research-article

A particle Swarm Optimization-based Heuristic for Optimal Cost Estimation in Internet of Things Environment

Authors:

Muddsair Sharif,

Siegfried Mercelis,

Johann Marquez-Barja,

Peter HellinckxAuthors Info & Claims

BDIOT '18: Proceedings of the 2018 2nd International Conference on Big Data and Internet of Things

Pages 136 - 142

https://doi.org/10.1145/3289430.3289433

Published: 24 October 2018 Publication History

Abstract

The Internet of Things (IoT) imparts a significant impact on everyday lifestyle seamlessly connecting people, information and businesses across the globe. The integration of digital economy with the Internet of Things (IoT) paradigm in recent years has enabled fundamental shifts in development of technology. As a result, massive devices such as sensors and smart objects with divergent capabilities and limited resources (exchangeable energy, processing power, storage capabilities) have commenced to form disordered interactions. In order to improve network performance with subsequent cost-effectiveness and efficient resource utilization an effective distribution of these IoT resources is required. Considering heterogeneity and widespread use of IoT a highly beneficial resource allocation is warranted. In this paper, a Particle Swarm Optimization (PSO) base meta-heuristic is presented which encompasses distribution of blocks of codes. Our findings demonstrate that Particle Swarm Optimization advantages in terms of cost-benefits outweighs other resource allocation algorithm such as Sequential Resource Allocation (SRA) and multi-level graph partitioning.

References

[1]

M. Satyanarayanan, "The Emergence of Edge Computing," in Computer, vol. 50, no. 1, pp. 30--39, Jan. 2017.

Digital Library

[2]

E. Ahmed et al., "Bringing Computation Closer toward the User Network: Is Edge Computing the Solution?," in IEEE Communications Magazine, vol. 55, no. 11, pp. 138--144, NOVEMBER 2017.

[3]

M. M. Shurman and M. K. Aljarah, "Collaborative execution of distributed mobile and IoT applications running at the edge," 2017 International Conference on Electrical and Computing Technologies and Applications (ICECTA), Ras Al Khaimah, 2017, pp. 1--5.

[4]

T. Taleb A. Ksentini and M. Chen. A markov decision process-based service migration procedure for follow me cloud", IEEE ICC 2014 - Communication QoS, Reliability and Modelling Symposium jun. 2014.

[5]

T. Taleb and A. Ksentini. An analytical model for follow me cloud, Globecom 2013 - Communications QoS, Reliability and Modelling Symposium, 2013.

[6]

S. Wang. Dynamic service placement in mobile micro-clouds, PhD. dissertation, imperial college London, United Kingdom 2015.

[7]

Urgaonkar, R., Wang, S., He, T., Zafer, M., Chan, K., and Leung, K. K. (2015). Dynamic service migration and workload scheduling in edge-clouds. Performance Evaluation, 205--228.

Digital Library

[8]

SE Mahmoodi, RN Uma, KP Subbalakshmi. Optimal joint scheduling and cloud offloading for mobile applications - IEEE Transactions on Cloud Computing, 2015.

[9]

Y. Wen W. Zhang and D. O. Wu. Collaborative task execution in mobile cloud computing under a stochastic wireless channel, IEEE transection on wireless communication, Vol. 14, No. 1, January 2015

[10]

J.Zhang, K.B.Letaief J.Liu, Y.Mao. Delay-optimal computation task scheduling for mobile-edge computing systems, in proceeding ieee int. symposium. inf. theory (isit), 2016.

[11]

Peter Hellinckx Muddsair Sharif, Siegfried Mercelis. Context-aware optimization of distributed resources in internet of things using key performance indicators.

[12]

J. D. Ullman. 1975. NP-complete scheduling problems. J. Comput. Syst. Sci. 10, 3 (June 1975).

Digital Library

[13]

M. H. Su J. Blythe Y. Gil C. Kesselman G. Mehta K. Vahi G. B. Berriman J. Good A. Laity J. C. Jacob E. Deelman, G. Singh and D. S. Katz. A framework for mapping complex scientific workflows onto distributed systems.

[14]

Tim Verbelen, Tim Stevens, Filip De Turck, and Bart Dhoedt. Graph partitioning algorithms for optimizing software deployment in mobile cloud computing. Future Gener. Comput. Syst., February 29, 2013.

Digital Library

[15]

Siddeswara Mayura Guru Rajkumar Buyya Suraj Pandey, Linlin Wu. A particle swarm optimization-based heuristic for scheduling workflow applications in cloud com- puting environments.

[16]

Pappas N-Fitzgerald E Yuan D. Angelakis V, Avgouleas I. Allocation of heterogeneous resources of an iot device to flexible services, 26 february 2016.

[17]

James Kennedy and Russell Eberhart. Particle swarm optimization, in proceedings of the ieee international conference on neural networks, volume iv, piscataway, n.j., 1995.

[18]

Shuihua Wang Yudong Zhang and Genlin Ji. A comprehensive survey on particle swarm optimization algorithm and its applications, Mathematical Problems in Engineering, Volume 2015.

[19]

P. N. Kechagiopoulos and G. N. Beligiannis. Solving the urban transit routing problem using a particle swarm optimization based algorithm, applied soft computing journal, vol. 21, 2014.

[20]

B. Qolomany, M. Maabreh, A. Al-Fuqaha, A. Gupta and D. Benhaddou, "Parameters optimization of deep learning models using Particle swarm optimization," 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), Valencia, 2017.

[21]

W. Liu, X. Deng and H. Shi, "Research on Algorithm of PSO in Image Segmentation of Cement-Based," 2016 7th International Conference on Cloud Computing and Big Data (CCBD), Macau, 2016.

[22]

S. Doctor, G. K. Venayagamoorthy and V. G. Gudise, "Optimal PSO for collective robotic search applications," Proceedings of the 2004 Congress on Evolutionary Computation, Portland, OR, USA, 2004.

[23]

S. Wang, M. Zafer and K. K. Leung, "Online Placement of Multi-Component Applications in Edge Computing Environments," in IEEE Access, vol. 5, 2017.

[24]

Pablo Cingolani. Particle swarm optimization simulator, {online}. available: http://jswarm-pso.sourceforge.net.

[25]

Sharif Muddsair, Mercelis Siegfried, Van den bergh Wim, Hellinckx Peter, Towards real-time smart road construction: efficient process management through the implementation of Internet of Things BDIOT2017: International Conference on Big Data and Internet of Things, December 20-22, 2017, London, United Kingdom-p. 174--180

Digital Library

Cited By

Amirghafouri FNeghabi AShakeri HSola Y(2025)Nature‐Inspired Meta‐Heuristic Algorithms for Resource Allocation in the Internet of ThingsInternational Journal of Communication Systems10.1002/dac.614138:5Online publication date: 17-Feb-2025
https://doi.org/10.1002/dac.6141
Salari MGhanbari MSkitmore MAlipour M(2024)Developing a framework for selecting alternative materials for construction projects using BIM and the particle swarm optimization algorithmConstruction Innovation10.1108/CI-12-2023-0309Online publication date: 17-Sep-2024
https://doi.org/10.1108/CI-12-2023-0309
Jain SAgarwal M(2022)Analysis and Comparison of Swarm Intelligence Algorithm in IoT: A SurveyProceedings of the Third International Conference on Information Management and Machine Intelligence10.1007/978-981-19-2065-3_1(1-7)Online publication date: 4-Aug-2022
https://doi.org/10.1007/978-981-19-2065-3_1
Show More Cited By

Index Terms

A particle Swarm Optimization-based Heuristic for Optimal Cost Estimation in Internet of Things Environment
1. Software and its engineering
  1. Software organization and properties
    1. Contextual software domains
      1. E-commerce infrastructure

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cover image ACM Other conferences

BDIOT '18: Proceedings of the 2018 2nd International Conference on Big Data and Internet of Things

October 2018

217 pages

ISBN:9781450365192

DOI:10.1145/3289430

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Deakin University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 October 2018

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BDIOT 2018

BDIOT 2018: 2018 2nd International Conference on Big Data and Internet of Things

October 24 - 26, 2018

Beijing, China

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Overall Acceptance Rate 75 of 136 submissions, 55%

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Cited By

Amirghafouri FNeghabi AShakeri HSola Y(2025)Nature‐Inspired Meta‐Heuristic Algorithms for Resource Allocation in the Internet of ThingsInternational Journal of Communication Systems10.1002/dac.614138:5Online publication date: 17-Feb-2025
https://doi.org/10.1002/dac.6141
Salari MGhanbari MSkitmore MAlipour M(2024)Developing a framework for selecting alternative materials for construction projects using BIM and the particle swarm optimization algorithmConstruction Innovation10.1108/CI-12-2023-0309Online publication date: 17-Sep-2024
https://doi.org/10.1108/CI-12-2023-0309
Jain SAgarwal M(2022)Analysis and Comparison of Swarm Intelligence Algorithm in IoT: A SurveyProceedings of the Third International Conference on Information Management and Machine Intelligence10.1007/978-981-19-2065-3_1(1-7)Online publication date: 4-Aug-2022
https://doi.org/10.1007/978-981-19-2065-3_1
Hatti DSutagundar A(2021)Swarm intelligence based MSMOPSO for optimization of resource provisioning in Internet of ThingsRecent Trends in Computational Intelligence Enabled Research10.1016/B978-0-12-822844-9.00028-1(61-82)Online publication date: 2021
https://doi.org/10.1016/B978-0-12-822844-9.00028-1
Ren XZhang ZChen SAbnoosian K(2020)An energy‐aware method for task allocation in the Internet of things using a hybrid optimization algorithmConcurrency and Computation: Practice and Experience10.1002/cpe.596733:6Online publication date: 30-Sep-2020
https://doi.org/10.1002/cpe.5967

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