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. 2021 Apr 21;21(9):2914.
doi: 10.3390/s21092914.

An Algorithm to Minimize Energy Consumption and Elapsed Time for IoT Workloads in a Hybrid Architecture

Julio C S Dos Anjos  1 , João L G Gross  1 , Kassiano J Matteussi  1 , Gabriel V González  2 , Valderi R Q Leithardt  3   4 , Claudio F R Geyer  1
Affiliations

An Algorithm to Minimize Energy Consumption and Elapsed Time for IoT Workloads in a Hybrid Architecture

Julio C S Dos Anjos et al. Sensors (Basel). .

Abstract

Advances in communication technologies have made the interaction of small devices, such as smartphones, wearables, and sensors, scattered on the Internet, bringing a whole new set of complex applications with ever greater task processing needs. These Internet of things (IoT) devices run on batteries with strict energy restrictions. They tend to offload task processing to remote servers, usually to cloud computing (CC) in datacenters geographically located away from the IoT device. In such a context, this work proposes a dynamic cost model to minimize energy consumption and task processing time for IoT scenarios in mobile edge computing environments. Our approach allows for a detailed cost model, with an algorithm called TEMS that considers energy, time consumed during processing, the cost of data transmission, and energy in idle devices. The task scheduling chooses among cloud or mobile edge computing (MEC) server or local IoT devices to achieve better execution time with lower cost. The simulated environment evaluation saved up to 51.6% energy consumption and improved task completion time up to 86.6%.

Keywords: Internet of things; cost minimization model; energy consumption; mobile edge computing; scheduling algorithm.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
System architecture.
Figure 2
Figure 2
Task allocation for Application 1 and Application 2.
Figure 3
Figure 3
Task allocation behavior vs. IoT device battery energy consumption when the LSL is reached. (a)Task allocation in the system. (b) Battery energy consumption of IoT devices.
Figure 4
Figure 4
Analytic analysis of energy model accuracy.
Figure 5
Figure 5
Cost policies for input data size variation in Application 1. (a) Cases A, B and C. (b) Case D.
Figure 6
Figure 6
Task allocation for different task generation rates.
See this image and copyright information in PMC

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