Delay-Aware and Energy-Efficient Task Offloading in Fog-Enabled IoT Networks

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Shahryari Om Kolsoom; Pedram Hossein; Khajehvand Vahid; Dehghan Takht Fooladi Mehdi

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Journal Paper

Paper Information

Journal: JOURNAL OF SOFT COMPUTING AND INFORMATION TECHNOLOGY (JSCIT) Year:2023 | Volume:12 | Issue:1 Page(s): 32-47

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Title

Delay-Aware and Energy-Efficient Task Offloading in Fog-Enabled IoT Networks

Author(s)

Shahryari Om Kolsoom | Pedram Hossein | Khajehvand Vahid | Dehghan Takht Fooladi Mehdi | Issue Writer Certificate

Keywords

Task Offloading

Internet of Things

Fog Computing

Resource Allocation

Genetic Algorithm

Abstract

Fog Computing has emerged as a promising technique to provide agile and pervasive computing services to the Internet of Things devices (IDs) and to support complicated IoT applications. Fog Computing brings computation resources to the edge of the network, near to the IDs, and provides low-latency services to users. By offloading computational tasks to fog nodes having greater computing capacities, can address the contradiction between the limited battery capacity of IDs and high computational intensity demand of tasks. Hence, the quality of service (QoS) demands of users can be fulfilled. Although Task Offloading to fog nodes leads to saving in energy consumption in the battery of IDs, it causes to increase in task completion time due to occurred delay in transmitting the task to the edge of the network. In this paper, to balancing the trade-off between energy consumption and task completion time, a Task Offloading scheme is proposed. The main objective of the proposed scheme is to minimize offloading overhead in terms of the weighted sum of energy consumption and task completion time by optimizing offloading decision, the destination of offloading, and computation Resource Allocation. We employ fuzzy logic to determine the weighting coefficient effectively. Task Offloading to fog nodes is formulated as a mixed-integer nonlinear programming (MINLP) problem, which is NP-hard. A sub-optimal algorithm based on Genetic Algorithm (GA) is proposed to solve the formulated problem. Extensive simulations prove the convergence of the proposed algorithm and its superior performance in comparison with some baseline schemes.

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