Joint optimisation of radio and infrastructure resources for energy-efficient massive data storage in the mobile cloud over 5G hetnet
Abstract
This study formulates the problem of radio resource and data transmission strategy in a dense HetNet enhanced with mobile cloud computing and mobile edge computing technologies as a combinatorial optimisation problem. An ant colony optimisation (ACO)‐based energy‐aware algorithm is proposed as solution approach, which consists of a data transmission management scheme and a physical resource blocks/bandwidth allocation scheme. Simulation results show that compared with baseline approaches [(greedy and data split multiple user equipment (UEs) algorithms]), the proposed ACO‐based algorithm achieves: (i) about 99% of reduction in terms of transmission delay under varying data file sizes (resp. varying number of UEs in the network); (ii) about 99% of reduction in terms of transmission energy dissipated by an UE during transmission of a file under varying data file sizes (resp. varying number of UEs in the network); (iii) improved performance with an increase in the number of small base stations in the network, (iv) significant reduction in the energy transmitted (by the UE) during transmission of big data files to the cloud storage; and (v) some stability irrespective of the variation in network conditions such as increasing the number of active UEs or increasing the number of UEs in the network.
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Machine learning exhibits great potential for data feature discovery to predict data usage behavior for developing control algorithms, such as data traffic estimation for network congestion avoidance or user access tracking for privacy preservation.
“ML also exhibits great potential on data feature discovery to predict data usage behaviour for developing control algorithms, such as data traffic estimation for network congestion avoidance or user access tracking for privacy preservation [226].”
From Blockchain for 5G and beyond networks: A state of the art survey (2020) · cites this paper as [226]
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- Authors
- Siddavaatam, R., Woungang, I., & Anpalagan, A.
- Affiliations
- Toronto Metropolitan University (CA)
- Corresponding author
- Woungang, Isaac
- Venue
- IET Wireless Sensor Systems
- Published
- 2019-10
- Volume
- 9
- Issue
- 5
- Pages
- 323-332
- ISSN
- 2043-6386 (ISSN-L)
- 2043-6394
- OpenAlex
- W2949044235
- Cited by
- 10
- References
- 25
- Citations by year
- 2023: 1 · 2022: 4 · 2021: 2 · 2020: 3
- FWCI
- 1.23
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IET Wireless Sensor Systems (publishedVersion)
- Funders
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced MIMO Systems Optimization
- Keywords
- heterogeneous networks, mobile cloud computing, mobile edge computing, energy efficiency, massive data storage, radio resource allocation, bandwidth allocation, ant colony optimization, data transmission strategy, transmission delay, small cells, 5G
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