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Title
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A particle swarm optimization-based heuristic for optimal cost estimation in Internet of Things
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Author
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Abstract
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| 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 fmdings 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. |
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Language
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English
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Source (book)
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BDIOT 2018: 2nd International Conference on Big Data and Internet of Things : proceedings, October 24-26, 2018, Beijing, China
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Publication
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New york
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Assoc computing machinery
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2018
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ISBN
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978-1-4503-6519-2
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978-1-4503-6519-2
978-1-4503-6519-2
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Volume/pages
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(2018)
, p. 136-142
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ISI
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000455369000027
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Full text (Publisher's DOI)
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Full text (publisher's version - intranet only)
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