

matching, home appliances, and elevators etc. are

some applications where FL is very effective. FL is

also suitable for optimized clustering and routing to

find different objectives. Sometimes non-optimal

solutions are generated. This issue can be resolved

by re-learning of fuzzy rule base.

Gupta et al. proposed a FL based FCH protocol

to addresses cluster-head election for WSNs. In this

approach, cluster-heads are selected by the sink node

in each round. For each node, residual power, node

density and nodes’ intra cluster distance are

considered as inputs to evaluate the criteria to be the

cluster head. Node density is calculated as number of

nodes by which the concerned node is surrounded,

and intra cluster distance is treated as nodes’

centrality with respect to the cluster. The node energy

and node density linguistic variables have three

levels: low, medium and high. Intra cluster distance

has the levels: close, moderate and far. The output

for presentation of the node’s chance to become

cluster head was levelled into seven stages: “very

small, small, rather small, medium, rather large, large,

and very large”. The fuzzy rule base look like: if the

residual energy and node density is high and intra

cluster distance is close then there is very large

chance for the node to become cluster head. In this

manner there are 3^3=27 rule base permutations. To

demonstrate medium and adequate fuzzy sets,

triangle membership functions are used. Trapezoid

membership functions are used to demonstrate low,

high, close and far fuzzy sets. As far as performance

is concerned, FCH has a substantial edge over

network lifetime in comparison to LEACH routing

protocol. Gupta et al. claims that round in which first

node is dead, is about 1.8 times better than LEACH.

However, this protocol is not scalable due to a non-

distributed approach.

“Cluster head election using fuzzy logic (CHEF)”,

presents a clustering method in distributed manner via

fuzzy logic approach. Initially CHEF selects CHs on

the basis of probability approach. Operational CHs

are chosen from initially selected CH list using

remaining power, and intra cluster separation of

nodes. Fuzzy inference computes the input

parameters. The result parameter chance is indicator

to choose a node as CH. Nodes having higher chance

value are the candidate for CH. It has a major

disadvantage that input variable intra cluster distance

does not suit for network sizes apart from

200m×200m.

“Energy aware distributed dynamic clustering

protocol using fuzzy logic (ECPF)” performs all the

operations in setup and steady state phases. Cluster

Head election and formation of cluster are done in set-

up phase. TDMA schedule formation and data

communication happens in steady state phase. Degree

and centrality of node are the input parameters and

fuzzy output cost is treated as the decision making

value. Every node will wait for a delay time (1/

residual energy). If no tentative CH is received by

node within its delay time, it declares itself as tentative

CH and broadcasts a message which includes its id,

fuzzy cost, and its status. Now it checks in the cluster

whether there is any other node with lower fuzzy cost

value. If it does not find any, concerned node declares

itself as the CH, and informs every member node with

final CH message within its cluster range.

ICACSE 2021 - International Conference on Advanced Computing and Software Engineering

226

3.2 Particle Swarm Optimization

(PSO)

Nature inspired the invention of “Particle Swarm

optimization technique by Eberhart and Kennedy”.

This strategy adopts the social behaviour of flying

birds in a flock where all the birds have equal status

and decision making capacity. They haphazardly

discover their food by that bird, which is closest to the

food position. All the animals have property to move

in the group, especially birds and fishes. They never

crash into one another because of the fact that each

individual from the flock follows their head bird and

alters its position and speed accordingly. This

phenomenon also reduces the effort time for

searching the food. position and area of food are

broadcasted by all the birds in the flock. In PSO,

‘bird’ represents a solitary solution. In some cases it

is also called as particle. All the particles have its

wellness value to access the nature of the solution.

“Two-tier Particle Swarm Optimization (PSO)”

routing convention has been created by numerous

specialists to solve the clustering and routing issues.

With the aid of PSO, it is now possible to choose

optimal cluster head to improve network lifetime,

throughput, scalability, and delay etc. in wireless

sensor network. Routing protocol adopts “particle

encoding scheme” and wellness output to access the

optimal route from source to destination. Authors,

discusses about two issues: routing and clustering.

PSO with multi-objective wellness function has been

executed to mimic the routing method. LP and NLP

formulations are used to improve the behaviour.

“Optimized energy efficient routing protocol

(OEERP)” is mentioned. This methodology increases

network lifetime by consistently depleting nodes’

power. Proposed methodology has no reference point

based transmission to arrive at the passage. One

disadvantage of “OEERP” methodology is that

remaining sensor nodes are considered in set-up

stage, which reduces framework lifetime as contrast

with different methodologies. During formation of

clusters, few nodes do not get included in any cluster.

This phenomenon gives birth to residual node

formation. Such remaining nodes transmit the

detected information either legitimately to the BS or

by getting suitable gateway node through control

messages. Excessive increase in control messages

results in reduced network lifetime. Authors,

proposed Enhanced optimized energy efficient

routing protocol (E-OEERP). This protocol

minimizes the chance of residual nodes creation to

improve the energy efficiency. Clustering and routing

are performed with the assistance of “Particle swarm

optimization (PSO) and Gravitational search

algorithm (GSA)” in route construction phase.

Saranraj et al. combined ACO with PSO to create

Particles with “Ant Swarm optimization” for finding

CH in a wireless sensor network. Authors applied

pheromone path to the PSO for the particles’ position

synchronization. This method attains the best

objective value to find the optimal path from source

to sink node. Authors combined PSO and neural

networks characteristics to make a scalable and

secure system. They figured out that fixed base

station frequently experiences hotspot issue as they

have more traffic density close to the sink hub. To

improve the hot spot problem, the authors presented

algorithm for mobile sink nodes with control

parameters to improve delay and network life.

Particle Swarm Optimization Routing (PSOR)

protocol has been proposed to create best path for less

energy consumption in data communication. Though

there are many routes between source and destination,

this protocol uses leftover node power as a fitness

function to discover the optimized route. We come to

conclusion that PSO is good for single-hop

communication, however it is not efficient for multi-

hop communication.

3.3 Firefly Algorithm (FA)

Firefly calculation (FA) is another enhancement

method initially proposed by Dr. Xin She. This

strategy copies the manner in which genuine flies get

pulled in to one another dependent on flash light.

Fireflies produce unique pattern by their flash

dependent on the species. Fireflies attract each other

with two fundamental patterns: mating and preys.

Female fireflies answer with some remarkable flash

light pattern to the male in mating case. The

separation between fireflies is contrarily relative to

the light emitted by fireflies. This implies fascination

between fireflies is dependent on the intensity of

Light emitted by them. As the separation increases,

received light brightness will diminish. This behavior

is inherited in firefly algorithm where fireflies are

represented as generated solutions and fitness

function is linked with the light intensity. WSN can

be implemented with the help of firefly algorithm. It

uses various parameters like remaining power, intra

and inter cluster node distance, node density etc.to

optimize path between Cluster Head and base station.

In firefly algorithm was implemented by

decreasing fitness value as hop count of any route

increases. This is worth in WSN to conserve the

energy of nodes, and add residual energy in its fitness

value. Authors, proposed a power saving algorithm

Computational Intelligence in WSN for Network Life Optimization

227

using ACO and firefly algorithms. They claim that

FA outperforms the ACO for less distant routes while

ACO is good in the case of longer routes. Mobile sink

node has been introduced in a paper proposed by

authors, named as mobile data transporter (MDT). It

gathers data from every sensor node to send the

collected stuff to the BS. In this FA approach, average

path length decreases in comparison to Ant Colony

Optimization. Firefly algorithm (EDFA) is presented

to solve vehicular routing problem with time

windows (VRPTW). This algorithm aims to optimize

(min) the number of possible paths in a network.

Algorithm is suitable for multi-objective optimization

problems. This technique faces the problem of delay

in path search.

3.4 Genetic algorithm (GA)

Genetic algorithm (GA) is one of the techniques

proposed by Holland et al., which solve search and

optimization problems. This technique is based on the

Darwin theory of biological evolution, reproduction

and “survival of the fittest”. It copies the behaviour of

genes transfer from parents to children through

crossover, mutation and selection operators. In

selection phase, few genes are chosen for crossover

and mutation; genes get swapped in crossover for

children production, whereas new attributes are added

in mutation phase. The same characteristic is

mimicked in Genetic Algorithm. In this algorithm,

population is constituted as chromosomes and each

string of chromosome is written as binary or real

numbers. First of all, the random generation of

population is performed, then process of selection,

crossover and mutation generate next generation of

population. Strength of produced chromosome in a

population is examined by the objective function. In,

GA has been taken to optimize the inter node distance

for energy conservation. The objective function

incorporates the transmission distance between nodes

and CH within a cluster and from the cluster head to

the BS. Node with maximum residual battery power

and minimum intra cluster distance is selected as CH,

to minimize the communication cost and increase the

life of network. Authors presented an algorithm

which reduces the chance of weak node consideration

in any route selection. Authors, considered clustering

and routing issues using Genetic Algorithm (GA)

,which gives better result. According to paper

presented, authors have used the advantages of

genetic algorithm and simulated annealing together

for efficient energy utilization via efficient route

selection. Author applied GA on hierarchical based

clustering protocol to make network properties better.

To maximize the network lifetime and to minimize

the average intra cluster distance, paper applied GA

based algorithm. This algorithm is not suitable in the

paradigm of mobile sensor nodes.

3.5 Ant Colony Optimization (ACO)

Darigo and Gambardella in 1997 proposed Ant

Colony Optimization technique. It mimics ants’

behaviour. It tackles the issue of ideal path discovery

between source and goal, based on genuine ants'

characteristic. In beginning, ants move in any

direction to search food. Upon the successful

discovery of food source, ants turn towards colony.

Ants release pheromones while going back to home

which in turn guides way for food source. Different

ants follow a similar way to reach on food source.

When these ants copy the same path, fair amount of

Pheromones are deposited to indicate a stronger path.

The quantity of deposited pheromones is directly

proportion to quality and magnitude of food source.

At a point of time when food sources diminish,

quantity of deposited pheromones also decreases to

inform the ants about less or no availability of food.

Authors in applied ACO to find optimal path for data

communication in WSN. ACO is applicable in the

case of predefined source and destination. It works

well only for symmetric paths. According to authors,

Pheromone quantity is computed in terms of hop

count between source and destination. Nodes receive

data values as Destination Address (DA), Next hop

(NH), Pheromone value (PH) and stores in routing

table. An algorithm named “optimal-distance based

transmission strategy (ODTS)” based on ACO

optimization is mentioned. This strategy searches for

the optimal distance among the sensor nodes for

cluster head selection, which ultimately improve

energy efficiency and life of the network. To

minimize the earlier death of sensor nodes, ACO

based load balancing in WSN is presented in article.

4 COMPARISON AND

RESEARCH GAP

Summary of various surveys for different class of

problems and applications are summarized in Table I.

We derive the conclusion as mentioned below:

 Residual energy is the basic criteria for most

routing protocols to improve the network life

time.

 Most routing protocols have complete

knowledge of the network.

ICACSE 2021 - International Conference on Advanced Computing and Software Engineering

228

 Node coverage issue were not discussed in depth.

 Fault tolerant and scalability are less explored.

 Cross-layer methods, mobility, non-uniform

deployment, etc. are not discussed much.

 Most routing protocols assumed BS as

stationary. Use of multiple BS is also not taken

into consideration.

 Novel approaches should be addressed for

mobility.

 The relation between heterogeneity and routing

is not addressed.

 Most researchers have considered performance

metrics such as intra cluster distance, power

consumption,

 Network life, packet delivery ratio and delay.

The metrics such as reliability, load balancing,

computational

 Conversion of simulated experiments into real-

world applications is a big challenge for future.

5 CONCLUSION

Various Optimization techniques like Fuzzy Logic,

ACO, FA, PSO, GA were used for optimization in

WSN. The parameters mentioned in Table-I have

been considered for the comparison of these

optimization techniques. In this study, we have

surveyed some challenges of routing in WSN. Few

optimization methods are discussed here to suggest

the best technique for a particular application.

Though many optimization techniques are available,

still there are plenty of open issues and challenges for

pursuance of optimal solution in a Wireless Sensor

Network. Most of these algorithms are still being

improved by the researchers. The tabulated results

given in this article may help researchers working in

this field. This paper gives insight about some

challenges also, which are not explored yet. This

article will probably guide new researchers to fill the

gap in the area of Wireless Sensor Network.



Table 1.

Parameters ACO PSO FA GA Fuzz

Representatio

Undirected Graph Dimensions for

vector position and

speed

Distance

based

attraction

Random binary

number

Multidimensional

vector values between

0 and 1

Operators Pheromone updates

and trial evaporation

Evaluation and

update

Current state

Attraction,

intensity of

light

Selection, crossover,

mutation

Fuzzy aggregation

operator, Ordered

Weighted Averaging

Control

Parameters

Magnitude of ants,

iteration, pheromone

decay rate

Position, magnitude

of pheromone,

Range, weight,

iterations

Force of

attraction,

light intensity

Population size,

selection procedure,

crossover and

mutation probability,

magnitude of

chromosomes

Fuzzy Membership

Function

Node

Deployment

Placed in distributed

manner, used in

dynamic

lications

Random, Centralized

nodes deployment

Random

manner

Both Distributed and

random both

Clustering

and routing

Explore closest

route between

source and

destination for better

transmission

Find optimal path by

choosing high energy

nodes as CH in every

round

Choose cluster

head on

distance basis

Number of

predefined clusters

are chosen to reduce

communication

distance

Selects CH on basis of

energy, concentration

and centrality etc.

Advantages 1.Can be used in

dynamic

applications like

travelling salesman

problem

1. It finds best

positioned nodes for

CH. 2. Inherently

continuous, 3. no

overlapping and

mutation calculation

Used in

optimization

with multi

objective

functions

1. Solve complex

problems where

parallel operations

are required

2. Discrete in nature

1. Ideal for problems

with imprecise and

vague data

2. Can Model

nonlinear problems

of arbitrary

complexit

Disadvantage

1. Only local search

2. More energy

Consumption for

more number of

aths

1. Not suitable for

distributed paradigm

2. Suitable for

coordinate system

only

Suitable for

nodes which

are deployed

randomly.

Suited for arbitrarily

placed sensor nodes.

1.The results are

perceived based on

assumption, 2. not

accurate always

Computational Intelligence in WSN for Network Life Optimization

229

REFERENCES

Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci,

E., 2002. Wireless sensor networks: a survey. Comput.

Networks 38, 393–422. https://doi.org/10.1016/S1389-

1286(01)00302-4.

Want, R., Farkas, K.I., Narayanaswami, C., 2005. Guest

Editors’ Introduction: Energy Harvesting and

Conservation. IEEE Pervasive Comput. 4, 14–17.

https://doi.org/10.1109/MPRV.2005.12

Raghunathan, V., Schurgers, C., Sung Park, Srivastava,

M.B., 2002. Energy-aware wireless microsensor

networks.IEEE Signal Process.Mag.19,40–50

https://doi.org/10.1109/79.985679

Anastasi, G., Conti, M., Di Francesco, M., Passarella, A.,

2009. Energy conservation in wireless sensor networks:

A survey. Ad Hoc Networks 7, 537–568.

https://doi.org/10.1016/J.ADHOC.2008.06.003

Zengin, A., & Tuncel, S. (2010). A survey on swarm

intelligence-based routing protocols in wireless sensor

networks. International Journal of Physical Sciences, 5,

2118–2126.

Parwekar, P., Rodda, S., & Kalla, N. (2018). A study of the

optimization techniques for wireless sensor networks

(WSNs). In Information systems design and intelligent

applications (pp. 909–915). Berlin:Springer.

Ali, Z., & Shahzad, W. (2013). Analysis of routing

protocols in ad hoc and sensor wireless networks based

on swarm intelligence. International Journal of

Networks and communications, 3, 1–11.

Shamsan Saleh, M., Ali, B. M., Rasid, M. F. A., & Ismail,

A. (2014). A survey on energy awareness mechanisms

in routing protocols for wireless sensor networks using

optimization methods. Transactions on Emerging

Telecommunications Technologies, 25, 1184– 1207

Saleem, M., Di Caro, G. A., & Farooq, M. (2011). Swarm

intelligence-based routing protocol for wireless sensor

networks: Survey and future directions. Information

Sciences, 181, 4597–4624.

Gui, T., Ma, C., Wang, F., & Wilkins, D. E. (2016). Survey

on swarm intelligence-based routing protocols for

wireless sensor networks: An extensive study. IEEE

International Conference on Industrial Technology

(ICIT), 2016, 1944–1949.

Zungeru, A. M., Ang, L.-M., & Seng, K. P. (2012).

Classical and swarm intelligence-based routing

protocols for wireless sensor networks: A survey and

comparison. Journal of Network and Computer

Applications, 35, 1508–1536.

Guo, W., & Zhang, W. (2014). A survey on intelligent

routing protocols in wireless sensor networks, Journal

of Network and Computer Applications, 38, 185–201.

Gupta I, Riordan D, Sampalli S., Cluster-head election

using fuzzy logic for wireless sensor networks,

Proceedings of the 3rd Annual Communications

Networks and Services Research Conference 2005,

p.255–260.

Eberhart, R., & Kennedy, J. (1995). A new optimizer using

particle swarm theory. In MHS’95. Proceedings of the

sixth international symposium on micro machine and

human science (pp. 39–43).

Kuila, P., & Jana, P. K. (2014). Energy efficient clustering

and routing algorithms for wireless sensor networks:

Particle swarm optimization approach. Engineering

Applications of Artificial Intelligence, 33, 127–140.

Chand, K. K., Bharati, P. V., & Ramanjaneyulu, B. S.

(2012) Optimized energy efficient routing protocol for

life-time improvement in wireless sensor networks. In

IEEE-international conference on advances in

engineering, science and management (ICAESM-2012)

(pp. 345–349).

RejinaParvin, J., & Vasanthanayaki, C. (2015). Particle

swarm optimization-based clustering by preventing

residual nodes in wireless sensor networks. IEEE

Sensors Journal, 15, 4264–4274.

Saranraj, G., & Selvamani, K. (2017). Particle with ant

swarm optimization for cluster head selection for

wireless sensor networks. Journal of Computational and

Theoretical Nano science, 14,2910–2914.

Stephen, K. V. K., & Mathivanan, V. (2018). An energy

aware secure wireless network using particle swarm

optimization. In 2018 Majan international conference

(MIC) (pp. 1–6).

Wang, J., Cao, Y., Li, B., Kim, H.-J., & Lee, S. (2017).

Particle swarm optimization-based clustering algorithm

with mobile sink for WSNs. Future Generation

Computer Systems, 76, 452–457.

Sarangi, S., & Thankchan, B. (2012). A novel routing

algorithm for wireless sensor network using particle

swarm optimization. IOSR Journal of Computer

Engineering (IOSRJCE), 4, 26–30.

Yang, X.-S. (2010). Nature-inspired metaheuristic

algorithms. Bristol: Luniver Press.

Manshahia, M. (2015). A firefly-based energy efficient

routing in wireless sensor networks. African Journal of

Computing & ICT, 8, 27–32.

Okwori, M., Bima, M., Inalegwu, O., Saidu, M., Audu, W.,

& Abdullahi, U. (2016). Energy efficient routing in

wireless sensor network using ant colony optimization

and firefly algorithm. In International conference on

information and communication technology and its

applications (pp.28–30).

Yogarajan, G., & Revathi, T. (2018). Nature inspired

discrete firefly algorithm for optimal mobile data

gathering in wireless sensor networks. Wireless

Networks, 24, 2993–3007.

Osaba, E., Carballedo, R., Yang, X.-S., & Diaz, F. (2016).

An evolutionary discrete firefly algorithm with novel

operators for solving the vehicle routing problem with

time windows. In Natureinspired computation in

engineering, pp. 21–41. Berlin: Springer.

Holland, J. (1975). Adaptation in natural and artificial

systems: an introductory analysis with application to

biology. In Control and artificial intelligence,

Cambridge: MIT Press

Deif, D. S., & Gadallah, Y. (2013). Classification of

wireless sensor networks deployment techniques. IEEE

Communications Surveys & Tutorials, 16, 834–855.

ICACSE 2021 - International Conference on Advanced Computing and Software Engineering

230

Aziz, L., Raghay, S., Aznaoui, H., & Jamali, A. (2016). A

new approach based on a genetic algorithm and an

agent cluster head to optimize energy in wireless sensor

networks. In 2016 international conference on

information technology for organizations development

(IT4OD) (pp. 1–5).

Yao, G.-S., Dong, Z.-X., Wen, W.-M., & Ren, Q. (2016). A

routing optimization strategy for wireless sensor

networks based on improved genetic algorithm. Journal

of Applied Science, Engineering and Technology, 19,

221–228.

Gupta, S. K., & Jana, P. K. (2015). Energy efficient

clustering and routing algorithms for wireless sensor

networks: GA based approach. Wireless Personal

Communications, 83, 2403–2423.

Chakraborty, S. K. M., & Naskar, M. K. (2011). A Genetic

algorithm inspired routing protocol for wireless sensor

networks. International Journal of Computational

Intelligence Theory and Practice, 6, 1–8.

Shurman, M. M., Al-Mistarihi, M. F., Mohammad, A. N.

Darabkh, K. A., & Ababnah, A. A. (2013). Hierarchical

clustering using genetic algorithm in wireless sensor

networks. In 2013 36th international convention on

information and communication technology,

electronics and microelectronics (MIPRO) (pp. 479–

483).

Heidari, E., & Movaghar, A. (2011). An efficient method

based on genetic algorithms to solve sensor network

optimization problem. arXiv preprint arXiv

:1104.0355.

Darigo, M., Gambardella, L., & Maria, L. (1997). Ant

colonies for the travelling salesman problem. Bio-

Systems, 43, 73–81.

Sudarshan D Shirkande, Rambabu A Vatti, “ACO based

routing algorithm for ad-hoc (WSN, MANET)

networks: survey” International Conference on

Communication Systems and Network Technologies

2013.

Varnika Bains, Kanchan Sharma “Ant colony based

routing in wireless sensor networks” International

Journal of Electronics and Computer Science

Engineering ISSN 2277-1956

Xuxun Liu, “An optimal-distance based transmission

strategy for lifetime maximization of wireless sensor

networks”, IEEE Sensors journals, Vol.15, No.6, June

2015.

Mamta Tewari, Kunwar Singh Vaisla “Optimized Hybrid

Ant Colony and Greedy Algorithm Technique based

Load Balancing for Energy Conservation in WSN”

International Journal of Computer Applications,

October 2014

J.-M. Kim, S.-H. Park, Y.-J. Han, and T.-M. Chung,

“CHEF: Cluster Head Election mechanism using Fuzzy

logic in wireless sensor networks,” in Proceedings of

the 10th International Conference on Advanced

Communication Technology, pp. 654–659, Gangwon-

Do, South Korea, February 2008. Wireless

Communications and Mobile computing 13

H. Taheri, P. Neamatollahi,O. M. Younis, S. Naghibzadeh,

and M. H. Yaghmaee, “An energy-aware distributed

clustering protocol in wireless sensor networks using

fuzzy logic,” Ad Hoc Networks, vol. 10, no. 7, pp.

1469–1481,2012

Computational Intelligence in WSN for Network Life Optimization

231