Minimizing energy expenditures using genetic algorithm for scalability and longlivety of multi hop sensor networks

Adeel Abro; Deng Zhongliang; Kamran Ali Memon; Khalid H. Mohammadani; Noor Ul Ain; Saleemullah Memon; Imran Memon; Muhammad A. Panhwar

doi:10.1109/ICEIEC.2019.8784682

Minimizing energy expenditures using genetic algorithm for scalability and longlivety of multi hop sensor networks

Adeel Abro
, Deng Zhongliang
, Kamran Ali Memon^*
, Khalid H. Mohammadani
, Noor Ul Ain
, Saleemullah Memon
, Imran Memon
, Muhammad A. Panhwar

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Scopus citations

Abstract

Along with implementations in tracking and monitoring systems, Sensor Networks (SNs) have evolved for many years and proved as an ultimate solution for dealing with sensing, controlling and mobility issues of physical phenomenon. Depending on the efficiency of the routing paradigm that are being used, the computing and processing power is minimal given the limited SNs batteries. In this paper, we use a Genetic Algorithm (GA) for multi hop scenario in extensive experiments with 20-90 nodes and analyze the performance of the proposed algorithm in terms of energy expenditures, scalability and longlivety of the SN. GA sinks nearly all packets in 18000 rounds as compared less efficient threshold sensitive energy efficient sensor network (TEEN) protocol under various deployments. In analysis for distance of multiple hops from/to the respective sink, the proposed algorithm performed fairly better than the TEEN approach in maximizing the sensor activity by saving energy resulting the increased lifetime of the network. Further, the algorithm is scalable and any number of nodes can produce the optimized results. The work can be extended to format some new scenarios and optimize routes with the help of GA and other algorithms typically used in optimization.

Original language	English
Title of host publication	ICEIEC 2019 - Proceedings of 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication
Editors	Wenzheng Li, Guomin Zuo
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	183-187
Number of pages	5
ISBN (Electronic)	9781728111896
DOIs	https://doi.org/10.1109/ICEIEC.2019.8784682
State	Published - Jul 2019
Externally published	Yes
Event	9th IEEE International Conference on Electronics Information and Emergency Communication, ICEIEC 2019 - Beijing, China Duration: 12 Jul 2019 → 14 Jul 2019

Publication series

Name	ICEIEC 2019 - Proceedings of 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication

Conference

Conference	9th IEEE International Conference on Electronics Information and Emergency Communication, ICEIEC 2019
Country/Territory	China
City	Beijing
Period	12/07/19 → 14/07/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

Energy Consumption
Lifespan
Optimal path
Routing
Sensor Network
the meta-heuristic algorithm

ASJC Scopus subject areas

Computer Networks and Communications
Hardware and Architecture
Information Systems
Information Systems and Management
Safety, Risk, Reliability and Quality

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ICEIEC.2019.8784682

Cite this

Abro, A., Zhongliang, D., Memon, K. A., Mohammadani, K. H., Ul Ain, N., Memon, S., Memon, I., & Panhwar, M. A. (2019). Minimizing energy expenditures using genetic algorithm for scalability and longlivety of multi hop sensor networks. In W. Li, & G. Zuo (Eds.), ICEIEC 2019 - Proceedings of 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication (pp. 183-187). Article 8784682 (ICEIEC 2019 - Proceedings of 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICEIEC.2019.8784682

Abro, Adeel ; Zhongliang, Deng ; Memon, Kamran Ali et al. / Minimizing energy expenditures using genetic algorithm for scalability and longlivety of multi hop sensor networks. ICEIEC 2019 - Proceedings of 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication. editor / Wenzheng Li ; Guomin Zuo. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 183-187 (ICEIEC 2019 - Proceedings of 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication).

@inproceedings{e9fe4bd179a542b082f4eb32621ce6f3,

title = "Minimizing energy expenditures using genetic algorithm for scalability and longlivety of multi hop sensor networks",

abstract = "Along with implementations in tracking and monitoring systems, Sensor Networks (SNs) have evolved for many years and proved as an ultimate solution for dealing with sensing, controlling and mobility issues of physical phenomenon. Depending on the efficiency of the routing paradigm that are being used, the computing and processing power is minimal given the limited SNs batteries. In this paper, we use a Genetic Algorithm (GA) for multi hop scenario in extensive experiments with 20-90 nodes and analyze the performance of the proposed algorithm in terms of energy expenditures, scalability and longlivety of the SN. GA sinks nearly all packets in 18000 rounds as compared less efficient threshold sensitive energy efficient sensor network (TEEN) protocol under various deployments. In analysis for distance of multiple hops from/to the respective sink, the proposed algorithm performed fairly better than the TEEN approach in maximizing the sensor activity by saving energy resulting the increased lifetime of the network. Further, the algorithm is scalable and any number of nodes can produce the optimized results. The work can be extended to format some new scenarios and optimize routes with the help of GA and other algorithms typically used in optimization.",

keywords = "Energy Consumption, Lifespan, Optimal path, Routing, Sensor Network, the meta-heuristic algorithm",

author = "Adeel Abro and Deng Zhongliang and Memon, \{Kamran Ali\} and Mohammadani, \{Khalid H.\} and \{Ul Ain\}, Noor and Saleemullah Memon and Imran Memon and Panhwar, \{Muhammad A.\}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 9th IEEE International Conference on Electronics Information and Emergency Communication, ICEIEC 2019 ; Conference date: 12-07-2019 Through 14-07-2019",

year = "2019",

month = jul,

doi = "10.1109/ICEIEC.2019.8784682",

language = "English",

series = "ICEIEC 2019 - Proceedings of 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "183--187",

editor = "Wenzheng Li and Guomin Zuo",

booktitle = "ICEIEC 2019 - Proceedings of 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication",

address = "United States",

}

Abro, A, Zhongliang, D, Memon, KA, Mohammadani, KH, Ul Ain, N, Memon, S, Memon, I & Panhwar, MA 2019, Minimizing energy expenditures using genetic algorithm for scalability and longlivety of multi hop sensor networks. in W Li & G Zuo (eds), ICEIEC 2019 - Proceedings of 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication., 8784682, ICEIEC 2019 - Proceedings of 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication, Institute of Electrical and Electronics Engineers Inc., pp. 183-187, 9th IEEE International Conference on Electronics Information and Emergency Communication, ICEIEC 2019, Beijing, China, 12/07/19. https://doi.org/10.1109/ICEIEC.2019.8784682

Minimizing energy expenditures using genetic algorithm for scalability and longlivety of multi hop sensor networks. / Abro, Adeel; Zhongliang, Deng; Memon, Kamran Ali et al.
ICEIEC 2019 - Proceedings of 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication. ed. / Wenzheng Li; Guomin Zuo. Institute of Electrical and Electronics Engineers Inc., 2019. p. 183-187 8784682 (ICEIEC 2019 - Proceedings of 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Minimizing energy expenditures using genetic algorithm for scalability and longlivety of multi hop sensor networks

AU - Abro, Adeel

AU - Zhongliang, Deng

AU - Memon, Kamran Ali

AU - Mohammadani, Khalid H.

AU - Ul Ain, Noor

AU - Memon, Saleemullah

AU - Memon, Imran

AU - Panhwar, Muhammad A.

PY - 2019/7

Y1 - 2019/7

N2 - Along with implementations in tracking and monitoring systems, Sensor Networks (SNs) have evolved for many years and proved as an ultimate solution for dealing with sensing, controlling and mobility issues of physical phenomenon. Depending on the efficiency of the routing paradigm that are being used, the computing and processing power is minimal given the limited SNs batteries. In this paper, we use a Genetic Algorithm (GA) for multi hop scenario in extensive experiments with 20-90 nodes and analyze the performance of the proposed algorithm in terms of energy expenditures, scalability and longlivety of the SN. GA sinks nearly all packets in 18000 rounds as compared less efficient threshold sensitive energy efficient sensor network (TEEN) protocol under various deployments. In analysis for distance of multiple hops from/to the respective sink, the proposed algorithm performed fairly better than the TEEN approach in maximizing the sensor activity by saving energy resulting the increased lifetime of the network. Further, the algorithm is scalable and any number of nodes can produce the optimized results. The work can be extended to format some new scenarios and optimize routes with the help of GA and other algorithms typically used in optimization.

AB - Along with implementations in tracking and monitoring systems, Sensor Networks (SNs) have evolved for many years and proved as an ultimate solution for dealing with sensing, controlling and mobility issues of physical phenomenon. Depending on the efficiency of the routing paradigm that are being used, the computing and processing power is minimal given the limited SNs batteries. In this paper, we use a Genetic Algorithm (GA) for multi hop scenario in extensive experiments with 20-90 nodes and analyze the performance of the proposed algorithm in terms of energy expenditures, scalability and longlivety of the SN. GA sinks nearly all packets in 18000 rounds as compared less efficient threshold sensitive energy efficient sensor network (TEEN) protocol under various deployments. In analysis for distance of multiple hops from/to the respective sink, the proposed algorithm performed fairly better than the TEEN approach in maximizing the sensor activity by saving energy resulting the increased lifetime of the network. Further, the algorithm is scalable and any number of nodes can produce the optimized results. The work can be extended to format some new scenarios and optimize routes with the help of GA and other algorithms typically used in optimization.

KW - Energy Consumption

KW - Lifespan

KW - Optimal path

KW - Routing

KW - Sensor Network

KW - the meta-heuristic algorithm

UR - https://www.scopus.com/pages/publications/85071117625

U2 - 10.1109/ICEIEC.2019.8784682

DO - 10.1109/ICEIEC.2019.8784682

M3 - Conference contribution

AN - SCOPUS:85071117625

T3 - ICEIEC 2019 - Proceedings of 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication

SP - 183

EP - 187

BT - ICEIEC 2019 - Proceedings of 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication

A2 - Li, Wenzheng

A2 - Zuo, Guomin

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 9th IEEE International Conference on Electronics Information and Emergency Communication, ICEIEC 2019

Y2 - 12 July 2019 through 14 July 2019

ER -

Abro A, Zhongliang D, Memon KA, Mohammadani KH, Ul Ain N, Memon S et al. Minimizing energy expenditures using genetic algorithm for scalability and longlivety of multi hop sensor networks. In Li W, Zuo G, editors, ICEIEC 2019 - Proceedings of 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication. Institute of Electrical and Electronics Engineers Inc. 2019. p. 183-187. 8784682. (ICEIEC 2019 - Proceedings of 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication). doi: 10.1109/ICEIEC.2019.8784682

Minimizing energy expenditures using genetic algorithm for scalability and longlivety of multi hop sensor networks

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this