Broadcast Tree Construction for Shortest Path Finding with Secure

Data Aggregation Techniques in Wireless Sensor Networks

Vidya H. Deshmukh

1,2

, Pramod Jagan Deore

1,2

and S. B. Deosarkar

Dr. Babasahabe Ambedkar Technological University, Lonere, India

R. C. Patel Institute of Technology, Shirpur, India

Department of Electronics and Telecommunication Engineering, India

Keywords: WSN, Shortest Path Finding, Data Aggregation, HMAC, Broadcast Tree Generation, Routing Protocols,

Sensing Nodes, Optimization Techniques.

Abstract: A wireless sensor network (WSN) consists of a large number of deployed sensing devices that consume

little power and computing capabilities. Data packet switching in sensing devices is commonly achieved by

using multi-hop transmission due to the limited range of connectivity and the dense distribution of sensor

nodes. Dynamic routing in WSN has been increasingly popular in recent years. In modern times, sensor

networks extensively employ the multi-path data transmitting technique to optimize system performance by

using the available bandwidth. The main objective of this paper is to elucidate the concept of multi-path

routing, along with its inherent challenges and fundamental justifications for its application in sensor

networks. This study presents a method for finding the shortest path using the Broadcast Tree Construction

(BTC) algorithm, which incorporates data aggregation techniques to reduce network overhead during

communication. This method is suitable for both cluster networks and Wireless Sensor networks. The

suggested work was assessed utilizing the NS2 environment in a thorough experimental investigation. Four

alternative procedures were used to explore the system efficiency. Through rigorous experimental

investigation, the system is able to conserve 15% of energy while operating with 100 internal nodes.

Additionally, it improves network life by 7% when utilizing the SAODV routing protocol. This

performance is superior to the distance-based method of determining the shortest path in a WSN. It also

improves the data transfer rate and decreases the number of lost packets compared to previous methods of

identifying the shortest path in WSN’s.

1 INTRODUCTION

Restricted wireless sensor nodes have been

introduced in recent advancements in wireless

communication systems and the manufacturing of

cheap wireless devices. WSN has been used for

numerous applications, including medicine, target

detection, and surveillance systems, due to their

simplicity of deployment and non - linear and none

of the edge devices (Le, Chong, et al. , 2010). The

main function of sensor nodes in any application is

to detect the target area and send the collected data

to the base station for additional analysis. The lack

of efficiency in sensor node resources and the

unpredictable nature of limited wireless connections

The user's text is (Kenc and Boudec, 2008), along

with the varied throughput requirements of diverse

applications, offer many difficulties in developing

effective WSN communication protocols (Wang,

Niu, et al. , 2007). Meanwhile, finding appropriate

network algorithms to meet the varied performance

needs of various workloads is a critical problem in

wireless mesh networking. Several routing methods

have been suggested in this area to enhance the

efficiency needs of multiple applications via the

network topology of the distributed sensing protocol

stack.

The battery powers the sensor nodes, which in

many cases, these devices cannot be changed. When

the power goes out, and the network stops working,

they die. As a result, a routing method is critical for

extending the battery's life and effectively managing

the battery. This feature encourages the development

of energy-saving routing methods. Data is sent via

intermediary sensor nodes in a WSN, which is a

multi-hop infrastructure. The connections between

Deshmukh, V. H., Deore, P. J. and Deosarkar, S. B.

Broadcast Tree Construction for Shortest Path Finding with Secure Data Aggregation Techniques in Wireless Sensor Networks.

DOI: 10.5220/0013600100004664

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 3rd International Conference on Futuristic Technology (INCOFT 2025) - Volume 2, pages 685-693

ISBN: 978-989-758-763-4

685

iot devices are very vulnerable to failure. The

probability of connection failure directly impacts the

data packet transmission and the network's

dependability. This problem drives the development

of dependable routing methods. The movable sink

may be used to address the energy hole issue.

However, managing a transportable sink is a time-

consuming job. Several routing algorithms function

in the mobile sink context, but they have drawbacks

such as inefficient management, high power

consumption, and a lower data delivery ratio. To

extend the broadcaster's life, it's critical to control

the mobile sink effectively. The produced data

should approach the network device as soon as

possible in many instances. The later part latency is

increased by the lack of a routine route, the

placement of the sink, and the probability of

hardware failures. As a result, methods to decrease

latency must be included.

The primary goal of this study is to decrease

energy usage while also reducing delay. Some

methods may be used by the routing algorithm to

enhance energy consumption and network lifespan.

Below are a few smart energy methods that will be

explored:

Energy model: In any routing algorithm, the

energy storage system of the WSN nodes may assist

to enhance connection speeds (Le, Chong, et al. ,

2010). The precise definition of the energy balance

equation may provide a more accurate estimate of

the residual energy within every node. It simplifies

and clarifies observation. The network lifespan may

be increased by using a model with a comprehensive

perspective and a proper methodology.

Reduce the control packet overhead: A sensor

network uses the most energy during communication

systems (Kenc and Boudec, 2008). The shortest path

and preservation in the routing algorithm for

neighbour node identification need many control

signals to be sent between sensor nodes. The

proposed method must limit the network's

unnecessary circulation of control packets. The

volume of the received packets may have an impact

on the total amount of energy used.

Permit multi-hop communication:

Straightforward data transfer requires more energy

then multi-hop data transfer (Wang, Niu, et al. ,

2007). The sensor node must maximize the wireless

transmission capacity in direct connection, which

produces higher expenditure at each node. To cut

emissions, the routing algorithm must address these

problems.

Transmission range modification: WSN is

several co networks in which data must pass via

intermediary nodes to reach its destination. During

implementation, it is common to discover that the

following accessible access points are almost always

near the sender node. As a result, instead of

transmitting data at full power, the RSSI (Xu, Liu, et

al. , 2010) may modulate the network throughput.

This method may assist in decreasing energy usage

and increasing network lifespan (Quang and

Miyoshi, 2010).

Data aggregation: At some time, comparable

data packets may be consolidated, and the collected

information can be sent to the sinkhole (Batwada,

Tripathi, et al. , 2012). Combining similar data

across the network reduces traffic on the web (Paul,

Nandi, et al. , 2008). Collisions and energy

consumption are decreased as a result of less traffic.

To extend the broadcaster's lifespan, the routing

algorithm must use the clustering algorithm.

Using MAC protocol: The node in the network

detects its surroundings, produces data, and sends it

to the reservoir (Karaki and Kamal, 2004). Sensor

networks must go into standby mode even when

they're not seeing or routing. As a result, for

networking energy saving, an appropriate Proposed

technique is needed.

Minimize the collision: Every data could reach a

wireless network device without even any

intervention in the packet forwarding (Chong and

Kumar, 2003). Each node must be capable of

communicating in a traffic delays atmosphere,

according to the standard. Alternatively, it may

result in data recapture, which directly impacts the

platform's fuel efficiency.

The primary contribution of our research is

outlined below.

The objective is to create an algorithm that finds

the shortest path from the source node to the base

station.

To provide a robust data aggregation technique

at the Cluster Heads' location.

To deploy the suggested solution across multiple

network protocols, including AODV, DSDV,

SOADV, and DSR.

The rest of the paper is divided into the

subsequent sections: Section II examines various

contemporary methodologies that previous

researchers have devised to uncover routes between

source and sink nodes. Part III demonstrates the

recommended system structure and execution, while

part IV outlines the proposed method specification

for implementation. The experimental configuration

for evaluating the proposed work and the results

obtained using our strategy, together with a

comparison study against various cutting-edge

INCOFT 2025 - International Conference on Futuristic Technology

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methodologies, is detailed in section V. The

conclusion and potential future developments are

addressed in Section VI.

2 LITERATURE SURVEY

Since the transmitted signal is dependent on

electricity, many studies have looked at the impact

of power in improving broadband services. The

maximum lifespan for the shortest route aggregation

tree was investigated by Luo et al.. The issue was

modified to a job scheduling scheme for each level

of a structure resembling a fat tree. The researchers

showcased the possibility of managing the problem

with a time complexity by using the min-cost max-

flow approach. They demonstrated the suitability of

their choice by showing that the produced shortest

route tree is superior, and their simulation confirmed

that it beats the random approach.

An efficient way for low latency data

transmission in wireless sensor networks (WSNs) is

the use of the lowest route routing, as described in

(Vetrivelan, 2019). A proposed routing system in

Wireless Sensor Networks (WSNs) is LWSP

(Shortest path without Latency), which aims to

efficiently determine the shortest paths with

minimum command processing overhead time and

low latency. Based on performance tests and

simulation data, our proposed protocol demonstrates

superior performance compared to existing protocols

in terms of latency and additional processing time

for commands. The shortest path technique is used

for the establishment of a wireless sensor network

(Nakas, Christos, et al. , 2020). The purpose is to

implement the quickest route method in a wired

communication network, which will facilitate the

transmission of protocol and ensure real-time arrival

at the destination. This will include determining and

identifying the shortest path algorithm in a wireless

communication system.

An extensive examination of energy-conserving

techniques in wireless sensor networks (Kandris,

Dionisis, et al. , 2020). This paper provides an

analysis of both conventional and modern methods

proposed for attaining energy-efficient routing in

Wireless Sensor Networks (WSNs). The protocols

were classified into four main areas based on their

important structural or operational characteristics:

Customized System, Network Structure, Topology,

and Reliable Routing. They analyzed the pros and

cons of many common examples of the

aforementioned techniques. They have a same goal:

to save energy by optimizing their routing strategies.

Our investigation uncovered several variances in

both the structural and functional aspects.

A recent publication (Osamy, Walid, et al. ,

2019) has presented new findings on the uses of

wireless sensor networks. A comprehensive and

current survey of traditional and emerging

applications of Wireless Sensor Networks can

greatly enhance comprehension of this scientific

domain and the recognition of inventive uses. To

achieve this purpose, we outline the main categories

of applications in Wireless Sensor Networks and

analyze specific instances of each category. Their

distinctive characteristics are examined, along with

their benefits and drawbacks. Subsequently, there is

an examination of the many concerns linked to each

of these specific groups. Lastly, there are a few

concluding remarks.

The article titled "Tree building and scheduling

techniques based on simulated annealing decrease

duration in wireless sensor networks" discusses the

use of simulated annealing to reduce the duration in

wireless sensor networks (Zantalis, Fotios, et al. ,

2019). In order to gather data with a specific

schedule for node transmissions, the researchers

devised an innovative method for constructing trees

using simulated annealing, known as SATC. The

delivery time for aggregated data to the sink is

reduced by 50% utilizing SATC. The proposed

strategy utilizes the average time delay as the fitness

value for the simulated annealing (SA) process. This

fitness value is assessed using routing aware MAC

scheduling techniques. Simulation is used to

evaluate the efficiency of the proposed method and

compare it to current state-of-the-art techniques,

primarily by analyzing average delay and latency.

Smart transportation is increasingly being

influenced by the growing importance of machine

learning and the Internet of Things (Khedo,

Bissessur, et al. , 2020). Intelligent transportation

encompasses several aspects such as optimizing

routes, managing parking, improving street lighting,

preventing and detecting accidents, addressing road

imperfections, and implementing infrastructure

applications. The objective of this article is to

provide a comprehensive summary of machine

learning (ML) techniques and Internet of Things

(IoT) applications in Intelligent Transportation

Systems (ITS) in order to get a thorough

understanding of the advancements in these fields

and identify any possible areas that have not been

adequately addressed. The analyzed articles indicate

a possible deficiency in machine learning

applications for Smart Lighting Systems and Smart

Parking Applications. Furthermore, the prevailing

Broadcast Tree Construction for Shortest Path Finding with Secure Data Aggregation Techniques in Wireless Sensor Networks

687

ITS applications favored by scholars include route

optimization, parking management, and accident

detection.

The researchers in (Tasgaonkar, Pankaj, et al. ,

2020) developed an earthquake early warning

system with the aim of increasing the duration of

time available before an earthquake occurs, allowing

individuals to take precautionary measures. The

researchers established a Wireless Sensor Network

(WSN) on Mauritius, an island characterized by

significant seismic activity. The technique utilizes

primary waves to observe seismic activity. The

system determines the local velocity and hypocentre

location by analyzing the time delay between the

arrival of P-waves at the sensors. The paper titled

"Vehicle Search and Traffic Estimation for

Intelligent Transportation Systems Using Sensor

Technologies" is referenced as (Tasgaonkar, Pankaj,

et al. , 2020). Vehicle detection strategies include

both invasive and non-intrusive sensors. The

objective of this research is to provide a

comprehensive inventory of the sensors and

technologies used in vehicle identification and

traffic estimation. By establishing a connection with

the monitoring station on the vehicle's existence on

the road, these sensors will provide crucial

information. Sensors and communication

technologies are extensively used in intelligent

transportation systems. An assessment is conducted

to evaluate the most recent tools and techniques used

to determine the number of vehicles, their

classification, location, speed, traffic volume,

density, and traffic estimation. Sensor fusion enables

the seamless integration of data from several

sources, hence enhancing accuracy.

The Social Internet of Vehicles employs a Cross-

Layer Protocol for Traffic Management (Jain,

Bindiya, et al. , 2018). A considerable quantity of

sensors transmit data via wireless means in the

proposed Vehicular Social Networks that rely on the

VIoT. The wide range of hardware capabilities and

quality of service requirements for different

applications hinders the effectiveness of traditional

layered protocol solutions and modern cross-layer

solutions for wireless sensor networks. The

innovative Vehicular Social Network Protocol

(VSNP) based on Wireless Sensor Networks (WSN)

in the context of Vehicular Internet of Things

(VIoT) provides an optimal level of global

connectivity and outperforms current layered

systems. The introduction of the new SIoT cross-

layer module is the first phase in establishing

dependable vehicle-to-vehicle communication and

optimizing traffic management. We presented a

methodology for effectively handling traffic

congestion and enhancing road safety in the context

of VIoT.

Deep learning is used in data-driven pavement

imaging. An evaluation of analysis and automated

problem-solving ((Gopalakrishnan, Kasthurirangan.,

et al. , 2018). An exposition of recent research in

this field, highlighting present achievements and

challenges. The provided information includes a

comparison of deep learning software frameworks,

network architecture, hyper-parameters utilized in

each study, and the performance of crack detection.

This serves as a solid basis for future research in the

field of intelligent pavement and asset management

structures. The work continues by proposing future

research directions, including the use of deep

learning techniques to accurately identify and

classify various types, quantities, and severities of

distresses in both 2D and 3D pavement photos.

Utilizing GPS trace, autonomously detect traffic

signals, street intersections, and urban roundabouts

during the act of driving(Organero, Mario, et al. ,

2018). A novel approach is centered on the

automated identification of street elements such as

traffic signals, intersections, and circular junctions.

These elements may be used to generate street maps

and fill them with traffic-related infrastructure

characteristics such as traffic signals. The system

utilizes just the residual GPS data obtained from the

mobile device while driving to reduce system

demands and streamline data collecting from many

users with little effect. The GPS data is used to

construct time series for speed and acceleration. At

first, an outlier identification method is used (which

may be caused by infrastructure components or

specific traffic situations). Deep learning is used to

analyze speed and acceleration patterns at each

anomaly in order to extract essential characteristics,

which are identified as a traffic signal, pedestrian

crossing, urban roundabout, or another component.

The paper titled "Duty-Cycle Multi-hop Wireless

Sensor Network with Structure-Free Broadcast

Scheduling (Chen, Quan, et al. , 2021)" is being

referred to.

1. Instead of depending on a predetermined

structure, a two-step scheduling technique is

suggested to concurrently generate the broadcast tree

and calculate a timetable that avoids collisions. As

far as we know, this is the first endeavor to combine

these two types of processes.

The paper introduces concurrent broadcasting, an

innovative transmission mechanism for wireless

networks, and investigates other methods to further

reduce the broadcast latency.

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3. The introduction of broadcasting algorithms

that may independently create a series of broadcast

schedules without a pre-determined tree was made

possible by taking into account collisions in both the

current and prior broadcast schedules, as well as the

broadcasting of simultaneous messages.

The most efficient path with risk management

for a malfunctioning wireless sensor network (Wang

and Na, 2019). Compromised Regions (CRs) consist

of a cluster of densely connected nodes (CNs) and

seem to provide a greater threat to networks

compared to individual CNs. In order to protect

against CR attacks, we have devised a Secure

Shortest Path Routing Algorithm (SPRA) that

redirects packets around, rather than through, CRs.

For instance, a source node computes the most direct

path to a sink node, avoiding any intersections with

CRs. It then identifies agent nodes along the route

indirectly by using a set of virtual locations.

Ultimately, a complex system using geographical

data is created to ensure that packages may be

passed via intermediary nodes until they are

successfully delivered to the final destination node.

In a series of experiments, we evaluate our strategy

by comparing it to the dynamically Greedy

Perimeter Stateless Routing (GPSR) and Directed

Diffusion (DD) methods.

The issue of transmitting data along the most

direct route (Younes, 2018). The problem of the

smallest broadcast tree was discussed and an

efficient genetic algorithm was shown to solve it.

The approach examines the connection, cost, and

bandwidth matrices of a network and constructs a

tree of least-cost routes. This tree is rooted at a

specific node s and takes into account bandwidth

limits. The construction of the tree is based on the

minimal broadcast tree algorithm. The genetic

algorithm (GA) was evaluated using three distinct

situations, and the results conclusively showed that

the proposed GA is very efficient. The paper WSN

(Sun, Xiao, et al. , 2019) proposes a method where

the integrated two-hop neighbourhood information

is used to create a transmission radius that is twice

as large, resulting in efficient code transmission. The

TNI-DBR technique is introduced to efficiently and

quickly provide codes in duty cycle-based WSNs by

using a Two-hop Neighbourhood Info with double

Broadcast Radius. Here are the most notable

breakthroughs discussed in the essay. The TNI-DBR

approach utilizes surplus energy to increase the

broadcast radius in the given region, enabling a

greater number of nodes to receive up-to-date code

via a continuous broadcast, hence decreasing the

latency in code dissemination. Unlike traditional

methods of distributing code, which choose

broadcasting nodes based on information from

nearby nodes within one hop.

3 PROPOSED SYSTEM DESIGN

The below figure 1 describes the proposed system

architecture for secure pathfinding using a broadcast

reconstruction algorithm. During the execution of

the proposed system, first generate multiple clusters

in a network, each cluster having a cluster node that

contains at least one cluster head; others are cluster

members. The two basic algorithms have been

demonstrated for data aggregation and secure data

transmission between source to destination. In order

to the selection of source is the cluster head and

destination is the base station. The cluster head

eliminates data redundancy while BTC gives

assurance, that recommended channel or path is

stronger than other available resources. The major

benefit of the proposed BTC, that reduces the

network overhead and higher energy consumption. It

selects strong nodes which are having higher energy

from the neighbour's list. Based on that, BTC

constructs the entire path and transmit the data

security. As a result, both algorithms improve

quality of service of the entire simulation.

Figure 1: Proposed System Architecture using Minimal 10

Nodes by Minimal Spanning Tree

Existing hop-to-hop connectivity in wireless

sensor networks is thought to be exposed to data

transmission vulnerability. Due to the higher cost of

packet transmission of hop-by-hop communication,

the current system employs a security technique

known as broadcast tree construction and

authentication among network resources. The

identity of intermediary nodes may be compromised

from hop to hop, posing a security risk. They

employ trust computation of each node at the node

level for communication or packet transfer to

prevent security threats. Message transmission in the

Broadcast Tree Construction for Shortest Path Finding with Secure Data Aggregation Techniques in Wireless Sensor Networks

689

current architecture occurs selective neighbour node

using BTC algorithm between the source and

destination nodes, resulting it reduces the overhead

between nodes. In wireless sensor communication.

4 ALGORITHM DESIGN

This section displays a data aggregation process

and a data transmission method that finds the

shortest route effectively. By reducing network

overhead and eliminating data redundancy, data

aggregation helps make data transfer between source

and sink nodes more efficient. Concurrently, internal

nodes that use a lot of power are reduced via tree

building. Together, the lists of internal nodes and

their neighbours allow this method to build a reliable

route up to the destination. Lightweight data

communication and the assurance that no data will

leak during transmission are both provided by this

approach.

4.1 Broadcast Tree Construction for

shortest path finding methods

Input: source node denoted as Src_node and sink

node Snk_node is a collection of adjacent nodes.

The neighbor node has an empty list, with the node's

identification as Node_id and its energy level. The

variable "Node_energy" is declared.

Output: Calculate the most efficient route from

the source node to the sink node or base station.

Step 1: Generate and initialize the network Src_node

and Snk_node with energy.

Step 2: Detect or choose the source file from the

Src_node.

Step 3: Check whether the value of file_data_values

is not null.

Step 4: Continuously read each byte, denoted as

'bytes', from the file data values until the

file_data_values is null.

Step 5: Transmit the data and initialize cost_filed_A,

cost_filed_B, parent_filed_A, parent_fileld_B

Step 6: Iterate over the nodes until the current node

is not null.

The value of cost_filed_A is equal to the value of

node[i].The variable "_energy" is assigned the value

of "node[i]" from the parent field

"parent_filed_A_values"._id cost_filed_B_values =

node[i+1]._id The energy parent field B values are

equal to the node ID of the next node in the

sequence.

Step 7: Check whether the value of

cost_filed_A_values is greater than the value of

cost_filed_B_values.

The value of cost_filed_B_values is nil.

The value of parent_filed_B_values is nil.

Otherwise, the values of parent_filed_A will be

equal to the values of parent_filed_B.

The values of cost_filed_B_values are assigned to

cost_filed_A_values. The values of

parent_filed_B_values are set to null.

The value of cost_filed_B_values is nil.

Step 8: Terminate the while loop.

Step 9: proceed check-out when reach to Snk_node

The method determines the next node by

considering possible neighboring nodes, using the

Parent_field as the node identifier and the cost_field

as the remaining energy of the chosen node. The

system develops routing and route based on energy

trust to ensure that no interruptions occur in

communication.

4.2 Data Aggregation Protocol

Input: The cluster head has received data in the

current stack, which is stored in the Rec_Data[]

array. The most recent packet received is stored in

the New_Rec_data[] array.

Output: true if data aggregation is feasible;

otherwise, false.

Step 1: Iterate over each read of data from

Rec_Data using the equation (1) below.

The equation (1) represents the sum of the values

in the array "Rec_Data" from index 0 to index n.

Step 2: Compute the cosine similarity between

the arrays Data[] and New_Rec_data. The result will

be a binary value (0 or 1) obtained by using the

function Calc_Cosine_Similarity on the elements

Data[k] and New_Rec_data.

Step 3: Terminate the for-loop

Step 4: Retrieve the Result_state and return it.

The method mentioned above performs data

aggregation and validates the received data by

comparing it with the presently received data. This

approach mitigates the duplication of data by

consolidating the information collected from various

sensor nodes. Additionally, it reduces the network

overhead involved in communication and conserves

energy.

5 RESULTS AND DISCUSSIONS

An analysis of the current system as well as its

potential successors is provided in this section. After

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690

outlining our experimental methodology and making

use of several measures including throughput,

packet delivery ratio, cost, and time, the

quantitatively evaluates the study. Many methods

exist for processing, filtering, and displaying vector

and scalar data. We are located in the project folder's

results directory. The findings of the simulation are

stored in the Tr file. Our graph tool will be used by

the mechanism. This file will display the result

parameters in relation to the x and y-axis

parameters. Any graphing program should be able to

plot files with the awk extension. In Table 1 below,

you can see the NS2 setup settings established for

the suggested Ubuntu open-source simulation.

The setup environment for the proposed IEEE

802.11n implementation is described in Table 1.

Using the SAODV Protocol, the findings shown in

Figures 2–6 below show how the proposed system

calculates quality-of-service parameters.

Table 1: Parameters and Values in wireless network with

100 nodes in NS2.

Figure 2: The suggested SAODV system's throughput

compared to other protocols.

The suggested simulation's throughput as a

function of simulation time is shown in Figure 2,

which is located above. In accordance with Table 1,

the network setup settings have been fine-tuned.

With IEEE 820.11n, which offers bandwidth of up

to 600 MB, the starting input nodes have been set at

100. Even while simulation time has grown with

SAODV, throughput has declined with DSR,

DSDV, and AODV, but it has improved with

SAODV.

Figure 3: The suggested SAODV system's end-to-end

delay SAODV metric in contrast to competing methods

Finding the logarithm of the time delay as shown

in Figure 3 provides the basis for calculating the

end-to-end latency. A packet's success rate is

determined by two factors: the amount of packets

transmitted by the internal load and the number of

packets received by the destination node. The

reduced packet overhead experienced by SAODV

producers during communication is also shown in

this experiment.

Figure 4: Packet drop rate vs simulation time

An example of how the packet drop rate is

determined is shown in figure 4, which is the result

of subtracting the number of packets successfully

received by the destination node from the number of

packets sent by the sensor node. During connection

5 1015202530

THROUGHPUT

TIME (IN SECONDS)

DSDV

DSR

AODV

SAODV

5 1015202530

END TO END DELAY

TIME (IN SECONDS)

DSDV

DSR

AODV

SAODV

5 1015202530

packet drop rate

time (in seconds)

DSDV

DSR

AODV

SAODV

Broadcast Tree Construction for Shortest Path Finding with Secure Data Aggregation Techniques in Wireless Sensor Networks

691

between heterogeneous nodes, DSDV causes a high

packet loss rate but SAODV causes a lower one.

Figure 5: Evaluation of the suggested SAODV system's

packet delivery ratio relative to competing protocols

Figure 5 shows the communication packet

delivery ratio vs simulation time. The packet

delivery rate is based on the number of successfully

delivered packages sent by the source node. Both

internal node communication and source-to-sink

node communication may benefit from a comparable

strategy.

Figure 6: Compared to competing protocols, the suggested

SAODV system's packet control overhead

Figure 6 displays the results of the entire

experiment analysis, which includes a number of

elements and a system performance analysis using

several methods. Compared to other alternative

Quality of Service (QoS) criteria, SAODVs deliver

superior outcomes. Across all trials, SAODV

outperformed AODV, DSDV, and DSR by an

average of 3-5%.

6 CONCLUSIONS

This article introduces the method, which helps

reduce power consumption and find the shortest path

for data transmission in a wireless sensor network.

Parent filed cost field and neighbour nodes.

Identification is an important factor of the BTC

algorithm. Before data transmission, we applied the

data aggregation technique protocol at the cluster

head level, which eliminates the data redundancy.

Both algorithms provide different advantages to

effective simulation. IT systems also skip the

distributed path selection problem and cut

generation during the data transmission. Data

aggregation reduce the commutation cost and reduce

memory and power consumption, respectively. We

have conducted four separate tests using the AODV,

SAODV, DSR, and DSDV procedures in our

experimental research. From what we can tell, when

it comes to SAODV, the system manages to keep

internal nodes' energy consumption at 15% while

improving the network life at 7%. Improve this

study in the future by including packet scheduling

and other methods for detecting and preventing

network assaults.

Table 2: Performance evaluation of proposed system for IEEE 802.11

Details of

technical

parameters

Output

achieved

Outcome inferred from output Impact Improvement

Without

BTC

With

BTC

Throu

ut 4.1 bit/s Hi

h throu

ut even hi

h traffic

enerate

3.5 bit/s 4.1 bit/s 0.6 bit/sec

E2E delay 0.019 sec Reduce E2E delay by using BTC based data

transmission

0.028sec 0.019

sec

0.09 bit/sec

Packet Drop 1.9 Low packet drop rate than traditional WSN

and cluster networking

4.7 1.9 3.8

Packet

Deliver

99.2 Achieve good packet delivery rate even we

chan

ed no. of nodes and size of data

95.20 99.2 4.0

Overhead 0.75 Packet overhead is almost noting. 1.85 0.75 1.10

100

101

5 1015202530

PACKET DELIVERY

TIME (IN SECONDS)

DSDV

DSR

AODV

SAODV

0.5

1.5

5 1015202530

PACKET OVERHEAD

TIME (IN SECONDS)

DSDV

DSR

AODV

SAODV

INCOFT 2025 - International Conference on Futuristic Technology

692

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