Deterministic static sensor node placement in wireless sensor network based on territorial predator scent marking behavior
An optimum sensor node placement mechanism for Wireless Sensor Network (WSN) is desirable in ensuring the location of sensor nodes that offers maximum coverage and connectivity with minimum energy consumption. This paper proposes a sensor node placement algorithm that utilizes a new biologically ins...
Saved in:
Main Authors: | , , |
---|---|
Other Authors: | |
Format: | Article |
Published: |
2023
|
Subjects: | |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.uniten.dspace-29441 |
---|---|
record_format |
dspace |
spelling |
my.uniten.dspace-294412023-12-28T12:13:06Z Deterministic static sensor node placement in wireless sensor network based on territorial predator scent marking behavior Abidin H.Z. Din N.M. Radzi N.A.M. 52165115900 9335429400 57218936786 Biological inspired Connectivity Coverage Deterministic Energy Sensor node placement Wireless sensor network An optimum sensor node placement mechanism for Wireless Sensor Network (WSN) is desirable in ensuring the location of sensor nodes that offers maximum coverage and connectivity with minimum energy consumption. This paper proposes a sensor node placement algorithm that utilizes a new biologically inspired optimization algorithm that imitates the behaviour of territorial predators in marking their territories with their odours known as Territorial Predator Scent Marking Algorithm (TPSMA). The main objectives considered in this paper are to achieve maximum coverage and minimum energy consumption with guaranteed connectivity. A simulation study has been carried out to compare the performance of the proposed algorithm implemented in two different single objective approaches with an Integer Linear Programming based algorithm and another biological inspired algorithm. The proposed single objective approaches of TPSMA studied in this paper are TPSMA with minimum energy and TPSMA with maximum coverage. Simulation results show that the WSN deployed using the proposed TPSMA sensor node placement algorithm is able to arrange the sensor nodes according to the objective required; TPSMA with maximum coverage offers the highest coverage ratio with fewer sensor nodes up to 100% coverage while TPSMA with minimum energy consumption utilized the lowest energy as low as around 4.85 Joules. Full connectivity is provisioned for all TPSMA approaches since the constraint of the optimization problem is to ensure the connectivity from all sensor nodes to the sink node. Final 2023-12-28T04:13:06Z 2023-12-28T04:13:06Z 2013 Article 2-s2.0-84890219411 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84890219411&partnerID=40&md5=bb06c620363235a0718bee62e23acb7b https://irepository.uniten.edu.my/handle/123456789/29441 5 3 186 191 Scopus |
institution |
Universiti Tenaga Nasional |
building |
UNITEN Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Tenaga Nasional |
content_source |
UNITEN Institutional Repository |
url_provider |
http://dspace.uniten.edu.my/ |
topic |
Biological inspired Connectivity Coverage Deterministic Energy Sensor node placement Wireless sensor network |
spellingShingle |
Biological inspired Connectivity Coverage Deterministic Energy Sensor node placement Wireless sensor network Abidin H.Z. Din N.M. Radzi N.A.M. Deterministic static sensor node placement in wireless sensor network based on territorial predator scent marking behavior |
description |
An optimum sensor node placement mechanism for Wireless Sensor Network (WSN) is desirable in ensuring the location of sensor nodes that offers maximum coverage and connectivity with minimum energy consumption. This paper proposes a sensor node placement algorithm that utilizes a new biologically inspired optimization algorithm that imitates the behaviour of territorial predators in marking their territories with their odours known as Territorial Predator Scent Marking Algorithm (TPSMA). The main objectives considered in this paper are to achieve maximum coverage and minimum energy consumption with guaranteed connectivity. A simulation study has been carried out to compare the performance of the proposed algorithm implemented in two different single objective approaches with an Integer Linear Programming based algorithm and another biological inspired algorithm. The proposed single objective approaches of TPSMA studied in this paper are TPSMA with minimum energy and TPSMA with maximum coverage. Simulation results show that the WSN deployed using the proposed TPSMA sensor node placement algorithm is able to arrange the sensor nodes according to the objective required; TPSMA with maximum coverage offers the highest coverage ratio with fewer sensor nodes up to 100% coverage while TPSMA with minimum energy consumption utilized the lowest energy as low as around 4.85 Joules. Full connectivity is provisioned for all TPSMA approaches since the constraint of the optimization problem is to ensure the connectivity from all sensor nodes to the sink node. |
author2 |
52165115900 |
author_facet |
52165115900 Abidin H.Z. Din N.M. Radzi N.A.M. |
format |
Article |
author |
Abidin H.Z. Din N.M. Radzi N.A.M. |
author_sort |
Abidin H.Z. |
title |
Deterministic static sensor node placement in wireless sensor network based on territorial predator scent marking behavior |
title_short |
Deterministic static sensor node placement in wireless sensor network based on territorial predator scent marking behavior |
title_full |
Deterministic static sensor node placement in wireless sensor network based on territorial predator scent marking behavior |
title_fullStr |
Deterministic static sensor node placement in wireless sensor network based on territorial predator scent marking behavior |
title_full_unstemmed |
Deterministic static sensor node placement in wireless sensor network based on territorial predator scent marking behavior |
title_sort |
deterministic static sensor node placement in wireless sensor network based on territorial predator scent marking behavior |
publishDate |
2023 |
_version_ |
1806423329718927360 |
score |
13.222552 |