an energyefficient traffic-less channel scheduling based data ...

European Journal of Molecular & Clinical Medicine

ISSN 2515-8260 Volume 7, Issue 11, 2020

5704

AN ENERGYEFFICIENT TRAFFIC-LESS CHANNEL

SCHEDULING BASED DATA TRANSMISSION INWIRELESS

NETWORKS

D.Saravanan1, Dr.D.Stalin David2, S.Usharani3, D.Raghuraman4,D.Jayakumar5, Dr.U.Palani6,

R.Parthiban7

Associate Professor1,3,5,7,Department of CSE, IFET College of Engineering, Villupuram,

Assistant Professor2, Department of CSE, IFET College of Engineering, Villupuram,

Senior Assistant Professor4,Department of CSE, IFET College of Engineering, Villupuram,

Professor6, Department of ECE, IFET College of Engineering,

[email protected], [email protected], [email protected],

[email protected], [email protected],

[email protected],[email protected]

Abstract

Wireless sensor networks are the most common of all other networks. This is an

infrastructure that totally involves the infrastructure, or a set of sensor nodes. It is commonly

used in military and search and rescue activities, such as node mobility, without proposals to

prioritize and drop data packets. The output of the network is thus totally degraded. Take the

Energy Efficient Traffic-less Channel Scheduling (EETCS) scheme guidelines to address this

dilemma. It could be that, regardless of the suggested algorithm, the length of the equilibrium

schedule is unbalanced due to non-uniform density or variation. First, to increase the network

lifespan, estimate the node energy initial stage before scheduling the transmission channel.

The schedule lengths can also be imbalanced due to the disparity in regions' heterogeneity

levels.In this proposed method of calculating the Edge Support (SES), Channel Traffic

Weightage (CTW) and Transmission Channel Support (TCS) sequence, the transmission path

to boost network capacity is planned. Energy usage, throughput ratio and distribution ratio

are evaluated to approximate the performance of the proposed EETCS method and equate it

with other existing approaches.

Keywords: Wireless sensor network, Edge Support, Traffic-less Channel Scheduling, Channel

Traffic Weightage (CTW)

Introduction

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There is a set of nodes called node sensors in a wireless sensor network. There is an

integrated CPU, a radio and one or two sensors for both of them. These nodes work together to

gather the physical attributes of the local climate, such as temperature and humidity, within the

monitoring area. Data obtained by these sensor nodes can be used in a number of top-level

applications, such as tracking, networks and surveillance systems for various natural phenomena

and ecosystem monitoring. Sensor nodes have a battery life that is reduced. The sensor is

difficult to put in some applications, touching impractical positions. It's impractical to expect

manual action to upgrade the battery. In reality, these sensor nodes are predicted to be redundant

in the foreseeable future with technical developments and would still be sold before they can be

expelled.A node must be able to handle the small battery energy capacity and can only survive

for a brief amount of time without power control.

The sensor network consists of a vast number of sensor nodes, with dense or internal

phenomena being deployed. There is no need to predetermine the position of the sensor node. In

inaccessible terrain or emergency relief activities, this facilitates arbitrary setup. This also

suggests, on the other hand, that sensor network protocols and algorithms must have capability

for self-organization. The collective endeavours of sensor nodes are another aspect of sensor

networks. The node of the sensor is fitted with an onboard processor. Instead, the sensor node

has its own computing power to conduct basic calculations locally and send as much partially

processed data as possible by submitting raw data to the node responsible for the fusion.



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Figure 1. Scheduling data and channel in WSN

Routing problems in wireless sensor networks give the classic balance between response

speed and reliability a very daunting challenge. The restricted processing and coordination

abilities of overhead sensor nodes must be weighted by this trade-off. Overhead is mainly

measured in wireless sensor networks in bandwidth usage, resource consumption, and mobile

node processing demands. The basis for the routing of successful challenges is the discovery of a

plan to balance these competing conditions.In addition, the special aspects of wireless networks

face critical concerns. To face this challenge, the current routing protocols of the built ad hoc

network are appropriate. To attain reliability and scalability in large networks, hybrid strategies

depend on the existence of network structures. The network connects to the node to which it is

allocated in these methods, is dynamically managed like a weekend, and is grouped into clusters

adjacent to each other. Under high dynamic environments, conventional routing algorithms in ad

hoc networks frequently display the least favorablebehavior. Usually with overhead routing

protocols which, with growing network size and dynamics, grow dramatically. Big overheads

can overload network infrastructure easily. In major networks, it also takes a lot of intermodal

communication to operate conventional routing protocols. Global floods are being used in some

situations to accomplish loop-free routing.

Related work

Energy is spent on idle control, redundant transmission, and wasteful resource usage

within a wireless sensor network (WSN) with multiple aggregate nodes. Proposing near-optimal

data aggregation paths and heuristics for service cycle preparation through MDAR, implying

energy efficiency and late binding within acceptable limits O-MAC[1, 2]. Specify a distribution

approach to address the problem of aggregation scheduling in the WSN work cycle by defining

distribution-delayed and efficient data collection scheduling (DEDAS-D). Our approach

illustrates an analysis that is a good way of addressing this problem.

Group-based distributed data aggregation Scheduling algorithms can reduce data

aggregation delays by multi-channel, multi-power wireless sensor networks[3], and multi-power

and multi-channel (DMPMC) algorithms have been suggested, but distributed. Low transmission

power is used to transfer packets within a cluster in order to conserve resources, and higher



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power is used to relay packets between clusters. For typical solutions, the tree form is used. In

certain cases, though, a set volume of data can be aggregated into a single data packet[4]. This

prompted us to research the issue of minimizing data aggregation latency without allowing the

packet radio sensor network to aggregate a certain volume of data.The problem of data collisions

is called minimum-latency collision-avoidance multiple-data-aggregation scheduling

(MLCAMDAS) avoidance minimum delay collisions. In this approach two reinforcement

learnings (RL) [5], especially Deep Deterministic Gradient Decent (DDPG) and Q-learning

(QL), understanding the environment and using train unmanned reconnaissance aircraft, effective

Scheduling is provided.

Consider the question of minimal data aggregation latency, where the data can be

compressed at a scalable aggregation rate so a channel in Multi-data aggregation scheduling and

multi-channel (MLCAMDAS-MC) problems might be delegated to the sensor[6]. It has been

suggested that two distributed aggregation algorithms concurrently produce aggregate trees and

conflict-free timetables to take advantage of all neighbor’s in a valid time slotDR is also the most

frequent micro-vascular complication of diabetes. The eye is one of the first places where micro-

vascular damage becomes apparent. Though diabetes is still incurable, treatments exist for DR,

using laser surgery and glucose control routines [7]. Compared to pre-centralized and dispersed

methods, convergence delay and usage of usable time slots are greatly enhanced.At the same

time, many adaptive techniques have been proposed without increasing aggregation delay to

resolve network topology changes. This paper, focusing on data access control and WSN hybrid

transmission control Mathematical Morphology brings a fancy result during the discrete

processing. At last, we consider every discrete region according to the tumor's features to judge

whether a tumor appears in the image or not [8], is the key to solving this issue. For the first

time, tricky optimization problems are recommended based on these two statistical models of

control behaviour, including several variables like data utility, energy usage, network reliability

and data error rates.

The general K-hop wireless sensor network [9] is suggested based on the survey

addresses the issues in cryptographic approaches such as storage / computational overhead, the

trade-off between the security and elasticity, trust assurance against the attacks respectively. Our

theoretical analysis provides the best k to obtain the longest network life. A new energy supply



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plan for the whole grid is proposed by the integration of SWIPT and EH from RF and

environmental energy Automatic detection of diabetic retinopathy in retinal image is vital as it

delivers data about unusual tissues which is essential for planning treatment [10]. The sensing

data transmitting schedule for all sensor nodes is derived for this purpose, after which the

configuration of the frame and the behavior are evaluated in real time. The problem of

formulation optimization is the lack of stubbornness in the representation of essential parameters,

called reliability criteria given in the form of failure probabilities, in the form of close to the

achievement rate of effective fading power[11]. Use the regression of the first data to estimate

parameters for the 3D UAV orbital function via logic to solve this problem ("S" shape).

"We have developed a new approach called "Content-based adaptation and dynamic

scheduling using two wireless sensor network methods" to increase lifetime and provide energy-

saving WSN[12]. During the dynamic data set, the state of one node is changed, and each node

adapts to the new state depending on the quality of the sensed data packet. For network

optimization, consider two minimum overall energy consumption thresholds and a minimum

maximum energy consumption per node[13]. It also offers formulations and algorithms for

optimum transmit throughput scheduling in a pure agglomeration physical interference model.

To move the sink node, a novel Pair-based Sink Relocation Scheme (PSRS) is proposed that

effectively extends the lifespan of the network[14].To enforce a route change based on a

Destination Centered Guided Acyclic Graph (DODAG) that changes the route by considering

three rules. The goals of this paper overlap cast in thick in-vehicle WSNs, where all nodes would

theoretically enter the sink node directly. A technique of cross-layer low-latency topology

management and TSCH scheduling (LLTT)[15] is proposed that provides the TSCH schedule

with a very high time slot utilization and minimizes contact latency. It first selects a topology

that increases the capacity of parallel TSCH communications for the network.

Implementation of proposed method

In this work, technology transmission data obtained on the basis of the first energy level

is proposed and the occurrence of traffic congestion is also minimized. The scheduling of node

creation in a heterogeneous network is different from that of a homogeneous network. The

channel listens to this network for a specified amount of energy and time, at least equal to the



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duration of synchronization [16]. If the node does not hear a timetable from another node at the

expiration of this waiting time. Centered on Sequence Edge Support (SES), Channel Traffic

Weightage (CTW) and Transmission Channel Support, it automatically selects its own schedule

(TCS).It should be noted that neighboring nodes may still not be able to find each other due to

delays or loss of synchronization packets.

Figure 2 Proposed method block diagram

It is dedicated to the transmission of isochronous packets and the second subinterval is

used for the transmission of data packets. Data packet transmission uses exclusive access fixed to

the channel during TCS data transmission. This access procedure ensures that neighboring nodes

receive data packets synchronously at the same time.

Node Energy Estimation

The set of input data is then needed depending on the framework chosen for the wireless

sensor network node implementation. The stores in the table look for nodes in the wireless sensor

network while measuring various approaches to network consumption flooding techniques. The

key users of electricity connect on cellular networks[17]. It is observed to listen to the nodes

involved in data transmission that need almost a lot of energy for short-range communication.To

send messages of request that route in the positive direction (the non-negative direction towards

the destination, using the lookup stored in the table of each node that was initialized at first

energy value). An identifier that contains both source and destination nodes is allocated. The

Node

scheduler

Channel

Selection

Topology

construction

Sequence Edge

Support (SES)

Channel

Traffic

Weightage

(CTW) Transmission Channel

Support (TCS)

Data

scheduler

Energy

Estimation



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packet is used to set the value of energy and distance. If the node energy at which the magnitude

of the distance is measured is smaller than the expected threshold to be applied to the routing

table. The remaining energy (E res) is called the sum of energy remaining in the node of the

current case. To become a CH, a node should have more residual energy than its neighbor’s.

𝐸(𝑡) = (𝑛𝑡𝑝 ∗ 𝛼) + (𝑛𝑟𝑝 ∗ 𝛽)--- (1) // 𝛼, 𝛽 constant range of the node.

Consider E-i as the node's initial capacity, and n-tp,n-rp is the transmitting and receiving

packet node. Energy consumption (E(t)) in this node should be determined using the time span of

equation 1 series T. By restricting factors such as liquidity, malicious behavior, and the creation

of unauthenticated nodes and bits per energy transfer, it may decrease a node's energy

consumption.

Data channel scheduling and routing using EETCS

Reducing the sum of transmitted data between high-cost transmitting nodes. The

approach used by the data minimizes the amount of replication needed to be submitted by the

aggregation. When a node wants to determine its slot and parent, it has the following pieces of

information: I the number of incoming packets (ii) the nature of each incoming packet, to

decrease the data gathered by the sensor before sending it to the actual node. This approach is

used to approximate the Edge Support (SES) node sequence, Channel Traffic Weightage (CTW)

and Transmission Channel Support (TCS) to prepare the channel for data processing.



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Figure 3 edge node and channel selection process

Algorithm step:

Input: Routing Table, Node ID (𝑛𝑖𝑑), Energy

For each n in nodes

To categorize the node based on energy 𝑛𝑒𝑛𝑒𝑟𝑔𝑦,

Node threshold value𝑡ℎ𝑛,

Estimate the sequence Edge Support =𝑛SES,

Estimate theChannel Traffic Weightage =𝑛CTW,

Estimate theTransmission Channel Support =𝑛𝑇𝐶𝑆

If (𝑛𝑒𝑛𝑒𝑟𝑔𝑦>𝑡ℎ𝑛) then

𝑛SES= 𝑎𝑟𝑔 𝑚𝑎𝑥 {∑𝜎𝑛+𝜎𝑛+1

2

𝑛𝑖=𝑜 } --- (2)

𝑛CTW=∫ ∑ 𝑀𝑐𝑐(𝑛(𝑖)).𝑀𝑜𝑏𝑖𝑙𝑖𝑡𝑦<𝑁𝑀𝑇ℎ

𝑠𝑖𝑧𝑒(𝑀𝑐𝑐)𝑖=1

𝑠𝑖𝑧𝑒(𝑀𝑐𝑐) --- (3)

The channel transmission estimation is based on time-variant data moving and size of bandwidth

used.

𝑛𝑇𝐶𝑆=∮ 𝛼(𝑡)𝑒𝑛(𝑖).𝜑(𝑡)+𝑛(𝑏)

𝑖𝑑𝑖=0

2𝜋 --- (4)

End

EETCS =𝑛SES ∗ 𝑛CTW ∗ 𝑛𝑇𝐶𝑆 --- (5)

If (EECS<th)

Channel allocate and add route on routing table

End

End



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The reduction in traffic load would increase the lifespan of the network, and the proposed

MER algorithm will reduce the use of resources. The data would be processed depending on the

appropriate data and then forwarded to the destination. It would help to reduce and absorb

transmission capacity. The definition of the multipath function proposed is to broadcast the load

of traffic between the two or more routes.

Result and discussion

The service and efficiency of the wireless sensor network was evaluated in this experimental

study with the suggested Energy Efficient Traffic-less Channel Scheduling (EETCS) process.

The built Java framework and 250 nodes are used in this process.

Table 1 Simulation parameters

Parameters Value

Platform Advance java

Number of node 250

Traffic mode CBR (Constance Bit Rate)

Simulation Time 10min

The proposed simulation parameters of the system are shown in Table 1. With a 3-second

delay, WSN node motions are confined to a 500m x 500m region. EETCS is used in this

suggested methodology to test QoS parameters such as packet distribution ratio (PDR),

throughput (TH), energy usage, and output scheduling.

Throughput =𝑷𝒂𝒄𝒌𝒆𝒕𝒔 𝑹𝒆𝒄𝒆𝒊𝒗𝒆𝒅 (𝒏) ∗ 𝑷𝒂𝒄𝒌𝒆𝒕 𝒔𝒊𝒛𝒆

𝟐𝟎𝟎



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Figure 4 Comparison of throughput ratio

Figure 4 shows the existing methods like DTMC, DEF, RPRDC and the proposed

method EETCS comparison.

Table 2 Analysis of Throughput Ratio

Method 75 Nodes 150 Nodes 250 Nodes

Throughput in %

DTMC 71 75 77

DEF 74 77 81

RPRDC 76 79 85

ATL 87 92 97

CRL 89 94 98.7

EETCS 91.2 95.8 98.9

In this throughput ratio analysis, the proposed method provides 98.7% for 250 number of

nodes, and existing method DTMC, DEF, RPRDC are provided less than 77%, 81%, and 85% of

throughput ratio.

0

20

40

60

80

100

120

D T M C D E F R P R D C A T L C R L E E T C S

Thro

ugh

pu

t in

%

THROUGHPUT

75 Nodes 150 Nodes 250 Nodes



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Figure 5 Comparison of Energy Consumption

In Figure 5, this results in the average energy consumption of various nodes per 10-50

seconds. The suggested EETCS scheme has the lowest energy consumption as compared to the

DTMC, DEF, RPRDC, and CRL schemes.

Table 3 Comparison of network life time

Method 75 Nodes 150 Nodes 250 Nodes

Life time in %

DTMC 14 17 21

DEF 16 20 25

RPRDC 18 22 29

ATL 45 60 70

CRL 53 69 78

EETCS 64 72 89

The EETCS algorithm achieved higher lifetime performance compare to existing method

simulated conditions, it is shown in table 2.

0

5

10

15

20

25

70 90 110 130 150 170 190 210 230 250

Ener

gy c

on

sum

pti

on

in (

j)

Number of node

ENERGY CONSUMPTION DTMC

DEF

RPRDC

ATL

CRL

EETCS



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Figure 6 network life time comparisons

Above Figure 6 indicates the estimated network lifespan of the current DTMC, DEF,

RPRDC, CRL and EETCS methods suggested. In this study, this strategy has an average

network life of 89 percent. Similarly, the latest DTMC, DEF, RPRDC, and CRL approaches

have 21%, 29%, and 70% of the average network lifespan.

Conclusion

Load delivery is meant to prevent the issues of congestion in the network and to improve

the rate of data throughput. This study focused on developing appropriate routing protocols that

can be useful for increasing the network lifespan. Proposing the EETCS approach for calculating

data scheduling based on transmission efficiency across the network. The first transmission

energy calculation of the network in the initial stage to stop the fault node. Overall efficiency of

89 percent of network life, 98.8 percent of throughput and 10j of energy usage in this proposed

work relative to the current method

Reference

14

16

18

45

53

64

17

20

22

60

69

72

21

25

29

70

78

89

0 10 20 30 40 50 60 70 80 90 100

DTMC

DEF

RPRDC

ATL

CRL

EETCS

life time in %

network life time

250 nodes

150 nodes

70 nodes

2 per. Mov. Avg. (250 nodes)



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on MRI Images and Multi Phase level set Formulation”, Biomedical and Pharmacology Journal,

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5717

[12]D Stalin David, 2016, Robust Middleware based Framework for the Classification of

Cardiac Arrhythmia Diseases by Analyzing Big Data, International Journal on Recent

Researches In Science, Engineering & Technology, 2018, Volume 4, Issue 9, pp.118-127.

[13]D Stalin David, 2020, ‘Diagnosis of Alzheimer's Disease Using Principal Component

Analysis and Support Vector Machine, International Journal of Pharmaceutical Research,

Volume 12, Issue 2, PP.713-724.

[14]D Stalin David, 2020, ‘An Intellectual Individual Performance Abnormality Discovery

System in Civic Surroundings’ International Journal of Innovative Technology and Exploring

Engineering, Volume 9, Issue 5, PP.2196-2206.

[15]D Stalin David, 2020, ‘Machine learning for the prelude diagnosis of dementia’,

International Journal of Pharmaceutical Research, Volume 13, Issue 3, PP.2329-2335.

[16]Stalin David D, Saravanan D, 2020, ‘Multi-perspective DOS Attack Detection Framework

for Reliable Data Transmission in Wireless Sensor Networks based on Trust’, International

Journal of Future Generation Communication and Networking, Volume 13, Issue 4, PP.1522–

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[17]J. K. S and D. S. David, "A Novel Based 3D Facial Expression Detection Using Recurrent

Neural Network," 2020 International Conference on System, Computation, Automation and

Networking (ICSCAN), Pondicherry, India, 2020, pp. 1-6, doi:

10.1109/ICSCAN49426.2020.9262287.

[18] R.Parthiban, Dr.K.Santhosh Kumar, Dr.R.Sathya, D.Saravanan,” A Secure Data

Transmission And Effective Heart Disease Monitoring Scheme Using Mecc And Dlmnn In The

Cloud With The Help Of Iot”, International Journal of Grid and Distributed Computing, ISSN:

2005 – 4262, Vol. 13, No. 2, (2020), pp. 834 – 856.

[19] R.Bhavya, G.I.Archanaa, D.Karthika, D.Saravanan,” Reflex Recognition of Tb Via Shade

Duplicate Separation Built on Geometric Routine”, International Journal of Pure and Applied

Mathematics 119 (14), 831-836.

[20] D Saravanan, R Bhavya, GI Archanaa, D Karthika, R Subban,” Research on Detection of

Mycobacterium Tuberculosis from Microscopic Sputum Smear Images Using Image

Segmentation”, 2017 IEEE International Conference on Computational Intelligence and

Computing Research (ICCIC).

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[21] D Saravanan, R Parthiban,” Automatic Detection of Tuberculosis Using Color Image

Segmentation and Statistical Methods”, International Journal of Advance Research in Science

and Engineering, Volume 6, Issue 10.

[22] U.Palani, D.Saravanan, R.Parthiban, S.Usharani,” Lossy Node Elimination Based on Link

Stability Algorithm in Wireless Sensor Network”, International Journal of Recent Technology

and Engineering (IJRTE), Volume 7, Issue 6S5.

[23] S.G.Sandhya, D.Saravanan, U.Palani, S.Usharani,” Handover Priority to the Data at Knob

Level in Vanet”, International Journal of Recent Technology and Engineering (IJRTE), Volume

7, Issue 6S5.

[24] D.Saravanan R.Parthiban, U.Palani S.G.Sandhya,” Sheltered and Efficent Statistics

Discrimnation for Cluster Based Wireless Antenna Networks”, International Journal of Recent

Technology and Engineering (IJRTE), Volume 7, Issue 6S5.

[25] D.Saravanan1, Dr. K.Santhosh Kumar2,, R.Sathya3, U.Palani4, “An Iot Based Air Quality

Monitoring And Air Pollutant Level Prediction System Using Machine Learning Approach –

Dlmnn”, International Journal of Future Generation Communication and Networking, Vol. 13,

No. 4, (2020), pp. 925–945.

[26] Raghu Raman D, Saravanan D, Nivedha R,”An Efficacious E-Portal for Rancher to Buy

Seeds and Humus”, International Journal of Recent Technology and Engineering (IJRTE),

Volume-8, Issue-1S5, June 2019.

[27] M.Sudha, D.Saravanan,S.Usharani,” Security Improvement of Dropper Elimination Scheme

for IoT Based Wireless Networks”, International Journal of Engineering Trends and Technology

(IJETT) ,Volume-45 Number3 -March 2017.

[28] K. Gayathri,D.Saravanan,” An Innovative IOT security Enhancing Schema Based Gamed

Theory Decryption Percentage”, International Journal of Advanced Research in Science and

Technology, Volume 6, Issue2, 2017, pp. 666-671.

[29] E.Kowsalya, D.Saravanan, R.Parthiban,” Energy Aware Resource Allocation for

Throughput Maximized IOT Network”, International Journal of Computer Trends and

Technology (IJCTT) – Volume 45 Issue 2- March 2017.

[30] Stalin David D, Saravanan D, “ Enhanced Glaucoma Detection Using Ensemble based CNN

and Spatially Based Ellipse Fitting Curve Model”, Solid State Technology, Volume: 63 Issue: 6.



5719

[31] Stalin David D, Saravanan D, Dr.A.Jayachandran “ Deep Convolutional Neural Network

based Early Diagnosis of multi class brain tumour classification system”, Solid State

Technology, Volume: 63 Issue: 6.

[32] D.Jayakumar, Dr.U.Palani, D.Raghu Raman, Dr.D.StalinDavid, D.Saravanan, R.Parthiban,

S.Usharani “Certain Investigation On Monitoring The Load Of Short Distance Orienteering

Sports On Campus Based On Embedded System Acceleration Sensor”, European Journal of

Molecular & Clinical Medicine ISSN 2515-8260 7 (9), 2477-2494.

[33]Dr.U.Palani, D.Raghu Raman, Dr.D.StalinDavid, R.Parthiban, S.Usharani,

D.Jayakumar,D.Saravanan " An Energy-efficient Trust Based Secure Data Scheme In Wireless

Sensor Networks”, European Journal of Molecular & Clinical Medicine ISSN 2515-8260 7 (9),

2495-2510.

[34]R.Parthiban, S.Usharani, D.Saravanan, D.Jayakumar, Dr.U.Palani, Dr.D.StalinDavid,

D.Raghu Raman, "Prognosis Of Chronic Kidney Disease (Ckd) Using Hybrid Filter Wrapper

Embedded Feature Selection Method”, European Journal of Molecular & Clinical Medicine

ISSN 2515-8260 7 (9), 2511-2530.

[35]D.Raghu Raman, D.Saravanan, R.Parthiban,Dr.U.Palani, Dr.D.Stalin David, S.Usharani,

D.Jayakumar, " A Study On Application Of Various Artificial Intelligence Techniques On

Internet Of Things”, European Journal of Molecular & Clinical Medicine ISSN 2515-8260 7 (9),

2531-2557.

[36] Dr. D. Stalin David,R. Parthiban, D.Jayakumar, S.Usharani, D.RaghuRaman, D.Saravanan,

Dr.U.Palani,”Medical Wireless Sensor Network Coverage And Clinical Application Of Mri

Liver Disease Diagnosis”, European Journal of Molecular & Clinical Medicine ISSN 2515-8260

7 (9), 2559-2579.

[37] D Saravanan, J Feroskhan, R Parthiban, S Usharani, “Secure Violent Detection in Android

Application with Trust Analysis in Google Play”, Journal of Physics: Conference Series 1717

(1), 012055.

[38] D Saravanan, E Racheal Anni Perianayaki, R Pavithra, R Parthiban,” Barcode System for

Hotel Food Order with Delivery Robot”, Journal of Physics: Conference Series 1717 (1),

012054.



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[39] D Raghu Raman, S Gowsalya Devi, D Saravanan, ”Locality based violation vigilant system

using mobile application”, 2020 International Conference on System, Computation, Automation

and Networking (ICSCAN-IEEE).

[40] R Parthiban, R Ezhilarasi, D Saravanan, “Optical Character Recognition for English

Handwritten Text Using Recurrent Neural Network”, 2020 International Conference on System,

Computation, Automation and Networking (ICSCAN-IEEE).

[41] R Parthiban, V Abarna, M Banupriya, S Keerthana, D Saravanan, ” Web Folder Phishing

Discovery and Prevention with Customer Image Verification”, 2020 International Conference on

System, Computation, Automation and Networking (ICSCAN-IEEE).

[42] K Dhivya, P Praveen Kumar, D Saravanan, M Pajany,”Evaluation of Web Security

Mechanisms Using Vulnerability & Sql Attack Injection”, International Journal of Pure and

Applied Mathematics, Volume 119, Issue 14, 2018.

[43] D Saravanan, R Parthiban, S Usharani, K Santhosh Kumar,”Furtive Video Recorder Using

Intelligent Vehicle with the Help of Android Mobile”, International Journal of Pure and Applied

Mathematics, Volume 119, Issue 14, 2018.

[44] K Dheepa, D Saravanan, R Parthiban, K Santhosh Kumar,”Secure And Flexible Data

Sharing Scheme Based On Hir-Cp-Abe For Mobile Cloud Computing”, International Journal of

Pure and Applied Mathematics, Volume 119, Issue 14, 2018.

[45]D.Saravanan S. Usharani,”Survey on Security Based Novel Context Aware Mobile

Computing Scheme Via Crowdsourcing”, International Journal of Scientific Research in

Computer Science, Engineering and Information Technology.

[46] S. Usharani, D.Saravanan,”Security Improvement of Dropper Elimination Scheme for Iot

Based Wireless Networks”, International Journal of Engineering Trends and Technology.

[47] D.Saravanan,”An Innovative Iot Security Enhancing Schema Based Gamed Theory

Decryption Percentage”,International Journal of Advanced Research in Science and Technology.

[48]R Parthiban, S Usharani, D Saravanan,”Survey of Security Improvement of Dropper

Elimination Scheme for IoT Based Wireless Networks”, International Journal of Engineering

Trends and Technology (IJETT).



5721

[49] S Karthika, Mr D Saravanan, “Dynamic taxi-sharing system using Dual-side searching

algorithm”, International Journal For Research In Advanced Computer Science And

Engineering.

[50] M. Rajdhev, D. Stalin David, "Internet of Things for Health Care", International Journal of

Scientific Research in Computer Science, Engineering and Information Technology

(IJSRCSEIT), ISSN : 2456-3307, Volume 2 Issue 2, pp. 800-805, March-April 2017.

[51] P. Prasanth, D. Stalin David, "Defensing Online Key detection using Tick Points",

International Journal of Scientific Research in Computer Science, Engineering and Information

Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2 Issue 2, pp. 758-765, March-April

2017.

[52] A. Sudalaimani, D. Stalin David, "Efficient Multicast Delivery for Data Redundancy

Minimization over Wireless Data Centres", International Journal of Scientific Research in

Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307,

Volume 2 Issue 2, pp. 751-757, March-April 2017.

[53]R. Abish, D. Stalin David, "Detecting Packet Drop Attacks in Wireless Sensor Networks

using Bloom Filter", International Journal of Scientific Research in Computer Science,

Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2 Issue 2,

pp. 730-735, March-April 2017.

[54] A. Vignesh, D. Stalin David, "Novel based Intelligent Parking System", International

Journal of Scientific Research in Computer Science, Engineering and Information Technology

(IJSRCSEIT), ISSN : 2456-3307, Volume 2 Issue 2, pp. 724-729, March-April 2017.

[55] D Stalin David, 2016, Robust Middleware based Framework for the Classification of

Cardiac Arrhythmia Diseases by Analyzing Big Data, International Journal on Recent

Researches In Science, Engineering & Technology, 2018, Volume 4, Issue 9, pp.118-127.

[56] D. Jayakumar; Dr.U. Palani; D. Raghuraman; Dr.D. StalinDavid; D. Saravanan; R.

Parthiban; S. Usharani. "CERTAIN INVESTIGATION ON MONITORING THE LOAD OF

SHORT DISTANCE ORIENTEERING SPORTS ON CAMPUS BASED ON EMBEDDED

SYSTEM ACCELERATION SENSOR". European Journal of Molecular & Clinical Medicine,

7, 9, 2021, 2477-2494.



5722

[57] R. Parthiban; S. Usharani; D. Saravanan; D. Jayakumar; Dr.U. Palani; Dr.D. StalinDavid; D.

Raghuraman. "PROGNOSIS OF CHRONIC KIDNEY DISEASE (CKD) USING HYBRID

FILTER WRAPPER EMBEDDED FEATURE SELECTION METHOD". European Journal of

Molecular & Clinical Medicine, 7, 9, 2021, 2511-2530.

[58] Dr.U. Palani; D. Raghuraman; Dr.D. StalinDavid; R. Parthiban; S. Usharani; D. Jayakumar;

D. Saravanan. "AN ENERGY-EFFICIENT TRUST BASED SECURE DATA SCHEME IN

WIRELESS SENSOR NETWORKS". European Journal of Molecular & Clinical Medicine, 7, 9,

2021, 2495-2510.

[59] Dr. D. Stalin David; R. Parthiban; D. Jayakumar; S. Usharani; D. RaghuRaman; D.

Saravanan; Dr.U. Palani. "MEDICAL WIRELESS SENSOR NETWORK COVERAGE AND

CLINICAL APPLICATION OF MRI LIVER DISEASE DIAGNOSIS". European Journal of

Molecular & Clinical Medicine, 7, 9, 2021, 2559-2571.

[60] D.Raghu Raman; D. Saravanan; R. Parthiban; Dr.U. Palani; Dr.D.Stalin David; S. Usharani;

D. Jayakumar. "A STUDY ON APPLICATION OF VARIOUS ARTIFICIAL INTELLIGENCE

TECHNIQUES ON INTERNET OF THINGS". European Journal of Molecular & Clinical

Medicine, 7, 9, 2021, 2531-2557.

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