ERAM-EE: Efficient resource allocation and management strategies with energy efficiency under fog–internet of things environments

Citation

Periasamy, Prakasam and Ujwala, R. and Srikar, K. and Durga Sai, Y.V. and Preetha, K.S. and Sumathi, D. and Sayeed, Md. Shohel (2024) ERAM-EE: Efficient resource allocation and management strategies with energy efficiency under fog–internet of things environments. Connection Science, 36 (1). ISSN 0954-0091

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Abstract

ABSTRACTDue to technological advancements, most devices are generating a significant amount of data which needs appropriate technology to handle the data generated by IoT devices. Fog computing addresses this challenges in a decentralised manner. This paper proposes an efficient resource allocation and management strategies with energy efficiency (ERAM-EE) to effectively allocate available resources in Fog-enabled networks. The ERAM-EE algorithm utilises the channel gain matrix of the interconnected network to assign IoT devices to Fog nodes (FNs) through resource blocks (RBs) with three stages. In the initial stage, one FN is assigned to each IoT device through a single RB by calculating the maximum value of the channel gain. In the subsequent stage, the remaining RBs are assigned to unassigned FNs for future task-offloading processes. Finally, the unassigned RBs are allocated to IoT devices by calculating the maximum channel gain of the Fog–IoT networks. Simulated results indicate that the ERAM-EE scheme confirms that each IoT device is mapped with minimum one FN and RB for effective task scheduling and resource management. Analysis reveals that the ERAM-EE method achieved an increase in EE of up to 7, 8, and 18 Mbit/J compared to existing schemes for varying IoT devices, FNs and RBs respectively.

Item Type: Article
Uncontrolled Keywords: Internet of things
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 29 May 2024 03:48
Last Modified: 29 May 2024 03:48
URII: http://shdl.mmu.edu.my/id/eprint/12467

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